EFFECT OF SOLAR PV ON GENERATION DISPATCH IN NEW ZEALAND

Transcription

1 EFFECT OF SOLAR PV ON GENERATION DISPATCH IN NEW ZEALAND DECEMBER 2017 TECHNICAL REPORT

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3 49TTable of Contents Table of Contents EXECUTIVE SUMMARY... VII 1 INTRODUCTION Programme Overview The PV Generation Investigation Project The New Zealand power system Objectives of this study STUDY METHODOLOGIES AND ASSUMPTIONS Study scenario and input data Methodology to investigate generation balancing STUDY RESULTS Ramp up margins Maximum rate of change of net demand Thermal plant utilisation KEY FINDINGS AND CONCLUSIONS Generation balancing Study limitations Market considerations RECOMMENDATIONS System solar PV capacity System planning Longer term factors A1 ADDITIONAL SCENARIO FIGURES A1.1 Autumn A1.2 Winter A1.3 Spring A1.4 Summer A2 METHODOLOGY DETAIL A2.1 Data preparation A2.2 NFR estimation model A2.3 FIR validation check A2.4 CVP values A3 EMERGING ENERGY PROGRAMME: PLAN AND OUTCOME STRATEGY A3.1 Emerging Energy Technologies Outcome Strategy Map GLOSSARY OF TERMS AND ACRONYMS BIBLIOGRAPHY Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. iii

4 Table of Figures Table of Figures Figure 1: Example net national demand profiles for New Zealand with various levels of total solar PV installed... 5 Figure 2: Summer (5 Jan 2016) national net demand, with and without modelled PV generation... 9 Figure 3: Winter (28 Jul 2015) national net demand, with and without modelled PV generation Figure 4: Example of a PV generation profile at a selected site on a clear, sunny day (14 January 2005) Figure 5: Comparison of winter period generation mix for 2013 and Figure 6: Flow diagram for data preparation of sunny-day solar PV GXP generation profiles Figure 7: EPEC normalised Northland PV generation data for a selected five-day interval Figure 8: Comparison of scenario ramp up margins with 4 GW solar PV (vertical bars represent the seasonal time of sunset) Figure 9: Comparison of scenario ramp up margins with 10 GW solar PV (vertical bars represent the seasonal time of sunset) Figure 10: Net system demand for weekday 4 GW solar PV scenarios Figure 11: Comparison of average thermal generation over the evening period by season and day with 4 GW solar PV installed Figure 12: Comparison of average thermal generation over the evening period by season and day with 10 GW solar PV installed Figure 13: Key scenario metrics for evening period (16:00 to 19:30) by season and day with 4 GW solar PV installed Figure 14: Key scenario metrics for evening period (16:00 to 19:30) by season and day with 10 GW solar PV installed Figure 15: Study scenario national PV generation and net demand over evening period for 18 April Figure 16: Scenario ramp up margins and actual system ramping over evening period for 18 April Figure 17: Scenario generation mix and HVDC transfer over evening period for 18 April 2015 with 4 GW solar PV Figure 18: Scenario generation mix and HVDC transfer over evening period for 18 April 2015 with 10 GW solar PV Figure 19: Study scenario national PV generation and net demand over evening period for 19 April Figure 20: Scenario ramp up margins and actual system ramping over evening period for 19 April Figure 21: Scenario generation mix and HVDC transfer over evening period for 19 April 2015 with 4 GW solar PV Figure 22: Scenario generation mix and HVDC transfer over evening period for 19 April 2015 with 10 GW solar PV Figure 23: Study scenario national PV generation and net demand over evening period for 14 April Figure 24: Scenario ramp up margins and actual system ramping over evening period for 14 April Figure 25: Scenario generation mix and HVDC transfer over evening period for 14 April 2015 with 4 GW solar PV Figure 26: Scenario generation mix and HVDC transfer over evening period for 14 April 2015 with 10 GW solar PV Figure 27: Study scenario national PV generation and net demand over evening period for 1 August Figure 28: Scenario ramp up margins and actual system ramping over evening period for 1 August Figure 29: Scenario generation mix and HVDC transfer over evening period for 1 August 2015with 4 GW solar PV Figure 30: Scenario generation mix and HVDC transfer over evening period for 1 August 2015 with 10 GW solar PV Figure 31: Study scenario national PV generation and net demand over evening period for 2 August Figure 32: Scenario ramp up margins and actual system ramping over evening period for 2 August Figure 33: Scenario generation mix and HVDC transfer over evening period for 2 August 2015 with 4 GW solar PV Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. iv

