Solar intermittency: Australia s clean energy challenge

Transcription

1 ENERGY TRANSFORMED FLAGSHIP Solar intermittency: Australia s clean energy challenge Characterising the effect of high penetration solar intermittency on Australian electricity networks June 212

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3 Solar intermittency: Australia s clean energy challenge Characterising the effect of high penetration solar intermittency on Australian electricity networks Saad Sayeef, Simon Heslop, David Cornforth, Tim Moore, Steven Percy, John K Ward, Adam Berry and Daniel Rowe June 212 This project has been supported by the Australian Government through the Australian Solar Institute (ASI). The Australian Government, through ASI, is supporting Australian research and development in photovoltaic and concentrating solar power technologies to help solar power become cost competitive with other energy sources. 1

4 Acknowledgements This work is supported by the Australian Government through the Australian Solar Institute (ASI), part of the Clean Energy Initiative. The authors would like to thank David Swift (AEMO), Michael Lyons (AEMO), Chris Stewart (AEMO), Michael Redpath (AEMO) and Mark Amos (ENA) for their support and contributions to the project, and look forward to a continuing partnership. The authors would also like to thank the following people and their organisations for their support of the work carried out in this report: Desert Knowledge Australia Solar Centre (DKASC) Ga Rick Lee Lyndon Frearson The University of Queensland Paul Meredith Craig Froome Enquiries should be addressed to: Saad Sayeef (saad.sayeef@csiro.au) Copyright and Disclaimer 212 CSIRO To the extent permitted by law, all rights are reserved and no part of this publication covered by copyright may be reproduced or copied in any form or by any means except with the written permission of CSIRO. Important Disclaimer CSIRO advises that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, CSIRO (including its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it. The views expressed herein are not necessarily the views of the Australian Government, and the Australian Government does not accept responsibility for any information or advice contained herein. 2 Solar intermittency: Australia s clean energy challenge

5 Table of Contents Executive summary List of acronyms Introduction Definitions Current state of worldwide generation intermittency research Characterisation of solar power variability Impacts/observations: high penetration solar intermittency Predicted impacts of high penetration intermittent renewable generation Changes required to accommodate high penetration IRG Work required to facilitate high penetration IRG Summary Comparison between wind and solar intermittency Correlation with load Inherent variability Load-following and regulation requirements Summary Comparison between concentrating solar thermal (CST) and PV power generation Intermittency Variability Storage and capacity factor Summary High penetration intermittent renewable generation (HP-IRG) in Australia Potential electricity network impacts Accommodation of HP-IRG in Australia Summary Renewable generation intermittency IEA Task IEA Task 14 background Current and predicted levels of intermittent generation penetration Intermittency observations and impacts Procedures currently in place to mitigate issues associated with intermittent generation Existing activities to further knowledge on intermittent generation Future plans of action and expected changes to manage increased penetration Solar intermittency Australian industry experts Industry workshop on Renewable Generation Intermittency Solar Intermittency survey Growth in energy generation from solar Large-scale versus small-scale solar systems Resolution of solar data Solar forecasting Unique aspects of the Australian electricity network

6 8.8 Ancillary services to cater for solar intermittency Other issues reported by Australian industries Intermittency ramp rates and timescales analysis Desert Knowledge Australia Solar Centre (DKASC) Intermittency data analysis DKASC 196 kw PV system Intermittency data analysis CSIRO 22 kw PV system Intermittency data analysis UQ 1.2 MW PV system Comparison of ramp events for 196kW and 22kW PV system Model to examine likely impacts of intermittency on Australian electricity networks Solar irradiance and PV power spectrum Model linking solar intermittency with generation output Solar prediction Summary of key findings Further work required References Appendix A Intermittency workshop program Appendix B Survey of stakeholder perspectives on solar intermittency in the Australian electricity network Appendix C Solar PV technologies at the DKASC Appendix D User interface for power fluctuation simulation model Solar intermittency: Australia s clean energy challenge

7 List of Figures Figure 1 Unique network aspects in Australia Figure 2 PV output profiles across various zip codes in western US for a July morning [6] Figure 3 Example output profile of a CST plant on a day in May in California [6] Figure 4 Average January profiles (California) for load demand (pink triangles), PV (purple squares) and CST generation [6] Figure 5 Average July profiles (California) [6] Figure 6 Variation in monthly solar energy for three years (24-26) [6] Figure 7 Hourly variability of load and solar generation [6] Figure 8 Predicted hourly net load variability with 33% renewable penetration in the Californian grid in 21 [1]..28 Figure 9 Hourly net load variability by hour of day [1] Figure 1 Frequency of different ramp rate events as a percentage of total PV capacity for PV system in Golden, Colorado [11] Figure 11 Reduction in variability of solar irradiance when the outputs of multiple sites are aggregated [12] Figure 12 Cumulative distributions (95th to 1th percentiles) of irradiance and PV power changes over various time periods for one highly variable day for 13.2 MW system in Nevada [12] Figure 13 Irradiance and net system power output during cloud passage [14] Figure 14 Total site power and PV generation over a 6-minute period [14] Figure 15 Power output of a 4.6 MW PV system on a partly cloudy day in Arizona [39] Figure 16 Predicted load duration curve, CAISO July 27, with 1, 3 and 5% solar [13] Figure 17 Dispatch order for Californian fuel mix with 3% PV penetration (CAISO July 27) [13] Figure 18 Dispatch order for US fuel mix with 3% PV penetration (CAISO July 27) [13] Figure 19 Aggregate hourly output for Northern Europe across four months [18] Figure 2 Monthly wholesale prices ( /MWh) across different historical weather patterns [18] Figure 21 Average and marginal relative cost of wind as a function of wind energy penetration due to varying levels of curtailment [19] Figure 22 Dispatch for week in April no solar [6] Figure 23 Dispatch for same period as Figure 22 above 25% solar [6] Figure 24 Spot price duration curve with 25% solar in Arizona [6] Figure 25 Required LFC as % of installed PV [21] Figure 26 Graph showing impact of incorporating hour-ahead wind forecast on AGC unit [1] Figure 27 Impact of system flexibility on curtailed energy for ERCOT [19] Figure 28 Wind penetration level vs. required curtailment [19] Figure 29 Required curtailment with various levels of solar power added to the renewable mix [19] Figure 3 Required curtailment with storage [19] Figure 31 Traditional and emerging practice in capacity planning [1] Figure 32 Power profile required of an energy storage unit to level a cloud transient in a PV system [2] Figure 33 Energy storage required to provide the power profile above, as a function of PV system rating and duration of the cloud transient [2] Figure 34 Correlation between wind and load in Denmark, January Figure 35 Correlation of solar production with load demand [6] Figure 36 Wind penetration (%) per decile [1] Figure 37 Solar penetration (%) per decile [1] Figure 38 Capacity factor vs. decile for wind and solar (21T scenario)

8 Figure 39 Correlation between wind, solar and load for each of the four seasons [24] Figure 4 Wind power output for one wind turbine (doubly fed induction generator) [25] Figure 41 Cumulative distributions (95th and 1th percentiles) of six individual PV plants within a ~2 square kilometre area in Las Vegas [12] Figure 42 Wind variability (deltas) for CAISO 125MW capacity [1] Figure 43 Load, wind and solar duration curves for CAISO (21X scenario) [1] Figure 44 Correlation vs. distance for wind and solar [26] Figure 45 Duration curves wind and solar [26] Figure 46 Example for load-following (5-min) and regulation (1-min) metric definition [1] Figure 47 5-min delta over three hours one July morning, 23 [1] Figure 48 1-min delta over three hours one July morning, 23 [1] Figure 49 SEGS IV hybrid parabolic trough (yellow) and gas generation (green) [28] Figure 5 Output for 64 MW parabolic trough 1-sec sample rate. Top: sunny day, bottom: partly cloudy [28] Figure 51 Output for 1 MW PV plant 1-sec sample rate. Top: sunny day, bottom: partly cloudy [28] Figure 52 The effect of storage [82] Figure 53 PV capacity factors [32] Figure 54 Comparison change in UCL due to battery storage [33] Figure 55 An ideal generation profile for solar PV, compared with a real profile from a cloudy day in winter Figure 56 A day in the NEM [34] Figure 57 Standard deviation of net load for increasing PV capacity per block [35] Figure 58 Australian generation by fuel type excluding distributed generation and off-grid private sources (21) [34] vs. US Generation mix (25) [36] Figure 59 Voltage difference between the common high-voltage measuring point and PV system output [37] Figure 6 Single line diagram for electrical system at Main Stadium in Kaohsiung, Taiwan [38] Figure 61 PV generation profile used for study in [38] Figure 62 PV deployment scenarios: Europe 27, Norway and Turkey [4] Figure 63 Increase of renewable energy sources in Germany [51] Figure 64 Ratio of installed PV capacity over average annual load for German DSOs in 21 [51] Figure 65 HELCO PV installations as % of Annual High Peak, Hawaii [52] Figure 66 PV Dissemination target of Japan [53] Figure 67 Projected European generation mix from five different studies [54] Figure 68 PV power output for three consecutive days at the Atlantic City Convention Centre [58] Figure 69 Net load before and after PV on PEPCO feeder [58] Figure 7 Curtailment and ramp-up rates for a 5 kw system [58] Figure 71 Flicker guidelines [58] Figure 72 PV Curtailment Ohta City Project, Japan [53] Figure 73 Smoothing effect due to spatial diversity in the Osaka area of Japan [53] Figure 74 Voltage regulation at CSU (Phase I) [6] Figure 75 PEPCO 2 MW system [58] Figure 76 Image of NEDO Smart Grid clustered PV Figure 77 Voltage control in NEDO Smart Grid clustered PV generation system Figure 78 NEDO Smart Grid setup and characteristics Figure 79 Reduction in curtailment through flexibility technologies [6] Figure 8 Global growth of PV capacity [63] Solar intermittency: Australia s clean energy challenge

9 Figure 81 Scenes from the intermittency workshop held in Melbourne Figure 82 Plant power output (kw) and solar Irradiance (W/m 2 ) at the DKASC for 1-month period from October, Figure 83 Irradiance (W/m 2 ) and power output (kw) for the DKASC on 26th May, Figure 84 Frequency spectrum of power output data recorded over a 1-month period at the DKASC Figure 85 Distribution of ramp-up events in 1-second periods for 196 kw PV system Figure 86 Distribution of ramp-down events in 1-second periods for 196 kw PV system Figure 87 Distribution of ramp-up events in 2-second periods for 196 kw PV system Figure 88 Distribution of ramp-down events in 2-second periods for 196 kw PV system Figure 89 Distribution of ramp-up events in 3-second periods for 196 kw PV system Figure 9 Distribution of ramp-down events in 3-second periods for 196 kw PV system Figure 91 Distribution of ramp-up events in 4-second periods for 196kW PV system Figure 92 Distribution of ramp-down events in 4-second periods for 196kW PV system Figure 93 Distribution of ramp-up events in 5-second periods for 196 kw PV system Figure 94 Distribution of ramp-down events in 5-second periods for 196 kw PV system Figure 95 Distribution of ramp-up events in 6-second periods for 196kW PV system Figure 96 Distribution of ramp-down events in 6-second periods for 196kW PV system Figure 97 Ramp-up rates for 196 kw PV system 2, 3, 4 and 5-second variations Figure 98 Ramp-down rates for 196 kw PV system 2, 3, 4 and 5-second variations Figure 99 Output from PV and CSP plants without dedicated thermal storage: the role of thermal inertia. Source: Mehos et al., IEEE Power & Energy Magazine, May/June Figure 1 Average line-to-neutral AC voltage of the three phases of DKASC for 1-month period from October, Figure 11 Distribution of average line-to-neutral AC voltage measured at the DKASC over ten months Figure 12 Irradiance, output power and line-to-neutral AC voltage (top to bottom) at the DKASC over one week (7 13 February, 211) with partly cloudy days Figure 13 Irradiance, output power and line-to-neutral AC voltage (top to bottom) at the DKASC over one day (7 February, 211) Figure 14 Irradiance, output power and line-to-neutral AC voltage at the DKASC on 19 December, 21 (cloudy) and 2 December, 21 (clear) Figure 15 (a) Voltage and insolation, and (b) voltage and output power, at the DKASC along one day (7 February, 211) Figure 16 (a) Voltage and insolation, and (b) voltage and output power, at the DKASC during a 5-minute period at midday on 7 February, Figure 17 Distribution of ramp-up events in 5-second periods for 22 kw PV system Figure 18 Distribution of ramp-down events in 5-second periods for 22 kw PV system Figure 19 Distribution of ramp-up events in 1-second periods for 22 kw PV system Figure 11 Distribution of ramp-down events in 1-second periods for 22 kw PV system Figure 111 Distribution of ramp-up events in 5-second periods for 22 kw PV system Figure 112 Distribution of ramp-down events in 5-second periods for 22 kw PV system Figure 113 Distribution of ramp-up events in 6-second periods for 22 kw PV system Figure 114 Distribution of ramp-down events in 6-second periods for 22 kw PV system Figure 115 Power output profiles of the PV arrays on four UQ buildings and total power output for an intermittent day (normalised)

10 Figure 116 Power output profiles of the PV arrays on four UQ buildings and total power output for an intermittent 1.5 hour period (normalised) Figure 117 Distribution of ramp-up events in 1-minute periods for 1.22 MW PV system Figure 118 Distribution of ramp-down events in 1-minute periods for 1.22 MW PV system Figure 119 Distribution of ramp-up events in 2-minute periods for 1.22 MW PV system Figure 12 Distribution of ramp-down events in 2-minute periods for 1.22 MW PV system Figure 121 Distribution of ramp-up events in 3-minute periods for 1.22 MW PV system Figure 122 Distribution of ramp-down events in 3-minute periods for 1.22 MW PV system Figure 123 Distribution of ramp-up events in 4-minute periods for 1.22 MW PV system Figure 124 Distribution of ramp-down events in 4-minute periods for 1.22 MW PV system Figure 125 Distribution of ramp-up events in 5-minute periods for 1.22 MW PV system Figure 126 Distribution of ramp-down events in 5-minute periods for 1.22 MW PV system Figure 127 Distribution of ramp-up events in 6-minute periods for 1.22 MW PV system Figure 128 Distribution of ramp-down events in 6-minute periods for 1.22 MW PV system Figure 129 Comparison of output power 1-second ramp events for 196kW and 22kW PV system Figure 13 Comparison of output power 2-second ramp events for 196kW and 22kW PV system Figure 131 Comparison of output power 5-second ramp events for 196kW and 22kW PV system Figure 132 Actual data from DKASC showing power and voltage fluctuations Figure 133 Simulation model for investigating effects of intermittency Figure 134 Voltage at the PV array for low penetration and a strong grid Figure 135 Voltage at the PV array for low penetration and a weak grid Figure 136 Voltage at the PV array for high penetration and a strong grid Figure 137 Voltage at the PV for high penetration on weak grid Figure 138 Irradiance (solid line) and output power, both normalised, recorded at DKASC during a 15-min period on 27 Jan, Figure 139 Spectrum of irradiance recorded at DKASC during a 1-month period for data sampled at 1-second interval Figure 14 Spectrum of output power from DKASC 196kW PV plant recorded for a duration of 1 months at a 1-second interval Figure 141 Output real power, actual (green line) vs. predicted (red line), for two consecutive days at the DKASC, (a) 18 February, 211, and (b) 17 February, Figure 142 Output real power, actual (green) vs. predicted (red), over a 5-hour period on 17 February, 211 at DKASC Figure 143 Spectrum of irradiance recorded at CSIRO during a 2-week period for data sampled at 5-second interval Figure 144 Spectrum of output power from CSIRO 22kW PV plant recorded for a duration of 2 weeks at a 5-second interval Figure 145 Output real power, actual (green line) vs. predicted (red line), for two intermittent days at CSIRO Figure 146 An artificial neural network for predicting future solar power levels. The only input is the current solar power output Figure 147 A sine-curve approximation of solar output across a day Figure 148 An advanced artificial neural network for predicting future solar power levels Figure 149 An advanced artificial neural network (with memory) for predicting future solar power levels Figure 15 Predicted and observed solar power levels for 3 days of data. Outputs are based on the advanced neural network (with memory). Observed data is taken from real-world measurements of solar irradiance Solar intermittency: Australia s clean energy challenge

11 List of Tables Table 1 Scales at which PV is connected Table 2 Potential power system impacts of intermittency over various intermittency timescales Table 3 Timescales for loss of irradiance ramps correlation vs. distance between plants [12] Table 4 Expected factors limiting PV penetration level [2] Table 5 Number of tap changes with and without PV (simulated) [43] Table 6 Reduction in curtailment required for various storage capacity Table 7 Battery cost ($/kwh) [3] Table 8 Features of CST plants versus PV plants Table 9 Percentage of RES for Spain [55] Table 1 PV installation in China [57] Table 11 China s PV targets for 215 and 22 [57] Table 12 Voltage fluctuations solutions PEPCO 2 MW PV Table 13 Voltage fluctuation solutions PEPCO 1.9 MW PV [58] Table 14 Japan s island demonstration sites Table 15 NSW total connections, installed capacity and applications as at October 21 [64] Table 16 PV system connections in NSW by distribution area [64] Table 17 Estimates of Australian solar penetration (PV and CST) by 216, 221 and Table 18 Number of occurrences of ramp-up events with various ramp rates for different timescales 196kW PV Table 19 Number of occurrences of ramp-down events with various ramp rates for different timescales 196kW PV Table 2 Number of occurrences of ramp-up events with various ramp rates for different timescales 22 kw PV Table 21 Number of occurrences of ramp-down events with various ramp rates for different timescales 22 kw PV Table 22 Variance of PV output power for four UQ buildings and total combined output power Table 23 Number of occurrences of ramp-up events with various ramp rates for different timescales 1.22 MW PV Table 24 Number of occurrences of ramp-down events with various ramp rates for different timescales 1.22 MW PV Table 25 Conflicting outcomes in existing literature

12 Executive summary Solar intermittency and grid integration are two fundamental barriers to the uptake of large-scale solar power in Australia and around the world. Whilst much is said about the effect of intermittency on electricity networks, the information shared and views expressed are often anecdotal, difficult to verify and limited to a particular technical, geographical or social context. There is surprisingly very little real-world data on how intermittency, particularly solar intermittency, affects electricity networks. This report provides an in-depth analysis of worldwide research and practical results on renewable generation intermittency, examining what common conclusions can be drawn from other efforts in this area, and how these may apply in the Australian context. This project, Characterising the Effect of High Penetration Solar Intermittency on Australian Electricity Networks, produced several critical findings that help to understand the challenges and opportunities behind intermittency and grid integration. These are listed below. Key Finding 1: Intermittency could stop the adoption of renewable generation Australia is already facing the situation where, in some network areas, the installation of additional renewable generation has been stopped. This is a conservative response to a lack of information about network problems intermittent renewable generation might cause and/or concerns about the mitigation measures required to address them, including cost and availability. This needs to be urgently addressed, through rigorous analysis of both network simulations and trial deployments in the context of Australian electricity transmission and distribution systems. 1 Solar intermittency: Australia s clean energy challenge

13 Key Finding 2: Existing research has conflicting outcomes, suffers from a lack of quality data and consequently often overemphasises anecdotal evidence Key Finding 3: There is considerable intermittency in the existing electricity system Key Finding 4: The effect of solar intermittency is not uniform Some studies report significant cost savings can be achieved by displacing generation fuels, primarily natural gas, with renewable energy sources, supported by accurate forecasts. Others conclude that increased penetration of intermittent renewable generation (IRG) will actually increase system costs due to the required upgrade of conventional generation equipment to achieve increased system flexibility. In studies analysing wind and solar variability some sources report wind to be less variable than solar at the second timescale, while others show wind to be more variable for the same timeframe. In other studies, this contradiction is repeated at minute and hour timescales and even when wind and solar generation are considered in aggregate. The existing electrical power system already incorporates significant load intermittency which is managed through generator dispatch and ancillary services mechanisms. As solar penetration levels increase, additional measures may be needed (for example, additional ancillary services). There is no uniform view on the level at which this will become significant, the requirements for additional ancillary services and how they can be met (possibly through more advanced control of the renewable generation itself). It is critical that this be determined. The effect of solar generation intermittency on the power system is context-specific and currently must be considered on a case-by-case basis. Intermittency exhibited by photovoltaics (PV), concentrating solar thermal (CST) and wind systems have different characteristics and consequently have different network impacts. There is an opportunity to develop a more generalised approach to network assessment, ameliorating the need for detailed modelling of individual systems. Key Finding 5: The amount of high penetration solar generation that can be integrated is application specific The amount of solar generation that can be integrated into utility power systems without compromising power quality, stability and reliability varies widely. The penetration level is dependent on assumptions about how the electrical system should operate, what additional measures are acceptable and what the wider system will look like in future. Re-evaluating network power quality standards (including voltage regulation for generation and load, and flicker requirements for generation) can significantly impact the costs of managing intermittency. Standards vary widely in Australia and around the world. A breach of a restrictive standard in one region may be no worse than complying with a relaxed standard in another. The appropriateness and adequacy of power quality standards, and how they can be cost-effectively met through the coordination of network, load and generation control needs to be evaluated. Key Finding 6: Solar intermittency can be managed A number of mechanisms can be employed to manage the impact of intermittency on electricity networks. Some of these include: using short-term energy storage systems strengthening the electricity network so that intermittency effects are not as localised controlling loads in response to network requirements deploying additional ancillary services (using conventional generators) curtailing the output of renewable generators. Depending on the effectiveness of solutions that combine these mechanisms, managing intermittency may require additional generating units for regulation duty or additional fast-response generators. The choice of measures will also affect the economics of solar generation. The effectiveness of these mechanisms both individually and cooperatively requires further investigation via modelling and experimental analysis as well as real-world Australian trials. 11

14 Key Finding 7: Accurate solar forecasting is essential Key Finding 8: Research and demonstration work is required in Australia Accurate forecasting is vital for the successful integration of large amounts of solar generation. Intermittency can be planned for and managed most cost-effectively with appropriate long (years), medium (months/days) and short term (minutes/seconds) forecasts. This is needed for network planning, and grid and market operation, including accurate generator unit commitment scheduling. In order to support this work, there is an immediate need for high resolution solar data from both large-scale solar systems and large numbers of small-scale solar systems aggregated. This will additionally support further investigation into the effects of temporal variances on the Australian electricity network. There is an immediate need (expressed at this project s industry forum) to develop tools that enable the impact of small scale PV on distribution networks to be assessed. Of particular concern to utilities is the perception that PV causes over-voltage problems. This issue needs to be resolved in the context of Australian network configurations (which tend to be sparse and have higher impedance than observed in overseas studies). In assessing future high solar penetration scenarios, it is necessary for analysis carried out in other countries to be performed within an Australian context. Local research will need to determine the type of ancillary services required and whether existing mechanisms are sufficient for intermittency compensation. These impacts need to be assessed for both large-scale and small-scale solar systems. Future work should include: Development of evaluation tools for DNSPs to assess the impacts of, and develop appropriate mitigation responses for coping with, increasing levels of PV within the distribution network Reconciliation of conflicting information in scientific literature on the impacts of intermittent renewable generation Undertaking a large-scale assessment of the characteristics of generation, load and networks in Australia to determine the applicability of international results and the extent to which Australian networks might require special consideration Consequently, the requirement for intermittency mitigation measures (for example network storage, load management, generation curtailment or additional ancillary services) and the most cost-effective approaches to meeting these at different penetration levels can be assessed Collection of high resolution (temporal and spatial) solar data to support: Development of accurate solar forecasting tools, both for long-term planning and short-term network management Assessment of different large and small scale PV architectures Making detailed case studies and investigations publicly available, about specific intermittency issues and situations (both on actual networks and via modelling and experimental analysis), for assessing the issues and required solutions These should be relatively detailed and could be performed by research groups in conjunction with industry. The modelling, experimental analysis and investigation of case studies would be required at all levels and timescales (e.g. distribution through to system level, and short through to long timeframes). Maintaining industry engagement as initiated through the intermittency workshop and stakeholder perspectives survey undertaken in this project to ensure: Research is relevant and appropriate to Australian industry and the Australian context, including appropriateness to the existing systems and regulatory environment A shared vision which fosters greater renewable generation penetration. 12 Solar intermittency: Australia s clean energy challenge

15 As well as in-depth analysis of worldwide intermittency research and practical results carried out in this report, high-resolution data was obtained from three Australian PV plants of different sizes and analysed to evaluate output power fluctuation ramp rates. An investigation of ten months of solar data from the Desert Knowledge Australia Solar Centre (DKASC) in Alice Springs, Australia, at 1-second resolution, found that high ramp rate events occur more frequently at smaller timescales, with observed power output reductions exceeding 66% of plant rating within a ten second period. Further data collected and analysed came from a 22 kw PV system at the CSIRO Energy Centre in Newcastle with 5-second resolution, representing a small-scale PV system, and from a 1.22 MW PV system at the University of Queensland (currently Australia s largest flat panel PV system) with 1-minute resolution to represent a large-scale PV system. Models were also developed to examine the likely impacts of fast output power fluctuations on different types of Australian electricity networks and to estimate the power output of PV plants using solar irradiance and plant size information. The network impacts model expanded an existing CSIRO model and was used to examine the likely impacts of output power fluctuations seen at the DKASC on various types of Australian electricity networks at different penetration levels. Four different scenarios comprising weak and strong grids with high and low level penetration were modelled. The model showed that if solar generation, PV in this case, is attached to a rural feeder where the grid is likely to be weak, increased penetration would likely cause an increase in voltage swings observed. This could have adverse impacts on the network s stable operation. The four scenarios presented here are general representations of common Australian electricity networks, but detailed information on network characteristics, such as feeder impedance, is needed to evaluate the likely specific impacts of solar intermittency. The PV output power estimation model allows estimation of the output power ramp rate probability density function. This can be used to study the effect of a particular PV array on the local network. Through simulation, and using historical or predicted irradiance levels, the developed model can be used to predict the number of short-term fluctuations, the magnitude of ramp events and the overall AC and DC output power of an existing or proposed PV plant. One feature the model considers is that the larger the collector area, the longer it takes for large passing clouds to cover an entire PV plant. 13

16 Project goals This is the Final Report for the Australian Solar Institute (ASI) funded project entitled Characterising the Effect of High Penetration Solar Intermittency on Australian Electricity Networks. The broad aims of this project were to: consult key solar industry stakeholders to understand and assemble a list of key concerns regarding solar intermittency in Australia develop a model linking solar intermittency to generation output assess the current state of research worldwide and the unique aspects of Australian deployments develop an understanding of how solar intermittency differs from wind. Project-related activities 1) Industry Workshop on the Effect of High Penetration Solar Intermittency on Australian Electricity Networks, held at the AEMO offices in Melbourne on 4 April, 211 Brought together over 4 key experts from network service providers, power system operators, large-scale renewable system operators and other industry players from around Australia to jointly report on the effects of renewable generation intermittency on electricity grids identify the key concerns regarding intermittency in Australia 2) A follow-up survey was conducted to get a better understanding of effects and concerns regarding intermittency in Australia from industry experts. 3) Data collection High resolution 1-second data collected from the Desert Knowledge Australia Solar Centre (DKASC) 196 kw 5-second data for CSIRO Energy Centre office building rooftop PV system 22 kw 1-minute data for Australia s largest flat panel PV system at the University of Queensland 1.22 MW 4) Publication: 1 st International Workshop on Integration of Solar Power into Power Systems held on 24 October, 211, in Aarhus, Denmark Abstract submitted on 3 June, 211 Notification of acceptance received on 27 June, 211 Full paper submitted on 15 September, 211 Paper presented in the Keynote Session of the Workshop on 24 October, Solar intermittency: Australia s clean energy challenge

17 List of acronyms AC ADES AEMO AGC Ah APF AS ASI AWEFS BAPV BIPV BOM CAISO CAT CCS CDF CECRE CECRE CF CIGS CSI RD&D CSIRO CSP CST CSU DC DFT DG DKA DKASC DNSP DSM DSO DSO ED EDP EMS ENA ERCOT ESAA Alternating Current Association pour le Développement de l Energie Solaire Australian Energy Market Operator Automatic Generation Control Amp-hour Absorbing Power Factor Australian Standard Australian Solar Institute Australian Wind Energy Forecasting System Building Applied Photovoltaics Building Integrated Photovoltaics Bureau of Meteorology California Independent System Operator Centre for Appropriate Technology Carbon Capture and Storage Cumulative Distribution Function Control Centre of Renewable Energies Clean Energy Council Capacity Factor Copper indium gallium di-selenide California Solar Institute Research, Development, Deployment and Demonstration Commonwealth Scientific and Industrial Research Organisation Concentrating Solar Power Concentrating Solar Thermal Colorado State University Direct current Discrete Fourier Transform Distributed Generation Desert Knowledge Australia Desert Knowledge Australia Solar Centre Distribution Network Service Provider Demand-Side Management Distributed System Operator Distribution System Operators Economic Dispatch Energias de Portugal Energy Management System Energy Networks Association Electric Reliability Council of Texas Energy Supply Association of Australia 15

18 ESM EU EV FCAS FFT FNN FRV GUI GW GWh HELCO HIT HP-IRG Hrs HTF IEA IEEE IRG km kva kvar kw kwh kwp LFC LS-PV LTC LV MBTU METI MJ MV MVA MW MWh MWp NEDO NEM NER NERC NREL NSW Energy Sector Model European Union Electric Vehicle Frequency Control Ancillary Services Fast Fourier Transform Forum for Network Technology / Network Operation Fotowatio Research Ventures Graphical User Interface Gigawatts Gigawatt-hour Hawaii Electric Light Company Heterojunction with Intrinsic Thin Layer High Penetration Intermittent Renewable Generation Hours Heat Transfer Fluid International Energy Agency Institute of Electrical and Electronic Engineers Intermittent Renewable Generation Kilometers Kilovolt-amp Kilovolt-amp reactive Kilowatts Kilowatt-hour Kilowatts peak Load Frequency Control Large-Scale Photovoltaics Load Tap Changing Low Voltage Kilo-British Thermal Unit Ministry of Economy, Trade and Industry Megajoule Medium Voltage Megavolt-amps Megawatts Megawatt-hour Megawatts peak New Energy and Industrial Technology Development Organisation National Electricity Market / Net Energy Metering National Electricity Rules North American Electric Reliability Corporation National Renewable Energy Laboratory New South Wales 16 Solar intermittency: Australia s clean energy challenge

19 NT NWIS NYISO OECD PCC PCS PEPCO POI PSH PSSE PV PVPS RES RET ROCOF s SAIFI SCADA SEGS SRRL SWIS TES THD TNSP TOU TSO UCL UQ USA V Var VDE VG VRLA W WA WECC Wh Northern Territory North-West Interconnected System New York Independent System Operator Organisation of Economic Co-operation and Development Point of common coupling Power Control System Potomac Electric Power Company Point of interface Pumped Storage Hydro Power System Simulator for Engineering Photovoltaic Photovoltaic Power Systems Renewable Energy Sources Department of Resources, Energy and Tourism Rate of change of frequency second System Average Interruption Frequency Index Supervisory Control and Data Acquisition Solar Energy Generating Systems Solar Radiation Research Laboratory South-West Interconnected System Thermal Energy Storage Total Harmonic Distortion Transmission Network Service Provider Time-of-use Transmission System Operators Unserved Critical Load University of Queensland United States of America Volt Volt-Amp Reactive Association for Electrical, Electronic and Information Technologies Variable Generation Valve-regulated lead-acid Watts Western Australia Western Electricity Coordinating Council Watt-hour 17

20 1 Introduction Whilst much is said about the effect of renewable energy intermittency on electricity networks, the information shared and views expressed are often anecdotal, difficult to verify and limited to a particular technical, geographical or social context. There is surprisingly very little real-world data on the effects of intermittency on electricity networks, particularly in regard to solar intermittency. Investigations into the issue in Australia are particularly scarce for both photovoltaics (PV) and concentrating solar thermal (CST), especially when considering unique network aspects such as the distance covered by Australia s electrical infrastructure and its limited level of interconnection (see Figure 1). Analysis of the effects of solar intermittency and operational considerations are vital for accelerating solar technology commercialisation and deployment in Australia. The largest concentration of research into the effect of intermittent renewable generation (IRG) on electricity networks has been in the wind industry, with one of the most widely cited works being that of the Institute of Electrical and Electronics Engineers (IEEE) Accommodating Wind s Natural Behaviour Advances in Insights and Methods for Wind Plant Integration [1]. The issues raised in such wind industry research papers include, but are not limited to: dispatchability, balancing, variability, uncertainty, market operation and impact on system reliability. There is no consideration of whether these issues apply to solar generation and if so, how they relate and the extent of the commonality. Recognising this general lack of information, this report provides an in-depth analysis of worldwide research and practical results on renewable generation intermittency, examining what common conclusions can be made from other efforts in this area, and how these might apply in the Australian context. Some of the most significant work in this area is being undertaken by the California Solar Initiative Research, Development, Deployment and Demonstration (CSI RD&D) Programme, exploring the planning and modelling for high penetration PV on the California transmission and distribution network [2]. This work is specifically targeted towards California, and only examines photovoltaic generation. More broadly, the current worldwide state of the art in solar intermittency study is represented by the information being collated by the International Energy Agency (IEA) Task 14 on high penetration PV. This work is predominantly general in nature, collating already available data rather than conducting targeted experiments, and again focuses on PV intermittency. The US Department of Energy (DoE) is also facilitating many activities to investigate high solar penetration issues. Their High 18 Solar intermittency: Australia s clean energy challenge

21 Penetration Solar Portal [3] contains links to information on various case studies, investigations, analysis and technical discussions on relevant high solar penetration topics. Market dynamics Network subject to Australian usage, weather and environmental Low levels of Large coverage area Unique network aspects in Australia High impedance network compared with Europe Low average density Large number of long skinny feeders Areas of high density *Australian load profiles and environmental conditions such as fire and flood Figure 1 Unique network aspects in Australia There is no common definition of high penetration intermittent generation, but there is consensus amongst the parties developing the IEA Task 14 that a high penetration situation exists if additional efforts are necessary to optimally integrate the dispersed generators. Building on this philosophy, a working definition of high penetration intermittent generation was developed and is presented in Section 2 of this report. Section 3 summarises the current state of research on renewable generation intermittency, focussing on centralised and distributed photovoltaics (PV) and Concentrating Solar Thermal (CST) although wind intermittency is also addressed briefly. The literature shows that one of the main challenges for the power network is overcoming issues with the instantaneous penetration of intermittent solar generation, that is the fraction of total system load being provided by solar generation at a given instant. Solar generation is viewed as negative load and when it is combined with the actual system load yields a net load which corresponds to the power that must be supplied by other resources on the system. The effect of renewable generation intermittency on net load variability, as covered in the existing literature, is also given in this section. Observations of actual cases of high penetration solar intermittency are also presented. Section 3.3 summarises studies examining the likely impacts of increased levels of intermittent renewable generation through simulation and modelling. Changes to operations, infrastructure, planning and power quality management that might be required to manage increased levels of intermittent renewable generation, as suggested in the literature, are discussed in Section 3.4. To understand the differences between wind and solar intermittency, several aspects of both wind and solar generation intermittency are discussed in Section 4. This includes correlation of both generation types with load, their inherent variability over different timescales, and load-following and regulation requirements to meet the new variability in net load introduced by solar and wind. The differences between CST and PV power intermittency are discussed in Section 5, which includes comparisons of the two different solar generation types. 19