5 Table of Figures Figure 34: Scenario generation mix and HVDC transfer over evening period for 1 August 2015 with 10 GW solar PV Figure 35: Study scenario national PV generation and net demand over evening period for 28 July Figure 36: Scenario ramp up margins and actual system ramping over evening period for 28 July Figure 37: Scenario generation mix and HVDC transfer over evening period for 28 July 2015 with 4 GW solar PV Figure 38: Scenario generation mix and HVDC transfer over evening period for 28 July 2015 with 10 GW solar PV Figure 39: Study scenario national PV generation and net demand over evening period for 31 October Figure 40: Scenario ramp up margins and actual system ramping over evening period for 31 October Figure 41: Scenario generation mix and HVDC transfer over evening period for 31 October 2015 with 4 GW solar PV Figure 42: Scenario generation mix and HVDC transfer over evening period for 31 October 2015 with 10 GW solar PV Figure 43: Study scenario national PV generation and net demand over evening period for 1 November Figure 44: Scenario ramp up margins and actual system ramping over evening period for 1 November Figure 45: Scenario generation mix and HVDC transfer over evening period for 1 November 2015 with 4 GW solar PV Figure 46: Scenario generation mix and HVDC transfer over evening period for 1 November 2015 with 10 GW solar PV Figure 47: Study scenario national PV generation and net demand over evening period for 27 October Figure 48: Scenario ramp up margins and actual system ramping over evening period for 27 October Figure 49: Scenario generation mix and HVDC transfer over evening period for 27 October 2015 with 4 GW solar PV Figure 50: Scenario generation mix and HVDC transfer over evening period for 27 October 2015 with 10 GW solar PV Figure 51: Study scenario national PV generation and net demand over evening period for 9 January Figure 52: Scenario ramp up margins and actual system ramping over evening period for 9 January Figure 53: Scenario generation mix and HVDC transfer over evening period for 9 January 2016 with 4 GW solar PV Figure 54: Scenario generation mix and HVDC transfer over evening period for 9 January 2016 with 10 GW solar PV Figure 55: Study scenario national PV generation and net demand over evening period for 10 January Figure 56: Scenario ramp up margins and actual system ramping over evening period for 10 January Figure 57: Scenario generation mix and HVDC transfer over evening period for 10 January 2016 with 4 GW solar PV Figure 58: Scenario generation mix and HVDC transfer over evening period for 10 January 2016 with 10 GW solar PV Figure 59: Study scenario national PV generation and net demand over evening period for 5 January Figure 60: Scenario ramp up margins and actual system ramping over evening period for 5 January Figure 61: Scenario generation mix and HVDC transfer over evening period for 5 January 2016 with 4 GW solar PV Figure 62: Scenario generation mix and HVDC transfer over evening period for 5 January 2016 with 10 GW solar PV Figure 63: Example of unprocessed TNL data over 4 years for a selected GXP Figure 64: Example of processed TNL data for a selected GXP Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. v

6 Table of Figures Figure 65: Probability distribution function for selected GXP daily peak values Figure 66: NFR values and island load for the SI ACCE FIR NFR type (Jan 2015 May 2016) Figure 67: Resulting normal probability distribution function curves for initial and final island load values Figure 68: Final processed estimation model NFR means and standard deviations by risk class for North Island FIR Figure 69: Final processed estimation model NFR means and standard deviations by risk class for North Island SIR Figure 70: Final processed estimation model NFR means and standard deviations by risk class for South Island FIR Figure 71: Final processed estimation model NFR means and standard deviations by risk class for South Island SIR Figure 72: Linear regressions for DC risks classes, 1 Jan 2015 to 31 March Figure 73: Linear regressions for AC risks classes, 1 Jan 2015 to 31 March Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. vi