22 Section 6 presents studies completed in other countries which are considered relevant for informing the integration of high penetration intermittent generation in Australia. Both system and distribution level studies are included. Findings from some of these studies can be applied in Australia, but similar studies would need to be carried out in an Australian context, examining unique Australian conditions, such as reduced grid interconnection, disparate population densities and long and skinny feeders, before any significant conclusions can be made here. This project builds upon the early work of the ASI-supported Australian PV Association s International Energy Agency (IEA) Task 14 investigation into High Penetration PV in Electricity Grids, by investigating CST as well as PV. Section 7 of this report discusses key intermittency issues attributed to PV installations in various countries around the world, identified at the IEA Task 14 High Penetration Photovoltaics workshops in December 21 and May 211. Section 8 of this report explores solar intermittency in the operation of Australian electricity networks. An Australian industry workshop and follow-up survey were conducted to obtain the views of key solar industry experts on solar intermittency in the Australian context. Invited attendees and submissions included responses from Australian utilities, power system operators, large-scale renewable system operators, renewable energy developers and other solar and electricity industry players, on solar intermittency in the Australian context. The discussions and findings from both the workshop and survey are presented in this Section. Apart from the daily sun cycle, clouds are the main cause of solar generation intermittency. Variations in irradiance depend on cloud height, sun elevation and wind speed. Irradiance fluctuations have been observed and analysed in Germany [7], Japan [8], Belgium [9] and USA [66], but analyses of power output fluctuations are particularly scarce. Ten months of 1-second resolution solar data from the Desert Knowledge Australia Solar Centre (DKASC) in Alice Springs, Australia, was collected and analysed to evaluate the occurrences of power fluctuations and the various ramp rates investigated. To study the variations of output power from a small-scale and large-scale PV plant, 5-second data from the CSIRO Energy Centre s office building rooftop PV system and 1-minute data from Australia s largest flat-panel PV system at the University of Queensland were also collected and analysed. Section 9 of this report presents an analysis of the various PV plant output power ramp rates observed and considers the frequency of their occurrence. The data for line-to-neutral AC voltage measured at the DKASC were also analysed and the correlation between variations in irradiance, output power and voltage was observed. The effects of intermittent power generation upon electricity networks are strongly influenced by the type of network to which they are connected and the type and amount of generation installed, however most fall into the categories of stability and voltage effects. A simulation model developed at CSIRO was used to examine the likely impacts of output power fluctuations seen at the DKASC on various types of Australian electricity networks with different penetration levels of solar power. Four different scenarios comprising weak and strong grids with high and low level penetration of solar power were modelled. Section 1 of this report explores the likely effects of output power fluctuations seen at the DKASC on various Australian electricity networks. The power generated by solar power plants has an intermittent character due mainly to atmospheric effects such as insolation variability resulting from cloud movement, however differences are present between thermal and PV technologies and their response to changes in insolation. One example involves the generally low inertia to change in output of PV plant when compared to thermal systems, which have a level of inherent thermal storage, while the reliance of concentrating solar thermal (and PV) systems on direct irradiance can be contrasted to non-concentrating PV s acceptance of both global and direct irradiance. Further, consideration of the effects of shading and partial shading indicates that the larger the PV plant, the longer it takes for a cloud cover spread to shade a significant proportion of the entire field. An analytic model has been developed in which the PV plant power output is described as the signal output of a first order low-pass filter whose input signal is the solar irradiance signal. Using historical or predicted irradiance levels, the developed model, explained in Section 11, can be used to simulate and predict the power output of an existing or proposed PV plant. This allows estimation of the output power ramp rate probability density function, which can be used to estimate the effect of a particular PV array upon the local network. 2 Solar intermittency: Australia s clean energy challenge

23 2 Definitions In seeking to define high penetration intermittent generation, the IEA Task 14 group notes that Although up to now, no common definition of high penetration PV scenarios exists, there is consensus amongst the parties developing this Task that a high penetration situation exists if additional efforts will be necessary to integrate the dispersed generators in an optimum manner. We build on this philosophy, by suggesting that a high penetration intermittent renewable generation (HP-IRG) scenario exists where it is the variability of the intermittent generation rather than the loads within a network segment that is the dominant factor in determining the need for substation, network or control upgrades. This definition can then be expressed mathematically, based on a comparison of the largest net variability as seen in both loads and generation: HPIG exists if Pg i Pg (i 1) i (i 1) for i such that P i P (i 1) is maximised where Pg i is intermittent power generation (kw) at time i i P i i Pg i 1 Note that the variability of loads and generation needs to be assessed over a timeframe appropriate for the network characteristics under consideration as is the case when assessing other network performance characteristics such as voltage and frequency fluctuations. This is for the purpose of assessing the time interval τ. 21

24 Further, commonly described scales at which PV is connected are defined below in Table 1. Table 1 Scales at which PV is connected Identifier Small-scale Utility-scale Large-scale Connection Point 23V/4V (or 24V/415V) Low Voltage Distribution Network > 23V/4V (24V/415V) Distribution Network 66kV Transmission and Sub-transmission Network 22 Solar intermittency: Australia s clean energy challenge

25 3 Current state of worldwide generation intermittency research This section summarises the current state of research on renewable generation intermittency, for wind, centralised and distributed photovoltaics (PV) and concentrating solar thermal (CST). 3.1 Characterisation of solar power variability Materials discussing the variability, correlation with load and spatial diversity aspects of distributed and concentrated solar power are covered in this section. CST is addressed only briefly, reflecting the small amount of published material currently available on the topic. Clouds are the main reason PV generation experiences intermittency (excluding diurnal intermittency). PV generation can drop by 6% within seconds [4] due to a reduction in solar insolation. The time taken for the cloud to pass is dependent upon cloud height, sun elevation and wind speed. These factors need to be considered in solar power production forecasting [5]. I n [4], it is noted that the variability of solar systems can be characterised across two dimensions, temporal and spatial. The analysis in the existing literature covered in this section investigates intermittency at different timescales, and the significance of these timescales to grid operation and their potential power system impacts in the temporal domain are included in Table 2. Table 2 Potential power system impacts of intermittency over various intermittency timescales Timescale of Intermittency Seconds Minutes Minutes to hours Hours to days Potential Power System Impact Power quality (e.g. voltage flicker) Regulation reserves Load following Unit commitment 23

26 The spatial impacts of variability on the electricity grid will vary because of the smoothing effect, which describes the solar power production decorrelation that occurs as spatial separation is increased. Despite this, even if this aggregated generation is smoothed, some potential impacts act at a local level and some at a global level. For example, while power quality concerns may be limited to a single feeder (tens of square kilometres), load balancing is an issue which occurs across a much larger area (say, an entire network segment). For this reason, it is clear that an understanding of variability at both tens of kilometres as well as thousands of kilometres (or however large the load balancing area) is required. According to [6], the variability of PV generation is reasonably insignificant on cloudless and consistently overcast days (albeit at reduced power output). It is on partly cloudy days that variability in insolation becomes a major concern. At present, although these variations can cause local problems, the bulk grid is only concerned if the outputs of a large number of PV installations vary at the same time, due to the modest amount of PV installed compared to total electrical lo ad. Figure 2 shows PV output profiles representing relatively large regions in the western USA Zip code 92 Zip code 91a Zip code 95a Zip code 91b Zip code 95 Zip code 9 Zip code 921 Zip code 956 Zip code 94a Zip code 945 MW Hour of day Figure 2 PV output profiles across various zip codes in western US for a July morning [6] Although reliant on direct beam irradiance, concentrating solar thermal (CST) is inherently less variable than PV due to the thermal mass present in the energy distribution system, which can be water, oil or molten s alts [6]. The graph bel ow in Figure 3 is a normal day for a large CST plant in California. The CST plant in the study had six hours of thermal storage which was dispatched to a typical utility load pattern. 24 Solar intermittency: Australia s clean energy challenge

27 5 Sungen & Luz 4 3 MW Hour of day Figure 3 Example output profile of a CST plant on a day in May in California [6] Correlation of solar and load Neither PV nor CST correlates greatly with the morning and afternoon load (demand) peaks. Figure 4 shows correlation between CST, PV production profile and load. Peak PV production occurs between the morning and afternoon load peaks. The CST system shown includes thermal storage, reducing the time to the peak afternoon load to about 3 hours. Without thermal storage the production profile would be simila r to PV [6]. These profiles vary throughout the year with PV and CST generating for longer periods during the summer months. At the height of summer (June/July for the Northern Hemisphere and January/February for the Southern Hemisphere), both PV and CST generation begin to overlap with the afternoon load peak, CST (with thermal storage) in particular, as can be seen in Figure Load (MW) Load PV CSPws Solar (MW) Hour of Day Figure 4 Average January profiles (California) for load demand (pink triangles), PV (purple squares) and CST generation [6] 25

28 6 15 Load (MW) Load PV CSPws Solar (MW) Hour of Day Figure 5 Average July profiles (California) [6] Variability of renewable sources and net load An interesting finding is the variability of monthly solar energy production over the course of three years as discussed in [6]. Figure 6 shows the variation year-to-year for each month over the entire footprint of the study. Variation is likely to be greater over smaller areas. Knowing what kind of variability to expect at certain times of the year will be important for forecasting and planning purposes. Total Energy (GWh) PV CSP 2 1 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month of Year Figure 6 Variation in monthly solar energy for three years (24-26) [6] One of the main challenges to the power system is characterised by the instantaneous penetration of intermittent solar generation, i.e. the fraction of total system load being provided by solar generation at a given instant in time. Further analysis in [6] looked into net load. Solar generation is viewed as negative load, and when this is combined with the actual system load yields a net load, which corresponds to the power that must be supplied by other resources on the system. The variability of load and solar will drive the change in production of the other generation resources on the system. When load and solar power are both coincidentally increasing or decreasing, the need for other generation to vary their output 26 Solar intermittency: Australia s clean energy challenge

29 power will decrease. Of concern to utilities is when load and solar power move in opposite directions (e.g. increasing solar output power and decreasing load, and vice versa) at the same time creating large changes in the net load. Figure 7 shows hourly changes of solar generation and load over a year with 25% solar penetration (relative to total annual load energy). The four quadrants can be described as: Q1: concurrent increase in solar and load Q2: increase in solar and decrease in load Q3: concurrent decrease in solar and load Q4: decrease in solar and increase in load A noteworthy aspect is how much greater the variation in solar power is compared to load with solar showing variations of up to 15 GW and load barely 4 GW. Summer (pink dots) sees the most extreme variations. Hours above and below the 5 MW dotted lines correspond respectively to net load decreases and increases greater than 5 GW. These large variations in the net load have the potential to stress the grid and operating reserves. Looking at Q1 and Q3 (concurrent changes), load changes regularly by over 2 GW at the same time as solar power changes by more than 1 GW (Q1) and 5 GW (Q3). This results in a change in net load of 8 GW up and 3 GW down. According to [3] utilities in California are concerned with changes in net load over 5 GW. The data for solar generation (25% penetration level) simply scaled up a 5% penetration scenario, and does not take into account the effect of increased spatial diversity on the reduction of solar power variability. Realistically, the hourly variability of solar power would be lower with a more geographically diverse installation of solar generation plants. Solar Delta (MW) (25% scenario) Increased L-S down-ramps Q2 Load and Solar Spring Summer Fall Winter Q1 Load and Solar Q3 Q4 Increased L-S up-ramps Load Delta (MW) Figure 7 Hourly variability of load and solar generation [6] A study on the net load variability in the Californian grid was reported i n [1]. Figure 8 illustrates the distributions of hourly change in total load (delta) for load-only and net load (total load taking into account wind and solar generation as negative load) for a model of the Californian power grid assuming 33% penetration of renewable energy, wind and solar in this 27

30 case. Net load is indicated in the figure by L-W-S. The projection in this study is based on 26 data. The data is split up into deciles. The 1st decile is a measurement of delta when load is between 9% and 1% of peak load (top 1% of peak load hours) with the 1th decile being a measurement of delta for loads up to 1% of peak load. The thin lines above and below the bars represent the standard deviation of the positive and negative deltas respectively. An increase in hourly net load variability can be seen in majority of the deciles, more pronounced in the higher deciles, with the integration of 33% renewable energy. The standard deviation of net load variability in the 1th decile is seen to increase by 47% with the integration of 33% intermittent renewable energy penetration. According to [1], the required flexibility to account for the variability of the renewable resource is three standard deviations. System flexibility can be described as the general characteristic of the ability of an aggregated set of generators to respond to the variation and uncertainty in net load Load L-W-S 2 MW Decile Figure 8 Predicted hourly net load variability with 33% renewable penetration in the Californian grid in 21 [1] Figure 9 shows the results of a similar analysis of net load variability, but in this case for each hour of the day. At 6am, we have similar maximum and minimum deltas but the average hourly variation is seen to increase by 25% from 2MW to 25MW in the scenario with 33% intermittent renewable penetration. For the afternoon peak, the averages are similar but the maximum and minimum deltas are significantly larger for net load with solar and wind energy integrated. The standard deviation is also noticeably larger for net load, increasing from approximately 13 to 16 MW (around 23% increase). 28 Solar intermittency: Australia s clean energy challenge

31 8 6 4 Load L-W-S 2 MW Hour Figure 9 Hourly net load variability by hour of day [1] Ramp rates A ramp rate analysis for a PV system at the NREL/SRRL site in Golden, Colorado, USA, was perform ed in [11] where data was accumulated over a 1-year period at 1-minute inte rvals. Figure 1 shows the distribution of 1-minute and 15-minute ramp rate events and the number of high ramp rate events, both positive and negative, was seen to be much greater for the 1-minute data compared to the 15-minute data. Data of this nature would assist in determining the degree of flexibility required in the network to compensate for the variability of PV systems Frequency in daylight hours minute 15 minutes.5-5% -4% -3% -2% -1% % 1% 2% 3% 4% 5% Bin % change per minute Figure 1 Frequency of different ramp rate events as a percentage of total PV capacity for PV system in Golden, Colorado [11] 29

32 3.1.4 Spatial diversity Changes in the position of the sun affect the output of all PV plants in a nearly uniform and highly correla ted way [12]. Changes in PV output due to clouds are not driven by a similar uniform process. Clouds move across plants affecting one part of a plant before another, or leaving some parts of the plant unobstructed as the cloud passes. The relative reduction in solar irradiance ramps for the aggregate of multiple plants relative to a single point is demonst rated in Figure 11. The reduction in variability due to spatial diversity is seen within large-scale PV plants as well. The graph in Figure 12 compares the variation of a single insolation point to the variation of the output of an entire PV plant rated at 13.2MW. Unfortunately, information on the layout of the PV panels and the area of the plant were not available. The output power did not experience a change of more than 2% in any 1-second period, whereas there were variations of nearly 8% in the irradiance level. The average correlation between the output of one inverter and all the other inverters in the same plant was found to be about 5%, which is an indication of diversity of output within the same PV plant. The smoothing of PV output across plants when aggregated is also inves tigated in [12] for 1-minute and 1-minute timescales. The study comprised of six individual PV plants within an approximate 2 square kilometre area in Las Vegas, USA. For the 1-minute timescale, the maximum ramp rate of the six plants ranged from 3-5% and this was seen to reduce to around 2% when the outputs of all plants were aggregated. A similar reduction in variability due to aggregation is seen for the 1-minute timescale. Irradiance (Site 5 May 7, 1999) 2 Second Change in Irradiance (Site 5 May 7, 1999) ^2) ^2) : 12: 18: 6: 12: 18: Time of Day -1 Time of Day Figure 11 Reduction in variability of solar irradiance when the outputs of multiple sites are aggregated [12] 3 Solar intermittency: Australia s clean energy challenge

33 min min 1-sec Power Irradiance Figure 12 Cumulative distributions (95th to 1th percentiles) of irradiance and PV power changes over various time periods for one highly variable day for 13.2 MW system in Nevada [12] The distance between plants before correlation of irradiance ramps is lost for various timescales is d iscussed in [12]. These are for plants located in the Great Plains. Irradiance ramps over timescales of 3 minutes were uncorrelated for sites around 5 km apart. Ramps over timescales of 6 minutes were uncorrelated for sites in the order of 15 km apart and ramps over timescales of 15 minutes and shorter were uncorrelated for all distances between sites down to the minimum spatial resolution of 2 km between sites. Table 3 Timescales for loss of irradiance ramps correlation vs. distance be tween plants [12] Distance between plants (km) Required time scale (min) for loss of correlation 2 < d < 5 > 15 5 < d < 15 > 3 > 15 > 6 31

34 3.2 Impacts/observations: high penetration solar intermittency The impacts of PV power plants are associated with voltage profiles, electrical losses, power factor, capacity planning, power quality, system operations and protection. Currently utility-scale solar PV plants have nominal capacities that are compatible with distribution substation MVA ratings. During normal operations, energy market operators control and dispatch conventional generators to minimise the cost of producing electricity while maximising system reliability. Each generation unit is committed and loaded according to its heat rate, fuel cost and availability, associated transmission losses, and output ramp rate, to satisfy the electricity demand reliably at the lowest possible cost [44]. However, unlike operators of conventional generation units, renewable generation system operators have no control over the availability and quantity of solar and wind resources, as weather variations dictate the generation output of these units. The inclusion of intermittent energy technologies in the system means conventional generators not only must follow the usual load demand changes, but also make up for the output variations caused by intermittent generators. Other techniques that can be used to compensate for the output variations of intermittent generators include energy storage, load response and curtailment of generation from intermittent sources. Five normal functions of the generation operations that could be affected are: Load-frequency control: When load exceeds generation, the system frequency will drop, and vice versa. When a change in the system frequency is detected, power system operators increase or decrease the output of conventional generators to match the load. Intermittent renewable generation technologies generally have not participated in system frequency regulation and have output power that is independent of system frequency. However, through output curtailment, there is no engineering reason why renewable generation could not participate in the FCAS1 Lower markets of the Australian National Electricity Market (NEM). An example of this is Germany, where the grid operator has the capability of turning down PV output for large PV plants if required. Load following: If an increase in solar or wind power does not coincide with a system load increase, other generating units in the system will have to be off-loaded so as to utilise the solar or wind power while keeping the system balanced. When meteorological conditions cause a decrease of solar or wind power, output from other units has to increase to take up the generation slack. Networks normally use intermediate plants to follow the load. The integration of high penetration intermittent generation may result in increased load-following duties for the conventional generators assigned for system regulation. Ramping rate: Ramping rate represents the generator s ability to change its output. The ramping rate of on-line generators has to be able to follow the combined load changes and output fluctuations of intermittent generators when intermittent generation is added to the electricity system; for example, load increase and intermittent generation decrease simultaneously, or vice versa. Unloadable generation: The down-ramping rate of a generator may be different from its up-ramping rate. Both are important to meet the normal system load-following requirement. The amount of generation that can be off-loaded (down ramping) is called unloadable generation. In order to accommodate the maximum output from intermittent generating technologies, system operators have to make certain that on-line conventional generators can be backed down quickly enough, particularly when facing a simultaneous sudden increase of intermittent generation output and a reduction of system load. Such an accommodation to absorb energy from intermittent generation cannot be made by tripping off a unit because the unit may be needed again soon after being taken off-line. Operating reserve: The impact on the electric system operating reserve is also related to the intermittency of solar and wind generation technologies. Operating reserves are maintained to guard against sudden loss of generation and unexpected load fluctuations. Any load and generation variations that cannot be forecast have to be considered when determining the amount of operating reserve. Carrying operating reserves is expensive. If the short-term fluctuations of intermittent renewable generators cannot be predicted accurately, more operating reserves will have to be scheduled to ensure the 1 The control of frequency on the power system is managed through the dispatch of frequency control ancillary services (FCAS). The Australian Energy Market Operator (AEMO) procures FCAS to ensure that when an event occurs on the power system (e.g. loss of the largest generator or loss of an interconnector and subsequent islanding of a region) frequency is maintained within these standards. 32 Solar intermittency: Australia s clean energy challenge

35 system can be kept regulated within standards. This requirement will increase the cost of integrating intermittent solar and wind systems. These five potential impacts on operational requirements mean an increased proportion of conventional generation units may need to be brought on-line or put on regulating duty to manage higher penetration intermittent renewable generation, which may increase the system operating cost. One of the objectives of sub-task 3 of the IEA Task 14 Photovoltaics Workshop is to look into the impacts of high penetration solar on distribution grids. Presentations at recent meetings in Colorado and Lisbon included case studies showing impacts which give reason for concern. The issues of concern include: fast ramp times in PV output due to cloud activity making voltage and frequency regulation difficult displacement of conventional generation providing ancillary grid services aggregate loss of PV during faults and contingencies (due to under-voltage and under-frequency) insufficiently accurate forecasting making scheduling difficult. According to [13], conventional generators are forced to be more flexible with their output, resulting in a higher per unit cost. Adequate system flexibility is a key requirement for managing increased levels of intermittent renewable gene ration; Section 3.4 discusses how flexibility requirements can be met. This section presents observations of actual case studies of high penetration PV intermittency. Measured data was given for a report on a PV plant in Gardner, Massachusetts and a small scale study done in Italy. Unfortunately the majority of work discovered is modelling of impacts rather than observation of impacts. Cloud activity is the main reason for variability in PV output. High frequency sampling (1-second) at the Gardne r, Massachusetts [14] site shows the kind of ramp rates possible in PV output and net load due t o passing clouds. Figure 13 shows the impact of a passing cloud on the irradiance and net load of the site, which consists of 53 residential properties and 28 PV installations. The irradiance is seen to initially dip sharply just after 14:13, causing the net load to increase sharply as the cloud edge begins to move across the site. During the one minute and thirty seconds period between the onset and completion of the cloud passing, the net load is seen to increase by 45kW from a condition where the site was exporting 1kW to one of importing 35kW. At 14:15, the irradiance is seen to increase from being near zero to 85% in 4 seconds, and the site is once again exporting power. This case study shows how rapidly the net load of a system can vary significantly. 33

36 Beam Irradiance 6 B Phase Power :13:5 14:13:36 14:14:6 14:14:36 14:15:6 14:15:36 14:16:6 Time of Day EDST 25 Sept 87 Figure 13 Irradiance and net system power output during cloud passage [14] Figure 14 shows the site power flow (P34) over a 6-minute period with passing clouds. Even taking into account the smoothing effect aggregation has on PV output, large drops in power output are observed, including a drop from 5kW to 8kW in approximately one minute Real Power P34 Real Power (4 PV) -1 13:52:51 13:53:28 13:54:3 13:54:37 13:55:12 13:55:47 13:56:21 13:56:54 13:57:3 13:58:5 Time of Day Sept 25, 1987 Figure 14 Total site power and PV generation over a 6-minute period [14] 34 Solar intermittency: Australia s clean energy challenge

37 Large variations in PV output for systems with high penetration PV will result in proportionally large variations in net load. Existing methods for managing large scale intermittent generation are not expected to be suffici ent. Again, Section 3.4 contains further discussion on how large scale intermittent generation may be managed. A good example of output variations that can be expected from a large-scale PV system can be seen from the output of a 4.6MW PV system located in Springerville, Arizona, US. The extent of intermittency exhibited by such a large s ystem can be seen in Figure 15, where the PV output data was sampled every ten seconds. Large abrupt power output drops, from about 4 kw to 5 kw, can be seen to occur over extremely short timeframes. 4 Real Power Output (kw) Seconds Figure 15 Power output of a 4.6 MW PV system on a partly cloudy day in Arizona [39] A near-occurrence of instability caused by intermittency of wind generation is when ERCOT 2 was forced to declare emergency conditions when an abrupt loss of 1,2 MW of wind energy production caught them by surprise on 26 February, 28 [39]. The sharp drop in production occurred during a three-hour period when overall electricity loads were increasing. This threatened the stability of the power grid and had the potential to cause rolling blackout. Large-scale PV penetration may cause similar problems due to significant magnitude and ramp rates of power output variations that can be seen to occur, an example b eing the case shown in Figure 15. Installed PV capacity in Germany has increased greatly in recent years. By the end of 21 approximately 8% of cumulative installed PV capacity (about 14 GW) was connected to the low voltage network [4]. Before a transitional arrangement was introduced by the VDE FNN e.v. in April 211, low voltage generation plants were required to be switched off immediately if system frequency increased to 5.2 Hz. In a worst case scenario, up to about 9, MW of power from PV systems would disconnect from the network if system freque ncy increased to 5.2 Hz [4]. That would cause a large instantaneous loss of PV power on the network and a sharp increase in the load seen by centralised generators. This in turn could affect the stability of the grid. Reaching a system frequency value of 5.2 Hz during normal operations is as yet quite unlikely but any unexpected large-scale disturbance followed by an abnormal system condition would pose significant risks and cause the system frequency to increase due to an oversupply of electrical power. An example of this is the European power grid failure in 26 due to power imbalance [41] and the blackout in Italy in 23 [42]. In both cases, Germany belonged to an exporting network region in which the frequency value increased to 5.2 Hz. The European grid is designed only for a sudden loss of 2 The Electric Reliability Council of Texas (ERCOT) manages the flow of electric power to 23 million Texas customers, representing 85 percent of the state s electric load. 35

38 3, MW of generating capacity. If similar disturbances were to occur on sunny days with the current PV capacity during high supply from those PV systems, their power infeed would be lost. On sunny days the current PV capacity in Germany exceeds the current maximum value of 3, MW by several times. As a result, there would be a high probability of a large-scale failure to the electrical supply in those parts of Europe affected by this phenomenon. Note that the 5.2 Hz issue in Germany is not specifically an intermittency issue, more one of inverter or regulatory parameter settings. The power quality provided by a photovoltaic system is described by the voltage, frequency, harmonics of voltage and curren t, flicker and power factor [15]. An experimental analysis aimed at evaluating the effects of many PV plants on the power qualit y of a grid was performed in [15]. In this case, it was found that the Total Harmonic Distortion (THD) of the current injected into the grid, independent of grid characteristics, may at times exceed standard limits. Moreover, the THD for voltage could exceed standard limits in the case of high impedance networks, typical at the distribution level. These findings were based on systems ranging from 16 to 4 kw. The penetration level of these systems is not mentioned, but considering their size it is unlikely to be large. Based on the findings of this report, higher levels of distortion are likely to be observed with increased penetration, especially in grids with relatively high impedance (suburban and rural grids). It is important to note that these results were unlikely due to harmonics caused by the PV inverters (limits for harmonic distortion in inverters, as set in Australian Standard AS4777, are significantly lower than those allowed for loads), but rather that by partly meeting the real power requirements (reducing the fundamental frequency component) of the network segment, the harmonics due to loads become more apparent. Consequently, while there is unlikely to be any significant difference in the total harmonics due to high PV penetration levels, the reduced real power requirements can lead to an increased THD ratio. 3.3 Predicted impacts of high penetration intermittent renewable generation There have been a number of studies looking into the likely impacts of increased levels of intermittent renewable generation (IRG). They cover areas such as effects on fault response of systems, voltage and frequency regulation, fuel mix, generation flexibility and cost. These are all estimations of impacts, achieved through simulation and modelling. The predicted impacts of high penetration intermittent renewable generation include the displacement of conventional generation units, requirement of curtailing renewable generation output, load frequency control and associated costs. A thorough survey of papers is given in [16] covering the impact of variable renewable generation power fluctuations on system performance. A general observation is that integrating renewable energy sources in power system grids will have impacts on optimum power flow, power quality, voltage and frequency control, system eco nomics and load dispatch. From [ 13], the load duration curve in Figure 16 shows original (without PV) net load and the predicted impact of incorporating 1, 3 and 5% PV penetration. Original net load data is from the California Independent System Operator (CAISO) for July 27. Actual PV production data from CAISO during the period has been scaled to give penetration levels of 1, 3 and 5%. Some reduction in peak load is seen (far left), and for 3 and 5% PV, a large impact on minimum net load is observed. This will affect the generation portfolio, as it may be necessary to have conventional generation capable of running at a reduced minimum load. A reduction in minimum load due to increased PV penetration will therefore have impac ts on the fuel mix. The graph in Figure 17 shows how the typical Californian fuel mix will meet net load demand with 3% solar penetration. In this case, only gas fired generation will be affected. Solar could be valued by the savings achieved through reduced gas generation. 36 Solar intermittency: Australia s clean energy challenge

39 5 MW Load Net Load 1% PV Net Load 3% PV Net Load 5% PV hours Figure 16 Predicted load duration curve, CAISO July 27, with 1, 3 and 5% solar [13] 5 45 Natural Gas MW Petroleum Coal Renewable Hydro Nuclear hours Figure 17 Dispatch order for Californian fuel mix with 3% PV penetration (CAISO July 27) [13] 37

40 The standard US fuel mix has a greater proportion of coal fired generation. The rate of power change of coal fired generation is largely limited by thermal inertia and unit specific fuel system limitations, making them inflexible. If the same load duration curve is used for the US fuel mix, an incursion into coal fi red generation occurs, as seen in Figure 18. This raises questions of generation flexibility and how coal fired generation will manage requirements to reduce output to allow integration of increased variable renewa ble generation. It is suggested in [13] that cheaper less flexible plants will need to be replaced with more flexible expensive plants. 5 MW Natural Gas Petroleum Coal Renewable Hydro Nuclear hours Figure 18 Dispatch order for US fuel mix with 3% PV penetration (CAISO July 27) [13] Further c ost impact studies are discussed in [17]. These studies looked into costs associated with increasing wind generation for systems: Xcel Energy North (Minnesota), Californian Independent System Operator (CAISO) and New York Independent System Operator (NYISO). The Minnesota Department of Commerce study (September 24) assumed 15% (15 MW) wind penetration, and the costs resulting from plans and procedures required to accommodate such wind penetration level came to no higher than $4.6/MWh. For CAISO, about 23-25% wind capacity is assumed. At the regulation timeframe (seconds) a maximum wind cost of $.46/MWh was evaluated. For the load following time scale (ten minutes to a few hours), results focussed on the dispatch stack as a result of the variability introduced by wind. Due to the numerous conventional generators available no impact was measured. The (NYISO) study was the most extensive. Wind penetration of 1% (33 MW in 34MW system) is assumed and encompasses all timeframes. This comprehensive cost analysis looked into operating system costs, impact on customer payments, and reduction in emissions from conventional plants and impacts of wind forecasting. Wind generation was modelled using existing weather data and projected out to 28 on expected demand. The conclusion from the NYISO study was that the New York power system can reliably accommodate at least 1% penetration of wind generation with only minor adjustments to its planning, operating, and reliability practices. 36MW of additional generation would, however, be required to maintain frequency at the no-wind level. In this study, it was reported that significant cost savings could be achieved mainly due to the displacement of fuel, primarily natural gas, by wind and by having accurate forecasts. 38 Solar intermittency: Australia s clean energy challenge

41 Conclusions from [13] are summarised as: costs are moderate at load following and regulation level costs are greater at scheduling level (if wind scheduled in and wind doesn t blow then conventional generation required to ramp up) with accurate wind forecasting mitigating this impact the greater the area of load balancing and robustness of the system the less the costs of wind integration. The PÖYRY study [18] for Europe claims there is a stark difference between the current output of intermittent renewables and what may be expected in 23 and wind and solar output will be highly variable and will not average out. Figure 19 shows expected wind and solar generation profiles in 235. This volatility directly impacts on cost, as shown in Figure 2. Prices are expected to become peakier and less predictable, representative of the nature of weather systems. There are likely to be short periods of very high cost when renewable generation is low and extended periods of low cost when renewable (and nuclear) generation is high. The significant difference in pricing volatility between 21 and 23 can be seen in Figure 2. January April July October Tech Solar Onshore Figure 19 Aggregate hourly output for Northern Europe across four months [18] France Feb Apr Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb Apr Jun Germany Aug Oct Dec 1 5 Weather years Figure 2 Monthly wholesale prices ( /MWh) across different historical weather patterns [18] 39

42 The costs associated with required curtailment of wind generation are analysed in [19]. Figure 21 shows the relative cost (relative to cost if no wind curtailment in place) of higher levels of wind penetration for different levels of flexibility (minimum load as a percentage of annual peak load). As stated before, system flexibility can be described as the general characteristic of the ability of the aggregated set of generators to respond to the variation and uncertainty in net load. There is considerable difference between the average and the marginal costs, especially at higher penetration levels. For example, to achieve 5% wind penetration level in a 9% flexible system (i.e. thermal generators are able to cycle down to 9% below the annual peak load), the average cost of wind generation would be about 1.2 times the base cost. However, at the margin, the last unit of wind generation installed to meet the 5% penetration level would cost about two times the base cost. For 8% penetration, higher flexibility is required and the average cost is just below 1.8 times the base cost but the marginal cost (not shown) is five times the base cost because of the high level of curtailment required % Flexibility Marginal 1% Flexibility Marginal 9% Flexibility Average % Flexibility Average % 1% 2% 3% 4% 5% 6% 7% 8% Figure 21 Average and marginal relative cost of wind as a function of wind energy penetration due to varying levels of curtailment [19] The hourly operation of the entire interconnected grid of the western United States with 25% solar penetration over a period 3 years was simulated in [6]. The dispatch profiles for a scenario with no solar power penetration and another with 25% solar penetration are shown in Figure 22 and Figure 23 respectively. A drastic change in dispatch is observed when the two scenarios are compared. Solar generation is seen to displace significant amounts of combined cycle generation with periods of zero production during peak periods. The generation from the coal units is also seen to be reduced during this time. The backing down of coal is a marked contrast to minimum load challenges associated with high wind generation scenarios that result in coal cycling during the night. Hydro generation is seen to shift from the load peak into subsequent load trough. Gas turbines are necessary on days with actual solar generation below forecast or with peak loads occurring after solar generation decreases [6]. It should be noted that the 1-hour resolution in this study is too slow for proper analysis of the impacts of rapid fluctuations in solar power generation, in the order of minutes or seconds, on the dispatch profile. 4 Solar intermittency: Australia s clean energy challenge

43 7 6 Nuclear Solar PV Pumped Storage Hydro Steam Coal Combined Cycle Hydro Solar CSP w. Storage Gas Turbine 5 MW MON APR1 TUE APR11 WED APR12 THU APR13 FRI APR14 SAT APR15 SUN APR16 Figure 22 Dispatch for week in April no solar [6] 7 6 Nuclear Combined Cycle Steam Coal Gas Turbine Solar Con. PSH Solar PV Hydro 5 MW MON APR1 TUE APR11 WED APR12 THU APR13 FRI APR14 SAT APR15 SUN APR16 Figure 23 Dispatch for same period as Figure 22 above 25% solar [6] 41

44 Analysis in [6] found savings are to be had from introducing solar power, by displacing gas and coal fired generation and from a price on carbon. For the Western Electricity Coordinating Council (WECC), under the assumptions outlined below, $11 billion a year is saved in 29 out of total operating costs of $43 billion, corresponding to a 25% reduction: $2/MBTU coal, $9.5/MBTU gas, $3/tonne CO 2 for 217 with 2% annual escalation extensive balancing area cooperation all units economically dispatched while respecting transmission limitations generation equivalent of 6% held in reserve, half spinning, half not spinning 25% solar penetration. The impact on spot price due to the integration of solar generation in Arizona was also analysed in [6]. It was found that adding new, zero marginal cost resources will generally decrease spot prices. However, large forecast errors with higher solar penetration cause expensive generation to be brought online to make up for these errors, leading to the observation of a diminishing benefit at high prices seen in Figure Spot Price ($/MWh) No solar 5% solar 1% solar 15% solar 2% solar 25% solar Hours Figure 24 Spot price duration curve with 25% solar in Arizona [6] A survey of papers on the likely factors limiting PV penetration is summarised in a r eport by Sandia Natio nal Laboratories [2] and presented in Table 4. The reported reasons restricting high PV penetration levels are broad and include: ramp rates of conventional generation reverse power swings frequency control voltage regulation. It should be noted that the different papers looked at different types of grid and penetration contexts. For example some looked at distribution system penetration while others looked at central generation penetration. The maximum PV penetration level is highly situation-depende nt and varies widely, as can be seen in Table 4 where the different papers looked at different scenarios. 42 Solar intermittency: Australia s clean energy challenge

45 Table 4 Expect ed factors limiting PV penetration level [2] Reference # Maximum PV penetration level Cause of the upper limit 2 5% Ramp rates of mainline generators. PV in central-station mode. 4 15% Reverse power swings during cloud transients. PV in distributed mode. 5 No limit found Harmonics. 6 > 37% No problems caused by clouds, harmonics, or unacceptable responses to fast transients were found at 37% penetration. Experimental + theoretical study. 8 Varied from 1.3% to 36% Unacceptable unscheduled tie-line flows, The variation is caused by the geographical extent of the PV (1.3% for central-station PV). Results particular to the studied utility because of the specific mix of thermal generation technologies in use. 1 1% Frequency control versus break-even costs. 11 Equal to minimum load on feeder Voltage rise. Assumes of LTCs in the MV/LV transformer banks. 12, 13 < 4% Primarily voltage regulation, especially unacceptably low voltages during false trips, and malfunctions of SVRs. 16 5% This is the level at which minimum distribution system losses occurred. This level could be nearly doubled if inverters were equipped with voltage regulation capability % or 5% Voltage rise. The lower penetration limit of 33% is imposed by a very strict reading of the voltage limits in the applicable standard, but the excursion beyond that voltage limit at 5% penetration was extremely small. One of the challenges of integrating high penetration solar into the grid is the Load Frequency Control (LFC) required to manage power fluctuations from installed PV systems. A study was performed to investigate the extent of LFC required to manage power fluctuations from PV systems installed at five sites across the city of Nagoya in Japan [21]. The average insolation across the sites is used to calculate the combined power output assuming the PV capacity is 2% of total generation. Figure 25 illustrates the LFC capacity required to manage the fluctuations in power output from the PV. The total number of days in the study is 3, which corresponds to the sum of the magnitude of each of the columns in the plots. The bottom plot (LFC capacity of generators = 1% of PV capacity) shows 17 days where the LFC could not manage the fluctuations in load due to the PV. Only when the LFC is at 5% of installed PV are the fluctuations in load for all days managed. This result shows the extent of ancillary services or additional generation required to manage PV power output fluctuations. The impact of intermittency on voltage regulation in distribution systems with high PV penetration levels was studied in [43], specifically voltage flicker and excessive transformer tap changes that may result from induced fluctuations in PV power. Actual load and solar irradiance data was used to simulate the impact of 2% PV penetration on a simple distribution system on partly cloudy days. Simulations were performed using 1-minute sampling rate and no flicker problems were encountered, but a significant increase in the number of transformer tap changes was observed, which would likely reduce the life expectancy of the load tap changing mechanism. The difference in the number of tap changes, with 1-minute and 5-minute time delays, when PV is integrated into the distribution system can be seen in Table 5. For 1-minute time delay, the number of tap changes was found to be four times higher with PV integrated compared to the base case without PV. The number of tap changes for the 5-minute delay scenario was 2.8 times higher than the corresponding base case. A higher rate of sampling (in the order of seconds) is needed to analyse voltage flicker more accurately as the 1-minute sampling interval is not sufficient to take into account the effects of faster changes in solar irradiance. 43