7 Executive Summary EXECUTIVE SUMMARY Transpower has initiated a programme of work to investigate the impacts on the power system from an anticipated increase in distributed, non-dispatchable and renewable generation, and from other emerging technologies in New Zealand. The aim of Transpower s Emerging Energy Programme is to identify potential compromise to Transpower s ability to meet the system operator Principal Performance Obligations (PPOs) with the introduction of new generation technologies. The alternative would involve a revision of the PPOs to accommodate these emerging technologies. The first part of the programme features the PV Generation Investigation Project, which studies the effect of PV generation technology on four areas of the power system: generation dispatch, frequency management, transmission voltage management and transient stability. Study reports have been produced for each of these areas, with this report covering the study of supply and demand balancing with high penetration levels of solar PV in the power system. All the project studies used a scenario of 4 GW [1] of solar PV capacity installed. This study revealed no immediate concerns for the ability of the New Zealand power system to balance supply and demand with increasing PV generation. Of particular interest was the ability of the power system to meet changing net demand in the early evening period, as the sun sets ahead of peak evening demand. The system handles foreseeable quantities of solar PV 1, even under conservative assumptions. This implies there is currently a sufficient quantity of fast ramping generating plant in operation in New Zealand to allow for significant solar PV uptake. Hydro generation is critical for power system solar PV limits, as it provides flexible generation that quickly and easily responds to changes in net demand. This study revealed that any issues with dispatching generation to meet demand in New Zealand will typically involve a localised or national lack of access to hydro ramping capacity. It is necessary to monitor and coordinate North Island generation and reserve availability, together with HVDC link availability, as ramping outcomes are sensitive to these factors. This enables the North Island to access the abundant ramping capacity offered by the hydro generation in the South Island. The study also found that while the maximum net demand ramp observed in the system during the early evening period increases considerably with high solar PV penetration, the impact on the energy provided by thermal generation is minor. The evolution of the market operation as a result of increasing solar PV, and the relative shift in the role of thermal generation from baseload to peak time operation, may have long-term consequences on the New Zealand generation fleet. These second order impacts were outside the scope of this study. 1 The high PV penetration scenarios tested were 4 GW and 10 GW total solar PV installed. These scenarios are explained in more detail in section 4.1. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. vii

8 Executive Summary Solar PV only complicates existing seasonal generation capacity constraints, rather than creating entirely new issues. Generation capacity at the winter peak remains the primary driver for system planning in high solar PV penetration scenarios. This is because mitigating issues from a supply standpoint appears to also address any ramp rate related problems. A high solar PV penetration does not appear to change the current seasonal capacity dynamic in the power system, and may only complicate this in rare circumstances. This study was subject to several limitations and lower level system constraints may exist locally or nationally regarding transient, variable day effects and system stability issues. The other studies in the PV Generation Investigation Project address some of these issues. The learnings gained in these studies will be useful in steering the future of the system operator service, electricity market design, industry regulations, policies and procedures for a period of increasingly decentralised supply and responsive consumer technologies. Ultimately, this understanding will facilitate an evolving power system which can continue to meet the changing needs of New Zealanders. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. viii

9 Section 1 Introduction 1 INTRODUCTION 1.1 Programme Overview Transpower's Emerging Energy Programme investigates the potential impacts on the power system resulting from an anticipated increase in distributed, non-dispatchable, renewable generation and other emerging technologies in New Zealand. The programme outlines the strategy Transpower has adopted to develop its capability and business processes to enable a successful integration of such technologies in the New Zealand power system. See Appendix A3 for a scope of work flowchart that summarises Transpower's Emerging Technologies Programme The growth of distributed, non-dispatchable renewable generation Distributed, non-dispatchable electricity generation, primarily PV generation, has grown rapidly in most regions around the world in recent years. The change in technology costs, consumer preferences, policies and environmental concerns, leads to this trend of growth [2]. PV generation uptake is still relatively low in New Zealand. However, the rate of growth is expected to increase for the foreseeable future, with PV generation projected to become a significant part of New Zealand's electricity supply mix. Other emerging technologies (such as energy storage devices, Home Energy Management Systems (HEMS), Electric Vehicles (EVs) and smart appliances) will also play a role in shaping the future of the New Zealand power system. New business models for energy trading and distributed generation ownership will facilitate consumer choice and change the way we produce and use electricity. Though the cumulative effect of these developments is highly interdependent and difficult to predict, the electricity industry will need to be proactive in meeting changing consumer expectations and a shifting market environment, to avoid significant business disruptions Assessing New Zealand's ability to adapt to new technologies The New Zealand power system has some unique features not the least of which is being an islanded system with a high proportion of electricity generated from hydro-power backed by storage. A 2008 study of the system's ability to accommodate wind generation indicated that hydro generation afforded a high degree of flexibility to accommodate intermittent generation. Transpower is assessing the possible future impacts of intermittent generation technologies on the power system and the policies it may need to adopt to continue to meet the PPOs in its role as system operator. These assessments will also provide useful context for the future development of the Electricity Industry Participation Code (the Code), including the PPOs The challenges due to the variability of PV Generation Electricity produced from solar irradiance depends on the position of the sun, which is predictable though variable. With consistently clear or overcast weather, PV generation output can be relatively steady; with output increasing in the early morning and Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 1