46 frequency (days) inf frequency (days) inf (a) capacity of generators available for LFC K a : 5% of PV capacity (1% of system capacity) (b) capacity of generators available for LFC K a : 3% of PV capacity (6% of system capacity) frequency (days) inf (c) capacity of generators available for LFC K a : 1% of PV capacity (2% of system capacity) Fig ure 25 Required LFC as % of installed PV [21] Table 5 Number of tap changes with and without PV (simulated) [43] Date (d/m/y) w/o PV and 1 min. delay w/o PV and 5 min. delay with PV and 1 min. delay with PV and 5 min. delay 11/7/ /7/ /7/ Total Various other studies have been conducted on the potential impacts of solar intermittency on different networks throughout the world, with varying levels of solar penetration and different generation and load profiles. Some general conclusions that can be drawn from these studies are: The amount of solar generation that can be integrated into the utility power system without compromising grid stability and reliability varies widely. The determining factors are the amount of a utility s load fluctuation and the regulating capability of existing conventional generating units. This observation indicates that the effect of solar generation intermittency on the power system is context specific and needs to be considered on a case-by-case basis. Therefore, a general cause-effect conclusion cannot be drawn. Although high penetration levels of solar generation have the potential to cause adverse network impacts, corrective measures are available, such as assigning more generating units to regulating duty or installing fast-response generation such as combined-cycle generators. These measures can be effective if carefully planned. Simulations have shown high penetration limits are possible after adding fast-response, combined-cycle generating units to the existing generation mix or fast-acting energy storage. Another method is to control the solar generation output under intermittent cloud coverage during periods of peak system demand when the network has fewer generating units on standby and less on-line regulating capacity. However, these corrective measures may cause the system to deviate from its optimal operating condition, adversely affecting the economics of solar generation. 44 Solar intermittency: Australia s clean energy challenge

47 The worse-case cloud patterns turned out to be cumulus (fast-moving, well-defined clouds with clear sky between the clouds) and squall (a solid line of dark clouds moving across a clear sky). The squall line cloud patter n can cause complete loss of PV generation [44]. The speed of clouds and size of the dispersed PV system area determine how fast the complete loss occurs. For a 1km 2 area, a squall line caused the loss of all PV generation within that area in 1.8 minutes [44]. The worst cumulus clouds, while causing less loss of PV generation than the squall line, might actually pose a more difficult problem for utilities, because their changes are random and their effect on the PV output is much less predictable than that of a squall line. 3.4 Changes required to accommodate high penetration IRG Forecasting To manage increased levels of IRG, changes to the power grid are predicted to be necessary. Changes to operations, infrastructure, and planning and power quality management are suggested in the literature. It is widely agreed that accurate forecasting is an essential element for the successful integration of large amounts of intermittent generation. It was mentioned in [6] that accurate forecasting is necessary for solar power to be economically viable. Forecasting at various timescales is required. More accurate day-ahead prediction of renewable resources is required for more accurate unit commitment [13]. Satellite images can be used to track cloud movement for forecasting at short timescales while numerical weather models can be used to predict insolation out to a number of days [1]. Figure 26 shows the impact of incorporating an hour-ahead wind forecast on the duty imposed on a proxy Automatic Generation Control (AGC) unit [1]. The power output of this proxy unit approximated the amount of regulation required of all units on AGC between 5-minute re-dispatches of the system. The blue line represents the original economic dispatch model with a persistence-based wind forecast and the yellow line represents the improved economic dispatch model. The green lines represent the minimum up and down regulation procured during the study period. Originally, the proxy AGC unit output was offset by an average of about -18 MW, as indicated by the heavy blue line. With a hour-ahead forecast included, the offset is reduced to about -6 MW, as indicated by the heavy yellow line. This shows that the Economic Dispatch (ED) units are better able to follow load with the improved wind forecast incorporated into the dispatch function. 6 4 Preg Original Preg Wind Forecast Reg Up Reg Down 2 MW :PM 5:PM 6:PM 7:PM Figure 26 Graph showing impact of incorporating hour-ahead wind forecast on AGC unit [1] 45

48 3.4.2 System flexibility Increasing system flexibility or decreasing the flexibility requirements of the system is another important determinant for increasing levels of intermittent generation. A study discussed in [1] claims a required increase in system flexibility of 4% for the 22 scenario (33% renewables) for normal load conditions and 5% for light load due to greater impact of renewables during such conditions. Due to increased variability, the number of start/stops increases and sustained load ramps (up and down) steepen. The load following capability will also need to increase above what is required for variation in load a lone due to renewable [1]. It is claimed in [13] that system flexibility can be increased through balancing the generation portfolio introduction of more flexible conventional generation redesign of power system to enable it to handle reverse power flow from distributed PV. Reducing net load variability reduces the required fle xibility of the system. Measures suggested in [13] are: energy storage load control increased control and communication ability to curtail Intermittent Renewable Generation (IRG) would reduce required flexibility spatial diversity of the resource. Experience with systems containing large amounts of variable renewable generation shows that flexibility of the portfolio balance is crucial for economic and stable operation [6]. For a CSP system, introduction of thermal storage would introduce greater flexibility to the production profile. It is predicted that more flexible generators, (ones which are able to not only vary their output but cycle on and off quickly), will find more situations where they can respond to network requirements and hence are likely to be dispatched more often. This is to manage what will be variable net load due to the large penetration of solar power. A measure of the flexibility of a system is its minimum load capability. In [1], it is suggested that Californian grid operators should plan for a combination of flexible generation and import-export agreements to allow for a smaller minimum net load (load minus wind and solar) and greater net load variability. A few different methods of system flexibility which would allow a larger penetration of intermittent renewable generation could consist of: Minimum turndown: Generation able to operate at a lower minimum power enables greater flexibility in the system and reduces the amount of renewable generation curtailment required. New generation will be required to operate at lower power levels and existing generation may be required to upgrade plant in order to enable operation at a lower power level. Diurnal start/stop: More generation able to operate economically on a diurnal cycle (run at the same time for the same period each day) could be scheduled. These generators could run at the morning and evening peaks only, and be off during what would otherwise be uneconomical times. Load participation: Large loads could take advantage of inexpensive power periods, further reducing the minimum operational load. Load shifting, for example in cold stores, and energy storage will also contribute. According to [1], the evaluation of the generation flexibility of a system should be done at the load following time scale in relation to the variability of the net load. It is also suggested that the selection of sites for renewable generation should also take into account the costs of required transmission to accommodate the new generation. It may be more feasible to place the renewable generation at a site where yield is less but connection costs are lower. It is also expected that existing contributors of frequency stability at the regulation timescale will step up to manage the increased variability greater IRG penetration will present. 46 Solar intermittency: Australia s clean energy challenge

49 3.4.3 Curtailment Curtailment requirements for ERCOT for varying levels of minimum load are analysed in [19] with penetration of up to 8% renewables. Curtailment of generation from variable renewable sources would be required if the generation portfolio is not sufficiently flexible to manage the increased fluctuations in net load introduced by the intermittent generation. Figure 27 shows the simulation results of two scenarios at ERCOT, one where the minimum load is 21 GW (65% below annual peak load of about 6 GW) and another with minimum load of 13 GW (78% below annual peak load). In the first case, 21% of the intermittent generation must be curtailed due to the minimum generation constraints of inflexible generation with wind and solar only contributing 2% of the energy demand. Less than about 3% curtailment is required in the second case by increasing flexible generation with renewables contributing 25% of the system s annual energy. Insufficient transmission capacity also impacts on flexibility. A real world example of this occurred in 29 where insufficient transmission from West Texas to loads in the Eastern USA resulted in 17% curtailment of wind generation [8]. MW Hours 21 GW system of 13GW MW Hours Figure 27 Impact of system flexibility on curtailed energy for ERCOT [19] 47

50 Further simulations on the ERCOT system shows the relationship between wind penetration level and required curtailment for different levels of System Flexibility (minimum load as a percentage of annual peak load). Figure 28 shows that the required amount of wind curtailment is significantly reduced with the presence of a larger amount of flexible generation. It can be seen that when the flexibility of the system is increased by 1% from 8% to 9% for a wind penetration level of 5%, the amount of wind generation curtailment required drops from about 45% to 2%. This shows how the level of system flexibility affects the amount of intermittent renewable generation that can be accommodated and utilised in a power system. It has to be noted that increased system flexibility level can incur increased operational costs which have to be taken into consideration during planning stages. 5% 45% 4% 35% 3% 25% 2% 15% 1% 5% 8% Flexibility (12 GW min load) 9% Flexibility (6 GW min load) 1% Flexibility ( GW min load) % % 1% 2% 3% 4% 5% 6% 7% 8% Figure 28 Wind penetration level vs. required curtailment [19] The required level of curtailment when solar power is added to the generation mix is shown in Figure 29 [19]. It is seen that the required curtailment of renewable generation is reduced with the integration of solar into the renewable mix, with minimum curtailment seen when the solar/wind ratio is at 3/7. The required curtailment is seen to increase beyond that of the wind-only scenario when the proportion of solar exceeds that of wind in the renewable mix. According to the author, this is due to the limited spatial diversity of the solar resources compared to the wind used in the model. With greater spatial diversity higher penetration of solar with less curtailment should be possible. Still, 8% penetration of intermittent renewable generation requires 42% curtailment in the wind-only scenario while the 3/7 mix only requires 33% curtailment. This shows the advantage of mixed renewable generation. 48 Solar intermittency: Australia s clean energy challenge

51 5% 45% 4% 35% 3% 25% 2% 15% 1% Solar/Wind Mix /1 2/8 3/7 4/6 6/4 8/2 5% % 2% 3% 4% 5% 6% 7% 8% Figure 29 Required curtailment with various levels of solar power added to the renewable mix [19] The effect of storage on the required curtailment of the above system was also simulated and the result is shown in Figure 3 [19]. For 24 hours of storage with 8% intermittent renewable penetration, the required curtailment reduces from 33% to 1%. Nonetheless, there is only so much storage can do, as shown in Table 6. Diminishing returns can be seen for the same increase in storage. Twelve hours of storage in the ERCOT region is equivalent to 34 GW of power capacity and 414 GWh of energy capacity. Current storage in all the US is about 21 GW, nearly all of which is pumped hydro. This analysis shows the scale of storage required to accommodate high penetration levels of intermittent renewable resources. 4% 35% 3% 25% 2% 15% 1% No storage 4 hours 8 hours 12 hours 24 hours 5% % 2% 3% 4% 5% 6% 7% 8% Figure 3 Required curtailment with storage [19] 49

52 Table 6 Reduction in curtailment required for various storage capacity Storage (hours) Drop in required curtailment (%) Interconnection According to [18], interconnection is a crucial aspect of integrating large amounts of intermittent generation. Pöyry, the authors, show the increase in load balancing area which comes from increased interconnection reduces overall variability without an increase in price in all countries with the exception of the Nordic countries. A price rise occurs there because the usually cheap hydro generation from this part of Europe is able to fetch higher prices elsewhere in other countries through interconnection. Pöyry also found the amount of backup plant required to account for variability is not offset greatly by increased interconnection. This is because weather systems at times stretch across thousands of kilometres, thereby impacting renewable generation across the area. The effect of the increase in load balancing area is nullified and operators are still required to maintain sufficient back-up plant to manage demand during these periods of very low renewable generation. Although the amount of backup plant required might not change, the frequency of deployment of such plants is likely to increase to account for the variability of intermittent renewable generation Increased control and communication It is suggested in [13] that as penetration levels of PV increase, to manage power quality issues arising due to its intermittent nature, communication between central control and distributed PV sources will be necessary. It has been generally recommended that an increase in control and communication will allow for more: effective management at the distribution level reducing losses improving power quality flexible system configuration increased capacity for system restoration more selective protection. New communications and control infrastructure will be required for the installation of new renewables. Minimising the cost associated with this new infrastructure will make renewables more attractive economically. According to [1], transmission installation costs can also be reduced via reduced required capacity through local control of renewables, including real-time power flow monitoring and local short-term forecasting. Further benefits and savings can be made through renewables providing ancillary services such as local frequency control and Var support Planning Planning and assessment of adequate resources to meet expected demand needs to take into account the requirement for flexibility [1]. Generation planning is shifting from planning for peak load towards planning for system energy [13]. System energy is centred on using net load as a basis for capacity planning which requires accurate renewable resource data. Traditionally, planning involved predicting the future demand and extrapolating the peak demand from this number. Required generation and transmission were then calculated with intermittent renewable generation (IRG) effectively ignored. Conventional generation would need to reduce output due to IRG leading to lower efficiency and greater operating costs. The emerging planning approach looks at net load; which incorporates the addition of IRG into planning. Demand is 5 Solar intermittency: Australia s clean energy challenge

53 forecast and offset by predicted installations of IRG. The contribution of this IRG and its expected impact on the variability of net load is estimated based on historical renewable resource data. Generation and transmission is then planned to meet forecast net load and associated variability. These traditional and emerging planning approaches are summarised in Figure 31. modify Figure 31 Traditional and emerging practice in capacity planning [1] Voltage regulation It is indicated in [2] that voltage fluctuations caused by cloud transients, amongst other sources, are of concern. Methods for mitigating this include the use of fast acting energy storage to smooth out voltage fluctuations. It is suggested the type of storage should be chosen to manage the power profile shown in Figure 32, an example where the PV power output drops to about 2% of its value under clear sky conditions because of cloud cover. The size of the required storage depends on the duration of the cloud transient. The irradiance is assumed to ramp at a rate of 2 W/ m 2 /s in the study (making t1 = t3 = 4 seconds in Figure 32) and it can be seen from Figure 33 that to level a cloud transie nt of a 2 kw system whose t2 aspect (flat part of Figure 32) is eight seconds long, would require about 53 Wh of energy from a fast acting energy storage source. It is seen that the size of energy storage required to level a 16-second cloud transient instead for the 2 kw system is 9 Wh, almost double that for the 8-second transient. The transient duration an energy storage can manage reduces hyperbolically with system size. A 2 Wh stored energy can manage five second transients for a 1 kw system but less than one second (8% reduction) for only a doubling in system size to 2 kw. P out.8 x P max Time t 1 t 2 t 3 Figure 32 Power profile required of an energy storage unit to level a cloud transient in a PV system [2] 51

54 x 1 4 Figure 33 Energy storage required to provide the power profile above, as a function of PV system rating and duration of the cloud transient [2] 3.5 Work required to facilitate high penetration IRG The literature suggests further research is necessary to assist in the transition to high penetration IRG. Areas include forecasting, acquisition of high resolution data on irradiance and PV output, and load profiles. Detailed studies on energy storage and a review of standards to integrate more IRG into grid operations are also m entioned. The following future work is suggested in [22]: Developing models representing aggregated behaviour of PV and updated transmission planning databas es to accommodate such models. This is reiterated in [13] Improve understanding on the role PV can play at the distribution and transmission level to quantify performance and economic impact on regulation and load-following. This would include required flexibility of non-variable generation. A dispatch strategy could be developed once PV behaviour is well understood Quantifying the economic and performance benefit of mitigating variability of PV. Customer load statistics are deemed important in [23]. Detailed load profile data for individual homes would allow for accurate modelling on the impact of PV and storage. The results of such modelling would be a reliable basis from which to gauge the economic viability of PV and storage. It is suggested in [13] to look in to standards, claiming there need to be concise and comprehensive practices and standards developed to handle distributed PV, making the process of further penetration easier. Also necessary for the uptake of high penetration solar power are: further simulation to determine the impact on power quality (voltage, etc.) and fault contribution of inverter connected PV pilot programs to test communication between centralised control, distributed PV and protection devices using the Internet quantifying the variability of the renewable resource, to determine the amount of flexibility required by the system modelling requirements: grid behaviour due to changes in insolation, Var support, dynamics of anti-islanding detection and fault response. 52 Solar intermittency: Australia s clean energy challenge

55 Models to accurately and comprehensively describe the behaviour of large scale solar plants are also requ ired, as is developing leading-edge solar forecasting [6]. The effective identification of grid locations best suited for PV installations at the distribution level should take into account: power quality impacts, or if the potential installation contributes to grid stability coincident load/generation load profiles grid impedance offsetting of transmission line upgrades Data High resolution insolation data, time synchronised across different spatial scales, is required for analysts to develop projections of intermittent renewable generation [12]. This is required as there will be different impacts requiring different responses at different spatial scales, including: large individual plants (1-1s km 2 ) dispersed PV plants connected to the same feeder (1-1s km 2 ) aggregate of all PV generation across the balancing area (1-1s km 2 ). To understand how regulation will be impacted, data will need to be gathered at high resolutions of up to 1 seconds, and synchronised with load data, to give a clear picture of the net impact of varying load and generation. Data will need to be gathered for a period of at least a year. Cross-disciplinary analysis projects are considered important as stated in [11]; The use of solar resource and meteorological data to address complex problems such as time-dependent utility load estimations, cloud transient effects on grid stability, and solar dispatching require close collaboration between analysts and utility planners, and the resource and meteorology community. Weather systems can cover very large areas, reducing the advantages gained from an increased load balancing area. According to [18], studies are required to determine the correlation of weather across the spread of the electricity grid ensuring links between areas with weak weather pattern correlation Energy storage and load response Investigation into energy management systems (EMS) is suggested in [2]. Algorithms, hardware and communication protocols (between utilities, DG and distributed storage) will need to be developed to optimise the use of energy sources. A well designed EMS will store energy when prices are low and inject power when prices are high or when auxiliary support (for voltage regulation for example) is requested. If they prove to be commercially viable, private sector investment could see a proliferation of such systems. Energy storage systems which can be integrated with PV also need to be identified. In aggregate, these systems could be utilised for ancillary services. To assess how distributed energy resources, in particular residential air-conditioning, could provide a responsive spinning reserve capacity, Lawrence Berkeley National Laboratory (coordinated by the Consortium for Electric Reliability Technology Solutions) carried out the Demand Response Spinning Reserve project. The demonstration showed it is technologically feasible to provide a spinning reserve ancillary service through demand-side resources. This could become a preferred option, since the response is near instantaneous. 53

56 3.6 Summary The current state of research worldwide on renewable generation intermittency, including centralised and distributed PV, concentrating solar thermal and wind, is summarised in this chapter. One of the main challenges to the power system is associated with the instantaneous penetration of intermittent solar generation. Solar generation is viewed as negative load. When this is combined with the actual system load it yields a net load, which corresponds to the power that must be supplied by other resources on the system. Of particular concern to utilities is load and solar power moving in opposite directions at the same time creating large changes in the net load. A study on the Californian grid reported an increase in net load variability, especially during low load periods, with the integration of 33% intermittent renewable energy penetration. Various other studies concluded that a high penetration of intermittent generation results in greater variability in the net load compared to the variability in the original load alone without solar or wind. In addition to the magnitude of net load variability, data on ramp rates of such variations would assist in determining the degree of flexibility required in the network to compensate for the variability of PV systems. Studies on the effect of spatial diversity on intermittency seen in output of PV systems showed reduced variability when the aggregate of multiple PV plants and also output of large-scale PV plants were considered. The impacts of PV power plants are associated with voltage profiles, electrical losses, power factor, capacity planning, power quality, systems operations and protection. Normal functions of conventional generation operations that could be impacted by high penetration solar intermittency, which may need more conventional generation units to be brought on-line or put on regulating duty thus increasing system operating cost, include: load-frequency control load following ramping rate of on-line generators unloadable generation to accommodate maximum output from intermittent generating technologies operating reserve. There is very little published literature which discusses observed impacts of high penetration solar intermittency. The majority of work discovered focussed on modelling impacts rather than actual observation of impacts. Studies have shown that adequate system flexibility is a key requirement for managing increased levels of intermittent renewable generation and that conventional generators are forced to be more flexible with their output, resulting in a higher per unit cost. A case study at Gardner, Massachusetts, analysing the impact of a passing cloud on the irradiance and net load of the site (53 residential properties and 28 PV installations) shows how rapidly the net load of a system can vary significantly. Large variations in PV output for systems with high penetration PV will result in proportionally large variations in net load putting added pressure on conventional generating resources on the system to vary their output rapidly. Existing methods for managing large-scale intermittent generation might not be sufficient. A good example of output variations that can be expected from a large-scale PV system can be seen from the output of a large existing array in the US, a 4.6MW PV system located in Springerville, Arizona. The PV output data was sampled every 1 seconds and large abrupt power output drops, from about 4 kw to 5 kw, were seen to occur over extremely short timeframes. It is suggested in the literature that cheaper less flexible plants will need to be replaced with more flexible expensive plants to accommodate high penetrations of solar generation. Otherwise, a significantly larger amount of ancillary services or additional generation may be required to manage PV power output fluctuations. A number of studies looked into the likely impacts of increased levels of intermittent renewable generation (IRG), estimating impacts through simulation and modelling. The predicted impacts of high penetration intermittent renewable generation include the displacement of conventional generation units, the need to curtail renewable generation output, load frequency control and costs associated with these. Some general conclusions can be drawn from these studies: The amount of solar generation that can be integrated into the utility power system without compromising grid stability and reliability varies widely. The determining factors are the amount of a utility s load fluctuation and the regulating capability of existing conventional generating units. This observation indicates the effect of solar generation intermittency on the power system is not uniform and is case sensitive. Hence, a general cause-effect conclusion cannot be drawn. 54 Solar intermittency: Australia s clean energy challenge

57 Although high penetration levels of solar generation have the potential to cause adverse network impacts, corrective measures are available, such as assigning more generating units to regulating duty or installing fast-response combined-cycle generators. These measures can be effective if carefully planned. High penetration limits have been shown to be possible (in simulations) after adding fast-response, combined-cycle generation units to the existing generation mix. Another method is to control the solar generation output under intermittent cloud coverage during periods of peak system demand when the network has fewer generating units on standby and less on-line regulating capacity. These corrective measures, however, may cause the system to deviate from its optimal operating condition, thus adversely affecting the economics of solar generation. The successful integration of large amounts of intermittent solar generation depends highly on the essential element of accurate forecasting. Forecasting at a range of timescales is necessary. More accurate day-ahead prediction of renewable resources is required for more accurate unit commitment. Forecasting at short timescales is needed to predict rapid power dips in the solar system output, while numerical weather models can be used to predict insolation out to a number of days. Increasing system flexibility or decreasing the flexibility requirements of the system is another important determinant for increasing levels of intermittent generation. Some ways by which the system flexibility can be increased include balancing the generation portfolio, introducing more flexible conventional generation and redesigning the power system to enable it to handle reverse power flow from distributed PV. Net load variability can be reduced in order to reduce the required flexibility of the system, the literature indicates, by such means as the use of energy storage, load control, increased control and communication, ability to curtail intermittent generation and spatial diversity of the resource. 55

58 4 Comparison between wind and solar intermittency Some general stat ements regarding the difference between wind and solar [13] that are of relevance to the intermittency issue: PV has no apparent inherent inertia. Wind delivers power through fixed-speed induction or synchronous generators which have an inherent inertia, reducing the rate of change of frequency (ROCOF) after a disturbance. Solar generation is thought to be easier to integrate into the system than wind generation because of its lesser impact on increased net load variability, compared to variability of load alone. This is due to the stronger correlation of solar generation output with load demand compared to wind generation. Solar generation often has higher value to the system than wind, because of its availability during higher load demand compared with wind, solar PV often tends to displace more expensive generation. These points are further explored below. 4.1 Correlation with load Figure 34 shows the load and wind profiles for Denmark in January 2. As expected, the load waveform has a regular repetitive pattern, which is not the case for wind production. If solar production was shown, it would show a repeating waveform and, at this level of resolution, would seem to closely correlate with load. The correlation of solar power, both PV and CST, with load demand over a period of a week in the western USA can be seen in Figure 35 [6]. In this figure, the load is equivalent to the sum of all production. It can be seen that power from both solar PV and CST systems correlate well with demand. Individual PV panels display extreme variation, but the variation they exhibit is smoothed out with aggregation. Because of this smoothing effect, the daily production profile of solar should be reasonably consistent., When this is combined with a repeating daily waveform shown in Figure 35, the profile of solar generation could be considered far more deterministic than wind. The reduced randomness in solar generation should reduce the complexity of scheduling dispatchable generation in comparison with wind. 56 Solar intermittency: Australia s clean energy challenge

59 Denmark load and wind power data from January Wind Load MW Hour Figure 34 Correlation between wind and load in Denmark, January MW Nuclear Combined Cycle Steam Coal PSH Solar Con. Hydro Solar PV MON JUL1 TUE JUL11 WED JUL12 THU JUL13 FRI JUL14 SAT JUL15 SUN JUL16 Figure 35 Correlation of solar production with load dema nd [6] Lookin g more closely into correlation with load, Figure 36 and Figure 37 show the penetratio n of wind and solar respectively at different load levels [1]. Three scenarios depicted for these two graphs; 26, 21T and 21X: 26 (base case) 21 MW of wind and 33 MW of solar. It is not specified what these values are as a percentage of overall generation 21T (2% renewable energy) 75 MW wind (16%) and 19 MW of solar (4%) 21X (33% renewable energy) 125 MW of wind (27%) and 26 MW for solar (6%). Load is lightest at the tenth decile and heaviest at the first. This data from CAISO [1] again shows solar has better correlation with load, with penetration the highest during the first decile while, in contrast, wind has its highest penetration in the tenth decile. These findings show correlation between solar and load to be greater, but it is important to bear in mind that wind generates throughout the night when there is light load, while solar does not generate. This may be a tipping factor 57

60 for the correlation to favour solar. It would be instructive to see the analysis repeated with wind generation during the night discarded. Looking specifically at the first decile (peak load) for the 21T scenario (16% wind and 4% solar as a percentage of total generation capacity) wind can be seen to only provide 4% of generation, which corresponds to a quarter of its full generation capability. For the same scenario, solar is seen to provide 2out of a possible 4% of total generation capability. 3% 25% 2% % 15% 1% 5% % Decile Figure 36 Wind penetration (%) per decile [1] 3.5% 3.% 2.5% % 2.% 1.5% 1.%.5%.% Decile Figure 37 Solar penetration (%) per decile [1] 58 Solar intermittency: Australia s clean energy challenge

61 Figure 38 shows the capacity factor for each decile for wind and solar for the 21T scenario. It can be seen that solar uses more of its capacity than wind during the top three load periods, making it a more efficient resource for reducing peak load Solar Wind Capacity Factor % Decile Figure 38 Capacity factor vs. decile for wind and solar (21T scenario) Another study in [24] analysed three years worth of load, wind and solar data from three sites two in California and one in Texas and performed a correlation on the data for each of the four seasons. As Figure 39 shows, there is a greater correlation between load and solar generation for all three sites. This reinforces the conclusion in [1] that solar generation has a stronger correlation with load demand. Winter Spring Summer Fall Wind vs. Load Solar vs. Load Combined vs. Load Wind vs. Solar Wind vs. Load Solar vs. Load Combined vs. Load Wind vs. Solar -1 Wind vs. Load Solar vs. Load Combined vs. Load Wind vs. Solar Figure 39 Correlation between wind, solar and load for each of the four seasons [24] 59

62 4.2 Inherent variability Power outputs from both solar and wind generating sources are known to vary considerably with varying irradiance and wind speeds respectively. Figure 4 shows the variation of wind power output from a single turbine over a period of 2 seconds with sub-second sampling time [25]. By looking at the turbine output between time = 8 seconds and time = 9 seconds, a drop of power output from 1.5MW to.9mw, corresponding to a drop in power of 4%, can be seen to occur in just 1 second. It is clear that large rapid drops in power output can be experienced by wind generating systems, similar to that seen in solar PV systems, at the 1-second timescale. 1.6 x1 6 Wind Power (W) Time (s) Figure 4 Wind power output for one wind turbine (doubly fed induction generator) [25] The variability of solar power over timescales of 1-minute and 1-minute for plants located in Las Vegas can be seen in Figure 41 [12]. Corresponding variability of a wind power generation system in California (CAISO) at 1-minute and 5-minute intervals is shown in Figure 42 [1]. Comparing the 1-minute deltas from both graphs, solar shows a maximum percentage change of 5% for individual plants and around 2% for all six plants in aggregate. Wind, on the other hand, (note Figure 42 would be considering the wind generation in aggregate) shows a maximum change (negative) of 5 MW or.4%. The installed capacity is 125 MW. The maximum delta for a 5-minute interval is 2 MW (negative) or 1.6%. Although the variation of wind power in this comparison appears insignificant compared with that of solar power (which can change up to 2% in aggregate over a 1-minute interval), it should be noted that the level of variability is highly dependent upon the number of sites and spatial diversity of the solar and wind sites. In this case, 6 solar plants were considered and the number of wind plants is unknown. 6 Solar intermittency: Australia s clean energy challenge

63 1% 1% 99% 99% 98% 97% 96% 95% Ft Apache Gd Canyon Luce Ronzone 98% Ft Apache 97% Gd Canyon Luce Spg Mtn Ronzone 96% LVSP Spg Mtn LVSP Total PV Total PV 95% % 1% 2% 3% 4% 5% % 1% 2% 3% 4% 5% 1-min Ramp (% of Capacity) 1-min Ramp (% of Capacity) Figure 41 Cumulative distributions (95th and 1th percentiles) of six individual PV plants within a ~2 square kilometre area in Las Vegas [12] min Wind delta (+) 1min Wind delta (+) 5min Wind delta (-) 1min Wind delta (-) 7 Times (%) Delta (MW) Figure 42 Wind variability (deltas) for CAISO 125MW capacity [1] 61

64 When variation is considered and analysed at the hourly level, greater variability is observed in wind systems. Figure 43 shows the duration curves for load, wind and solar for CAISO for a simulated scenario with 125 MW of wind and 26 MW of solar, for a total of 33% renewables penetration. These graphs are created by sampling the wind and solar power output every hour, then sorting the samples in descending order (highest output reading at hour 1, lowest at hour 876). Comparing the duration curves for wind and solar shows that wind has a steeper gradient; indicating greater variation hour-to-hour. If a non-variable resource (coal fired power station for example) is plotted on the same graph, an almost horizontal line would be observed due to its minimal hourly variation in output MW A = 22 load shapre B = 23 load shape C = 24 load shape Hour X-A, CAISO load, actual, MW X-A, Total Actual Solar X-A, Total Actual Wind X-B, CAISO load, actual, MW X-B, Total Actual Solar X-B, Total Actual Wind X-C, CAISO load, actual, MW X-C, Total Actual Solar X-C, Total Actual Wind Figure 43 Load, wind and solar duration curves for CAISO (21X scenario) [1] Analysing all the graphs illustrated in this section would indicate that in the second-to-second timeframe the output of individual wind turbines is similarly variable in nature to individual solar arrays. When wind and solar output are taken in aggregate and analysed at 1-minute to 1-minute intervals, wind seems to benefit more from the smoothing effect associated with aggregation, showing less variability than solar. Finally, when behaviour is observed at 1-hour intervals, again in aggregate, wind is shown to be more variable. The difference in correlation versus distance for both wind and solar systems was studied in [26] for sites in Sweden spread over approximately 7 square kilometres. The correlation between wind sites is seen to decrease with distance at a greater rate than for solar, as shown in Figure 44. This suggests the smoothing effect due to aggregation will be greater for wind than solar. The sample rate for this correlation analysis is given as one hour for both wind and solar. It is not specified whether the irradiance is averaged over the hour or an instantaneous measure. 62 Solar intermittency: Australia s clean energy challenge

65 1.8. x Wind power Figure 44 Correlation vs. distance for wind and solar [26] The smoothing effect due to aggregation of output from the Swedish wind and solar sites is shown using duration curves in Figure 45. The decrease in gradient of the aggregated curve is an indication of reduced variability. Again, it can be seen that the smoothing effect due to aggregation is greater for wind than for solar. This hourly load duration curve shows a different picture of the relative variability of wind and solar when compared with the hourly load duration curve shown in Figure 43 for the CAISO system. A steeper gradient for wind is observed for the CAISO system, implying greater variability, while a steeper gradient is seen for solar power output in the Swedish study, implying solar is more variable. From the results of these studies, one could deduce that variability of both solar and wind resources depends on factors such as geographical location, spatial diversity and size of the renewable generation system. Power Output (MW/TWh) Hours Figure 45 Duration curves wind and solar [26] 63

66 4.3 Load-following and regulation requirements Analysis in the CAISO report [1] examines the load-following (5-minute) and regulation (1-minute) requirements to meet the new variability in net load introduced by wind and solar. A metric was developed using synchronised 1-minute data of wind, solar and load. A 15-minute rolling average of the 1-minute data was calculated and the 5-minute delta was calculated on this rolling average. The 1-minute delta is the difference between the 15-minute rolling average and the 1-minute measure. Figure 46 illustrates an example of this metric MW Time in Minutes Figure 46 Example for load-following (5-min) and regulation (1-min) metric definition [1] Using this metric, 5-minute and 1-minute deltas on the 15-minute rolling average for the load, net load, wind and solar data were produced. These are illustrated in Figure 47 and Figure 48 respectively. Both the 5-minute and 1-minute deltas are for the same 3-hour period one morning in July, 23. The left y-axis scale applies to load and net load while right y-axis scale applies to the wind and solar. From Figure 47, the load following requirement ranges from 12 MW to 4 MW per 5 minutes. Over the 3 hour period the average load is 4 GW, 6 MW for wind (1.5% penetration) and 7 MW (1.75% penetration) for solar. Examining the 5-minute analysis, the standard deviation for wind is 18 MW (3% of the average) and 13 MW for solar (1.8% of the average). As expected, at such low penetrations there is little impact on the net load. What is of interest is the difference in variability between wind and solar for 5-minute and 1-minute intervals. From Figure 48, the 1-minute wind delta stays within 2MW/minute while the solar delta ranges between ±6MW/minute. Looking at the 1-minute analysis, the standard deviation for wind is 9 MW (1.5% of the average) and 22 MW for solar (3.1% of the average). So the 1-minute standard deviation increases relative to the 5-min by 5% for solar and decreases by 5% for wind. Based on the comparisons made above, something for utilities to consider is that a greater penetration of solar is likely to imply greater regulation requirements while a greater penetration of wind will require greater load-following requirements. However, it should be noted that solar and wind deltas at different timescales are situation dependent, and are dependent upon such factors as number and diversity of solar versus wind plants, and weather characteristics of where they are located. 64 Solar intermittency: Australia s clean energy challenge

67 5 25 Load Load (Delta MW per 5 Minutes) Load-Wind-Solar Wind Solar Wind and Solar (Delta MW per 5 Minutes) -1-5 Time Figure 47 5-min delta over three hours one July morning, 23 [1] 3 2 Load (Delta MW per 1 Minute) -3 Load Load-Wind-Solar Wind Solar 1 Wind and Solar (Delta MW per 1 Minute) : 7: 8: 9: Time Figure 48 1-min delta over three hours one July morning, 23 [1] 65

68 4.4 Summary Power output from both solar and wind generating sources are known to vary considerably with varying irradiance and wind speeds, respectively. Studies have shown the repeating daily production profile of solar generation correlates better with load and can be considered far more deterministic than wind generation. This reduced randomness in solar generation in comparison with wind should result in a reduced impact on the complexity of scheduling dispatchable generation. Analysis of results illustrated in the studies carried out on wind and solar variability indicates that the output of individual wind turbines is similarly variable in nature in the second-to-second timeframe to individual solar arrays. When wind and solar output are taken in aggregate and analysed at time intervals of 1-minute to 1-minute, wind seems to benefit more from the smoothing effect associated with aggregation, showing less variability than solar. When behaviour is observed at 1-hour intervals, again in aggregate, wind is shown to be more variable. A Swedish study observed that the correlation between wind sites decreases with distance at a greater rate than for solar, suggesting the smoothing effect due to aggregation will be greater for wind than solar. It should be noted that the level of variability is highly dependent upon the number of sites and spatial diversity of the solar and wind sites. 66 Solar intermittency: Australia s clean energy challenge

69 5 Comparison between concentrating solar thermal (CST) and PV power generation Intermittency The key difference between CST and PV is that generation from CST plants can be considered to have better capability of riding through cloud transients, i.e. smoother output with less variability, due to some storage inherent in all CST systems, simply through the thermal mass of the working fluid and broader system, even if there is no major storage element included. This is an important difference between CST and solar PV CST s relative controllability moves the discussion away from a comparison based on variability. It may resolve itself into a discussion on how these two technologies complement each other and would best contribute to the move to meeting the majority of our energy needs through variable renewable generation. The key differences between CST and other variable renewable energy generatio n, including PV, is summarised in a report by BrightSource [27], a developer and manufacturer of solar thermal power systems in the US: Solar thermal projects are inherently capable of more accurate forecasts as they are more independent of the variability of weather conditions. This independence is due to the output being moderated by the thermal mass of Heat Transfer Fluid (HTF). This makes them less susceptible to short-ter m fluctuations in output due to passing clouds. See section 5.1 for more on variability of CST systems. Solar thermal plants are able to utilise backup fossil fuel generation (hybridisation, see Figure 49) and thermal energy storage (TES) to increase their capacity factor. Forms of storage are obviously possible for PV plants but they are no t as cost-effective or efficient as CST with TES. See Section 5.2 for some details on storage capabilities of CST systems. Solar thermal plants are able to respond to system operator instructions in a similar manner to conventional units and are more able than PV to provide ancillary services. 67

70 (SEGS IV 1/5/1998) 4 35 Electric Output MWe Figure 49 SEGS IV hybrid parabolic trough (yellow) and gas generation (green) [28] These statements are generally supported by existing literature. At large scales, CST also has a better MW/km 2 than PV. Finally, CST is considered a more mature technology than PV (at a very large scale) with a greater number of large-scale systems operational for longer periods. One of the earliest large-scale PV plants is the 4.6 MW system in Springerville, Arizona, USA, which has been operating since 23. The SEGS I (13.8 MW) CST system has been operational since 1985 and includes 3 hours of TES. Some advantages of PV systems compared with CST systems are: PV systems do not necessarily require new land for their installation and can utilise building rooftops. This enables PV to be installed in already built up areas, with little impact on the surrounds and without the need to add new electrical infrastructure Distributed PV can be located close to loads, resulting in reduced line losses. There may also be the capacity to defer required upgrades of electrical infrastructure The process of installing a small-scale PV system does not require a large injection of capital or extensive studies to determine its feasibility, unlike as a CST system. This increases the likelihood of the installation going ahead, reducing the barriers to greater levels of renewable generation. 5.1 Variability :1 1:5 3:3 5:1 6:5 8:3 1:1 11:5 13:3 15:1 16:5 18:3 2:1 21:5 23:3 The North Ameri can Electric Reliability Corporation (NERC) completed a report [28] looking into the accommodatio n by the electrical grid of high levels of variable generation. Figure 5 shows the power output of a 64 MW parabolic trough solar thermal plant f or a sunny day (top plot) and a partly cloudy day (bottom plot). Figure 51 shows similar plots, but for a 1 MW PV plant located in Nevada. A comparison of the sunny day output profiles shows the PV plant is more variable, but with only small movements. A significant difference is evident when looking at the partly cloudy day output profiles, where the variability of the PV plant is far greater than for the CST plant. Parabolic trough type CST plants utilise a working fluid such as water or oil to transfer heat from the receiver to a heat exchanger. The working fluid has a thermal inertia and drops in irradiance do not result in an immediate drop in the temperature of this working fluid. This contributes to significant reduction in variability. Not all CST plants however utilise a working fluid. Parabolic dish type CST plants do not utilise water but their inherent thermal storage acts as a buffer to rapid ramp rates of their power output due to passing clouds. Similarly, Solar Tower type CST plants, such as the one installed at CSIRO Newcastle, which have thermal storage (e.g. molten-salt as the working fluid), are expected to exhibit a 75% capacity factor and with minimal variability in minute to minute output [28]. 68 Solar intermittency: Australia s clean energy challenge

71 CSP Output 5/1/28 MW CSP Output 3/25/28 MW Figure 5 Output for 64 MW parabolic trough 1-sec sample rate. Top: sunny day, bottom: partly cloudy [28] PV Output 5/1/ MW PV Output 3/25/ MW Figure 51 Output for 1 MW PV plant 1-sec sample rate. Top: sunny day, bottom: partly cloudy [28] 69

72 5.2 Storage and capacity factor A major storage advantage of a CST system over a PV system is that it can use thermal energy storage (TES) [29]. Storage for CST is more established than for PV with operational CST plants utilising TES throughout Spain and the USA. The authors are not aware of any large scale PV plants utilising storage, whereas SEGS I, which has three hours of TES, has been operating since An example of how the power output from a solar plant (in this case CST trough plant combined with TES) can be shifted through utilising storage to more closely match a load profile is shown in Figure 52. A thermal storage system for CST can be as simple as: a storage tank added to the loop through which the heat transfer fluid (HTF) flows the flow rate between the collectors and the storage tank, and the storage tank and the heat exchanger, are controlled to ensure a constant supply of heat to the heat exchanger, giving constant power output from the steam turbine the storage tank is drawn down during periods of cloud cover (or at nights) and re-filled during normal sunshine smoothing out output variability Hour Ending Figure 52 The effect of storage [82] A key advantage of TES in CST systems is that no additional energy conversion process is required resulting in more efficient addition of storage, unlike battery or mechanical storage for PV. Recent cost estimates puts the cost of adding TES to a CST system between $72 and $24 per kwh of electric storage capacity, along with high round trip efficiencies of up to 98% [29]. Table 7 gives estimates for the costs of battery storage using lead acid and VRLA types at $15 and $2 per kwh respectively, being the only types which would be considered competitive to TES [3]. 7 Solar intermittency: Australia s clean energy challenge

73 Table 7 Battery cost ($/kwh) [3] Technology Current Cost ($/kwh) 1-yr Projected Cost ($/kwh) Flooded Lead-acid Batteries $15 $15 CRLA Batteries $2 $2 NiCd Batteries $6 $6 Ni-MH Batteries $8 $35 Li-ion Batteries $13 $15 Na/S Batteries* $45 $25 Zebra Na/NiCl Batteries $8 $15 Vanadium Redox Batteries Nn/Br Batteries* 2 kwh = $18/kWh 1 kwh = $6/kWh 3 kwh/45 kwh = $5/kWh 2 MWh = $3/kWh Lead-carbon Asymmetric Capacitors (hybrid) $5 <$25 Low-speed Flywheels (steel) $38 $3 High-speed Flywheels (composite) $25/kW $8 Electrochemical Capacitors $356/kW $25/kW 25 kwh = $12/kWh 1 kwh = $5/kWh $25/kWh The increase in the Capacity Factor (CF), defined as the ratio of actual to potential power (nameplate size) supplied, of PV and CST plants with the addition of energy storage is also of interest. The 19.9 MW Torresol Gemasolar solar tower in Spain is able to manage 74% capacity factor with 15 hours of molten-salt storage [31]. Solar towers without storage can generally manage 2-25% capacity factor. PV systems without storage seem to offer similar capacity factors as CST without storage [32]. This is possibly due to the reliance of CST systems on direct beam irradiance and without storage, the capacity factor depends on the relative overall solar to electric power conversion efficiency of the technology itself. Figure 53 shows capacity factors for a number of PV systems, ranging from 13.4% to 26.9% with an average of around 23.6%. The findings are based on 3 years of hourly irradiance data, each system being a singular module ranging from 5W to 25W. Equivalent capacity factors between CST and the PV systems can be seen, but it is worth noting that the PV systems are single modules. Capacity factors would likely be different if these systems were analysed in aggregate..3 Average Capacity Factors Company A Company B Company C Company D Company E Figure 53 PV capacity factors [32] 71

74 Research in [33] considered how battery storage helped decrease the Unserved Critical Load (UCL) in a high PV penetration scenario. UCL is a proxy for SAIFI 3, which is defined as the annual average number of critical load interruptions experienced on a circuit. The study used ten, a hundred and a thousand houses in three different areas of the US, and compared UCL levels for systems with no storage and 1 kwh worth of battery storage. Figure 54 shows the results of the study. The most significant result was for the California (CA) 1 houses case, where the UCL dropped from around 85% to 5%. This can alternatively be seen as a 35% increase of critical load served this scenario consists of 1, 2.1 kw PV systems with 1 kwh battery storage. 1% % of original unserved 9% 8% 7% 6% 5% CO VA CA 1% % of original unserved 75% 5% 25% % CO VA CA Figure 54 Comparison change in UCL due to battery storage [33] This comparison between CST and PV systems looking into how much storage improves capacity factor is not an ideal one as it should really be done between systems of equivalent generation and storage capacity, and a large number of systems should be compared. However, there do not appear to be any large scale PV plants utilising storage. Note that the PV scenario used for the comparison is the best of those presented in Figure 54, and the Torresol Gemasolar solar tower has the best capacity factor of all operational CST systems. Although the comparison is somewhat mismatched, it gives some indication of the effectiveness of storage in improving the capacity factor for both CST and PV systems. 3 The System Average Interruption Frequency Index (SAIFI) is commonly used as a reliability indicator by electric power utilities and is the average number of interruptions that a customer would experience. 72 Solar intermittency: Australia s clean energy challenge

75 5.3 Summary The variability of CST plants was discussed in this chapter and compared with variations observed in PV plants. CST plants can essentially be considered to have better capability of riding through cloud transients. This is evident when looking at partly cloudy day output profiles: the variability of PV plants is seen to be far greater than for CST plants. The reason is that some storage is inherent in all CST systems, simply through the thermal mass of the working fluid and broader system, even if there is no major storage element included. Some advantages of CST systems over PV systems, and vice versa, are listed in Table 8. Table 8 Features of CST plants versus PV plants Advantages of CST plants CST projects are inherently capable of more accurate forecasts than PV plants as they are more independent of the variability of weather conditions. CST plants are able to utilise backup fossil fuel generation and thermal energy storage (TES) to increase their capacity factor. CST plants are able to respond to system operator instructions in a similar manner to conventional units and are more capable than PV to provide ancillary services. Advantages of PV plants PV systems do not necessarily require new land for their installation and can utilise building rooftops. Distributed PV can be located close to loads, resulting in reduced line losses. Installations of PV systems may help defer required upgrades to existing electrical infrastructure. Installing a small-scale PV system does not need large amounts of capital or extensive studies to determine its feasibility as a CST system does. This makes it more likely that the installation will go ahead. 73

76 6 High penetration intermittent renewable generation (HP-IRG) in Australia Electricity grids in Australia can be broadly divided into the National Electricity Market (NEM), which covers the entire east coast, and the other (smaller) grids. These include the West Australian South-West Interconnected System (SWIS) supplying Perth and surrounding regions, the North West Interconnected System (NWIS), and the many small grids supplying electricity to isolated townships and mining communities in Western Australia, Northern Territory and Queensland. The NEM operates on the world s longest interconnected power system, stretching approximately 5, kilometres from South Australia to northern Queensland [34], supplying the vast majority of the Australian population. Several key characteristics of the NEM distinguish it from the European electricity grid (Germany in particular): the NEM supplies a population roughly one-quarter the size of Germany s, but over a much greater area (South Australia, Victoria, New South Wales and Queensland have a combined area approximately ten times larger than Germany) the shape of the network is broadly long and thin, as opposed to a mesh-type dense, interconnected network like Germany the NEM supplies many remote and regional areas, and therefore includes many long, thin radial supply lines to transport electricity to these locations. These characteristics present different technical challenges from those in Europe and are likely to also provide unique challenges for the integration of HP-IRG. A large majority of the electricity generated from solar systems in Australia comes from decentralised rooftop systems connected to the LV network. In the future, large centralised solar systems, such as those proposed under the Australian Government s Solar Flagships program, could have very high levels of penetration in particular parts of the network. 74 Solar intermittency: Australia s clean energy challenge

77 6.1 Potential electricity network impacts An electric power system consists of a number of generators and a number of consumers, or loads. Conventional electricity networks do not incorporate storage (except at sub-cycle time scales). This means the energy generated must be exactly balanced by the energy consumed at all times. For example, if a person switches on an air conditioner (increasing the load), then a generator has to increase its output immediately to compensate. All generators have limits to the speed at which they can respond to a change in load (also known as ramp rate). When generators cannot respond fast enough, the power imbalance causes symptoms such as frequency and voltage fluctuations. Two characteristics of consumer loads help to prevent serious problems for the most part. First, their large scale effects are predictable. Engineers and planners working for utility companies and energy market operators know when to expect large increases in demand, and can schedule extra generation or alter maintenance schedules to ensure enough power is available. Also, these large scale effects are relatively slow moving, allowing time for generation to adapt to demand. Second, the small scale effects of demand, such as the precise time of a customer turning on their air conditioner, happen in a largely independent and uncorrelated manner (Mr Smith does not turn on his air conditioner at exactly the same time as Ms Jones). Consequently, these small scale effects tend to cancel out due to their randomness. Power utilities have dealt with these effects for many years, and are experienced and capable in maintaining this delicate balance. Just how difficult this is can be seen by the catastrophic power blackouts that occur from time to time, sometimes started by the most innocuous seed event. It may be thought that PV generation produces variation just like loads changing on the network. However, the intermittency of power from PV generation is different in several ways. First, large scale variation in PV generation is in the form of the classic sinusoidal function rising from a minimum in the morning to a maximum at noon, then tailing off again to sunset. This is in contrast to residential loads, which tend to peak in the evening. The whole system load, however, better correlates to the classic sinusoidal pattern associated with solar generation. Second, the small scale variation is caused by clouds and is not easily predictable. Although research efforts are being made in this area, it is still not clear what weather monitoring equipment is needed to do this accurately and efficiently, neither is it known which prediction methods are the most suitable. Third, power utilities do not have years of experience in dealing with PV generation and are therefore wary of adopting new technology that could upset the delicate balance that is the daily reality for maintaining a reliable power supply. Intermittent power generation can have a variety of effects upon the electricity networks to which they are connected. These effects are strongly influenced by the type of network and the amount of intermittent generation, but mostly fall into the categories of stability and voltage effects. High PV system penetration can be characterised by a single large generator in a given network area, or a large number of small generators in a given network area when compared with the load in the same network area. In addition to technical phenomena, there are effects upon the energy market of trading power generated by solar resources. The successful integration of high penetration solar power into the Australian electricity network is far from assured. The recent rapid uptake of rooftop solar panels around the country has raised concerns amongst various utilities on the impacts of high penetration solar PV. The installation of additional renewable generation has been stopped in certain areas in Queensland and Western Australia. In Queensland, some new applications for rooftop solar systems have been rejected and in Western Australia, limits have been set in some areas on how much intermittent renewable energy can be installed in a system without affecting the power supply. Horizon Power in WA has been rejecting applications for new installations in Exmouth and Carnarvon, and restricting new installations in Broome and Leonora [84]. The issue here is not specifically of intermittency but of voltage rise up to the maximum allowable level triggering the tripping out of the individual systems. This is a conservative response to a lack of information on the potential network problems intermittent renewable generation could cause or what mitigation measures are available to accommodate. 75

78 6.1.1 Voltage effects Voltage effects are illustrated by the figures in Section However, voltage variations are generally local effects that can cause inconvenience to a small number of people. Momentary high voltage can cause damage or shorten the life of equipment, and this is generally regarded as a symptom of adverse network issues. Voltage variations at the level of four volts, as illustrated in section 9.2.2, are unlikely to cause any noticeable network issues. However, utilities routinely maintain voltage near the maximum limit to avoid dropping too low when supplying large loads. When solar PV installations feed power back into the network, a small increase of four volts can push the voltage above this maximum limit. Although voltage variations are already caused by load changes, and utility companies already have mechanisms in place to deal with this, adding local generation can increase the range of voltage variation. The voltage limits are fairly broad and can be managed, but utility companies need to change voltage limits and practices in order to deal with this. At present, utility companies do not routinely monitor voltage at customers premises, so do not always possess the information required to accurately set local voltage levels. This is set to change with the installation of smart meters at residential properties. Amongst other things, these are capable of monitoring voltage. In this case, utilities may come under increasing pressure to rectify voltage issues. This may result in utilities exerting tighter control over voltage levels because of their regulatory requirement to comply with high and low voltage limits for the network under quality of service provisions. PV installations have another problem related to voltage, because each inverter has a voltage operating range. If the voltage at a customer premises is already near the maximum tolerated by the inverters of the PV systems, a small increase in voltage can exceed the maximum for the inverter, which causes the inverter to shut down. This will lead to the voltage falling again, and after some time the inverters may reconnect. So even a steady insolation level can result in fluctuating voltage and a barely functioning PV system. Many customers would be unaware this is happening as they have no technical knowledge in the area, and utilities may regard it as the customers problem. This issue is primarily due to inverter control settings, not inverter limits, which are in place to satisfy grid/utility/standards requirements. Intermittency of PV inverters will add to the existing variations in load. Load variation is a problem that utilities already understand and manage well. But PV intermittency will increase the variation, meaning utilities require more close monitoring and control of voltage. Although this sounds like a small problem, with obvious technical solutions, the increase in cost and the change of culture and practice required will pose a big problem to many utilities Stability effects Stability refers to the underlying balance of generation and consumption, manifested by steady and acceptable values for voltage and frequency. The frequency is generally considered such a good indication of generation and load balance that small changes can be used to attempt to avoid catastrophic collapse. In the event of a shortfall in generation, the frequency falls, triggering under frequency load shedding relays to reduce consumption. When this happens, customers are affected by blackouts in the areas where the relays operate. Contemporary power networks are designed around steady state theory and operate by allowing large safety margins in capacity. Reserve power is made available in the expectation that increases in demand can then be accommodated quickly without overloading the network. Large reserves of spinning fossil-fuel generators are undesirable, because of both increasing financial pressures and concerns about the environment. However, excessive reduction in safety margins may increase the risk of a catastrophic failure as a result of insufficient power quality and generation capacity to resolve a fault. Cascading failure can be defined as a sequence of dependant failures of individual components that successively weakens the power system [67]. The initial cause may be random but subsequent events are linked by electrical, control/protection devices or human error. Disturbances that cascade beyond the local area generally do so as a consequence of unexpected protection operation [68]. This type of blackout has attracted considerable attention because of the number of customers affected, and the obvious implications about the vulnerability and brittleness of the grid. There are many examples of this type of failure, most well-known occurring in August 23 when 5 million customers in the USA and Canada lost power for up to two days at a financial cost of $6 - $1 billion [69],[7]. 76 Solar intermittency: Australia s clean energy challenge

79 Stability problems are not confined to the deployment of solar PV generation, but can be triggered by a variety of causes, including extreme weather, unexpected operation of switchgear, and lack of capacity. However, there are some disturbing reports of instability caused by wind, and as PV is likely to cause similar problems, this should be a warning of what is possible [39][83]: An abrupt loss of 1,2 megawatts of wind energy production on Feb. 26 [28] caught the Electric Reliability Council of Texas (ERCOT) Inc. by surprise and forced it to declare emergency conditions ERCOT said the sharp drop in production during a three-hour period while overall electricity loads were increasing threatened the stability of the power grid and could have caused rolling blackouts. If a system can go unstable in the winter because 1,5 MW of expected wind turns into 4 MW wind and then fossil has to scramble to come online... that s a big issue. This abrupt drop in wind power production, a quicker than expected evening load ramp-up and an unexpected loss of conventional generation forced ERCOT to make an emergency declaration in response to a potential major grid stability problem [83]. With a more accurate generation and demand forecast, ERCOT could have scheduled additional generation to be available in advance of the evening load pickup and avoided the need for this emergency response. A detailed study of a large PV plant using a PSSE 4 model highlights the problem of the lack of knowledge of the effects of solar intermittency on the network: Although a few studies are found in the literature, there are no standard tools yet developed to analyze these complex scenarios. In most cases, custom models and tools are used along with some commercial programs to address the (stability) impact of large solar PV plants on electrical power systems. [45] These authors conclude that with large PV plant, the system is more vulnerable to stability problems. Those scenarios can drive the system to unstable operating point if protection is not designed with proper consideration. They also make the point that further research is required in order to understand these phenomena. Depending on the type of network, time of year, demand scenario and unit operation and maintenance schedule, the power network can be a finely balanced system that can be triggered into a state of catastrophic collapse by an event such as that discussed above. With increasing penetration of PV, some fear that the intermittent nature of PV generation may be able to provide just the trigger that can lead to such collapse. Although network operators already manage instability, an increasing penetration of PV systems will add another potential source of instability, and one network operators are not familiar with Ramp rate effects As discussed earlier, the rate of change of power, or ramp rate, is the amount by which the power output of a PV array changes within a given time period. If the ramp rate is too high, centralised power stations may be unable to react in time. The result of this would be voltage and frequency deviations. In extreme cases this would lead to power system instability, which could result in collapse and blackouts. In order to prevent such an occurrence, the most likely scenario is that power networks would be forced to provide rapid-peaking generation plants, such as gas, to provide voltage and frequency support. This is known as an ancillary service. Of course, storage can also be used to provide ancillary services, but this is not yet widely used. The ramp rate is also a measure of how fast ancillary services would have to react in order to stabilise the system. A small amount of intermittent generation (a small PV array) will have a smaller range of variation than a larger array, but the variation will occur over a smaller time scale than with a large array. There is evidence in the literature that as the penetration of PV rises, the ramp rate problem will be reduced, due to the averaging effect of many PV systems dispersed geographically [46]. However, there is also evidence that there may be a limit on the effect of geographical dispersion. For example a study on large scale PV sites across the American southwest showed that even spread over 28km, intermittency still requires very high ramp rate power generation [47]. Figure 55 shows ideal and actual power output from a photovoltaic array for a 24-hour period. The former is the theoretical maximum for a sunny day, while the latter is actual data taken from CSIRO s Energy Centre at Newcastle during a cloudy day in winter. 4 Power System Simulator for Engineering (PSSE) is a software simulation tool used for electrical transmission networks. 77

80 12 1 Actual Ideal Power (kw) : AM 4: AM 8: AM 12: PM 4: PM 8: PM 12: AM Time (hour:minute) Figure 55 An ideal generation profile for solar PV, compared with a real profile from a cloudy day in winter Based on these data, the maximum ramp rate under ideal conditions can be calculated as 8 Watts per second. However, the maximum ramp rate for a cloudy day was calculated as 73 Watts per se cond. A model has been developed based on the work of Marcos et al. [77] which allows prediction of the power output from the PV plant if the size of the PV array and the cloud c haracteristics are known. This model is described further in Section 11.1 of this report. The output of the model, which is the predicted PV plant output power, can be analysed to determine potential power ramp rates and the likeliho od of occurrence, using similar analysis to that performed in Section 9.2. This allows estimation of the probability density function of ramp rate, which can be used to predict the effects of a particular PV array upon the local network. Such prediction should be done with a clear understanding that every PV array will be different according to its size, type, local weather, local load profiles, network type and method of connection to the network. This information could then be used to estimate the amount of fast-acting generation required to provide the ancillary services for network stabilisation Capacity factor Apart from these obvious and immediate technical problems, there are other issues to be considered, including that the power supplied by a PV array is not schedulable, and even when stability is managed there is the issue of how power from a PV array can be traded on the electricity market. A PV array, will not always supply the rated power its nameplate size implies. The ratio of actual to potential power supplied is known as the capacity factor. There are various measures employed to characterise a PV array for its actual capacity and contribution to network generation needs. These measures can be useful in determining the level of backup generation required to offset the variability or lack of generation during periods of cloudy weather. To summarise: 1. Effective Load Carrying Capability a measure of how much extra load can be supported after adding PV without changing system reliability 2. Load Duration Capacity an average output of PV for the peak of the load duration curve 3. Demand Time Interval Matching the difference in peak demand with and without PV 4. Time Window Season probability of providing a minimum power output within a given time window 5. Relative measures of intermittency based on Fourier spectra or fractal measures within a given time scale 78 Solar intermittency: Australia s clean energy challenge

81 Using one of these measures an effective MWh can be calculated. This will be lower than total MWh served, in order to quantify the uncertainty. If a PV array contributes a total MWh and an effective MWh, the ancillary services cost can be charged as the difference between these. In other words, how much ancillary service is needed to make the effective MWh into the total MWh served? The more intermittent the PV, the bigger the difference between effective MWh and MWh served, the bigger the share of ancillary services for which it is charged will be. The capacity factor for wind power is considered to be between 1/6 and 1/3. At best case, 3 GW of wind needs to be installed to generate total annual energy equivalent to that from 1 GW of conventional generation [46]. According to the same paper (citing the author s own earlier work) Germany had to build nearly 5% reserve margin to provide back up for its wind generation. There is little reason to suppose solar PV will be any different. Wind projects in the USA are estimated to have a capacity factor of 4% at best, while coal fired has 6% and nuclear power over 9%. Solar PV is estimated to have a rapidly rising capacity factor currently at about 21% [46]. It cannot be assumed that the success of European countries such as Germany and Denmark in integrating renewable energy will be matched in Australia. Australia has a very different network structure, characterised by low populations and large distances, resulting in weak networks compared with the US and Europe. 6.2 Accommodation of HP-IRG in Australia Various articles in the existing literature discuss the work required to facilitate the integration of high penetration intermittent generation. The recommendations include: Develop models representing solar behaviour and net load. Accurate models are needed to quantify the performa nce and economic impacts on regul ation and load following according to [22] and for reliable forecasting [6] Determine the required flexibility of non-variable generation to manage the variability introduced by HP-IRG. Again, accurate models on solar are necessary to determine this [13]. Simulations to determine the impact of HP-IRG on power quality at the distribution level [13] Investigate how flexibility can be introduced (or variability mitigated) into electrical networks through the utilisation of energy storage and load participation. This leads to thinking about centralised control concepts and Energy Management Systems (EMS). For successful integration of HP-IRG into the Australian electricity network, it is necessary for this work to be performed within an Australian context. For example, to determine the extent of flexibility required in the Australian electricity network, studies which consider Australia s generation mix and the individual capabilities of all conventional generators need to be performed. To determine the impact of the introduction of storage for a certain feeder with large amounts of photovoltaics (PV), the local load profile and grid characteristics have to be known. The need for site specific studies is either mentioned explicitly or implied in the literature. It is stated in [1] that to understand how regulation will be impacted, data will need to be gathered at high resolutions of up to 1 sec. Data will also need to be synchronised with load data to give a clear picture of the net impact of varying load and generation. Obviously the solar and load data to be synchronised in this case needs to be sourced from the same geographical location. Another statement made in [11] is Site specific data is required to characterise the spatial variability of solar irradiance over areas of 1km2 or less. This would enable optimised site selection of PV plant improving performance. This data could also be utilised to develop site specific forecasting. From [23], Weather systems can cover very large areas, reducing the advantages gained from an increased load balancing area. Studies are required to determine the correlation of weather across the spread of the electricity grid ensuring links between areas with weak correlation and Detailed load profile data for individual homes would allow for accurate modelling on the impact of PV and storage. The results of such modelling would be a reliable basis on which to gauge the economic viability of PV and storage. This section of the report presents studies completed in other countries which are considered necessary to assist in the integration of HP-IRG. Findings from some of these studies can be applied in Australia, but ultimately similar studies will need to be carried out in an Australian context, taking into consideration unique Australian conditions. 79

82 6.2.1 System level studies The studies presented in this section are all considered system level studies and are mainly related to the flexibility of an electrical network. These studies would assist in identifying vulnerabilities in Australia s generation mix in relation to required system flexibility to manage HP-IRG, and help determine the level of intermittent renewables penetration Australian electricity networks can manage. The inputs used to complete these studies can be categorised as: System load profile A representation of the demand over the course of differing time intervals for differing sample rates Renewable generation profile A representation of the power generated from renewable energy sources over the course of differing time intervals for differing sample rates. Generation mix The mix of conventional generation (coal, gas, hydro and nuclear) which makes up the generation set for the system System flexibility Can be described as the ability of the aggregated set of generators to respond to the variation and uncertainty in net load [19]. Flexibility can be introduced into electrical networks by using energy storage and load participation (e.g. load shedding). Measures of system flexibility are [1]: Minimum load The lowest power output the generation set can manage without being forced to turn off. The lower the minimum load the less likely generation will be forced to shut down during periods of light net load brought on by high solar generation. Start/stop speed The time required for generators to start up and shut down Ramp rates The rate at which generation can increase or decrease output Load balancing area The area over which generation output is matched to demand. For the Australian National Electricity Market (NEM), this is the entire east coast of Australia Estimation of net load For a generation set designed to meet net load requirements as opposed to peak load, its flexibility is the measure of its adequacy. To determine the necessary flexibility of the generation set, an accurate estimation of net load for a given penetration level of renewable generation is required. Models characterising net load need to clearly represent the expected variability at the timeframes of interest. In the NEM, generators are scheduled and dispatched into production to match supply every five minutes [34], as can be seen in Figure 56. NEM planners need to work out how much generation flexibility is required to manage an expected increase in variability from the introduction of renewable generation. To assist in this process, the amount or extent of variability in net load the network might experience in a five-minute interval needs to be determined HOUR 18 CLOCK Midnight start/ 4. starts and ends 6 1 HOUR 45 CLOCK 15 Pre-dispatch forecast 1 Hour Pre-dispatch forecast Figure 56 A day in the NEM [34] 8 Solar intermittency: Australia s clean energy challenge

83 Figure 9 from Section is an example of a presentation method to characterise the variability in net load. This was for a study performed on the Californian grid where actual historical data was obtained for wind, load and solar. This data was projected forward for modelling 2% renewable penetration by 21 (21T) and 33% renewables by 21 (21X) and 22 (22). Figure 9 shows the expected variability of net load for the 21X scenario, 33% penetration of renewables. Also shown is the variability of original load (i.e. without intermittent generation) for comparison. This study could be replicated for the NEM at five-minute intervals instead of hourly, which would enable system planners to calculate the flexibility gap in its generation set. For the NEM to commission work of this nature, it would require a renewable generation output profile scaled up to the penetration level of interest as well as the load profile. An accurate scaled up profile of renewable generation for the NEM would ideally be a combination of: existing generation output data scaled up to some degree estimation of generation at new sites based on a wind and solar resource model. The Australian Energy Market Operator (AEMO) uses the Australian Wind Energy Forecasting System (AWEFS) which provides a wind forecast every five minutes. In the case that there is insufficient recorded data at existing and proposed wind generation sites, data from the AWEFS could be used instead. Unfortunately there is no equivalent resource for solar yet. It may be necessary to begin accruing irradiance data at existing and proposed sites at five-minute intervals and combine this with historical satellite data to develop an adequate solar model. The influence large amounts of PV have on the net load profile is looked into in more detail in [35]. The Japanese government has set a target of 53,MW of PV to be installed by 23. Given the fluctuating nature of PV generation, a proper evaluation on net load variability is deemed necessary. Nagoya City is the focus of the study; the city is broken up into a grid pattern with the resultant net load calculated for 8MW of installed PV with three different distributions. PV output is calculated using measured insolation for five sites located within Nagoya City and the load data sourced from recordings by the local electric power company in 2/21. The three cases are: Case 1: Most concentrated 5 blocks with 16MW of PV per block. The blocks are located in more densely habituated regions of the city. Case 2: 15 blocks of 53MW of PV per block. Case 3: Least concentrated 5 blocks of 16MW of PV per block. Figure 57 illustrates the fluctuation levels of net load for each of the abovementioned case in comparison to the case with no PV, showing how the impact varies according to distribution type. It is seen that the net load fluctuations are greater when PV generation is more centralised. The top left chart of Figure 57 shows the most variability in net load due to its greater concentration. The standard deviation of net load with PV is at times 2MW greater than without. The significant increase in apparent load fluctuations with the integration of PV would require significant measure for load frequency control [35]. Note the difference in variation between the most concentrated and the least concentrated (bottom chart) cases, with the increase in net load fluctuations being minor for the lower concentration case. The peak load for Nagoya City is around 25 GW, making 8 GW of PV correspond to a penetration level of 32%. At the system level, this study demonstrates the variation in influence PV has on net load according to its geographical orientation. If Australia was to have a target of high penetration installed PV, similar studies would need to be performed during the planning stages. For the NEM, findings from such studies are unlikely to be similar. The NEM covers the entire east coast, far greater than the area of Nagoya City, so the area over which the solar resource needs to be defined is also far greater with a greater diversity in PV generation across the network. The density of load is also far greater in Japan than in the NEM, meaning these are two very different electrical systems. The NEM, covering the entire east coast of Australia, supplies 25 GW on a typical day [34], the same as for Nagoya City, which is approximately 325 km 2 in size. 81

84 35 N = 5 3 P = 16 MW/block (1 block = 25km 25 2 ) N = 15 P = 53 MW/block 3 (1 block = 25km 2 ) N = 5 P = 16 MW/block (1 block = 25km 2 ) Figure 57 Standard deviation of net load for increasing PV capacity per block [35] Displacement of conventional generation It is important to understand that variability and uncertainty are inherent characteristics of all power systems including the Australian NEM. Loads, power lines, and generator availability and performance all have a degree of variability and uncertainty. Regulations, standards, and procedures have evolved over the past century to manage variability and uncertainty to maintain reliable operation while keeping costs down. In general, system operators and planners use mechanisms including forecasting, scheduling, economic dispatch and reserves to ensure performance that satisfies reliability standards in a least cost manner. A study in [13] looked into how the integration of PV at penetration levels of 1%, 2% and 3% would impact on the existing generation mix. Figure 16 from Section 3.3 shows the load duration curve from CAISO. The graph is based on 27 load and PV data projected into 21 for scenarios of 1%, 3% and 5% PV penetration. Overlaying Figure 16 over the generation mix gives Figure 17 and shows how the increased levels of solar displace generation in the existing fuel mix. Looking at the US fuel mix, the majority of the solar displaces gas fired generation with coal fired generation being displaced into for short periods. Figure 58 compares the Australian fuel mix for the NEM with the US. It can be seen that the US utilises approximately 5% coal fired generation whereas the NEM uses more than 8% and if this fuel mix was laid over Figure 16, there would be a major change in the Australian generation mix. High penetration intermittent generation creates a need for increased system flexibility. A study can be performed locally to determine feasible options that could be implemented or integrated into the different Australian electricity markets to cope with this requirement. Various ways in which the system flexibility in various Australian electricity networks can be enhanced need to be investigated. 82 Solar intermittency: Australia s clean energy challenge

85 These include: load control ancillary services spinning reserve energy storage renewable generation curtailment upgrading of conventional generation mix. Australia USA Black coal: 56.3% Brown coal: 24.8% Natural gas 3 : 12.2% Hydro: 5.% Wind 2 : 1.5% Oil and other:.2% Coal: 49.61% Gas: 18.77% Hydro: 6.5% Oil: 3.3% Biomass: 1.3% Other fossil:.6% Geothermal:.36% Wind:.44% Other unknown:.1% Solar:.1% Source: U.S. EPA, egrid year 25 data Figure 58 Australian generation by fuel type excluding distributed generation and off-grid private sources (21) [34] vs. US Generation mix (25) [36] Study how system flexibility limits intermittent renewable generation penetration levels A study performed on the Electric Reliability Council of Texas (ERCOT) [19] looked into the degree of curtailment of intermittent renewable generation (IRG) required for different mixes of wind and solar generation for varying levels of system flexibility. System flexibility refers to the flexibility of the conventional generation fleet which is characterised in terms of parameters such as minimum start-up and shut-down times, minimum stable generation and ramp rates. Some results from the study presented in Figure 28 in Section show how the system flexibility impacted on the amount of wind curtailed. At 5% wind penetration, curtailment dropped from 5% to 2% for a 1% increase in system flexibility. Inputs into this study included the load profile for ERCOT, the local wind generation profile and the theoretical minimum load levels. Based on the system flexibility, this study gives indication of the level of IRG penetration possible; when a percentage increase in IRG results in a similar percentage increase in curtailment then the limit is likely reached. The study also looked at required curtailment levels for various percentage mixes of wind and solar generation. The effect of storage on increasing system flexi bility was also inve stigated in the study, the results of which are shown in Figure 3 in Section It is seen that a minimum amount of four hours storage reduces the fraction of curtailed IRG from nearly 35% to less than 2% for 8% penetration of IRG. This study uses theoretical values for system flexibility, and for it to be applicable in Australia it would need to use the local generation mix and models of solar and wind generation based on local wind and irradiance data. Work of this type would be of great assistance in determining possible levels of intermittent renewables penetration for the system and to identify what particular generation, due to its relative inflexibility, is limiting the penetration level. A study could also be performed to investigate whether introducing storage would be a more feasible option of increasing system flexibility than introducing more flexible generation. This study could also determine the cost savings that could be achieved by introducing storage through alleviating the need to decommission inflexible generation and reducing the curtailment of HP-IRG. 83