10 Section 1 Introduction decreasing during the late afternoon. However, PV generation output can be highly variable with changeable and fast moving cloud. The variability and intermittent effects of PV generation can cause operational issues for grid management. The increase in inverter-based generation in the power system (replacing conventional synchronous generators) can alter the dynamic behaviour of the power system. Inverters are highly programmable making their behaviour less predictable. Furthermore, PV generation will be more distributed compared to the present centralised generation topology. This form of distributed generation presents challenges in studying the dynamic behaviour and real-time operation of the power system. However, the studies are needed in order to understand the effect of PV generation variability and intermittency on the power system, and in forecasting the likely impact on the reliability of the ancillary services. 1.2 The PV Generation Investigation Project Transpower's PV Generation Investigation Project is part of the wider Emerging Energy Programme to ensure a smooth integration of these new technologies in New Zealand. The PV Generation Investigation Project provides studies into PV generation technologies and can be broadly separated into four main areas: generation dispatch, frequency management, transmission voltage management and transient stability. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 2

11 Section 1 Introduction 1.3 The New Zealand power system Overview The New Zealand power system has several features which have the potential to impact the integration of distributed, non-dispatchable generation. The major factor is that ours is an isolated system with a high proportion of electricity generated from renewable sources which can vary in availability; namely hydro and wind generation. It is necessary to understand the impact to New Zealand's security of supply due to additional variable energy sources that are not highly correlated to either hydrology or wind resources. A significant increase in the share of PV generation in the generation mix may require changes to the existing transmission network equipment, operational processes, code and industry standards to: Secure adequate responsive generation (and possibly energy storage) capacity to manage the variable and intermittent nature of non-dispatchable PV generation. Introduce new equipment and operational measures to ensure adequate grid stability and control. Include distributed PV generation forecasting into scheduling processes. Ensure prices reflect economic costs. In reading the study reports produced for the PV Generation Investigation Project it is assumed the reader is familiar with the New Zealand power system, including the following key features: There is good generation mix with approximately 80% of electricity supply from variable renewable sources. There is existing thermal plant. There have been recent thermal plant retirements. There is existing wind penetration. It is a two island system; it is relatively small, with low inertia at times and large generating units present susceptibility to frequency disturbances. There is a mix of generation characteristics - fast ramping hydro, slower ramping thermal, constant geothermal, variable wind, etc. Transpower holds a classification of power system risks. Ancillary services are used to manage frequency - FIR, SIR, IL, FK, AUFLS (see the glossary for details of these) PV generation in New Zealand PV generation uptake in New Zealand has been relatively low. As of December 2017, New Zealand's installed PV generation capacity has grown to a total of 62 MW, with generation from residential, commercial and industrial sites. This growth places total installed PV Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 3

12 Section 1 Introduction generation capacity at a level similar to the smaller, run-of-river hydro stations in New Zealand. However, at typical New Zealand solar capacity factors, this installed generation supplies only around 0.1% of total national energy consumption. This level of PV generation capacity has not compromised our ability to operate the power system securely and economically, with the existing tools and policies. However, the rate of growth is rapid, with a doubling time for installed PV generation capacity of approximately 18 months. PV generation installations are expected to continue to grow, as falling costs and an expanding market drive an increasing pace of PV generation uptake. Integration of high levels of PV generation into the power system will impact the frequency response to system imbalance, for the reasons outlined below: It is distributed and non-dispatchable, and therefore offsets load behind the GXP, with limited system operator visibility. It is highly stochastic, with rapid changes in output possible, depending on the relevant temporal and spatial scales, type of weather, season and level of uptake. The normal PV generation profile is negatively correlated with demand at times of maximum peak PV generation during mid-day, resulting in low system inertia that is susceptible to frequency disturbance. Inverter-based PV generation exhibits different frequency behaviour when subjected to system imbalance compared to conventional synchronous generation Impacts on generation balancing A primary objective for Transpower in managing the power system in high solar PV penetration scenarios is maintaining the balance of supply and demand in real-time under all expected conditions. One of the most obvious challenges is that during daylight hours, the output from PV generation will force larger dispatchable units off the system, with several operational consequences and commercial considerations. A high PV penetration scenario involves a significant requirement for generation ramping due to a higher rate of change in net national demand. The effect will be most pronounced during the early evening period, as demand increases toward the evening peak and solar irradiance decreases (see Figure 1 below). Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 4

13 Section 1 Introduction Figure 1: Example net national demand profiles for New Zealand with various levels of total solar PV installed There must be enough offered, flexible generation (available to dispatch) on the power system capable of changing power output to maintain the balance between supply and demand under a high rate of net demand change. Failure to increase power generation rapidly enough will reduce our ability to manage system frequency and in extreme cases, may result in load shedding or system collapse. In the market context, the availability of sufficient flexible generating capacity requires generators to have commercial incentive to offer generation, subject to their cost structures and fuel availability. This dynamic means significant market changes may occur, with higher and more volatile energy and reserve prices being likely. To determine the New Zealand power system's capacity to accommodate PV generation, available generation ramping capacity must be tested against net demand in possible high solar PV scenarios. Cases that indicate a ramping deficit may represent a physical limitation of the power system to incorporate PV generation. Additionally, this analysis must cover a range of challenging system conditions, to ensure balancing of supply and demand is possible in all realistic circumstances. Here we have suggested generation flexibility is necessary with higher levels of PV penetration. On the demand side, the New Zealand wholesale electricity market currently has a mechanism to dispatch demand, although participation is modest. Ongoing market design initiatives may lead to a greater role for dispatched and autonomous load reduction in complimenting flexible generation as solar PV penetration increases. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 5

14 Section 1 Introduction 1.4 Objectives of this study The primary objective of this study is to investigate whether there is currently enough generation ramping capacity on the power system to meet changes in net demand in high solar PV penetration scenarios. This view will provide some tentative information regarding the impacts of solar PV. Transpower's programme of work prioritised this study due to the following considerations: Irradiance-derived PV generation data initially available exists with a time sampling interval of 10 minutes, which is sufficient for generation balancing (sunny-day) analysis, but less useful for stability (variable day) analysis. For stability analysis, geographic diversity and weather-dependent correlation effects become important. The stability study will require more advanced mathematical techniques and models, not yet developed. An additional advantage to conducting the generation balancing study first is that it provides several key outputs to be either used directly or referenced in later studies. These key outputs include: An analytical justification for assumptions regarding the approximate level of total national solar PV installed employed in subsequent studies Direction regarding the most critical aspects of solar PV impact to consider for further analysis Methodology for disaggregating and apportioning a given total national PV installed capacity down to the level of regions and individual Grid Exit Points (GXPs) Statistical techniques for estimating Net Free Reserve (NFR) values under highly modified power system conditions New Scheduling, Pricing and Dispatch (SPD) based study tools for solar PV modelling and market analysis Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 6

15 Section 2 Study methodologies and assumptions 2 STUDY METHODOLOGIES AND ASSUMPTIONS This study was based on a simulation of real-time dispatch processes using a version of the SPD software (specifically, a modified vspd). This software was the primary tool used in this analysis and is discussed in more detail in subsection The following subsections describe the study assumptions, scenarios, inputs and limitations. This report assumes the reader is familiar with the basic details of SPD and the design of the New Zealand electricity market, including; generation offers, load forecast and the 5- minute dispatch process. 2.1 Study scenario and input data In the operation of the market system, all case files, consisting of SPD solve input and output data, are archived for future reference. For this study, Real Time Pricing (RTP) case files were extracted for twelve selected study days. RTP cases are run in real-time at 5-minute intervals to provide an indication of intratrading period price signals. RTD operates every 5 minutes and is used for operational dispatch purposes. Functionally, these two case types are very similar, with the primary difference being that RTD solves for the following 5 minutes while RTP solves retrospectively for the preceding 5 minutes. The RTP case type chosen for this investigation has a simpler nodal demand allocation, which is more straightforward to modify to represent distributed generation. Due to the design of SPD and vspd software, it is impractical to re-solve for extensive periods of time for study purposes. Therefore, 12 sample study days were selected to provide representation of: All four seasons Examples of different types of daily profile (weekday, Saturday, Sunday) A wide range of demand profiles Different system conditions that can be expected on the power system Table 1 below lists the specific dates selected for the study. Daily profile Summer Autumn Winter Spring Weekday 5 Jan Apr Jul Oct 2015 Saturday 9 Jan Apr Aug Oct 2015 Sunday 10 Jan Apr Aug Nov 2015 Table 1: Selected study days for sunny-day PV investigation Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 7