86 6.2.2 Distribution level studies The following studies revolve around possible impacts of HP-IRG at the distribution level. The studies look into the impacts of large amounts of centralised and distributed solar on frequency and voltage regulation, power flow, harmonics and voltage fluctuation. An analysis on the voltage behaviour for a PV demonstration project in Ota, Japan is presented in [37]. The project consists of 553 PV systems, ranging in size from 3-5kW, in a 1km 2 area; 8% of homes have PV on their roofs with a total installed capacity of 2.1 MW. The main focus of the project was to look into the effectiveness of the Power Control System (PCS) installed at each inverter. The PCS uses active and reactive power control in combination with battery storage to manage voltage rise. Of interest i s the kind of variability in voltage levels seen at each of the homes, shown in Figure 59. The bottom graph is the aggregate PV output for the project for the day, where conditions were particularly sunny with very little fluctuation in output. The top plot of Figure 59 shows the kind of variation in voltage possible from house to house: at worst case there is almost a 4% variation at midday. The study claims higher voltages are observed for PV systems with higher impedance between their output and the nearest pole transformer, demonstrating that the degree of impact PV has on power quality is influenced by grid characteristics. The voltage fluctuation is within tolerance (±6V for Japan at 1V) but this is with PCSs and battery storage. The results are likely to be different for Ota without the power flow management in place. According to the Japan PV23 roadmap, 5% of residences are forecast to have PV on their roofs by (a) Delta Voltage (pu) Sum of PV output power (MW) (b) Time (hour) Figure 59 Voltage difference between the common high-voltage measuring point and PV system output [37] 84 Solar intermittency: Australia s clean energy challenge

87 To facilitate similar penetration levels and gain confidence in the management of increasing solar penetration levels in Australia, demonstration projects like Ota are a necessary preparation and need to occur locally. There are a number of long and skinny feeders in Australia, most of which are located in rural networks. For example, Ergon Energy has one of the largest distribution networks in the world, with electricity infrastructure assets across one million square kilometres of regional Queensland [79]. Its service area covers 97% of the state and has one of the lowest customer densities of any network in the western world. High penetration of solar power in such networks with high impedance feeders is likely to cause voltage fluctuations beyond acceptable limits due to intermittent solar power output caused by passing clouds. Similar studies to that carried out in Ota, Japan, need to be performed in local areas with high impedance feeders to investigate the impacts of high penetration solar on such networks. The impact of a 1 MW PV system on a local feeder is analysed in [38]. The system is installed at the Main Stadium of the 29 World Games in Kaohsiung, Taiwan and connected to a Taiwan Power Company (TPC) feeder. The single line diagram shown in Figure 6 is a representation of the electrical system for the stadium. The PV system is connected to Bus 7, labelled PVS. Simulations using this model and increasing levels of PV were conducted to determine the level of voltage fluctuation introduced into the feeder by the PV system. Figure 6 Single line diagram for electrical system at Main Stadium in Kaohsiung, Taiwan [38] A probability distribution of PV generation was developed using hourly historical weather data and a model of a solar cell. Figure 61 shows the resultant PV output profile. The sample rate was not provided. As voltage fluctuations are calculated at the same rate as the generated PV output profile, it is important that this rate is high enough to capture the change in PV output due to passing clouds. It is stated in [38] that the PV output profile is affected by the random passing of clouds. To investigate the impact of the PV the circuit breaker at MF65 is opened (making Bus 7 the end of the feeder) and the feeder tie switch is closed, leaving the PV system at Bus 7 as the only power source at that end of the feeder. The voltage fluctuation at MU67 is then examined through simulation using historical load data and the generated PV output profile mentioned above. The results show voltage fluctuation starts to exceed constraints of 2.5% when the capacity of the PV system is around 3.5 MW. The rating of the feeder is assumed to be around 6 MVA as power is supplied to the loads through three 2MVA transformers, indicating voltage fluctuation constraints are being breached with PV penetration of around 58%. 85

88 W/m KWh Hour Figure 61 PV generation profile used for study in [38] This study is of interest, but would benefit from measured high resolution irradiance data. The generation profile of Figure 61 also looks to be representative of a fairly sunny day, with not many fluctuations. An impact analysis using a partly cloudy day would likely show more extreme voltage fluctuations. Taiwan is looking to increase PV installation levels to 2 GW by 225 and studies like these are being conducted to try to better understand the impact of this goal. 6.3 Summary The Australian continent experiences a collection of unique conditions which, when combined, create the unique environment in which the Australian electricity network operates. The network configuration in Australia is different from that of the rest of the world. For example, the National Electricity Market (NEM) covers the entire east coast. Compared to areas in other countries where various studies have been carried out, this is far larger and has a greater diversity in PV generation across the network. Successfully integrating high penetration solar power into the Australian electricity network is highly dependent on mitigating the effects of intermittency. Some initial investigations have been carried out in other countries, but they were of limited scope and are not necessarily applicable in the Australian context. Various articles in the existing literature discuss the work required to facilitate the integration of high penetration intermittent generation. The recommended work required includes: development of models representing solar behaviour and net load determination of required flexibility of non-variable generation to manage the variability introduced by high penetration intermittent generation simulations to determine the impact of high penetration intermittent generation on power quality at the distribution level investigations into how flexibility can be introduced (or variability mitigated) into electrical networks through the utilisation of energy storage and load participation. For successful integration of high penetration intermittent generation into the Australian electricity network, it is necessary for this work to be performed within an Australian context. An example would be to determine the extent of flexibility required in the Australian electricity network. This would call for studies to be performed that consider Australia s generation mix and the individual capabilities of all conventional generators. In the NEM, generators are scheduled and dispatched into production to match supply every five minutes. NEM planners would need to work out how much generation flexibility is needed to manage the expected increase in variability from the introduction of intermittent 86 Solar intermittency: Australia s clean energy challenge

89 renewable generation. In the case of the NEM, the extent of variability in net load the network might experience in a five-minute interval needs to be determined. It was mentioned earlier in the report that accurate forecasting is an essential element for the successful integration of large amounts of intermittent solar generation and for solar power to be economically viable. The Australian Energy Market Operator (AEMO) uses the Australian Wind Energy Forecasting System (AWEFS) which provides a wind forecast every five minutes. There is however no equivalent resource for solar yet. To develop an adequate solar model, it may be necessary to begin accruing irradiance data at existing and proposed sites at five-minute intervals and combine this with historical satellite data. To facilitate high penetration levels and gain confidence in managing increasing solar penetration levels in Australia, demonstration projects at the distribution level are a necessary preparation, and need to occur locally. The NEM, for example, is exceptionally sparse by international standards, leading to higher characteristic impedances and consequently greater sensitivity to the behaviour of localised load and generation. High penetration of solar power in such networks with high impedance feeders is likely to cause voltage fluctuations beyond acceptable limits due to intermittent solar power output caused by passing clouds. Similar studies to those carried out in other parts of the world need to be performed in Australian locations which have high impedance feeders to investigate the impacts of high penetration solar on such networks. 87

90 7 Renewable generation intermittency IEA Task 14 The International Energy Agency (IEA) established Task 14 to focus on electricity grid configurations with a high penetration of Renewable Energy Sources (RES), where PV constitutes the main RES. No common definition of high penetration PV scenarios has been established, but there is common consensus within the Task Definition group that high penetration situation exists if additional efforts will be needed to integrate the dispersed generators optimally. A new definition, suggested by the authors, states that a high penetration intermittent generation scenario exists where it is the variability of the intermittent generation rather than the loads within a network segment that is the dominant factor in determining the need for substation/network or control upgrades. Task 14 aims to analyse particular issues related to the penetration of PV in electricity grids and to establish penetration scenarios in order to guide discussions on respective technical challenges. The program is addressing mainly technical issues relating to high penetration of PV in electricity networks. These include energy management, grid interaction, and aspects to do with local distribution grids and central PV generation scenarios. One characteristic of PV considered in the Task 14 work is the variable and fluctuating nature of generation associated with PV. This section summarises key intermittency issues presented and discussed at the IEA Task 14 High Penetration Photovoltaic workshops held in December 21 at Colorado, USA, and more recently in May 211 at Lisbon, Portugal. The Task 14 Work Group is made up of representatives from many countries including Japan, China, Austria, Germany, Spain, Portugal, the USA and Australia. 7.1 IEA Task 14 background The International Energy Agency (IEA), founded in 1974, is an autonomous body within the framework of the Organization of Economic Co-operation and Development (OECD) which carries out a comprehensive programme of energy co-operation among its twenty four member countries. The European Commission also participates in the work of the Agency. The IEA Photovoltaic Power Systems (PVPS) Implementing Agreement is one of the collaborative research and development agreements established within the IEA. Since 1993 its participants have been conducting a variety of joint projects in the applications of photovoltaic conversion of solar energy into electricity. 88 Solar intermittency: Australia s clean energy challenge

91 Task 14, entitled High Penetration of PV Systems in Electricity Grids, was set up for the period from 21 to 214. The basic ideas for the proposed task resulted from a workshop on The Role of PV in Smart Grids Integration of Renewable Energy Systems and Distributed Energy in Electricity Grids, Opportunities and Issues for Photovoltaics organised by the Australian PV Association in Sydney in November 28. The intention of the workshop was to tackle opportunities and research needs for grid-connected PV in a high penetration scenario. Following the ongoing deployment of grid-connected PV in electricity grids in a number of PVPS countries and the problems and issues associated with this development, a number of PVPS members and external experts identified a clear need for further action. The main goal of Task 14 is to promote the use of grid-connected PV as an important source in electric power systems at a high penetration level, where additional efforts may be necessary to integrate the dispersed generators in an optimum manner [48]. The objectives of this Task are to: develop and verify mainly technical requirements for PV systems and electric power systems to allow for high penetrations of PV systems interconnected with the grid discuss the active role of PV systems related to energy management and system control of electricity grids. 7.2 Current and predicted levels of intermittent generation penetration The current and predicted levels of intermittent generation penetration in various parts of the world, obtained from presentation materials of the IEA Task 14 meetings in Colorado and Lisbon, are summarised in this section. EU Nations, Turkey and Norway The potential growth of PV penetration in Europe 27 (European Union Nations), Turkey and Norway is shown in Figure 62. The paradigm shift scenario describes PV moving from being a passive to an active grid element, with the integration of smart inverters, energy management through Demand Side Management (DSM) and storage, and communication technology for eventual optimisation of energy flux over the entire network [49]. GW Paradigm Shi 12% Asymptote without Paradigm Shi Accelerated Growth 6% Indica ve Baseline 4% Possible evolu on a er Paradigm Shi Paradigm Shi Scenario 12% of electricity demand by 22 Accelerated Growth Scenario 6% of electricity demand by 22 Baseline Scenario 4% of electricity demand by 22 Sources: EPIA - EU TREN European Energy and Transport: trends to 23 - update 27 - Eurostat Data Portal - EU Joint Research Centre Photovoltaic Geographical Informa on System - A.T. Kearney analysis. EPIA 29 or22.eu Figure 62 PV deployment scenarios: Europe 27, Norway and Turkey [4] Denmark and Germany According to [5], the Danish Government intends to increase wind capacity by 1.3 GW by 212 and is targeting 5% (mostly wind) renewable energy generation by 225. Figure 63 illustrates the increase in renewable energy generation in Germany from 199 to 29. As of 29, PV and wind penetration stood at 9 GW and 26 GW respectively, meaning the total power generation from renewable energy sources, including hydro and biomass exceeds the minimum load of 4 GW. 8% of installed PV is connected to low voltage grids, with 7% of the installations having generating capacity of under 1 kwp (Note: the term kwp refers to kilowatt peak) [51]. The total PV capacity was expected to increase to 15 GW by September 21, a 67% increase in less than two years. There is an average installed PV capacity of 39kW/km 2 in Germany, with peak 89

92 generation reaching over 1 GW at times. The ratio of installed PV capacity to average annual load for all Distributed System Operators (DSO) in Germany is shown in Figure 64. It can be seen that about 1% of the DSOs have installed PV capacity exceeding their average annual load (i.e. ratio of installed PV capacity to average annual load greater than 1). Seven DSOs have an installed PV capacity of 3-4 times more than their average annual load. The average installed PV capacity for each German DSO is reported to be 34% of their respective average annual load in 21. September 21: 15 GWp PV Installed RES Capacity [GWel] minimum load Year PV (9GW) 8% in low voltage 7% < 1 kwp Wind (26 GW) Biomass (4 GW) Hydro (5 GW) Figure 63 Increase of renewable energy sources in Germany [51] 15,34 MWp/MW MWp/MW 3 MWp/MW Fi gure 64 Ratio of installed PV capacity over average annual load for German DSOs in 21 [51] 9 Solar intermittency: Australia s clean energy challenge

93 Hawaii The Hawaiian Electric Light Company (HELCO) has had a large increase in distributed solar in 29 and 21 [52]. The total PV installed capacity, comprising both Net Energy Metering (NEM) and non-export PV installations, in the HELCO network can be seen in Figure 65. The total PV installed capacity currently stands at 5.2% of the annual high peak demand. 25.5% of Hawaii s power is generated using renewable energy resources, made up of wind (12.9%), hydro and geothermal. The Hawaiian grid system can be characterised by: autonomous system (no interconnections) close to limits of stable operation high penetration of variable generation high penetration of distributed generation large amount of renewable energy from wind, geothermal and solar decreasing demand (negative load growth) in the east side of the island, where HELCO s generation is located, and a rapid growt h on the west side Non-export kw PV installa ons (nameplate) NEM kw PV installa ons (nameplate) projected 1 8 Current NEM: 2.2% Current total: 5.2% Projected total: 2.2% Figure 65 HELCO PV installations as % of Annual High Peak, Hawaii [52] 91

94 Japan The current and projected dissemination of PV in Japan is shown in Figure 66 [53]. A large proportion (about 7-8%) of Japan s PV capacity is installed on residential properties. A residential PV system subsidy program was introduced in January 29 to encourage a higher rate of PV uptake among residential customers. With the introduction of this new program, Japan s total PV installed capacity saw a 1-fold increase in five years from 1.4 GW in 25 to 14 GW in 21. This capac ity is predicted to double to 28 GW by 22. Figure 66 PV Dissemination target of Japan [53] 92 Solar intermittency: Australia s clean energy challenge

95 Europe A presentation of the predicted power generation mix for Europe from five different studies is illustrated in Figure 67 [54]. These levels of RES, carbon capture and storage (CCS) and nuclear are required to achieve complete decarbonisation of energy demand with an average level of 52 % expected for RES. Power genera on mix according to different studies %, 2-25 RES avg. 2 =52% Nuclear CCS 2% 2% 27% 35% 23% 31% 1% 6% 2% 15% 2% 34% RES 6% 38% 46% 62% Figure 67 Projected European generation mix from five different studies [54] A table illustrating the magnitude (in MW) and percentage of RES generation in Spain is shown in Table 9 [55]. The installed PV capacity has increased from 139 MW in 26 to 3634 MW in January 21, a 26-fold increase in four years. The contribution of wind and solar power (PV and CSP), as a percentage of Spain s total demand in 21 was recorded as 15.6% and 2.4% respectively. Spain s renewable energy plan targets a PV capacity of 8367 MW by 22. Neighbouring Portugal has a goal of 15 MW installed solar capacity by 22, with the majority of the installations in the southern part of the country where the irradiation level is highest [56]. Table 9 Percentage of RES for Spain [55] Technology MW % of total generation Wind power generation Solar PV Solar CSP 63.7 Biomass Special regime hydro Cogeneration Waste treatment Total 34,

96 China The growth of installed PV capacity in China since 24 is shown in Table 1 [57]. A majority of the installations were standalone systems (off-grid) until a sudden spike in grid-tied installations in 29. The installed PV capacity in China as at 21 stands at 8 MW, with 19 MW of Building Integrated PV (BIPV) and Building Applied PV (BAVP) installed in 21 alone. In addition, 65 large-scale PV plants, ranging in size from 1 MW to 2 MW, were installed by the end of 21 with a total capacity of 4 MW. Table 11 presents China s targeted PV capacity by 215 and 22 [57]. It can be seen that a large increase in PV capacity is predicted from both large-scale solar plants and building PV (both BIPV and BAPV) installations. Table 1 PV installation in China [57] Year Off-Grid (MW) On-Grid (MW) Annual Inst Cumulative (MW) Table 11 China s PV targets for 215 and 22 [57] PV Baseline Target in China (GW) Market Sector Rural electrification Communication and Industry PV Commercial Products BIPV and BAPV LS-PV Concentrating Solar Power (CSP) Total To assist in achieving this target, three Chinese government projects are currently active: In Western China, 28 MW of PV is planned consisting of two 3 MW and eleven units of 2 MW systems. The first phase of The Golden Sun demonstration project installed 21 MW in 29 with 5% subsidy for grid-connected and 7% subsidy for off-grid projects. The second phase began in October 21 with 5 projects approved totalling 272 MW. 1 GW of PV per year will be installed from 212. Phase 1 of a BIPV/BAPV initiative resulted in 9 MW of PV installed, with a subsidy of 1.27 Billion Yuan ($19 Million AUD). The second phase included 99 installations totalling 9.2 MW, with a subsidy of Billion Yuan ($18 million AUD), which began in April 21. The third phase began in January 211 and is looking at installing around 2 MW with 5% subsidy. The global cumulative PV installed capacity, as at the end of 21, is 39.6 GW [56]. 94 Solar intermittency: Australia s clean energy challenge

97 7.3 Intermittency observations and impacts An example of the impact of solar intermittency on generation output was presented by PEPCO Holdings Inc. for the Atlantic City Convention Centre PV system, as shown in Figure 68 [58]. The red line shows irradiance (scaled) and the blue line shows total system output (in kw) with data obtained every one minute. A direct relationship between solar irradiance and corresponding PV generation output can be seen for the three plots. KW KW Power Output (kw) : 7:44 8:28 9:12 9:56 1:4 11:24 12: 12:52 13:36 14:2 15:4 15:48 16:32 17:16 18: 18:44 19:28-5 Time Power Output (kw) : 7:44 8:28 9:12 9:56 1:4 11:24 12: 12:52 13:36 14:2 15:4 15:48 16:32 17:16 18: 18:44 19:28 Time 2 Power Output (kw) KW : 7:44 8:28 9:12 9:56 1:4 11:24 12: 12:52 13:36 14:2 15:4 15:48 16:32 17:16 18: 18:44 19:28 Time Figure 68 PV power output for three consecutive days at the Atlantic City Convention Centre [58] 95

98 The net load on a particular electrical feeder, both before and after the installation of a 1.7 MW PV plant, is shown in Figure 69. The variability of net load on a partly cloudy day after the installation of the PV plant, as seen in the middle plot, is much larger than the variability due to load alone, shown in the top plot of Figure 69. It is also worth n oting the difference in net load between the clear and partly cloudy days (two bottom plots of Figure 69) with PV having minimal impact on the net load variability for the clear day. MW MW MW Sunday April 25, 21 (Before PV) 12: AM 1:4 AM 2:8 AM 3:12 AM 4:1 AM 5:2 AM 4:24 AM 7:28 AM 8:32 AM 9:3 AM 1:4 AM 11:44 AM 12:48 PM 1:52 PM 2:58 PM 4: PM 5:4 PM 6:8 PM 7:12 PM 8:16 PM 9:2 PM 1:24 PM 11:28 PM Time Sunday May 23, 21 (1.7 MW PV: 73 F and cloudy) Time Cloud 12: AM 1:4 AM 2:8 AM 3:12 AM 4:1 AM 5:2 AM 4:24 AM 7:28 AM 8:32 AM 9:3 AM 1:4 AM 11:44 AM 12:48 PM 1:52 PM 2:58 PM 4: PM 5:4 PM 6:8 PM 7:12 PM 8:16 PM 9:2 PM 1:24 PM 11:28 PM Sunday May 3, 21 (1.7 MW PV: 89 F and sunny) Typical Load Curve w/o PV Time Clear Day 12: AM 1:4 AM 2:8 AM 3:12 AM 4:1 AM 5:2 AM 4:24 AM 7:28 AM 8:32 AM 9:3 AM 1:4 AM 11:44 AM 12:48 PM 1:52 PM 2:58 PM 4: PM 5:4 PM 6:8 PM 7:12 PM 8:16 PM 9:2 PM 1:24 PM 11:28 PM Figure 69 Net load before and after PV on PEPCO feeder [58] 96 Solar intermittency: Australia s clean energy challenge

99 Ramp Rates and Flicker As the output of any solar system is insolation dependent, curtailment of output is often required to maintain system balance. Figure 7 from [7] shows the kind of occurrences of various curtailment and ramp-up rates for a 5 kw PV system. One-second data was used in this analysis. The penetration percentage is not given here. The highest number of occurrences in the 5 kw system were for ramp rates between 8 kw/sec and 1 kw/sec, which correspond to.48 MW/ min and.6 MW/min respectively. An interesting comparison between the fluctuations caused by PV due to cloud transits and other loads is shown on a flicker curve for incandescent lamps in Figure 71 [58]. PV sits at around 3 fluctuations per hour with a.6% voltage change, just below the boundary of visibility. Number of occurrences Histogram of curtailment ramp rates during peak hours (1am-3pm) for a 5kW system using one second data, May 14 to June 9, 21 Number of occurrences Histogram of ramp up rates during peak hours (1am-3pm) for a 5kW system using one second data, May 14 to June 9, 21 Curtailment ramp rate (-kw/sec) Ramp up rate (kw/sec) Approximately.4 MW/minute For a 1MW system, the curtailment ramp rate would be between -13. kw/sec and -5 kw/sec or MW/min The ramp up rate would be between 12. kw/sec and 52 kw/sec or MW/min Approximately 1.7 MW/minute Fi gure 7 Curtailment and ramp-up rates for a 5 kw system [58] Flicker Curve House pumps Sump pumps Air-conditioning equipment Theatrical lighting Domestic refrigerators Oil burners Single elevators Hoists Cranes Y-delta changes on elevator-motorgenerator sets X-ray equipment Arc furnaces Flashing signs Arc-welders Manual spot-welders Drop hammers Saws Group elevators Reciprocating pumps Compressors Automatic spot-welders % VOLTAGE FLUCTUATION Border line of irritation Border line of visibility Typical maximum PV variations per hour (wind with puffy clouds).6% 1 Fluctuations per hour Fluctuations per minute Fluctuations per second.4% Composite curve of voltage flicker by General Electric Company, General Electric Review, August 1925, Kansas City Power & Light Company, Electrical World, May 1, 1934; T & D Committee, EEi, October 24, 1934, Chicago, Detroit Edison Company, West Pennsylvania Power Company, Public Service of Northern Illinois. Relations of voltage fluctuations to frequency of their occurrence (incandescent lamps) Figure 71 Flicker guidelines [58] 97

100 Impacts of increased PV penetration PEPCO reported that the severity of the impact of distributed PV generation on a feeder depends on a number of factors, including [58]: electrical characteristics of the feeder looking back at the electric system from the location of the DG (distributed generation) daily load profiles during various times of the year maximum output of DG substation transformer ratings locations and settings of regulators, capacitors, and reclosers. The issues arising from increased PV penetration in Hawaii [52] are: displacement of generation performing critical grid services increased variability making frequency control more challenging lack of visibility and control aggregate loss of PV during faults and contingencies (due to under-voltage and under-frequency) excess energy from non-dispatchable sources. Over-voltage is seen as a major issue caused by distributed PV generation. An example was obtained from the Ohta City Project in Japan where 73% of possible energy output from a PV system had to be curtailed for the purpose of volt age regulation, as shown in Figure 72 [53]. Array Output [PU] Solar Irradiance [kw/m 2 ] Output Loss Array Output Solar Irradiance PCS Output Voltage 1 PCS Output Voltage % of possible energy out was curtailed due to O.V /3/28 5:41 8: 12: 16: 24/3/28 17:56 Figure 72 PV Curtailment Ohta City Project, Japan [53] PCS Output Terminal Voltage [V] 98 Solar intermittency: Australia s clean energy challenge

101 In general, the issues Japan is currently experiencing, and expects to see more of with increased PV penetration, are: distribution level voltage and power flow fluctuations electrical islanding system level frequency instability difficulty in scheduling generation transmission expansion requirements for new renewable energy sites. The reduction in variability from spatial diversity in the Osaka area of Japan is shown in Figure 73 [53]. A significant amount of reduction in variability is seen for spatial diversity of 2 kms or more. Longitude (plane co-ordinates) min 5 min 1 min 2 min 3 min 6 min 18 min Figure 73 Smoothing effect due to spatial diversity in the Osaka area of Japan [53] Spain is currently facing a number of challenges integrating RES into their electric system and these include [55]: generation is not well correlated with consumption balancing generation and demand during off-peak hours variability and predictability dynamic behaviour during disturbances provision of ancillary services is impacted with increased non-dispatchable generation. To solve some of the abovementioned issues associated with an increased penetration of solar power in Spain, the following needs to be achieved [59]: load demand balance will need to be satisfied at the distribution level technical constraints to increased penetration are voltage regulation and reverse power flow new technologies require complex communication and control but may reduce required investment in network reinforcement. 99

102 7.4 Procedures cur rently in place to mitigate issues associated with intermittent generation Xcel Energy explains in [6] how effective their voltage regulation control has been for their Colorado State University (CSU) 5 MW PV System 2 MW for Phase I and 3 MW for Phase II. The characteristics of the feeder are: 76% penetration during light loads PV site is 1.4 km from substation grounded wye 13.2 kv two sets of three single phase bi-directional regulators. The voltage regulators are set upstream from the PV site set at a 1V Bandwidth, and are also used for line drop compensation. A quarter of a million operations were recorded due to the narrow bandwidth with no customer complaints received. The time-frame for this operation was not mentioned. Phase II will add an additional 3 MW of PV. Plans to manage voltage include: all capacitors on the feeder to be switched off to prevent over-voltage if over-voltage still occurs, the six units of 5 kw PV inverters will be set to 1 15 kvar (6 9 kvar total) leading in order to reduce the voltage by absorbing reactive power. Figure 74 shows the effectiveness of the voltage regulation during periods of reduced power output for Phase I of CSU. The voltage is seen to be well regulated despite drops in active power. 1 Solar intermittency: Australia s clean energy challenge

103 Value (kv) Power Factor (PF) Value (MVA) Value (MW) /12/9 11/14/9 11/15/9 11/16/9 11/17/9 11/18/9 11/19/9 11/2/9 9:43:1 Time 8 :46:56 (mm/dd/yy) y) 1:3:6 Maximum Minimum Average MW 3ø 4 wire /12/9 11/14/9 11/15/9 11/16/9 11/17/9 11/18/9 11/19/9 11/2/9 9:43:1 Time 8 :46:56 (mm/dd/yy) y) 1:3:6 Maximum Minimum Average MVW 3ø 4 wire /12/9 11/14/9 11/15/9 11/16/9 11/17/9 11/18/9 11/19/99 11/2/9 9:43:1 Time 8 :46:56 (mm/dd/yy) y) 1:3:6 Maximum Minimum Average 3ø 4 w Power Factor Second set. MVA PV system on line. Customer still has unbalance. Voltage steady thanks to regulator despite power drop 11/12/9 11/14/9 11/15/9 11/16/9 11/17/9 11/18/9 11/19/9 11/2/9 9:43:1 Time 8 :46:56 (mm/dd/yy) 1:3:6 Maximum Minimum Average kv RMS of V kv RMS of V kv RMS of V AC Currnet (Aac) /12/9 11/14/9 11/15/9 11/16/9 11/17/9 11/18/9 11/19/9 11/2/9 9:43:1 Time 8 :46:56 (mm/dd/yy) 1:3:6 Maximum Minimum Average Aac RMS of I Aac RMS of I Aac RMS of I Figure 74 Voltage regulation at CSU (Phase I) [6] PEPCO [58] trialled an Absorbing Power Factor (APF) solution combined with a Load Tap Changing (LTC) transformer and voltage regulator to mitigate voltage fluctuation issues for a 2 MW PV system shown in Figure 75. The results are shown in Table 12. The mitigation attempts result in little change in maximum voltage fluctuation at the PV site (4% reduction) and has no impact at the substation. It appears that the system is on dedicated feeders, so the PV would be the dominant source of the fluctuations. 11

104 69/12 kv Substation New Xfrm 69/12 kv 12 kv Bus 1 MW New Feeder NJ xxxx Approximately 59,1 ft of PAC Cable Recloser M POI ACE Pole Number to be determined PV Solar Generator 1 MW 69/12 kv Substation Recloser M PV Solar Generator 1 MW New Xfrm 69/12 kv 12 kv Bus New Feeder NJ xxxx Approximately 66, ft of PAC Cable POI ACE Pole Number to be determined Solution Figure 75 PEPCO 2 MW system [58] Table 12 Voltage fluctuations solutions PEPCO 2 MW PV Maximum Steady State Voltage at PV Site (V) Maximum Voltage Fluctuation at the PV Site (V) Maximum Voltage Fluctuation at the Substation Bus (V) Without mitigation absorbing power factor 13V LTC reference voltage Voltage regulator 3.4 miles downstream from Substation 118V Table 13 shows the results for voltage mitigations attempts (and their associated costs) using various solutions for a 1.9 MW system. The impact of the APF solution is far greater (48%) for the smaller system than for this 2 MW system where only a 4% reduction is obtained. Solution Table 13 Voltage fluctuation solutions PEPCO 1.9 MW PV [58] Maximum Steady State Voltage (V) Maximum Voltage Fluctuation at the PV site (V) Maximum Voltage Fluctuation at the Upstream Regulator (V) Without Mitigation Cost ($) Absorbing Power Factor Solution kVA/15kWh Battery kVA/3kWh Battery AAC Reconductor For the Absorbing Power Factor (APF) solution, a power factor of.97 is used. The APF solution is obviously the cheapest but its mitigation impact is limited. Comparing the 5kVA and 75kVA battery solutions in Table 13, the cost of the 75kVA battery is almost double that of the 5 kva for an additional 2% reduction in maximum fluctuation at the PV site, and only 1% at the regulator. The question is to what extent fluctuations need to be reduced. Spain has a requirement that any renewable generation installation greater than 1MW maintains an inductive power factor of between.98 and.99 to eliminate sudden changes in the voltage profile corresponding to the transitions in off-peak-intermediate-peak periods and to avoid system over-voltage problems. Requirements for wind turbines/facilities 12 Solar intermittency: Australia s clean energy challenge

105 to comply with new voltage ride-through standards have been introduced to reduce generation tripping due to voltage dips [55]. The effectiveness of these strategies was not mentioned. Portugal [56] also introduced a new code, through their Ministry of Economy and Innovation, for technical requirements for wind generation. This code will facilitate fault ride -through and reactive power requirements to manage voltage dips. Through its Golden Sun project, China [57] is seeing technical issues which need to be addressed, such as fluctuation in voltage and frequency, power quality, protection and energy management. The Golden Sun project resulted in 73 MW of BIPV and BAPV installed in Existing activities to further knowledge on intermittent generation In Denmark, EnergiMidt is developing the FUR Project as a test lab where new products can be tested and developed [5]. The objectives of the project include to: determine whether sample rate of 15-min or 1-min is required for load profiles estimate the maximum allowable installed PV capacity, which will be determined by node voltage limits and thermal limits of transformers. The FUR Project will utilise smart meters (already installed) and fibre optics for fast data transmission. Irradiance profiles will be sampled every minute. In Japan, NEDO smart grids were trialled between 22 and 27 with the aim of developing technology to prevent restriction of PV output [53]. A photo showing a residential area in Japan with clustered PV power generation system can be seen in Figure 76. Figure 76 Image of NEDO Smart Grid clustered PV Storage was used to reduce reverse power flow and regulate voltage. The battery is charged when the PV output is larger than demand, and discharged when demand exceeds PV output. Results of the voltage control using storage are shown in Figure 77. A total of 553 PV units were installed on residential properties with an average capacity of 3.85 kw per installation [53]. The total installed PV capacity of the smart grid setup was 2.13 MW. Each installation was coupled with a 4 kva inverter and lead-acid batteries, as seen in Figure 78. New anti-islanding detection techniques and methods to avoid anti-islanding interference amongst systems were developed. 13

106 Power (kw) Voltage (V) PV output (kw) Demand (kw) Received power (kw) PCS AC voltage (V) Target line voltage (V) -2-4 : 3: 6: 9: 12: 15: 18: 21: : Figure 77 Voltage control in NEDO Smart Grid clustered PV generation system Inverter (4kVA) Figure 78 NEDO Smart Grid setup and characteristics 14 Solar intermittency: Australia s clean energy challenge

107 Japan is also running demonstration projects of renewables on islands with small independent grids. The list of islands with a breakdown of the different renewable sources and storage installations can be seen in Table 14. It is worth noting the large wind power capacity in the island of Yonaguni relative to its maximum demand. The objectives of these projects are to: study the impacts on the system if large scale solar is introduced calculate the level of solar penetration possible acquire knowledge about grid stabilisation technologies analyse operational data of solar generation and battery storage test system stabilisation technologies. Table 14 Japan s island demonstration sites Island Wind (kw) Solar (kw) Battery (kw) Max Demand (kw) Miyako Tarama Yonaguni Kitadaito Japan is implementing METI s smart grid demonstration tests (21 212) by power utilities with the following objectives: introduce a low carbon grid with massive renewable penetration vision is to minimise CO 2 emissions and social costs look into possible impacts to power quality and security discover ways to enhance power quality by making the grid and consumers smarter. For these METI demonstrations, at the house level there will be: weather forecasting electric vehicles PV/appliance optimisation curtailment control. Gas utilities in Japan and NEDO are also running a demonstration project to optimise the distributed energy management of co-generation and PV. EDP of Portugal is investigating the impact of climate on the performance of commercial PV pane ls. Four locations with varying average temperatures, direct and indirect insolation have been selected [61]. The panels are also oriented in three ways, vertically (facade), horizontally and tilted. EDP is also working with Siemens to test Molten Salt Technology at a CSP plant at the University of Evora and in is involved in smart grids through their InovGrid/InovCity project. The InovGrid project is exploring active participation of clients through the use of smart meters and energy efficient initiatives. 15

108 7.6 Future plans of action and expected changes to manage increased p enetration In Austria, there is a business case for increased PV penetration to resolve grid bottlenecks [49]. Grid re-enforcement is not seen as the most cost-effective option, with PV estimated as providing a 5% increase in capacity f or 1% of the cost. The Danish government plans to implement smart grids as part of th eir energy strategy [5] and Germany expects the following to be required to allow for greater penetration [51]: demand to react to non-controllable generation energy management between generation, storage and loads renewable energy generation, loads and storage to provide ancillary services active control of distribution grids. Future work to be done by PEPCO [58] includes: Impact studies: High voltage at low load Power flow reversal Impact on under load tap changer at substation transformer with and without dynamic Var compensating inverter Var Control for mitigating voltage fluctuations: Non-dynamic for smaller systems.99 pf when PV at -5% output.98 pf when PV at 5-75% output.97 pf when PV at 75-1% output Dynamic Var control for larger systems Set voltage and frequency ride through parameters Control voltage at POI Requires Droop and time delay setting to mitigate interference with existing automatic line equipment HELCO believes they need the following to help mitigate issues they have been experiencing and to ensure grid stability [52]: remote monitoring and control (SCADA) forecasting more knowledge on intra-hour variability better prediction of net load provide monitoring and curtailment capabilities on the RE additions > 25 kw to be managed by system operator may not be feasible for small installations storage (centralised and distributed). May be technically feasible but not economic changes to the interconnection rules under-frequency and under-voltage ride through capability required provide for adjustable voltage and frequency trip settings to avoid nuisance tripping and coordinate with the under-frequency load shedding scheme. 16 Solar intermittency: Australia s clean energy challenge

109 Studies/work planned by HELCO include: generation mix best mix to provide system stability through faults and contingencies modifications to system protection (under-voltage and under-frequency) needed and associated requirements of PV (ride-through) inquiry into required operational changes (modification of reserves) collection of real time stability data using synchronized phasor measurements (synchrophasors provide real-time measurement of electrical quantities from across a power system) detect stability issues in real time validate stability models tom improve simulation of dynamic response investigate low cost communication and control of DG. For a high percentage of RES, it is predicted that large amounts of curtailment and flexible backup capacity will be required [54]. Figure 79 shows how curtailment could be reduced through the implementation of flexibility technologies. A reduction from 17% to 5% curtailment is projected with the deployment of Demand Side Management (DSM), Electric Vehicles for energy storage (EV), increased interconnectivity and general storage. Integrated EU - Solar focused (RES-E = 11%) Pumped Hydro 1 DSM 8 EV 6 EU Intercon 4 Gen. storage % 5% 125 Initial curt. 16 Pump 1 Therm. Flex 44 DSM EV Inter con - % curtailment vs. RES volume Gen. Stg. 36 Final curt. Figure 79 Reduction in curtailment through flexibility technologies [6] To manage the challenges presented from the integration of RES, Spain [55] is planning the following: power system studies into wind farm requirements wind and solar forecasting tools monitoring and controllability network developments/enhancements. Spain is planning to improve observability and control to counteract RES variability [55]. Observation will consist of: real time measurements (12 sec cycles) to assist in production forecasting and for avoiding demand forecasting errors. This will lead to more accurate evaluation of reserves and efficient dispatch increased observability decreases uncertainty and therefore increases system security. Increased controllability of RES is also planned. RED Electrica de Espana has developed CECRE, a control centre devoted to RES generation, especially wind. All RES is to be linked into this integrated control structure which provides supervision and control instructions. Dynamic voltage support is also to be managed through CECRE. In the long term, Spain is looking into: RES providing frequency support increased international exchange (power exchange with neighbouring countries) capacity storage through hydro-pump units and very fast thermal plants demand side management. 17

110 Portugal [56] has stated that the actions required to facilitate high penetration of renewable include: greater cooperation between TSOs and DSOs the implementation of new issues in planning, construction and operation revision of codes and rules at an early stage of the process specification of new technical requirements for stability, security and operation of electrical grids. The United States [62] has been working on a wide range of approaches to address high penetration issues through: development of advanced inverter technologies updating power system simulation and planning practices updating interconnection standards and codes documenting successful case studies on high penetration PV projects. China [57] is researching the following for two systems, one of which is a 1 MW plant already constructed and another a 2 MW system currently under construction: modelling of grid connected PV (GCPV) power system analysis including PV development of algorithms to determine install capacity limit of PV testing of inverter types: Scheduled, dual-mode, self-synchronising testing of DC-DC 1kW battery charger automatic monitoring and control of PV. China is also starting a new research project titled Key technologies of co-ordination between high penetration and multi PCCs (points of common coupling) PV System and distributed grid. Research will include: simulation of high penetration PV system in distributed grid PCCs layout and optimization of high penetration PV system energy storage system integration and bi-directional converter power quality control of regional high penetration PV systems new technology for island detecting and anti-islanding new relay of distributed grid suitable for PV characteristics monitoring and controlling system of regional PV systems energy management technology of regional PV systems. One of the conclusions made by the Task 14 working group at the recent meeting in Lisbon, Portugal, was that more sophisticated analysis tools are needed to analyse system balancing requirement and capability, including future technologies such as demand activation and EV charging control, to counteract the intermittent nature of generation from RES. Discussions at the IEA Task 14 workshops indicate that there has been substantial worldwide growth in intermittent generation from renewable resources, mainly PV and wind. Countries involved in Task 14, including Germany, Denmark, Spain, Japan and China, are currently experiencing a significantly high rate of increase in PV installations, both small- and large-scale systems, and expect to see an increase of their intermittent generation capacity by a few hundred percent by 22. The Task 14 representatives acknowledged one of the main challenges they expect to face with increased PV penetration is issues associated with the variability and predictability of solar systems. 18 Solar intermittency: Australia s clean energy challenge

111 8 Solar intermittency Australian industry experts Figure 8 shows the growth of installed PV capacity over the last two decades in various countries around the world [63]. The PV penetration level in Australia is still relatively low by some international comparisons, but PV uptake is growing significantly at present from a small base. The growth of PV installations in New South Wales in 21 can be seen from the number of connections and installations recorded throughout the year, shown in Table 15 [64]. The total installed PV capacity increased from 24.7 MW to 1.8 MW (i.e. an increase of 3%) in just nine months. 8 Cumulative Installed Capacity MW AUS DEU ESP ITA JPN KOR USA Figure 8 Global growth of PV capacity [63] 19

112 Table 15 NSW total connections, installed capacity and applications as at October 21 [64] Data/Network Energy Australia Integral Energy Country Energy Total Prior 1 Jan 21 Connections Number of connections 6,554 3,346 5,179 15,79 Capacity (MW) Average system size (kw) June 21 Connections Number of connections 1,52 8,557 9,436 28,513 Capacity (MW) Average system size (kw) Early October 21 Connections Number of connections 17,456 15,388 17,448 5,292 Capacity (MW) Average system size (kw) 1, Applications (includes connections) Number of applications 28,242 21,9 33,138 83,28 Capacity (MW) Average system size (kw) Differences in the rate of PV connections between the distribution network areas may be as a result of variation in population levels and dwelling types. Note that the distribution networks mentioned here recently changed their names: Energy Australia becoming Ausgrid, Integral Energy is now Endeavour Energy and Country Energy is Essential Energy. Table 16 shows reported connections as of 3 June, 21 on a per capita and per dwelling basis. Note that suitable dwellings are defined as owner-occupied freestanding or semi-detached households (as opposed to rented houses or units without roof access). Network Table 16 PV system connections in NSW by distribution area [64] Connections PV Systems Per 1 People PV Systems Per 1 Dwellings Systems Per 1 Suitable Dwellings EnergyAustralia 1, Integral Energy 8, Country Energy 9, Total and averages 28, Several programs funded by the Federal Government will see the development of large-scale solar plants, causing a large increase in the penetration of solar power in Australia. These programs include Solar Cities and the proposed Solar Flagships projects. This project is based around a macro-level examination of the solar intermittency issue and its likely impacts on Australia s electricity networks. A major part of the work done in the early stage of this project focussed on identifying and understanding the issues key solar industry stakeholders in Australia face due to solar intermittency. Another goal was to obtain stakeholders perspectives on what is needed to remove barriers caused by the intermittent nature of renewable generation, to enable large-scale solar deployment in Australia. An industry workshop and a survey were conducted to obtain the views of key solar industry experts on solar intermittency in the Australian context. 11 Solar intermittency: Australia s clean energy challenge

113 8.1 Industry workshop on Renewable Generation Intermittency The CSIRO, Energy Networks Association (ENA) and Australian Energy Market Operator (AEMO) hosted a day-long industry workshop on the effects of renewable generation intermittency on Australian electricity networks. The workshop was held at the AEMO premises in Melbourne on 4 April, 211, and brought together 4 industry experts from various organisations around Australia. These include the Energy Supply Association of Australia (ESAA), ElectraNet, the Department of Resources, Energy and Tourism (RET), Fotowatio Research Ventures (FRV), BP Solar, Pacific Hydro, AEMO, Western Power, Powercorp, the Clean Energy Council (CEC), Power & Water Corp, SP Ausnet, ActewAGL, Jemena, Energex, Ausgrid and Endeavour Energy. The main goal of the workshop was to understand the issues that are being faced due to solar intermittency and the ramifications and concerns Australian utilities, power system operators, large-scale renewable system operators and other industry players hold. The workshop was based around the need to understand and characterise solar intermittency from the perspective of system planners and operators to avoid unnecessary barriers to the rapid development and uptake of large-scale solar power. The workshop generated considerable discussion on the topic and featured a number of excellent presentations were by workshop speakers. The workshop focussed on: actual effects of intermittency currently experienced in Australia likely impacts and concerns about large-scale penetration of solar power state of progress in Australia and overseas technologies to address the issue of intermittency. The corresponding topics covered at the workshop include Renewable Resource Characterisation, Network Issues and Concerns at both the transmission and distribution levels, and Grid Events and Variability Management. A copy of the detailed workshop program can be found in Appendix A. Photos from the industry workshop held in Melbourne can be seen in Figure 81. Figure 81 Scenes from the intermittency workshop held in Melbourne 111

114 8.2 Solar Intermittency survey Following the workshop, CSIRO conducted a survey aimed at gathering more information about certain key issues of solar intermittency that are likely to impact the Australian electricity network. This was needed to further assist in identifying and incorporating the views of key industry experts on solar intermittency into the strategic direction of the project in order to support improved reliability, operation and efficiency of electricity networks with large-scale penetration of solar power. A copy of the survey can be found in Appendix B. The topics covered in the survey include: growth in energy generation from solar impacts of large-scale solar intermittency impacts of small-scale solar intermittency intermittency time-scales data collection and forecasting unique aspects of the Australian electricity network control and service mechanisms. For the convenience of respondents, the survey was web-based. It was sent to industry experts from various organisations around Australia, including those who participated in the intermittency workshop. Twenty responses were obtained and a considerable range of perspectives were gathered from the survey, as discussed in the following sections. 8.3 Growth in energy generation from solar The National Electricity Rules (NER) define an intermittent generator as one whose output is not readily predictable, including, without limitation, solar generators, wave turbine generators, wind turbine generators and hydro-generators without any material storage capability. This definition of an intermittent generator fits well with most industry experts understanding of solar intermittency, some to a higher extent than others. Some believe a good forecasting system, once developed, will be able to predict solar intermittency to a certain degree, as wind output is now predictable for the purposes of NEM (National Electricity Market) dispatch. Solar industry stakeholders were asked what proportion of Australia s generation they believe is likely to come from all forms of solar, including small- and large-scale photovoltaics (PV) and concentrating solar thermal (CST), in the next five, ten and fifteen years. The responses (averaged and rounded) are shown in Table 17, along with a comparison with corresponding estimates from the Energy Sector Model (ESM), a long-term investment planning tool for the Australian energy sector developed by CSIRO [65], for two carbon reduction strategies (CRPS-5 and G-25). The main reason for delayed uptake of solar in the ESM is mainly due to the cost competitiveness of wind. Time Table 17 Estimates of Australian solar penetration (PV and CST) by 216, 221 and 226 Responses of Australian solar industry stakeholders CPRS-5 strategy based prediction G-25 strategy based prediction 5 years (by 216) 4%.2%.4% 1 years (by 221) 8% 3.2% 3% 15 years (by 226) 18% 8% 8% Some factors that might constrain the growth of energy generation from solar in Australia are: cost-competitiveness of solar technologies vs. other types of generation government policies/intervention grid integration is a likely issue this depends on AEMO and TNSPs managing large-scale intermittency with significantly higher penetration levels of intermittent generation and distribution networks managing voltage fluctuations arising from distributed solar (mainly PV) export 112 Solar intermittency: Australia s clean energy challenge

115 lack of reliable and economic energy storage to reduce intermittency impacts lack of transmission networks in large-scale solar areas design of existing distribution systems. The limiting factors above can be overcome by: making solar technologies more cost-competitive in the energy market managing solar intermittency issues (some lessons can be learned from wind integration) greater investment in transmission network in remote areas with high solar resource a more favourable regulatory environment developing smart inverters strategic planning developing accurate prediction methods of solar power. 8.4 Large-scale versus small-scale solar systems Respondents indicated the need to investigate the impacts of large numbers of small-scale solar systems in the distribution network and large-scale solar systems in the transmission network separately. Large-scale solar systems are often located in remote areas, where the environmental conditions are noticeably different from urban areas in Australia, where majority of the small-scale solar systems are located. The urban areas are located in coastal regions of the country where cloud movements are more prominent and the grid is also relatively stronger than in remote areas, meaning the impacts on the electricity network are likely to be different. Small-scale solar systems are also more inverter operation dependent. Therefore, it was emphasised that the impacts of small-scale and large-scale solar systems need to be studied separately. The likely impacts of intermittency from large-scale solar systems listed by workshop participants include: larger system voltage and frequency variations mainly due to their location being remote areas with relatively weaker grids high electricity price volatility due to uncertainty increased uncertainty in the operational position of the conventional plant in the system which might lead to increased frequency variations if the additional control duty is not provided increased need for the quantity of ancillary services the need for reactive power considerations to maintain stability due to transients in the section of the power system network security is also a critical factor in the successful implementation of large-scale intermittent renewables as they create higher levels of uncertainty. Energy supply is an essential service, which if denied leads to societal breakdown. These impacts are important as they affect the viable penetration levels of solar systems. Displacement of conventional plant by highly variable solar/wind plant output has the potential to create load-generation imbalances, leading to frequency and voltage excursions. The extent of the impacts of intermittency from large-scale solar systems is not known at present, and more data and research are required to get a better idea. Accurate predictive tools will also help to minimise the impacts of intermittency from large-scale solar systems on the electricity network. The likely impacts on the electricity network due to intermittency from small-scale solar systems are expected to be more localised. They include: voltage and power fluctuations in close proximity to connection points of the solar systems adverse power quality which has the potential of causing equipment failure shutdown of inverters as a result of poor voltage regulation if not controlled impact on spinning reserve to maintain frequency. 113

116 These impacts are important and need to be addressed, as they will limit the penetration of small-scale solar (mainly PV) systems. Small-scale solar systems are mainly inverter-operation dependent and the limited controllability and protection capability of existing inverters might not be sufficient to help mitigate the adverse impacts of intermittency on the electricity network. Workshop participants believed aggregation of many small-scale solar systems will not only reduce variability but allow for better/smarter control to provide a dispatchable energy source. This allows the renewable source to be offered to energy markets, and either used for ancillary services, or matched with controllable loads to cancel out the variable energy source in terms of overall system impact. This will require forecasting tool improvements and good data collection on the penetration and performance from the distribution businesses. 8.5 Resolution of solar data Integration studies for high penetration scenarios of solar will require projections of variability from a large amount of solar power generation for both distributed and large utility-scale solar plants. Currently, solar data is only available with low time resolution. In Australia, very limited detailed solar information is available to study the impacts of solar intermittency on the stability of the grid. Solar data covering a large spatial extent is available from satellite data, which has an issue of compounding errors, and very limited data can be obtained from ground stations. The only source of long-term insolation data is the Bureau of Meteorology (BOM) which provides daily averages, but this source is currently being phased out as more reliance is being given to satellite data. Global insolation data from the BOM has 7% mean bias error on sunny days and 2% on cloudy days. The Department of Resources, Energy and Tourism (RET) reported they are currently working on improving the satellite system capability to obtain 1-second solar data. A number of key industry experts stated that much higher resolution solar data than is currently available is needed to study the impacts of solar variability on the stability of the grid. High resolution solar data from both large numbers of small-scale solar systems aggregated and large-scale solar systems is required to investigate the effects of a range of temporal variances on the Australian electricity network. The impacts of intermittency are very different at different locations in the grid and utilities expressed a major concern over the rural edge of network scenario. It was widely claimed that solar data resolution of sub-second to ten seconds is required to investigate power quality issues and to study the dynamic response of the power system to solar intermittency. Sudden shadows due to passing clouds appear to produce more rapid flicker than sudden wind changes. The time it takes for a passing cloud to shade an entire solar system depends on the size of the installation, cloud speed, cloud height, and other factors. Different intermittency time-scales are associated with different impacts, management strategies and costs. General issues important for different time scales when operating power systems with intermittent generation, as reported by industry experts are: dynamic response of the system, power quality (e.g. voltage flicker), frequency stability sub-second to 1 seconds market specific ancillary service product seconds to five minutes load following minutes to hours generation dispatch beyond five minutes. Due to the lack of inertia in most solar power systems, industry experts agreed that the higher ramp rates of solar systems, compared to other renewable generation, is a major concern. Information on different ramp rates and time-frames of intermittency is necessary to determine how quickly energy management systems have to respond. In order to determine both the ramp up and ramp down rates of solar systems, solar data with high resolution is required for both small-scale and large-scale solar installations. High resolution data, at least 1-second data, is also required to investigate how often solar output will significantly impact the grid for any given plant and location. 114 Solar intermittency: Australia s clean energy challenge

117 8.6 Solar forecasting Forecasts of solar output are required for periods ranging from days ahead down to hours and tens-of minutes ahead. In Australia, forecasts of solar generation would be required up to two years out, as is currently required for wind generation. Forecasts should include information about the expected output and the degree of uncertainty in the expected output to indicate particularly volatile periods. Short-term forecasts are aided by the fact that clouds can be observed. Forecasts are an important method for managing both the intermittency and uncertainty of solar generation and should be incorporated into system planning and operations. The Australian Energy Market Operator (AEMO) has critical forecasting needs: both short-term to long-term energy growth (2 years). AEMO requires five-minute pre-dispatch data and the Australian Wind Energy Forecasting System (AWEFS) was developed in response to the growth in intermittent generation in the National Energy Market (NEM). AWEFS was developed to provide AEMO with more accurate wind generation forecasts to facilitate the operation of the market. Questions were raised by industry experts as to whether AWEFS can be expanded for solar forecasting and what information is needed to be able to piggyback off wind forecasting systems. The size of wind and solar systems need to be noted in this discussion. Wind is mostly large-scale (mostly MW) while solar is both small and large-scale systems (both kw and MW). There were also questions about the possibility of applying the European ANEMOS short-term wind forecasting system to solar forecasting, and AEMO is currently in discussion with the federal government regarding this. Wind energy output can be controlled using pitch control and discussions arose during the workshop about possible methods to control solar output. The ability to curtail output does not exist for current rooftop solar systems. Solar appears more predictable than wind, however wind forecasting has taken five to seven years to develop and many issues are yet to be resolved. There are likely to be a lot of unknown areas which need to be investigated and resolved when developing a solar forecasting system. Solar intermittency is believed to be more predictable than wind intermittency as it is affected by fewer climatic events, such as cloud cover and temperature. The ramp rates for solar (mainly PV), however, are potentially significantly higher than wind due to the lack of inertia: wind turbines have rotary components which provide some inertia for wind systems. Solar output is more predictable in the very short term because the movement of clouds is visible, but accurate prediction for long-term solar output is needed to determine ways to effectively compensate for solar intermittency. Some key requirements for data collection and forecasting of solar believed to be important are: temperature, irradiance, location correlation between weather patterns in nearby geographical locations ability to obtain samples at all required time intervals (i.e. seconds, minutes, hours, days) cross-checking of data with satellite data seasonal data to cater for seasonal variations the requirement of a large network of ground based monitoring stations to correlate satellite data with their output, and this should be established and maintained for several years accuracy of data sky-view cameras for large-scale solar farm to monitor the movement of clouds. Data collection and forecasting differ between small- and large-scale solar systems in the following ways: large systems are site specific, whereas small systems over a large geographic area need more measurement points small systems are affected by environment and orientation more greatly, and these factors need to be measured as well at the solar resource the accuracy of data and forecasting might have an impact on power quality aspects for small scale systems, while it affects energy output for large-scale solar systems. The time-scales for data collection and forecasting of solar power systems, believed to be appropriate by industry experts, varied between sub-second and ten minutes. It was also mentioned that the time-scale can be larger for large-scale systems 115

118 as variations in power output are likely to be significantly lower due to larger area coverage. A number of industry experts indicated the need for further research to determine the appropriate time-scales for data collection and forecasting for solar energy systems of different sizes. It would also be of interest to the Australian power industry to know how these forecasting issues are being dealt with in other parts of the world. 8.7 Unique aspects of the Australian electricity network Some aspects of the Australian electricity network are different to the rest of the world. A few of these unique aspects raised by industry experts are: The configuration of the electricity network is different between east and west Australia. In the western half of the country, most loads are concentrated in Perth and the remainder are mostly small loads scattered over a vast area of land. There are many small standalone power systems in west Australia, serving towns with small populations. Solar penetration relative to loads is high in the west and power system operators in the area are currently experiencing problems as a result. Eastern Australia has significantly more urban areas as well as more remote towns, making the electricity network in the east more diverse. Compared with Europe, distribution impedance is higher in Australia. This causes voltage control concerns due to significant voltage rise issues and a greater potential for oscillations. The Australian solar resource is different from Europe, i.e. stronger. There is a need to investigate temperature dependence issues (for PV systems), reflections, etc. The power flows in Australia are different. There are more heavily interconnected systems overseas. Australia has a lot of long and stringy (skinny) transmission networks where the impacts of solar intermittency are likely to be different compared with meshed interconnected networks. The market dynamics in Australia are very different: for example, the dispatch of generation is determined on a five-minute basis. The weather patterns vary greatly across the network, e.g. east vs. west Australia. Different system frequency operating standards across the country. For example, one set applies to mainland Australia, another for Tasmania (as it is connected to the mainland by a DC link), a third for the SWIS in WA, a fourth for the NWIS in WA, another for the NT, etc. 8.8 Ancillary services to cater for solar intermittency When industry experts discussed ancillary services, they asked how much spinning reserve would be needed to support intermittent generation. Some experts believe solar intermittency can be compensated in the same way as for wind. Others believe an evolution of ancillary services is required, which include demand response and management, control of power flows, dynamic voltage/var control, integration of energy storage, aggregation, etc. Accurate forecasts feeding into dispatch will assist in managing regulation plant and ancillary services market. Industry experts were asked whether intermittency has an influence on the financial position of solar generators, and most of them believe it does. Information investors would need in order to provide better solar generation certainty includes: methods of mitigating power quality impacts from the connection of solar generation data on solar resource/measure of solar irradiance at site of solar generation which would be an assessment of energy production across a period of time, e.g. a year. A view of firm energy output would be required for contract purposes orientation of solar power plant knowledge about the capability of the network to sustain the solar generation during its intended hours of operation energy storage information. Some storage would be strongly recommended for the investment to compensate for intermittency. 116 Solar intermittency: Australia s clean energy challenge

119 Many people believe that existing mechanisms cannot adequately handle short-term fast fluctuations caused by variable solar resource and other environmental factors. There could be a need to investigate the possible rates of change of power and the performance of the network due to solar intermittency in order to determine the type of ancillary services required and to determine whether existing mechanisms are sufficient for intermittency compensation purposes. 8.9 Other issues reported by Australian industries The biggest issue currently being faced by many Australian utilities is over-voltage due to a large number of small-scale rooftop solar installations. There may be a need for dynamic voltage control at more localised levels and more sophisticated voltage regulation than what currently exists in order to solve the issue of network over-voltage. There was a significant amount of discussion over the AS4777 standard and the need for this standard to be revised in order to utilise solar inverters for the purpose of voltage regulation. Currently, these inverters simply turn off when the voltage or frequency limits are exceeded. Revisions of the standard that will allow inverters to perform the following operations, i.e. smart or intelligent inverters, were discussed by power system operators: operation in voltage regulation mode possibility of remote control/configuration of the inverters power factor/reactive power control emulation of spinning reserve via capacitors or storage. The frequency limits in the Northern Territory were changed to 46 Hz (lower) and 54 Hz (upper). This was done to avoid low-frequency tripping of inverters during system frequency drop events (i.e. frequency briefly dropping by several Hz during central generation events). A workshop participant reported that Alice Springs has a 12% penetration level of solar power at times and is experiencing reactive power (Var) generation issues. There is a high usage of air-conditioning during the daytime in the area, which is well correlated with the daily solar profile. 7% of the 28, people in Alice Springs changed their power usage behaviour and shifted their load usage to off-peak times with the introduction of the Time-of-Use (TOU) pricing. 117

120 9 Intermittency ramp rates and timescales analysis To demonstrate the effect of rapid fluctuations in solar radiation on PV systems, ten months of data from the Desert Knowledge Australia Solar Centre (DKASC), Australia, was obtained and analysed. The 1-second sampled data includes the irradiance and power output at the same site. The data also includes the average line-to-neutral voltage of the three phases at a centralised switchboard measured using a class.5 ION 755 energy meter. The main feeder connecting the Solar Centre to the nearest substation is approximately 35 metres long and connected from a 22kV step-down transformer. The PV system at DKASC can be considered a medium-scale installation among all currently existing and operating PV systems in Australia. To analyse PV power output ramp rates for a small-scale system and also a large-scale system in Australia, data was obtained for a 22 kw PV system at the CSIRO Newcastle site with 5-second resolution and for a 1.22 MW PV system at the University of Queensland (UQ) (currently the largest flat-panel PV solar power system in Australia) with 1-minute resolution. 9.1 Desert Knowledge Australia Solar Centre (DKASC) The Desert Knowledge Australia Solar Centre (DKASC) is a demonstration facility for commercialised solar technologies operating in the arid solar conditions of Alice Springs, Central Australia 5. Alice Springs is located on the Tropic of Capricorn with this location exhibiting an excellent solar energy resource. On average the area experiences more than 2 clear days every year and 9.6 sunshine hours per day, giving an average daily solar exposure of 22.3 MJ/m 2 6,7, a very high figure by both Australian and world standards. The DKASC was developed by CAT (Centre for Appropriate Technology Incorporated) Projects and Desert Knowledge Australia as a resource for the rapidly expanding solar industry in Central Australia. The site is a showcase system where 27 smaller PV systems with varying PV technology types are grouped together into a single solar precinct. A list of the different solar technologies installed at the Solar Centre can be found in Appendix C. The Solar Centre s aim is to promote understanding and confidence in solar technologies, and provide the industry with long-term 5 Desert Knowledge Australia Solar Centre website, 6 Climate statistics for Australian locations, Australian Government Bureau of Meteorology, 7 Alice Solar City Media Release, Another Solar First for Alice Springs, 28 July Solar intermittency: Australia s clean energy challenge

121 system level data proving the reliability of solar generators in an Australian context. A range of solar power technologies in a number of different configurations operate at the DKASC, including: amorphous silicon CdTe (Cadmium Telluride) thin-film array CIGS (Copper indium gallium diselenide) thin-film array concentrated PV HIT (Heterojunction with Intrinsic Thin Layer) hybrid silicon array monocrystalline silicon array polycrystalline silicon array. 9.2 Intermittency data analysis DKASC 196 kw PV system As the output of any solar system is insolation dependent, the variability of generated power due to uncontrollable intermittent solar irradiance needs to be compensated to maintain system balance. To determine the regularity and rate at which generated output power can change for a local solar power plant, ten months of data (October 21 to August 211) was collected at a sample rate of.1hz from the DKASC. The data collected and used for this analysis comprised total diffused horizontal irradiance, corresponding output power and line-to-neutral AC voltage. The various solar technologies at the DKASC combined realise a 196kW PV system. Figure 82 shows the output power (upper plot) and irradiance (lower plot) over the ten month period. A reduction in the amplitude of irradiance is seen for the second half of the ten month period which corresponds to the onset of winter months in 211. The corresponding power output does not show reduced amplitude. This is likely due to the tracking of the solar panels and the fixed nature of the pyranometer used to measure irradiance. Another factor may be the tilt angles of the various fixed panels which may be such that they result in a relatively even power output over the year, as opposed to peak output in summer or winter. Figure 83 shows the irradiance and corresponding power output at the DKASC on a partly cloudy day in May 211. A strong positive correlation can be seen between the irradiance and corresponding power output of the PV system. Figure 84 shows the power spectrum of the fluctuations from this system over the ten month period using ten-second resolution data. The y-axis, Y(f), represents the relative magnitude of the different cycles. As expected due to daily periodicity, the largest peak is seen at 24 hours (1.15 x 1-5 Hz), followed by peaks at 12 hours, 8 hours, 6 hours and so on Power (kw) Days (13/1/21-15/8/211) 2 Insola on (W/m2) Days (13/1/21-15/8/211) Figure 82 Plant power output (kw) and solar Irradiance (W/m 2 ) at the DKASC for 1-month period from October,

122 Irradiance Output power 15 Irradiance (W/m 2 ) Power Output (kw) : 7:12 8:24 9:36 1:48 12: 13:12 14:24 15:36 16:48 18: 19:12 Time of Day Figure 83 Irradiance (W/m 2 ) and power output (kw) for the DKASC on 26th May, hours 3 25 Y(f) hours hours 6 hours x 1-4 Frequency (Hz) Figure 84 Frequency spectrum of power output data recorded over a 1-month period at the DKASC 12 Solar intermittency: Australia s clean energy challenge

123 9.2.1 Ramp rates for different timescales Analysis has been conducted to evaluate the occurrences of power fluctuations over time for the DKASC. The power fluctuations were analysed using ramp rates as a measure, defined as the amount by which the power output of a PV array changes within a given time period. An irradiance increase leads to an increase in the plant output power (ramp-up). This is due to clouds leaving after previously shading panels or the sun rising at the start of a day. An irradiance reduction causes a drop in the plant output power (ramp-down), due to arrival of clouds shading the panels or the sun setting at the end of a day. High ramp-up and ramp-down events can also occur due to inverters tripping or reactivating. These events are also included in our analysis. Output power ramp events over time periods ranging from 1 seconds to 6 seconds were extracted from the raw data obtained from the DKASC. To obtain ramp rate distributions for timescales of twenty, thirty, forty, fifty and sixty seconds, instead of downsampling the 1-second data, ramp events and ramp rates for every interval of the different timescales were captured, meaning sampling intervals overlap through the dataset and no samples are neglected. For example, a 6-second block of data contains five 2-second ramp events 8 instead of three if the data was downsampled. The output power ramp rates for various timescales, Δt, were calculated as the difference, ΔP, between the power values at each end of the time interval divided by Δt, that is: P( t + t) P( t) Ramp _ rate = t (1) Analysis of the data showed there was a total of 8,72 occurrences of ramp-up rates of 2kW/s (1% of plant rating per second) or more for ten-second periods over the ten months. Figure 85 shows the 12 of these occurrences that were over 6kW/s which corresponds to increases in output power of at least 6kW (31% of plant rating) in ten seconds. The analysis also showed there were 8,31 occurrences of ramp-down rates of more than 2kW/s or more for ten-second periods throughout the ten months. Figure 86 shows the 32 of these occurrences which were for ramp rates of 6kW/s or more. Ramp-down rates of between 13kW/s and 14kW/s were observed to occur seven times, corresponding to a power drop of between 13kW and 14kW (66% and 71% of plant rating respectively) in just ten seconds. 1 9 Number of occurrences Ramp up rate (kw/s) Figure 85 Distribution of ramp-up events in 1-second periods for 196 kw PV system 8 First sample: -2 seconds, second sample: 1-3 seconds, third sample: 2-4 seconds, fourth sample: 3-5 seconds and fifth sample: 4-6 seconds 121

124 16 14 Number of occurrences Ramp down rate (-kw/s) Figure 86 Distribution of ramp-down events in 1-second periods for 196 kw PV system Occurrences of continuous ramping of the solar plant s output power for periods of twenty, thirty, forty, fifty and sixty seconds were also analysed. Figure 87 shows occurrences of ramp-up rates for variations recorded in twenty seconds. A total of 5,946 occurrences of output power ramp-up events with rates of at least 2kW/s over 2 seconds were recorded. The corresponding total number of ramp-down events for 2-second periods with ramp rates of more than 2kW/s was found to be 6,298, out of which 768 were fluctuations with rates of 4kW/s or more, that is, a drop in output power of 8kW (41% of plant rating) or more in 2 seconds. This can be seen in Figure 88. Three occurrences of ramp-down events with rates higher than 8kW/s were recorded which corresponds to a drop in output power of more than 82% of the plant rating. 3 Number of occurrences Ramp up rate (kw/s) Figure 87 Distribution of ramp-up events in 2-second periods for 196 kw PV system 122 Solar intermittency: Australia s clean energy challenge

125 35 Number of occurrences Ramp down rate (-kw/s) Figure 88 Distribution of ramp-down events in 2-second periods for 196 kw PV system The distribution of ramp-up event occurrences over 3-second periods with rates greater than or equal to 3kW/s is shown in Figure 89. There were 862 such events in total, including 56 occurrences of ramp-up events with rates greater than 4kW/s corresponding to output power increases of more than 12kW (61% of plant rating) in half a minute. Figure 9 shows the distribution of output power ramp-down events over 3-second periods for rates greater than 3kW/s, the total number of such events was found to be 1,29. The number of times the output power dropped at least 15kW (77% of plant rating) in half a minute was found to be 37. Figure 91 shows the distribution of ramp-up event occurrences over 4-second periods with rates greater than 2.5kW/s, corresponding to output power increase of at least 1kW in 4 seconds. There were 218 events of increase in power output of at least 12kW (61% of plant rating) in 4 seconds recorded. Figure 92 shows the distribution of output power ramp-down events over 4-second periods for rates greater than 2.5kW/s and five events of output power drops of more than 16kW (82% of plant rating) in 4 seconds were recorded. Figure 93 shows occurrences of various ramp-up rates for variations recorded in fifty second periods. A total of 1,344 occurrences of ramp-up rates of 2 kw/s or more were observed which corresponds to increases in power output of at least 1kW (51% of plant rating) in fifty seconds. The corresponding total number of ramp down occurrences for 2kW/s or more for fifty seconds is 1,515, of which 54 were for ramp-down rates of 3kW/s or more (greater than 77% drop in plant power output in fifty seconds), as can be seen in Figure

126 6 Number of occurrences Ramp up rate (kw/s) Figure 89 Distribution of ramp-up events in 3-second periods for 196 kw PV system 6 Number of occurrences Ramp down rate (-kw/s) Figure 9 Distribution of ramp-down events in 3-second periods for 196 kw PV system 124 Solar intermittency: Australia s clean energy challenge

127 8 7 Number of occurrences Ramp up rate (kw/s) Figure 91 Distribution of ramp-up events in 4-second periods for 196kW PV system 8 7 Number of occurrences Ramp down rate (-kw/s) Figure 92 Distribution of ramp-down events in 4-second periods for 196kW PV system 125

128 7 6 Number of occurrences Ramp up rate (kw/s) Figure 93 Distribution of ramp-up events in 5-second periods for 196 kw PV system 6 Number of occurrences Ramp down rate (-kw/s) Figure 94 Distribution of ramp-down events in 5-second periods for 196 kw PV system 126 Solar intermittency: Australia s clean energy challenge

129 The distribution of ramp-up event occurrences over 6-second periods with rates greater than or equal to 1.5kW/s is shown in Figure 95. The total number of such events was 2,38, including 18 occurrences of ramp-up events with rates greater than 2.5kW/s corresponding to output power increases of more than 15kW (77% of plant rating) in 6 seconds. Figure 96 shows the distribution of output power ramp-down events over 6-second periods for rates greater than 1.5kW/s, the total number of which was found to be 2,163. The output power dropped at least 15kW in 6 seconds 56 times Number of occurrences Ramp up rate (kw/s) Figure 95 Distribution of ramp-up events in 6-second periods for 196kW PV system 127

130 Number of occurrences Ramp down rate (-kw/s) Figure 96 Distribution of ramp-down events in 6-second periods for 196kW PV system The number of occurrences of ramp-up events over various time periods is summarised in Table 18. It can be seen that the majority of the fluctuations observed over all time periods are small variations with ramp rates of less than 1kW/s. A significant number of rapid increases in output power were observed with ramp rates of more than 5kW/s over 1- and 2-second periods which correspond to increases of more than 5kW (26% of plant rating) and 1kW (51% of plant rating) respectively in a short time frame. An illustration of the distribution of ramp-up events over time periods ranging from 2 seconds to 6 seconds is shown in Figure 97. Table 18 Number of occurrences of ramp-up events with various ramp rates for different timescales 196kW PV Timescale -1kW/s 1-2kW/s 2-3kW/s 3-4kW/s 4-5kW/s 5-6kW/s 6-7kW/s 7-8kW/s 8-9kW/s 9kW/s+ 1s s s N/A N/A N/A 4s N/A N/A N/A N/A N/A 5s N/A N/A N/A N/A N/A N/A 6s N/A N/A N/A N/A N/A N/A Note N/A refers to ramp-rate events that are not feasible such ramp rates over those timescales will exceed the capacity of the plant. 128 Solar intermittency: Australia s clean energy challenge

131 15 Number of occurrences Ramp up rate (kw/s) Ramp dura on (s) Figure 97 Ramp-up rates for 196 kw PV system 2, 3, 4 and 5-second variations Table 19 presents a summary of occurrences of ramp-down events over various time periods. Similar to ramp-up events, the majority of the total number of fluctuations over all time periods were small variations with ramp rates of less than 1kW/s. However, a larger number of ramp-down events with higher ramp rates were observed compared to ramp-up events. A significant amount of rapid drops in output power with ramp rates greater than 5kW/s, corresponding to at least 5kW (26%) and 1kW (51%) for 1- and 2-second time periods respectively, were observed and this is greater than that of corresponding ramp-up events. Figure 98 illustrates the distribution of ramp-down events over time periods ranging from 2 seconds to 6 seconds. This analysis demonstrates the possible significance of rapid fluctuations of PV power to grid stability. This effect can obviously be mitigated to some degree by distributing the PV systems over wider areas in order to de-correlate system behaviour. Table 19 Number of occurrences of ramp-down events with various ramp rates for different timescales 196kW PV Timescale -1kW/s 1-2kW/s 2-3kW/s 3-4kW/s 4-5kW/s 5-6kW/s 6-7kW/s 7-8kW/s 8-9kW/s 9kW/s+ 1s s s N/A N/A N/A 4s N/A N/A N/A N/A N/A 5s N/A N/A N/A N/A N/A N/A 6s N/A N/A N/A N/A N/A N/A Note: N/A refers to ramp-rate events that are not feasible such ramp rates over those timescales will exceed the capacity of the plant. 129

132 15 Number of occurrences Ramp down rate (-kw/s) Ramp dura on (s) Figure 98 Ramp-down rates for 196 kw PV system 2, 3, 4 and 5-second variations Data from a concentrating solar thermal (CST) plant could not be obtained for this project for the purpose of intermittency timescale and ramp rate analysis. However, depending on the type of CST technology used, CST systems may be less susceptible to rapid ramp-up or ramp-down situations due to i nherent thermal inertia in the system, and short-term cloud event ride through is believed to be achievable. Figure 99 shows the output from PV and CST plants without dedicated storage on a cloudy day. A significant number of rapid fluctuations are seen in the output power of the PV plant while the output power of the CST plant is fairly steady throughout the main part of the day with significantly slower changes. 13 Solar intermittency: Australia s clean energy challenge

133 Solar Output (% maximum output) 1% Cloudy Day (May 3) 9% 8% 7% 6% 5% 4% 3% 2% PV 1% Solar Thermal % 7: 9: 11: 13: 15: 17: 19: Time (hh:mm) Figure 99 Output from PV and CSP plants without dedicated thermal storage: the role of thermal inertia. Source: Mehos et al., IEEE Power & Energy Magazine, May/June Voltage variations Data of average line-to-neutral AC voltage of the three phases of the DKASC with 1-second resolution were also obtained for a period of ten months, from October 21 to August 211. The voltage readings were taken at the centralised switchboard using a class.5 ION 755 energy meter which monitors the main feed to the switchboard. This meter is located at the output of the PV plants. The DKASC PV system is connected at the end of a feeder to which no loads other than a few street lights are connected to. The main feed is approximately 35 metres long, 3-phase+neutral, connected from a 22kV step-down transformer. The 22kV substation is the first substation on the feeder into the Desert Knowledge precinct which becomes a 22kV ring supplying about 16 different buildings, comprising offices and teaching spaces. Figure 1 shows a plot of the voltage data over the ten month period. The maximum voltage recorded was V x 1 6 Figure 1 Average line-to-neutral AC voltage of the three phases of DKASC for 1-month period from October,

134 Figure 11 shows the distribution of voltage measured at the DKASC. For the analysis conducted in our study, voltage drops to zero (representing the loss of main grid supply) were ignored, focussing instead on the system during normal operation. The average line-to-neutral voltage of the three phases was found to be greater than 25V for 79.8% of the time over the ten months, and the proportion that was recorded to be over 253V (upper limit of grid voltage, i.e. 23V +1%) was 29.4%. The tap setting over the one-year period for which data was analysed was high (433V), resulting in observed voltage of about 25V almost all the time. The tap setting has been reduced to 415V in late November 211 for voltage to be around the 24V level. As there was no end consumer in this case, the high voltage had no effect on any load: there was none apart from some street lights. The trip settings of the PV inverters are set higher than what the Australian standard AS4777 allows, in agreement with the local utility, i.e. a bigger voltage window. Figure 12 shows the irradiance, output power and corresponding voltage recorded at the DKASC over a seven day period from 7 to 13 February 211. It can be seen that all seven days experienced a significant number of passing clouds, causing many spikes in the generated power output during the course of the week. A similar plot for the duration of one day is shown in Figure 13. To illustrate the impacts of clear and sunny days on the PV plant output power and voltage, Figure 14 shows the effects of passing clouds on the plant output power and voltage on two consecutive days, 19 (cloudy) and 2 (clear) December 21. A positive correlation between the irradiance, output power and output voltage can be seen from Figure 12, Figure 13 and Figure 14. In Figure 14, a spike in the output power and voltage waveforms can be seen just after midday on a clear day, on 2 December 211. This is likely due to the tripping of inverters, causing spikes in both the output power and line-to-neutral voltage Frequency (%) RMS AC Voltage (V) Figure 11 Distribution of average line-to-neutral AC voltage measured at the DKASC over ten months 132 Solar intermittency: Australia s clean energy challenge

135 Insola on(w/m2) Days 2 Power(kW) Days 26 Voltage(V) Days Figure 12 Irradiance, output power and line-to-neutral AC voltage (top to bottom) at the DKASC over one week (7 13 February, 211) with partly cloudy days Insola on(w/m2) 2 1 5:4 AM 9:29 AM 1:18 PM 5:7 PM 8:56 PM 7/2/211 2 Power(kW) 1 5:4 AM 9:29 AM 1:18 PM 5:7 PM 8:56 PM 7/2/ Voltage(V) :4 AM 9:29 AM 1:18 PM 5:7 PM 8:56 PM 7/2/211 Figure 13 Irradiance, output power and line-to-neutral AC voltage (top to bottom) at the DKASC over one day (7 February, 211) 133

136 Insola on(w/m2) Power(kW) :55 AM 1:22 PM 12:5 AM 12:18 PM 11:44 PM 2 1 Hours -1 1:55 AM 1:22 PM 12:5 AM 12:18 PM 11:44 PM 26 Hours Voltage(V) :55:2 AM 1:22:5 PM 12:5:3 AM 12:18 PM 11:44 PM Hours Figure 14 Irradiance, output power and line-to-neutral AC voltage at the DKASC on 19 December, 21 (cloudy) and 2 December, 21 (clear) To study the voltage behaviour at the DKASC plant, the voltage and irradiance over one day were plotted together for comparison, as shown below in Figure 15(a), with the voltage and power output for the same day shown in Figure 15(b). A positive correlation is again observed between the voltage, irradiance and output power of the plant. Fluctuations in the voltage seen outside daylight hours are likely due to variations in loads connected to adjacent feeders. The magnitudes of these fluctuations are, however, not as large as those observed during daylight hours due to passing clouds. 134 Solar intermittency: Australia s clean energy challenge

137 258 Insola on Voltage RMS AC Voltage (V) Insola on (W/m2) :4 AM 9:29 AM 1:18 PM 5:7 PM 8:56 PM 7/2/211 (a) 258 Power Voltage RMS AC Voltage (V) Power (kw) :4 AM 9:29 AM 1:18 PM 5:7 PM 8:56 PM 7/2/211 (b) Figure 15 (a) Voltage and insolation, and (b) voltage and output power, at the DKASC along one day (7 February, 211) 135

138 Si milar plots to Figure 15 but restricted to a 5-minute window around midday on 7 February 211 can be seen in Figure 16. Figure 16(a) compares the voltage and insolation fluctuations over the period, while Figure 16(b) compares the voltage and plant output power fluctuations. A strong positive correlation is again observed between all the three parameters Insola on Voltage RMS AC Voltage (V) Insola on (W/m2) :2 PM 13:36 PM 12:53 PM 1:1 PM 7/2/211 (a) Power Voltage 18 RMS AC Voltage (V) Power (kw) :2 PM 12:36 PM 12:53 PM 1:1 PM 7/2/211 (b) Figure 16 (a) Voltage and insolation, and (b) voltage and output power, at the DKASC during a 5-minute period at midday on 7 February, Solar intermittency: Australia s clean energy challenge

139 9.3 Intermittency data analysis CSIRO 22 kw PV system To analyse PV power output ramp rates for a small-scale solar system, high-resolution 5-second data was gathered for a 22 kw PV system located on the roof of the CSIRO Energy Centre office building in Newcastle. The data was collected for a period of two weeks and comprised solar irradiance (global horizontal irradiance) and PV output power. Occurrences of continuous ramping of the 22 kw PV system s output power for periods of five, ten, fifty and sixty seconds were analysed using the 5-second resolution data collected. The distributions of ramp events over the various timescales were obtained using the same method as that for the DKASC. Figure 17 shows the distribution of ramp-up rates for variations recorded in five seconds. A total of 247 occurrences of output power ramp-up events with rates of at least.5kw/s over 5 seconds were recorded in the two-week period. Of these occurrences 54 were over.9kw/s, corresponding to increases in output power of at least 4.5kW (2.5% of plant rating) in five seconds. The analysis also showed there were 253 occurrences of ramp-down rates of.5kw/s or more for five second periods throughout the two weeks. The distribution of ramp-down events with rates greater than.8kw/s can be seen in Figure 18. Ramp-down rates of over 1.3kW/s were observed to occur ten times, which corresponds to a power drop of at least 6.5kW (~3% of plant rating) in just five seconds. 3 Number of occurrences Ramp up rate (kw/s) Figure 17 Distribution of ramp-up events in 5-second periods for 22 kw PV system 137

140 15 Number of occurrences Ramp down rate (-kw/s) Figure 18 Distribution of ramp-down events in 5-second periods for 22 kw PV system Figure 19 and Figure 11 show the distribution of ramp-up and ramp-down events respectively in 1-second periods with rates of.6kw/s or more. A total of 3 occurrences of ramp-up events with rates of more than.8kw/s were observed translating to an increase in the PV system s power output of at least 8kW (36% of rating) in ten seconds. The corresponding number of occurrences of ramp-down events with rates of more than 8kW/s recorded was Number of occurrences Ramp up rate (kw/s) Figure 19 Distribution of ramp-up events in 1-second periods for 22 kw PV system 138 Solar intermittency: Australia s clean energy challenge

141 4 35 Number of occurrences Ramp down rate (-kw/s) Figure 11 Distribution of ramp-down events in 1-second periods for 22 kw PV system Figure 91 shows the distribution of ramp-up event occurrences over 5-second periods with rates greater than.1kw/s, corresponding to output power increase of at least 5kW in 5 seconds. There were 68 events of increase in power output of at least 1kW (45% of plant rating) in 5 seconds recorded. Similarly, Figure 112 shows the distribution of output power ramp-down events over 5-second periods and five events of output power drops of more than 15kW (68% of plant rating) in 5 seconds were recorded. The distribution of ramp-up event occurrences over 6-second periods with rates greater than or equal to.1kw/s is shown in Figure 113. The total number of such events in the two-week period was found to be 189, with nine occurrences of ramp-up events with rates greater than.24kw/s, corresponding to output power increases of more than 14.4kW (65% of plant rating) in 6 seconds. Figure 114 shows the distribution of output power ramp-down events over 6-second periods for rates greater than.1kw/s, the total number of which was found to be 117. The number of times the output power dropped at least 14.4kW in 6 seconds was found to be ten. 139

142 6 5 Number of occurrences Ramp up rate (kw/s) Figure 111 Distribution of ramp-up events in 5-second periods for 22 kw PV system Number of occurrences Ramp down rate (-kw/s) Figure 112 Distribution of ramp-down events in 5-second periods for 22 kw PV system 14 Solar intermittency: Australia s clean energy challenge

143 Number of occurrences Ramp up rate (kw/s) Figure 113 Distribution of ramp-up events in 6-second periods for 22 kw PV system Number of occurrences Ramp down rate (-kw/s) Figure 114 Distribution of ramp-down events in 6-second periods for 22 kw PV system 141

144 The number of occurrences of ramp-up events over various time periods for the 22kW PV system is summarised in Table 2. It can be seen that the majority of the fluctuations observed over all time periods are small variations with ramp rates of less than.2kw/s. A significant number of rapid increases in output power were observed with ramp rates of more than.8kw/s over 5- and 1-second periods which correspond to increases of more than 4kW (18% of plant rating) and 8kW (36% of plant rating) respectively in a very short time frame. Timescale Table 2 Number of occurrences of ramp-up events with various ramp rates for different timescales 22 kw PV -.1kW/s.1-.2kW/s.2-.3kW/s.3-.4kW/s.4-.5kW/s.5-.6kW/s.6-.7kW/s.7-.8kW/s.8-.9kW/s.9 kw/s+ 5s s s N/A N/A N/A N/A N/A 6s N/A N/A N/A N/A N/A N/A Note: N/A refers to ramp-rate events that are not feasible such ramp rates over those timescales will exceed the capacity of the plant. Table 21 presents a summary of occurrences of ramp-down events over various time periods. Similar to ramp-up events, majority of the total number of fluctuations over all time periods are small variations with ramp rates of less than.2kw/s. A considerable amount of rapid drops in output power with ramp rates greater than.8kw/s, corresponding to at least 4kW (26%) and 8kW (51%) drops in output power for 5- and 1-second time periods respectively, were observed. Table 21 Number of occurrences of ramp-down events with various ramp rates for different timescales 22 kw PV Timescale -.1kW/s.1-.2kW/s.2-.3kW/s.3-.4kW/s.4-.5kW/s.5-.6kW/s.6-.7kW/s.7-.8kW/s.8-.9kW/s.9 kw/s+ 5s s s N/A N/A N/A N/A N/A 6s N/A N/A N/A N/A N/A N/A Note: N/A refers to ramp-rate events that are not feasible such ramp rates over those timescales will exceed the capacity of the plant. 142 Solar intermittency: Australia s clean energy challenge

145 9.4 Intermittency data analysis UQ 1.2 MW PV system To analyse PV power output ramp rates for a relatively large-scale solar system, 1-minute data was gathered for a 1.22 MW PV system located at The University of Queensland s (UQ) St Lucia campus in Brisbane 9. This is currently Australia s largest flat-panel PV solar power system comprising 5,4 panels on the rooftops of four of UQ s biggest buildings. Solar data containing solar irradiance (global horizontal irradiance) and PV output power sampled every 1-minute for a period of five months in late 211 was obtained for intermittency analysis. A breakdown of the peak power ratings for the solar arrays on the four different UQ buildings is: UQ Centre, St Lucia kwp Multi-level Carpark # kwp Multi-level Carpark # kwp Sir Llew Edwards Building 89.8 kwp. There is also a concentrating PV array of 8kWp on site, the data from which was not included in this intermittency analysis. The power output profiles of the solar arrays on the four different UQ buildings on a day with many moving clouds can be seen in Figure 115. The output power is normalised against the individual peak rating. At around 12:35pm, the PV power outputs of all the four arrays are seen to drop by about 8% in a minute. The corresponding total power output is also seen to drop by a similar amount at the same instant. The percentage drops in magnitude of the total output power during the day is seen to be similar to each of the individual building PV output power. In this case, spatial diversity due to the spacing between the four UQ buildings did not contribute towards smoothing out the intermittency. This demonstrates a strong correlation between the outputs of all the four different installations. However, with the resolution of 1-minute, the effects of fast moving clouds and the corresponding correlation between the outputs cannot be analysed. Figure 116 shows a similar plot of the power output profiles of the solar arrays on the four UQ buildings and also the total power output but for a period of 1.5 hours in the morning of the same day. As expected, the output power for the two car park buildings can be seen to be very strongly correlated (they are located adjacent to each other), while the correlation is not as strong for the other two buildings. In this zoomed-in plot, a reduction in the magnitude of output power variations is observed in the total output power profile compared to the individual building output profiles. For example, at around 9:43am a 7-8% drop in power output is seen to occur in about 2-3 minutes in the individual building solar output profiles and the corresponding drop in total output power is about 5%. The variances of the output power for the individual installations and the total output power, for both the whole day and 1.5 hour periods, are shown in Table 22. It can be seen that the variance for the total output power is significantly smaller for the 1.5 hour period when compared to the individual buildings. This is, however, not the case for the whole day period where the total output power variance is similar to those of the individual PV installations. Table 22 Variance of PV output power for four UQ buildings and total combined output power Site Variance whole day (%) Variance 1.5 hours (%) Car park Car park Sir Llew Edwards Building UQ Centre Total output power UQ Solar, The University of Queensland, 143

146 Car Park 1 4:59 AM 6:27 AM 6:59 AM 7:31 AM 8:3 AM 8:35 AM 9:7 AM 9:39 AM 1:11 AM 1:43 AM 11:15 AM 11:47 AM 12:19 PM 12:51 PM 1:23 PM 1:55 PM 2:27 PM 2:59 PM 3:31 PM 4:3 PM 4:35 PM 5:7 PM 5:39 PM 6:11 PM 6:43 PM Car Park 2 4:59 AM 6:28 AM 7:1 AM 7:34 AM 8:7 AM 8:4 AM 9:13 AM 9:46 AM 1:19 AM 1:52 AM 11:25 AM 11:58 AM 12:31 PM 1:4 PM 1:37 PM 2:1 PM 2:43 PM 3:16 PM 3:49 PM 4:22 PM 4:55 PM 5:28 PM 6:1 PM 6:34 PM Sir Llew Edwards Building 4:59 AM 6:28 AM 7:1 AM 7:34 AM 8:7 AM 8:4 AM 9:13 AM 9:46 AM 1:19 AM 1:52 AM 11:25 AM 11:58 AM 12:31 PM 1:4 PM 1:37 PM 2:1 PM 2:43 PM 3:16 PM 3:49 PM 4:22 PM 4:55 PM 5:28 PM 6:1 PM 6:34 PM UQ Centre 4:59 AM 6:27 AM 6:59 AM 7:31 AM 8:3 AM 8:35 AM 9:7 AM 9:39 AM 1:11 AM 1:43 AM 11:15 AM 11:47 AM 12:19 PM 12:51 PM 1:23 PM 1:55 PM 2:27 PM 2:59 PM 3:31 PM 4:3 PM 4:35 PM 5:7 PM 5:39 PM 6:11 PM 6:43 PM Total Output :59 AM 6:21 AM 6:47 AM 7:13 AM 7:39 AM 8:5 AM 8:31 AM 8:57 AM 9:23 AM 9:49 AM 1:15 AM 1:41 AM 11:7 AM 11:33 AM 11:59 AM 12:25 PM 12:51 PM 1:17 PM 1:43 PM 2:9 PM 2:35 PM 3:1 PM 3:27 PM 3:53 PM 4:19 PM 4:45 PM 5:11 PM 5:37 PM 6:3 PM 6:29 PM 6:55 PM Figure 115 Power output profiles of the PV arrays on four UQ buildings and total power output for an intermittent day (normalised) 144 Solar intermittency: Australia s clean energy challenge

147 Car Park 1 8:59 AM 9:4 AM 9:9 AM 9:14 AM 9:19 AM 9:24 AM 9:29 AM 9:34 AM 9:39 AM 9:44 AM 9:49 AM 9:54 AM 9:59 AM 1:4 AM 1:9 AM 1:14 AM 1:19 AM 1:24 AM 1:29 AM 1:34 AM 1:39 AM Car Park 2 8:59 AM 9:4 AM 9:9 AM 9:14 AM 9:19 AM 9:24 AM 9:29 AM 9:34 AM 9:39 AM 9:44 AM 9:49 AM 9:54 AM 9:59 AM 1:4 AM 1:9 AM 1:14 AM 1:19 AM 1:24 AM 1:29 AM 1:34 AM 1:39 AM Sir Llew Edwards Building UQ Centre :59 AM 9:4 AM 9:9 AM 9:14 AM 9:19 AM 9:24 AM 9:29 AM 9:34 AM 9:39 AM 9:44 AM 9:49 AM 9:54 AM 9:59 AM 1:4 AM 1:9 AM 1:14 AM 1:19 AM 1:24 AM 1:29 AM 1:34 AM 1:39 AM :59 AM 9:4 AM 9:9 AM 9:14 AM 9:19 AM 9:24 AM 9:29 AM 9:34 AM 9:39 AM 9:44 AM 9:49 AM 9:54 AM 9:59 AM 1:4 AM 1:9 AM 1:14 AM 1:19 AM 1:24 AM 1:29 AM 1:34 AM 1:39 AM Total Output :59 AM 9:3 AM 9:7 AM 9:11 AM 9:15 AM 9:19 AM 9:23 AM 9:27 AM 9:31 AM 9:35 AM 9:39 AM 9:43 AM 9:47 AM 9:51 AM 9:55 AM 9:59 AM 1:3 AM 1:7 AM 1:11 AM 1:15 AM 1:19 AM 1:23 AM 1:27 AM 1:31 AM 1:35 AM 1:39 AM Figure 116 Power output profiles of the PV arrays on four UQ buildings and total power output for an intermittent 1.5 hour period (normalised) 145

148 Figure 117 shows the distribution of 964 ramp-up event occurrences over 1-minute periods with rates greater than 5kW/s, corresponding to output power increases of at least 3kW (24.6% of plant rating) in one minute. Ten of these events had ramp-up rates of greater than 12kW/s, i.e. output power rise of more than 72kW (59% of plant rating) in 1-minute periods. Similarly, Figure 118 shows the distribution of output power ramp-down events over 1-minute periods and 11 events of output power drops of more than 72kW in 1-minute periods were recorded in the five month period in which data was collected. The distributions of ramp events over the various timescales were obtained using the same method as that for the DKASC. Figure 119 and Figure 12 show the distribution of ramp-up and ramp-down events respectively in 2-minute periods with rates of 4kW/s or more. A total of six occurrences of ramp-up events with rates of more than 7kW/s were observed and this translates to an increase in the PV system s power output of at least 85kW (69% of rating) in two minutes. The corresponding number of occurrences of ramp-down events with rates of more than 7kW/s recorded was 3. The distribution of ramp-up event occurrences over 3-minute periods with rates greater than or equal to 2kW/s is shown in Figure 121. The total number of such events in the five month period was found to be 1,247 including two occurrences of ramp-up events with rates greater than 5kW/s corresponding to output power increases of more than 9kW (74% of plant rating) within three minutes. Figure 122 shows the distribution of output power ramp-down events over 3-minute periods for rates greater than 2kW/s, the total number of which was found to be 1,39. The output power was seen to drop by more than 9kW in 3 minutes once in the five-month period. Figure 123 shows the distribution of ramp-up event occurrences over 4-minute periods with rates greater than 2kW/s, which corresponds to output power increase of at least 48kW in four minutes. 14 events of increases in power output of at least 84kW (69% of plant rating) in minutes were recorded. Figure 124 shows the distribution of output power ramp-down events over 4-minute periods for rates greater than 2kW/s and 18 events of output power drops of more than 84kW (69% of plant rating) in four minutes were recorded, one of which was for output power drop greater than 96kW (79% of rating). Figure 125 shows occurrences of various ramp-up rates for variations recorded in 5-minute periods. A total of 453 occurrences of ramp-up rates of 1.5 kw/s or more were observed, corresponding to increases in power output of at least 45kW (37% of plant rating) in five minutes. The corresponding total number of ramp down occurrences for 1.5kW/s or more for 5-minute is 511, out of which three were for ramp-down rates of 3kW/s or more (greater than 74% drop in plant power output in five minutes), as can be seen in Figure 126. The distribution of ramp-up event occurrences over 6-minute periods with rates greater than or equal to 1kW/s is shown in Figure 127. The total number of such events was found to be 466, including one occurrence of ramp-up event with rate greater than 2.5kW/s, corresponding to output power increase of more than 9kW (74% of plant rating) within six minutes. Figure 128 shows the distribution of output power ramp-down events over 6-minute periods for rates greater than 1kW/s, the total number of which was found to be 525. The number of times the output power dropped at least 9kW in six minutes was found to be three. 146 Solar intermittency: Australia s clean energy challenge

149 4 35 Number of occurrences Ramp up rate (kw/s) Figure 117 Distribution of ramp-up events in 1-minute periods for 1.22 MW PV system 4 35 Number of occurrences Ramp down rate (-kw/s) Figure 118 Distribution of ramp-down events in 1-minute periods for 1.22 MW PV system 147

150 3 25 Number of occurrences Ramp up rate (kw/s) Figure 119 Distribution of ramp-up events in 2-minute periods for 1.22 MW PV system 3 25 Number of occurrences Ramp down rate (-kw/s) Figure 12 Distribution of ramp-down events in 2-minute periods for 1.22 MW PV system 148 Solar intermittency: Australia s clean energy challenge

151 Number of occurrences Ramp up rate (kw/s) Figure 121 Distribution of ramp-up events in 3-minute periods for 1.22 MW PV system Number of occurrences Ramp down rate (-kw/s) Figure 122 Distribution of ramp-down events in 3-minute periods for 1.22 MW PV system 149

152 25 Number of occurrences Ramp up rate (kw/s) Figure 123 Distribution of ramp-up events in 4-minute periods for 1.22 MW PV system 3 25 Number of occurrences Ramp down rate (-kw/s) Figure 124 Distribution of ramp-down events in 4-minute periods for 1.22 MW PV system 15 Solar intermittency: Australia s clean energy challenge

153 2 Number of occurrences Ramp up rate (kw/s) Figure 125 Distribution of ramp-up events in 5-minute periods for 1.22 MW PV system 25 2 Number of occurrences Ramp down rate (-kw/s) Figure 126 Distribution of ramp-down events in 5-minute periods for 1.22 MW PV system 151

154 25 2 Number of occurrences Ramp up rate (kw/s) Figure 127 Distribution of ramp-up events in 6-minute periods for 1.22 MW PV system 3 25 Number of occurrences Ramp down rate (-kw/s) Figure 128 Distribution of ramp-down events in 6-minute periods for 1.22 MW PV system 152 Solar intermittency: Australia s clean energy challenge

155 The number of occurrences of ramp-up events over various time periods for the 1.22MW PV system is summarised in Table 23. It can be seen that the majority of the fluctuations observed over all time periods are relatively small variations with ramp rates of less than 1kW/s. A significant number of rapid increases in output power were observed with ramp rates of more than 6kW/s over 1- and 2-minute periods which correspond to increases of more than 36kW (29.5% of plant rating) and 72kW (59% of plant rating) respectively in a short time frame. Table 23 Number of occurrences of ramp-up events with various ramp rates for different timescales 1.22 MW PV Timescale -1kW/s 1-2kW/s 2-3kW/s 3-4kW/s 4-5kW/s 5-6kW/s 6-7kW/s 7-8kW/s 8-9kW/s 9kW/s+ 1-min min min N/A N/A N/A 4-min N/A N/A N/A N/A 5-min N/A N/A N/A N/A N/A 6-min N/A N/A N/A N/A N/A N/A Note: N/A refers to ramp-rate events that are not feasible such ramp rates over those timescales will exceed the capacity of the plant. Table 24 presents a summary of occurrences of ramp-down events over various time periods for the 1.22MW PV system. Similar to ramp-up events, majority of the total number of fluctuations over all time periods are small variations with ramp rates of less than 1kW/s. A considerable amount of rapid drops in output power with ramp rates greater than 6kW/s, corresponding to at least 36kW (29.5%) and 72kW (59%) drops in output power for 1- and 2-minute time periods respectively, were observed. Table 24 Number of occurrences of ramp-down events with various ramp rates for different timescales 1.22 MW PV Timescale -1kW/s 1-2kW/s 2-3kW/s 3-4kW/s 4-5kW/s 5-6kW/s 6-7kW/s 7-8kW/s 8-9kW/s 9kW/s+ 1-min min min N/A N/A N/A 4-min N/A N/A N/A N/A 5-min N/A N/A N/A N/A N/A 6-min N/A N/A N/A N/A N/A N/A Note: N/A refers to ramp-rate events that are not feasible such ramp rates over those timescales will exceed the capacity of the plant. 153

156 9.5 Comparison of ramp events for 196kW and 22kW PV system Variations in output power of the 196kW PV plant at DKASC and the 22kW PV installation at CSIRO Energy Centre were compared for various timescales. Note that the data from DKASC was for a one-year period whereas only two weeks of data was able to be analysed for the 22kW PV system. The plots in Figure 129 show percentage variations in output power of the two PV plants over 1-second periods. The graphs with the blue bars represent the 196kW DKASC system while the ones with the green bars represent the 22kW CSIRO PV system. A similar trend can be seen for the variations in both the plants and the number of variations with higher ramp rates is significantly higher for the 22kW PV system considering the difference in the duration for which data was available for the two plants, i.e. two weeks for the CSIRO system versus a year for the DKASC system. The time it takes for a cloud cover spread to shade a smaller PV plant is smaller than for a larger PV plant, and therefore more rapid variations in output power with larger magnitude are more likely to occur in the 22kW plant compared with the 196kW plant. DSASC 196 kw CSIRO 22kW Number of occurrences Number of occurrences % increase in plant power output in 1 seconds % increase in plant power output in 1 seconds Number of occurrences Number of occurrences % decrease in plant power output in 1 seconds % decrease in plant power output in 1 seconds Figure 129 Comparison of output power 1-second ramp events for 196kW and 22kW PV system Similar comparisons were made for 2-second and 5-second periods, results of which are shown in Figure 13 and Figure 131 respectively. It can be seen that both PV systems experience high ramp rate events over short timeframes. The rate of occurrence of high ramp rate events is significantly higher for the 22kW PV system, as it is more likely that a larger proportion of the smaller PV plant will be shaded by a cloud cover spread than the larger 196kW PV system at DKASC. It is also more likely that the PV system at CSIRO s coastal location will be shaded by intermittent clouds than the inland system at DKASC. Ramp events for the UQ 1.22MV PV plant were not included in this comparison study as high-resolution data below 1-minute was not available. 154 Solar intermittency: Australia s clean energy challenge

157 DSASC 196 kw CSIRO 22kW 4 25 Number of occurrences % increase in plant power output in 2 seconds 4 Number of occurrences % increase in plant power output in 2 seconds 3 Number of occurrences Number of occurrences % decrease in plant power output in 2 seconds % decrease in plant power output in 2 seconds Figure 13 Comparison of output power 2-second ramp events for 196kW and 22kW PV system 1 DSASC 196 kw 35 CSIRO 22kW Number of occurrences % increase in plant power output in 5 seconds Number of occurrences % increase in plant power output in 5 seconds 25 2 Number of occurrences Number of occurrences % decrease in plant power output in 5 seconds % decrease in plant power output in 5 seconds Figure 131 Comparison of output power 5-second ramp events for 196kW and 22kW PV system 155

158 1 Model to examine likely impacts of intermittency on Australian electricity networks In order to understand and investigate the likely effects of various penetration levels of intermittent generation on different segments of Australian electricity networks, we consider four scenarios: 1. A weak (e.g. remote and relatively small) network with a small amount of PV generation For a weak network, intermittency effects will be more pronounced than in a larger network with a concordant potential capacity for stability issues. However, as only a small number of customers rely on the network, detrimental effects will be limited to a small number of consumers. If we consider an extreme case of a small isolated power system, such as a microgrid, even a small amount of intermittency may result in an imbalance of power production and consumption on short time scales 2. A weak (e.g. remote and relatively small) network with a large amount of PV generation For a weak network, the PV generation will have a strong effect upon the system stability 3. A strong (e.g. urban and relatively large) network with a small amount of PV generation Here, the PV intermittency will be largely absorbed by the strong network. However, the small amount of variation can cause local voltage problems. The typical case is a PV rooftop array where local voltage swings caused by the PV intermittency will not bring down the network or cause network level problems, but may cause voltage variation locally. The most likely detrimental outcome is that the PV inverter will detect voltages outside its allowed operating range and shut down. The effect of this will be that the consumer may not notice anything wrong but the system will operate at less than its planned production efficiency and as a result consumer revenue will be decreased. 4. A strong (e.g. urban and relatively large) network with a large amount of PV generation This situation has the potential to affect a large number of customers. 156 Solar intermittency: Australia s clean energy challenge

159 A simulation model developed at CSIRO was used to examine the likely impacts of output power fluctuations seen at the Desert Knowledge Australia Solar Centre (DKASC), Alice Springs, on various types of electricity networks in Australia. Figure 132 shows actual data recorded from the DKASC PV array over a 15 minute period on a partly cloudy day. As the insolation changes over the course of 15 minutes, the power supplied from the array can be seen to vary from a high of 167kW to a low of 2kW. At the same time, the voltage measured at the Solar Centre varies between 248V and 253V, giving a range of approximately 4V. Power from PV array (kw) Power Voltage :5: 12:55: 13:: 13:5: 13:1: Time (hh:mm:ss) Voltage (Volts) Figure 132 Actual data from DKASC showing power and voltage fluctuations The four scenarios mentioned at the beginning of this section were simulated using data from the DKASC as shown above in Figure 132. The model used is a small-signal (time se ries) model developed at CSIRO for studying the effects of renewable generation attached to the grid. As shown in Figure 133, the grid is represented as a generation source, with series impedance representing the lumped impedance of the generators and the line connecting it to the PV array at DKASC. In this way, it is possible to represent a strong or weak grid simply by changing the values of the lumped impedance specified as L and R in the diagram. The load can also be varied in the model to show the effects of different penetration levels at the point of coupling to the network. The DKASC array is represented as a current source with a nominal maximum (nameplate) capacity of 2kW. Grid Lumped impedance of grid L R DKASC PV array = Load Figure 133 Simulation model for investigating effects of intermittency The simulation is run for approximately 15 minutes of actual data obtained from DKASC, and the effects of intermittency on voltage and network stability are observed. The first scenario modelled is a strong network with low penetration, that is, the nameplate rating of the PV array is 1% of the local load. The load is assumed to be constant load changes are n ot considered, and reactive power is set at.75 p.u., in accordance with the values used by Lasseter and Piagi in [71]. As the PV array at the DKASC is rated at approximately 2kW, we used a load of 2MW to approximate a 1% penetration. In line with [72], we set the lumped grid impedance 157

160 to.1 p.u. based on the load of 2MW. Consistent with the assumption that line reactive impedance is much larger than resistance, we set R in the diagram to be one tenth of the impedance of L. Only the 24V side was considered. It is assumed that standard voltage regulation is installed and operational in order to correct for vo ltage drops expected at the load. Results for the simulation with low PV penetration and a strong grid are shown in Figure 134. The voltage variation is only.14v. However this kind of grid situation would probably only apply in an urban area close to a substation. Power from PV array (kw) Power 248 Voltage Time (seconds) Voltage (Volts) Figure 134 Voltage at the PV array for low penetration and a strong grid The second scenario also assumes a low PV penetration of 1%, but in a weaker grid. A weak grid by US and European experience would be.1 p.u. impedance, but we have chosen to simulate a rural grid under Australian conditions, so have ch osen an impedance of.2 p.u. The load is 2MW, representing a low penetration of 1% as before. Results are shown in Figure 135. Now the voltage can be seen to sag as power from the PV array drops, and has a range of.8v. This is still within the allowable range for voltage and would probably go unnoticed by customers. Power from PV array (kw) Power 248 Voltage Time (seconds) Figure 135 Voltage at the PV array for low penetration and a weak grid Voltage (Volts) The third scenario treats a strong grid with high penetration. As before, the strong grid is modelled as a lumped impedance of.1 p.u., and high penetration has been set at 4% of load, so the load used was 5kW, again with.75 p.u. reactive load. As shown in Figure 136, the voltage changes very little, only with a range of.35 V in spite of the high penetration, because the PV array is attached to a strong grid. This shows the importance of intermittent renewable generation being attached to a strong grid if this is possible. 158 Solar intermittency: Australia s clean energy challenge

161 Power from PV array (kw) Power 248 Voltage Time (seconds) Figure 136 Voltage at the PV array for high penetration and a strong grid Voltage (Volts) In the final scenario, we examine a high penetration of PV on a rural feeder. Here we assume a long feeder, typical of Australian conditions, where the grid impedance is co nsidered to be.2 p.u. The load is 5kW, giving a penetration of 4% at peak PV production. The results are shown in Figu re 137. Here the voltage swings by 4V, which is similar to the actual data recorded from the DKASC array compare with Figure 132. The voltage swing is still within the allowed range, but such variations can be of concern when the voltage is close to the upper or lower limits. Power from PV array (kw) Power Voltage Time (seconds) Figure 137 Voltage at the PV for high penetration on weak grid Voltage (Volts) Power generated by PV arrays is by nature intermittent, and can experience large swings in power output over only a few seconds or minutes even for an array of 2kW like the one at DKASC. When the penetration is low and it is attached to a strong grid this is not an issue, however, when attached to a rural feeder where the grid is not strong, an increase in penetration caused an increase in the voltage swings observed. If the penetration were increased on this type of feeder, the voltage swings would begin to impact on the operation of this part of the network. It is likely that PV inverters would trip off with these voltage swings, causing larger power fluctuation and therefore worsening the voltage swings. 159

162 11 Solar irradiance and PV power spectrum The power generated by solar power plants has an intermittent character, mainly because of atmospheric effects such as insolation variability resulting from cloud movement. However, differences exist between thermal and PV technologies and their response to changes in insolation. One example involves generally low PV plant inertia when compared with thermal systems which generally have a level of inherent thermal storage. Similarly the reliance of concentrating solar systems on direct irradiance can be contrasted with non-concentrating PV s acceptance of global irradiance. Further, consideration of the effects of shading and partial shading indicate that the larger the PV plant, the longer it takes for a cloud cover spread to shade the entire field. The following sub-section explores an analytic model developed to establish a relationship between solar irradiance and a PV plant s output power. This model is capable of estimating the PV plant s output power behaviour based on historical or forecasted irradiance data and the size of the PV plant Model linking solar intermittency with generation output Power fluctuations have been analysed in [1] and [74] in terms of power spectral density (PSD), that is in the frequency domain, using two months of 1-second data and two years of 1-minute data from a 4.6MWp PV array and a 135kWp sub-array within it. The power spectral density of the output of large-scale PV can provide insight into the character of both cyclic (daily, seasonal) and non-cyclic (weather-related) fluctuations associated with array output [75]. Power spectral analysis can indicate the type of firm power or demand response appropriate to compliment PV including required ramp rate [76]. The main conclusion drawn from these studies has been that the larger the PV plant, the greater the attenuation of high frequencies [75]. Our work is based on ten months of 1-second data of power output and irradiance from the Desert Knowledge Australia Solar Centre (DKASC) site in Alice Springs. Figure 138 shows the irradiance and the output power recorded for a 15-minute period around midday at the DKASC on 27 January, 211. Both signals have been normalised, the irradiance by 1W/m 2 and the power output by total PV rating (196kW). As expected, the power curve is smoother than the irradiance curve, because of the larger size of the PV plant when compared with the discrete character of the irradiance sensor. This is because the larger the size of the PV plant, the longer it takes for the cloud cover spread to shade the entire field. The PV plant power 16 Solar intermittency: Australia s clean energy challenge

163 output can be described as the signal output of a low-pass filter where the input signal is the incident irradiance [77]. This correlation between the irradiance and power output can somewhat be seen in Figure 138. It is a first order filter whose pole value is a function of the PV plant area :51 12:54 12:57 13: 13:3 13:6 13:9 Time of Day Figure 138 Irradiance (solid line) and output power, both normalised, recorded at DKASC during a 15-min period on 27 Jan, 211 Figure 139 shows the Discrete Fourier Transform (DFT) of the irradiance signal recorded over ten months from October 21 till August 211 at the DKASC, computed through a Fast Fourier Transform (FFT) algorithm presented as a Bode (magnitude) plot. The daily solar resource cycle is evidenced by a peak at 24 hours, indicated in Figure 139 by a dashed vertical line at log 1 f =-4.93 (f = 1.15 x 1-5 Hz). The frequency response for irradiance fluctuations has a characteristic slope of -.72 (green line). The same frequency domain analysis has been applied to the PV plant output power data recorded during the ten month period. Figure 14 shows the spectra of the power fluctuations using 1-second resolution data. The DKASC occupies approximately 4.14 hectares. The power spectrum can be described as two linear regions well fitted by functions given in (1) and (2) with characteristic slopes of -.72 (solid green line) and (dashed green line) respectively. The cross-point defines the cut-off frequency. Therefore, regarding power fluctuations, the PV plant size can be interpreted as a first order low-pass filter for the irradiance data. From this analysis, the cut-off frequency for the PV plant at DKASC was found to be.55hz (log 1 f = -2.26) which agrees well with observations made by the authors in [77]. This cut-off frequency is indicated by a dashed vertical line in the power spectrum in Figure 14. log1 ( magnitude) =.72log1( freq) +.22 log1( magnitude) = 1.38log1( freq) 1.26 (1) (2) 161

164 7 6 FFT Intensity Log (Magnitude) = -.72log(f) h peak Log F (log1(hz)) Figure 139 Spectrum of irradiance recorded at DKASC during a 1-month period for data sampled at 1-second interval PV output power FFT 13/1/21 to 15/8/211 FFT Intensity Log (Magnitude) = -.72log(f) +.22 Log (Magnitude) = -1.38log(f) Log Power Intensity h peak frequency Log F (log1(hz)) Figure 14 Spectrum of output power from DKASC 196kW PV plant recorded for a duration of 1 months at a 1-second interval 162 Solar intermittency: Australia s clean energy challenge

165 The authors in [77] have undertaken a similar exercise for several PV plants which have different areas and rated power sizes. The cut-off frequencies for PV plants of different sizes were found and a relationship between the cut-off frequency and size of a PV plant was determined by a function given in (3). It was established from the analysis of cut-off frequency values that the main power fluctuation smoothing factor is the plant area rather than its rated power. f = a S c b (3) where f c is the cut-off frequency in Hz, S is the PV plant size in hectares, a =.24 and b = According to (3), the cut-off frequency for the PV plant at the DKASC is.1 Hz (log 1 f = -2.). It should be noted that the DKASC consists of different types of solar panels, some with single- and others with double-axis tracking, with non-uniform ground coverage. The PV plant analysed in [77] involved arrays of the same type with single-axis tracking and a uniform ground coverage. Using the developed model, the power output of an existing or proposed PV plant can now be obtained via simulation for measurements of a given irradiance time series. A graphical user interface (GUI) for the developed model can be seen in Appendix B. Figure 141 shows simulated and actual real power data for two consecutive days, 17 and 18 February 211. The actual power output is shown in green, while the simulated output power is shown in red. Figure 142 shows the actual versus simulated power output over a five-hour period on 17 February 211. The similarity between the actual and predicted output power is clearly observed, indicating that the simulation model developed is valid. 163

166 (a) PV Output Power Actual vs. Predicted 15 Actual PV Output Predicted PV Output PV Output Power (kw) 1 5 7: 9: 11: 13: 15: 17: 19: Date/Time (b) PV Output Power Actual vs. Predicted Actual PV Output Predicted PV Output 15 PV Output Power (kw) 1 5 6: 12: 18: Date/Time Figure 141 Output real power, actual (green line) vs. predicted (red line), for two consecutive days at the DKASC, (a) 18 February, 211, and (b) 17 February, Solar intermittency: Australia s clean energy challenge

167 PV Output Power Actual vs. Predicted Actual PV Output Predicted PV Output 15 PV Output Power (kw) : 12: 13: 14: 15: 16: Date/Time Figure 142 Output real power, actual (green) vs. predicted (red), over a 5-hour period on 17 February, 211 at DKASC Similar work was carried out on two weeks of 5-second data of power output and irradiance from the CSIRO Energy Centre office building rooftop PV system in Newcastle. Figure 143 shows the DFT of the irradiance signal recorded over a two-week period in October 211 at the CSIRO Energy Centre, computed through a FFT algorithm. A peak at 24 hours is again seen in the DFT plot representing the daily solar resource cycle. Similar to the DKASC, the same frequency domain analysis was applied to the 22 kw PV plant output power data during the two-week period. Figure 144 shows the spectra of the power fluctuations using 5-second resolution data. In this case, due to the significantly smaller size of the CSIRO s office building PV system, a roll-off is not observed in the power spectra. The power spectra can be described as one linear region with characteristic slope of -.93 (solid green line), which is the same as that for the irradiance spectra. Therefore, it can be said that a linear conversion model of irradiance to PV power output can be applied for small-scale PV systems like the one at CSIRO to predict their power output. Figure 145 shows the actual and simulated power data for two intermittent days where the power was predicted using a linear conversion model. The actual power output is shown in green while the simulated output power is shown in red. 165

168 6 5 FFT Intensity Log (Magnitude) = -.93log(f) Log F (log1(hz)) Figure 143 Spectrum of irradiance recorded at CSIRO during a 2-week period for data sampled at 5-second interval 4 3 PV Output Power FFT 11/1/211 to 26/1/211 FFT Intensity Log (Magnitude) = -.93log(f) Log Power Intensity Log F (log1(hz)) Figure 144 Spectrum of output power from CSIRO 22kW PV plant recorded for a duration of 2 weeks at a 5-second interval 166 Solar intermittency: Australia s clean energy challenge

169 PV Output Power Actual vs. Predicted PV Output Power Actual vs. Predicted 25 Actual PV Output Predicted PV Output 2 Actual PV Output Predicted PV Output PV Output Power (kw) PV Output Power (kw) : 9: 11: 13: 15: 17: 19: Date/Time Figure 145 Output real power, actual (green line) vs. predicted (red line), for two intermittent days at CSIRO The irradiance-power conversion model developed here is most applicable to large-scale PV systems where the PV plant power output is smoother than the irradiance signal and can be described more appropriately as the signal output of a low-pass filter whose input signal is the incident irradiance. Irradiance and power output data from the 1.22 MW PV plant at The University of Queensland was also obtained with a resolution of 1-minute but was not analysed using this model as the resolution of the data is not high enough for this particular analysis. The model developed in this project can be used to serve several different purposes including: simulation of power fluctuations in any power network with PV plants of various sizes 6: 8: 1: 12: 14: 16: 18: Date/Time determination of energy storage or ancillary services requirement for a PV plant that is yet to be built. Solar irradiance data from the proposed site need to be obtained and used as input to the model for prediction of intermittent generation ramp rates, the likelihood of occurrence and the timescales over which they occur prediction of more accurate solar energy output, with a better estimated measure of intermittency at various timescales, using solar forecast data for potential PV plant sites. The following section of this report includes a discussion of CSIRO s work on predicting solar irradiance with a two-hour forecast window using neural network theory Solar prediction Solar prediction using large numbers of high-quality data streams is hardly a novel task, and the performance of existing studies suggest medium-term 1 predictions are sufficiently accurate for estimating the coarse behaviour of solar panel outputs. More challenging is the development of prediction mechanisms that operate with minimal data streams. This section is an excerpt from CSIRO s work [78], conducted under an Australian Government funded project as part of the Asia-Pacific Partnership on Clean Development and Climate, which addresses this challenging problem, harnessing advanced recurrent neural networks to generate medium-term predictions based only on sampling data available to any standard solar power system. The consequence is a predictive system that requires no additional sensing equipment and, as a consequence, carries reduced cost and complexity for real-world roll-outs A very quick introduction to neural networks Though artificial neural networks have gained much mainstream attention due to claims of simulating human brain activity, their operation is in fact much more straightforward and echoes, at best, only the very simplest components of how brain networks operate. In brief, in an artificial neural network, a collection of input neurons are mapped to a collection of output neurons via some set of intermediary (hidden) neurons. The neural network learns by reinforcing the weights of connections between neurons when outputs match user requirements. So, a rain predictor may have an input neuron 1 Medium-term in this context refers to at least two hours into the future 167

170 that states whether a day is cloudy and an output neuron that says whether the predictor thinks it will rain. Across some set of training examples, the neural network should learn to form a strong connection between the cloudiness of a day and the chance of impending rain. How the network learns such connections ultimately forms the core of most artificial neural networks research. While it is beyond the scope of this report to go into much depth here, note that the majority of successful prediction studies are based around minimising predictive errors across a large number of historical cases and endeavouring to integrate history and context into neural network algorithms. Importantly for the solar prediction domain, once a network is trained on some set of data (noting that training may continue at any point and with new or incoming data), a neural network is incredibly lightweight. In essence, once input data is supplied or calculated, any prediction is simply one single graph traversal to the output neuron. For most neural networks, this process can be performed on barebones systems with a tremendously fast turnaround that, for the solar prediction task described here, should never exceed more than a few seconds Solar prediction with minimal information In life, it is often the case that the more information we have to make decisions, the better those decisions. And so too with artificial neural networks. The challenge in making predictions for a solar-system with minimal communication and sensor requirements is that there is precious little information from which we can draw conclusions. The goal then is to leverage the data that is absolutely available in this case, solar power measurements from pre-existing inverter systems. As expected, forming forward solar predictions based only on the current solar power output is fraught. Even a relatively sophisticated neural network with two hidden layers (see Figure 146) and the effective and contemporary resilient training method fails to produce predictions that are, on average, within 3% of the actual output (using a year s worth of training data with 15 minute granularity and testing with 6 days of unseen data). Clearly, this is insufficient for most any application of the data and particularly in areas where load shedding and battery charging decisions hinge on at least relatively accurate assessments of future on-site generation. Current (W) Hidden Layer Hidden Layer Output Layer Avg. Over Next Two Hours (W) Figure 146 An artificial neural network for predicting future solar power levels. The only input is the current solar power output. 168 Solar intermittency: Australia s clean energy challenge

171 Taking the lead suggested by contemporary prediction research, this report suggests augmenting the network to capitalise upon history and context. If it is too much to expect a neural network to understand a complex weather system from a single snap-shot, perhaps the performance might improve if that snapshot comes with a story. For example, in addition to the current solar power output, the advanced neural network includes the average output produced over the last hour, the total output produced today and the total output produced yesterday. These simple additions, which carry practically no overhead (both with respect to hardware and software requirements), begin to elucidate trends in the weather pattern that the neural network may be able to capitalise upon. Moreover, the trends exist across multiple time-frames from the immediate to longer-term. As a further augmentation, the network also includes a sensible heuristic available to most anyone who is familiar with solar power. Specifically, solar output across a day tends to follow a positive sinusoidal curve which essentially maps the movement of the sun from East to West (see Figure 147). For every time step, the neural network is therefore supplied with a value that indicates where on this sine curve the current measurement was taken. Again, this supplies the neural network with context, providing it with sufficient information to assess whether the solar output is likely to increase or decrease from its current level Solar Output Indicator Morning Noon Evening Time of Day Figure 147 A sine-curve approximation of solar output across a day The augmented advanced neural network is illustrated in Figure 148. After resilient training on a year s worth of solar data, the predictions across 6 days have, on average, improved to being within 18.7% of the true output (with an inter-quartile range between 26.7% and 5.7%). If the network is further augmented to include historical layers that capture decision making data from the previous two time-steps (see Figure 149), the accuracy further improves to 17.7% (with an inter-quartile range of between 25.3% and 5.5%). This final accuracy level is impressive, but is perhaps best captured through illustration. See, for instance, Figure 15, which demonstrates the neural network s predictive power across 3 days. Note that the model only produces one poor prediction, with outputs for remaining days successfully capturing the smooth curves associated with clear skies and the more erratic curves familiar to cloudy days. That the neural network performs so well, despite a lack of data sources, is impressive and suggests that supplementing live solar data with static weather forecasts or historical weather data (which could be supplied to the model without excessive hardware costs) may yield results of very high fidelity indeed. Such further augmentations rest as an exciting topic of future work. 169

172 Current (W) Avg. Last Hour (W) Total Today (Wh ) Total Yesterday (Wh ) Point on Sine Curve Hidden Layer Hidden Layer Output Layer Avg. Over Next Two Hours (W) Figure 148 An advanced artificial neural network for predicting future solar power levels Inputs are: the current solar power output; the average solar power output over the last hour; the total solar output across the day so far; the total solar output yesterday; and an indicative point taken from the sine curve in Figure 147. Current (W) Avg. Last Hour (W) Total Today (Wh ) Total Yesterday (Wh ) Point on Sine Curve Hidden Layer Memory (t - 2) Hidden Layer Memory (t -1) Output Layer Avg. Over Next Two Hours (W) Figure 149 An advanced artificial neural network (with memory) for predicting future solar power levels Inputs include output and decision making memory from the previous two time steps (t-1 and t-2) and the inputs described in Figure Solar intermittency: Australia s clean energy challenge

173 Solar Irradiance (W/m 2 ) Observed Predicted Time Solar Irradiance (W/m 2 ) Observed Predicted Time Solar Irradiance (W/m 2 ) Observed Predicted Time Figure 15 Predicted and observed solar power levels for 3 days of data. Outputs are based on the advanced neural network (with memory). Observed data is taken from real-world measurements of solar irradiance. Providing surety and accuracy in predictions of generation behaviour facilitates methods for more intelligent control of resources. In particular, by understanding the likely future state of the solar plant, more sophisticated choices can be made in the areas of battery operation and load shedding. This section has shown that accurate prediction methods can be constructed for renewable generation with a two-hour forecast window. 171

174 12 Summary of key findings The current state of worldwide research on renewable generation intermittency was summarised in the early sections of this report. One of the main challenges to the power system is associated with the instantaneous penetration of intermittent solar generation. As solar generation is viewed as negative load, when this is combined with the actual system load, the characteristics of the resulting net load which has to be supplied by other generating resources in the system changes. Knowing what kind of variability to expect when high penetration solar power is integrated into the system is important for forecasting and planning purposes. Of concern to utilities is when load and solar power move in opposite directions at the same time, creating large changes in the net load. Various studies have shown that a high penetration of intermittent generation results in greater variability in the net load compared with the variability in the original load alone without solar or wind. There is very little published literature to be found discussing observed impacts of high penetration solar intermittency. The majority of work discovered focused on modelling impacts rather than actual observation of impacts. Studies have shown that adequate system flexibility is a key requirement for managing increased levels of intermittent renewable generation. As a result, conventional generators are forced to be more flexible with their output resulting in a higher per unit cost. A study carried out in Gardner, Massachusetts, shows how rapidly the net load of a system can vary significantly, which is likely to put added pressure on conventional generating resources on the system to vary their output rapidly. A good example of output variations that can be expected from a large-scale PV system can be seen from the output of a 4.6 MW PV system located in Springerville, Arizona, where large abrupt power output drops, from about 4 kw to 5 kw, were seen to occur over extremely short timeframes. The literature indicates that cheaper less flexible plants will need to be replaced with more flexible expensive plants to accommodate high penetrations of solar generation. Otherwise, a significantly larger amount of ancillary services or additional generation would be required to manage PV power output fluctuations. 172 Solar intermittency: Australia s clean energy challenge

175 Some general conclusions that can be drawn from these studies include: The amount of solar generation that can be integrated into the utility power system without compromising grid stability and reliability varies widely. The determining factors are the amount of a utility s load fluctuation and the regulating capability of existing conventional generating units. This observation indicates the effect of solar generation intermittency on the power system is not uniform and is case sensitive. Hence, a general cause-effect conclusion cannot be drawn. Although high penetration levels of solar generation have the potential to cause adverse network impacts, corrective measures such as assigning more generating units to regulating duty or installing fast-response combined-cycle generators are available. These measures can be effective if carefully planned. High penetration limits have been shown to be possible (in simulations) after adding fast-response, combined-cycle generating units to the existing generation mix. Another method is to control the solar generation output under intermittent cloud coverage during periods of peak system demand when the network has fewer generating units on standby and less on-line regulating capacity. These corrective measures, however, may cause the system to deviate from its optimal operating condition, thus adversely affecting the economics of solar generation. It is widely agreed that accurate forecasting is an essential element for the successful integration of large amounts of intermittent solar generation and for solar power to be economically viable. Forecasting at various timescales is required. More accurate day-ahead prediction of renewable resources is required for more accurate unit commitment. Short timescale predictions are also needed (which could be obtained by tracking cloud movements), while numerical weather models can be used to predict insolation out to a number of days. Power output from both solar and wind generating sources are known to vary considerably with varying irradiance and wind speeds respectively. Analysis of the results illustrated in the studies carried out on wind and solar variability indicate that the output of individual wind turbines and individual solar arrays are similarly variable in nature in the second-to-second timeframe. When wind and solar output are taken in aggregate and analysed at 1-minute to 1-minute intervals, wind seems to benefit more from the smoothing effect associated with aggregation, showing less variability than solar. When behaviour is observed at 1-hour intervals, again in aggregate, the opposite is true and wind is shown to be more variable. When considering the variability of PV plants versus CST plants, a significant difference is evident when looking at partly cloudy day output profiles, where the variability of PV plants is far greater than for CST plants. This is primarily because CST plants have more thermal inertia in their underlying equipment which acts as a buffer to avoid immediate drops in the temperature due to passing clouds, thereby contributing to significant reduction in variability. Existing literature was found to contain conflicting outcomes, possibly due to the lack of quality data, and consequently often overemphasised anecdotal evidence. Table 25 lists two examples of conflicting outcomes reported. By looking at the outcomes of the studies that report the comparison of solar and wind power variability, one could deduce that the variability of both solar and wind resources is dependent upon factors such as geographical location, spatial diversity and size of the renewable generation system. Discussions at recent IEA Task 14 workshops indicate there has been substantial growth worldwide in generation from intermittent renewable resources, mainly PV and wind. Countries involved in Task 14 including Germany, Denmark, Spain, Japan and China are currently experiencing a high rate of increase in PV installations, both small- and large-scale systems, and are expecting to see an increase in their intermittent generation capacity by a few hundred percent by 22. The Task 14 representatives acknowledged that an area of considerable challenge they expect to face with increased PV penetration is to do with the variability and predictability of solar power systems. Issues reported by various Task 14 representatives include large abrupt changes in generation output as a result of intermittency, increase in net load variability on a feeder upon installation of a large PV plant, and frequent occurrences of high ramp-rate variations in PV output power. 173

176 Table 25 Conflicting outcomes in existing literature Study outcomes A study by the New York Independent System Operator (NYISO) reported that significant cost savings could be achieved by integrating intermittent generation (wind in this case) due to the displacement of fuel, primarily natural gas, by wind and by having accurate forecasts. Analysis performed in a Western US study found that savings can be achieved from the introduction of solar power through the displacement of gas and coal fired power generation and a price on carbon. Large forecast errors would, however, cause expensive generation to be brought online. Analysis of results in a study carried out by the California Energy Commission on the California Independent System Operator (CAISO) system showed wind to be more variable than solar when both wind and solar power were considered in aggregate at 1-hour intervals. Corresponding conflicting outcomes The POVRY study for Europe reported that the volatility introduced by the highly variable wind and solar output will directly impact on cost. Prices are expected to become peakier and less predictable, representative of the nature of weather systems. Simulations performed in the Texas, US (ERCOT) grid where different mixes of wind and solar power were modelled reported that high penetration of intermittent generation will increase system costs due to the upgrade of conventional generation equipment required to achieve increased system flexibility. It was reported in a Swedish study that the smoothing effect due to aggregation is greater for wind than for solar at the hourly timescale, which conflicts results seen in the CAISO study. In order to investigate the issue of solar intermittency in the Australian context, an industry workshop and online survey were conducted by the project team to understand the issues key solar industry stakeholders are facing due to solar intermittency, and their perspectives on what is needed to remove barriers caused by the intermittent nature of solar. The key findings are: The impacts of intermittency from large-scale and small-scale solar systems on the electricity network need to be studied separately. Large-scale solar systems are often located in remote areas where the environmental conditions are noticeably different to urban areas in Australia where majority of the small-scale solar systems are located. The grid configuration and strength are also different in remote and urban areas; hence the impacts of solar intermittency on the electricity network are likely to be different. The ramp up and down rates of solar, more for PV than CST, are potentially significantly higher than wind due to the lack of inertia. It is important to investigate the ramp rates and time-frames of intermittency in order to determine how quickly energy management systems have to respond. Solar output is more predictable than wind in the very short term as the movement of clouds is visible, but accurate prediction for long-term solar output is required in order to determine ways of effectively compensating for solar intermittency. Accurate solar forecasting with high temporal resolution is required to manage solar intermittency issues. Higher resolution data than is currently available is required to study solar intermittency and its impacts on stability of the grid. High resolution solar data from both large numbers of small-scale solar systems aggregated and large-scale solar systems is required to investigate the effects of various temporal variances on the Australian electricity network. Solar intermittency will likely cause power quality issues which need to be investigated. Sudden shadows by clouds appear to produce more rapid flicker than sudden wind changes. Possible rates of change of power and the performance of the network due to solar intermittency need to be investigated in order to determine the type of ancillary services required and to determine whether existing mechanisms are sufficient for intermittency compensation purposes. Australian solar industry stakeholders have expressed considerable interest in further examining the issues detailed above and are keen to learn more about the likely impacts of solar intermittency on Australian electricity networks with an increasing penetration of both small- and large-scale solar systems. To demonstrate the effect of rapid fluctuations in solar radiation and to understand the variability of solar power plants, ten months of high-resolution 1-second data from the Desert Knowledge Australia Solar Centre (DKASC), Australia, was collected and analysed. The data comprised the total diffused horizontal irradiance, total output power of the PV arrays at the Solar Centre and line-to-neutral AC voltage. To study PV power output ramp rates for a small-scale system and also a 174 Solar intermittency: Australia s clean energy challenge

177 large-scale system in Australia, data was also collected and analysed for a 22 kw PV system at the CSIRO Energy Centre in Newcastle with 5-second resolution and for a 1.22 MW PV system at The University of Queensland (currently Australia s largest flat panel PV system) with 1-minute resolution. Analysis of the data collected has been conducted to evaluate the occurrences of power fluctuations over time for the DKASC. The raw data was processed and occurrences of continuous ramping of the solar plant s output power for periods of ten, twenty, thirty, forty, fifty and sixty seconds were extracted and studied. Analysis of 1-second ramp events resulted in a total of 422 occurrences of variations in PV plant output power of at least 6kW (31% of plant rating) in ten seconds. 7 occurrences of output power drops of between 13kW and 14kW (66% and 71% of plant rating respectively) in just ten seconds were also recorded. A significant number of events where the output power was seen to vary by more than 77% of plant rating over timeframes of twenty, thirty, forty, fifty and sixty seconds were also recorded at the DKASC. The data for line-to-neutral AC voltage measured at the switchboard at the DKASC were also analysed. A positive correlation between the variations in irradiance, output power and voltage was observed. A larger number of ramp-down events with higher ramp rates were observed compared with ramp-up events for this system. Analysis of the data for the 1.22 MW PV system at UQ showed 21 events of output power variations greater than 72 kw (59% of plant rating) in 1-minute periods in the five-month period in which data was collected. Three occurrences of output power drops greater than 9kW (74% of plant rating) in five minutes and another three occurrences of power drops greater than 9kW in six minutes were also recorded. This analysis demonstrates the possible significance of large fluctuations of PV power over very short timeframes to grid operation. In a high penetration scenario, these fluctuations could potentially cause large variations in instantaneous net load causing conventional generators in the power system to vary their output significantly very quickly. This shows the importance for a power system to have greater flexibility in order to accommodate high penetration of intermittent solar generation. A simulation model developed at CSIRO was used to examine the likely impacts of output power fluctuations seen at the DKASC on various types of Australian electricity networks with different penetration levels of solar power. The effects of intermittency on voltage and network stability were modelled and observed for four different scenarios, and are summarised as follows. Note that load variations were not considered in this study. Strong (e.g. urban and relatively large) grid with low penetration solar power (1% PV penetration with.1p.u. lumped grid impedance): This type of grid situation would probably only apply in an urban area close to a substation. Results show voltage variation of only.14v. Weak (e.g. remote and relatively small) grid with low penetration solar power (1% PV penetration with.2p.u. lumped grid impedance): This is to simulate a rural grid under Australian conditions with a low level of PV penetration. The voltage was seen to sag as power from the PV array drops and the range of voltage variation observed was.8v. This variation of voltage would probably go unnoticed by customers. Strong (e.g. urban and relatively large) grid with high penetration of solar power (4% PV penetration with.1p.u. lumped grid impedance): The voltage is seen to change very little, with a range of.35v. This is because the strong grid acts as a buffer for variations in PV generation. Weak (e.g. remote and relatively small) grid with high penetration of solar power (4% PV penetration with.2p.u. lumped grid impedance): This is to simulate a long feeder, typical of Australian conditions. Results show voltage swings of 4V, similar to the actual data recorded from the DKASC array over a 15-minute period. This shows that when solar generation, mainly PV, is attached to a rural feeder where the grid has high impedance, an increase in penetration is likely to cause large voltage swings to be observed. These increased voltage swings may have the potential to impact the stable operation of the network. It is also likely that PV inverters would trip off with these voltage swings, resulting in larger power fluctuations and therefore worsening the voltage swings. 175

178 The larger the PV plant, the longer it takes for a cloud cover spread to shade the entire field. An analytic model has been developed where the PV plant power output is described as the signal output of a first order low pass filter whose input signal is the irradiance signal. The pole value of the filter is a function of the PV plant area. Frequency domain analysis using Discrete Fourier Transform (DFT) was applied to both the irradiance and PV plant output power data recorded during the ten-month period. The cut-off frequency for the PV plant at the DKASC was determined to be.55hz which agreed well with observations made in existing literature. The developed model can be used to predict the power output of an existing or proposed PV plant via simulation for measurements of a given time irradiance time series. This would allow estimation of the probability density function of output power ramp rates which can be used to predict the effects of a particular PV array upon the local network. The actual power output of the PV plant at the DKASC was compared with the power output predicted by the developed model using irradiance signal as its input. The similarity between the actual and predicted output power was clearly observed, indicating the validity of the simulation model developed. 176 Solar intermittency: Australia s clean energy challenge

179 13 Further work required Australia is fortunate to have numerous abundant energy sources, including solar energy. The continent also experiences a collection of unique conditions which, when combined, create the unique environment in which the Australian electricity network operates. The successful integration of high penetration solar power into the Australian electricity network is far from assured. Although some initial investigations have been carried out in other countries, these have been of limited scope and are not necessarily applicable in the Australian context. The network configuration in Australia is different from that of the rest of the world. The National Electricity Market (NEM) covers the entire east coast which is far larger and has a greater diversity in PV generation across the network when compared with areas in the countries where various studies have been carried out. Though widespread, the NEM is also exceptionally sparse by international standards, leading to higher characteristic impedances and consequently greater sensitivity to the behaviour of localised load and generation. Specific areas in need of future work include relevant parties to: Develop evaluation tools for DNSPs to assess the impacts and develop appropriate mitigation responses to cope with increasing levels of PV within the distribution network Reconcile conflicting information from the scientific literature on the impacts of intermittent generation Undertake a large-scale assessment of the characteristics of generation, load and networks in Australia to determine the applicability of international results, and the extent to which the Australian networks do or do not require special consideration consequently, the requirement for intermittency mitigation measures (for example network storage, load management, generation curtailment or additional ancillary services) and the most cost-effective approaches to meeting this, at different penetration levels, can be assessed 177

180 Collect high resolution (temporal and spatial) solar data to support: development of accurate solar forecasting tools, both for long-term planning and short-term network management assessment of different large and small scale PV architectures Perform further modelling and real-world experimental analysis for assessing and managing intermittency: relatively detailed modelling and experimental analysis is required at all levels and timescales (e.g. distribution through to system level, and short through to long timeframes) Maintain industry engagement, as initiated through the intermittency workshop and stakeholder perspectives survey undertaken in this project to ensure: research is relevant and appropriate to the Australian context, including appropriateness to the incumbent systems and regulatory environment a shared vision with greater renewable generation is fostered. In order to better evaluate the likely impacts of solar intermittency on various types of Australian electricity networks, detailed information on the characteristics of the networks is needed, for example impedances of feeders representative of the different types of networks operated by specific utilities. There is a lack of readily accessible quality information available in Australia on grid integration issues. This, however, does not seem to be as much the case in the United States. NREL and Sandia, along with other institutions in the US, have been carrying out many relevant studies and correspondingly publishing many reports which are publicly available. The U.S. Department of Energy (DoE) proactively supports studies and activities in the area and disseminates high quality information to a variety of stakeholders. An example is DoE s SunShot Initiative and the high penetration solar portal [3], the latter containing links to information and also relevant DoE-supported workshops with key stakeholders which address many high solar penetration issues. There is a strong need in Australia for something similar, that: encourages analysis and investigation in this area and provides openly accessible published reports and information brings together key industry players in a dialogue to discuss the issues makes relevant information accessible to all stakeholders (e.g. a portal). This will enable Australian renewable energy stakeholders including industry, research, government and financiers to better understand the issues and opportunities surrounding high penetration solar intermittency, to direct resources more effectively and collaborate more efficiently to overcome the barriers preventing the uptake of high penetration solar power in Australia. 178 Solar intermittency: Australia s clean energy challenge

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185 Appendix A Intermittency workshop program MORNING Monday April 4, 211 1:-1:15am 1:15-1:3am Welcome Mark Amos, ENA Introduction Mark Paterson, CSIRO 1 RENEWABLE RESOURCE CHARACTERISATION 1:3-11:am 1.1 Solar resource variability and uncertainty what do we know? Gavin Street, BP Solar What data do we currently have and with what resolution (time and space)? How does geographical diversity affect variability of renewable generation? Variability comparison of PV, CSP and wind What are expected or typical capacity factors? 11:-11:3am MORNING TEA 11:3-12:noon 1.2 Forecasting renewable resource Kate Summers, Pacific Hydro Forecasts for different time horizons Short-term forecasts by observing clouds. Use of sky imagers to indicate approaching clouds Longer-term forecasts by using numerical weather models to predict solar insolation for multiple days ahead Effect on planning optimal size, positioning, etc 2 NETWORK ISSUES AND CONCERNS 12:-12:3pm 2.1 Distribution level Mark Amos, ENA With current level of solar penetration, what are the current issues and how are they impacting the Australian distribution systems? Impact on total net load at points of supply, has there been an observable increase in load variability in association with solar penetration? Impact on voltage regulation, power quality, etc Likely impacts with integration of solar power on a larger scale from utilities perspective 12:3-1:3pm LUNCH What is the accepted penetration limit of solar/renewables? Can this figure be referenced? 183

186 AFTERNOON Monday April 4, 211 1:3-2:pm 2.2 Transmission level Dr. Zoran Bozic, Western Power Is the intermittent nature of solar power affecting transmission systems with current level of solar penetration? If yes, what are they and how are they impacting the Australian transmission systems and market operation? Impact on power system frequency, voltage regulation, power quality, capacity reserves, etc Likely impacts of solar power integration on a larger scale 3 GRID EVENTS AND VARIABILITY MANAGEMENT 2:-2:3pm 3.1 Variability of solar output due to grid events Max Rankin, SP AusNet Step changes in solar output occurring from simultaneous inverter trips Monitoring of such drop-outs by installers Utility solar case studies 2:3-3:pm 3.2 Managing intermittency Ben Skinner, AEMO Mitigation techniques Energy storage systems Load management possibilities and ways of minimising energy storage needs 3:-3:3pm Capabilities for dispatch AFTERNOON TEA 4 OPEN FORUM INDUSTRY SOLAR INTERMITTENCY ISSUES SOUGHT 3:3-4:3pm Perspectives from: Utilities Power system operators Large-scale renewable system operators Other industry players? 5 OPEN DISCUSSION OF NEXT STEPS 4:3-5:pm 5:pm What happens now? END of Workshop 184 Solar intermittency: Australia s clean energy challenge

187 Appendix B Survey of stakeholder perspectives on solar intermittency in the Australian electricity network 185

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196 Appendix C Solar PV technologies at the DKASC Technology and site Size (kw) Amorphous silicon, Kaneka 6. CdTe thin film, Calyxo 5.4 CdTe thin film, First Solar 6.96 CIGS thin film, Q-Cells 5.61 CIGS thin film, Solco Choice Electric 5.76 Concentrated PV, Solfocus 2 X 8.4 HIT hybrid silicon, Sanyo 6.3 Large scale trackers, DEGERenergie 6 X 5.25 Large scale trackers, ADES Monocrystalline silicon, BP Solar 5.1 Monocrystalline silicon, Q-Cells 5.64 Monocrystalline silicon, Sungrid 5.4 Monocrystalline silicon, SunPower 5.85 Monocrystalline silicon, Trina 5.25 Polycrystalline silicon, BP Solar 4.95 Polycrystalline silicon, Evergreen 4.92 Polycrystalline silicon, Kyocera 5 X 1.8 Polycrystalline silicon, Q-cells 5.64 Polycrystalline silicon, Sungrid 5.4 Roof mounted polycrystalline, BP Solar 4.95 Solar Compass, BP Solar 4 X 1.98 Solar Forest 1 Axis trackers, Kyocera 5 X 1.8 Solar Forest 2 Axis trackers, Kyocera 5 X 1.8 Solar Forest hydraulic trackers, Kyocera 2 X 1.8 Solar Water System, Trunz.756 Upgraded metallurgical grade silicon, Q-cells 5.85 Total PV plant rating kW 194 Solar intermittency: Australia s clean energy challenge

197 Appendix D User interface for power fluctuation simulation model 195

198 CONTACT US t e enquiries@csiro.au w FOR FURTHER INFORMATION CSIRO Energy Transformed Flagship Dr Saad Sayeef t e saad.sayeef@csiro.au w YOUR CSIRO Australia is founding its future on science and innovation. Its national science agency, CSIRO, is a powerhouse of ideas, technologies and skills for building prosperity, growth, health and sustainability. It serves governments, industries, business and communities accross the nation.