16 Section 2 Study methodologies and assumptions Each of the RTP archive cases contains all necessary input data to carry out an SPD solve, including nodal demand, generation offers, non-conforming demand bids, a timestamped transmission network model, model overrides and constraints Hydrology and system dispatch This study assumed that hydrology is favourable (i.e. a wet year scenario) and hydro generating capacity was therefore cleared first in the market for both energy and instantaneous reserve, before other types of generation. Assumed offer information reflects this relative merit order for dispatch. Given that hydro generating plant typically offers higher maximum ramp rates, this effectively means that generating plant represented at the lower end of the supply curve is quicker to change output than the generation at the higher end of the curve. This wet year scenario is conservative regarding testing the limits of balancing of supply and demand on the power system. If base-loaded first, hydro generation ramping capacity will be less available to meet any high rate of change in net demand which may occur in high solar PV penetration scenarios. This scenario is where any failure to dispatch generation fast enough to meet changing demand is most likely to occur. In practical terms, this scenario simulated a case in which hydro generation offers were typical of offers during times of very low water values Demand Gross nodal demand remained the same as occurred on each selected study day. This data is recorded in the RTP case files and comes from real-time SCADA 5-minute average values. These values were offset by the nodal PV generation profiles, described in subsection (with additional detail in Appendix A2.1). This produced new net demand profiles representing residual GXP load in a high solar PV penetration scenario. The nodal PV generation offset was based on a disaggregation of an assumed total national solar PV installed capacity to council regions and GXPs. The PV generation offset considered GXP type, demand characteristics and regional solar irradiance data. GXPs are typically situated near population or industrial centres and are located geographically within council regions, as summarised in Table 2. Note that more than one market GXP may be present per physical substation. Region GXP(s) within region Northland 8 Auckland 34 Waikato 52 Bay of Plenty 17 2 Corresponds to a situation where generators are actively avoiding hydro fuel spill. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 8

17 Section 2 Study methodologies and assumptions Region GXP(s) within region Gisborne 0 Hawke s Bay 11 Taranaki 15 Manawatu-Wanganui 24 Wellington 25 Tasman 2 Nelson 1 Marlborough 7 West Coast 15 Canterbury 53 Otago 15 Southland 8 Table 2: Number of GXPs present within each council region An example of this solar PV offset for aggregate national demand in summer is provided in Figure 2 below. Note that the midday demand, and the timing and magnitude of the demand peaks, change with a high penetration of PV generation. This effect varies by season, as shown by the winter example illustrated in Figure 3. Figure 2: Summer (5 Jan 2016) national net demand, with and without modelled PV generation Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 9

18 Section 2 Study methodologies and assumptions Solar irradiance data Figure 3: Winter (28 Jul 2015) national net demand, with and without modelled PV generation This study used cleaned and processed point-source PV generation data 3. The data had the following attributes: A sampling interval of 10 minutes Values provided for each of the 16 council regions Geographical coordinates (longitude and latitude) defining the location of the measurement site used to determine the solar irradiance Time: from 2000 to 2015 inclusive (841, minute values, across 5844 days) Power generation calculated from measured global horizontal solar irradiance values using a time-dependent PV module model (normalised, watts generated/watts installed) Simulations assumed PV modules are installed with a 0-degree azimuth (north facing) and 30-degree tilt and that the PV module performance is typical of those currently available on the market A total of 71 National Institute of Water and Atmospheric Research (NIWA) sites with reliable irradiance data were used in the final calculation of the 16 representative regional locations. The meteorological stations equipped with pyranometers for irradiance measurements are typically located at regional airports or on council buildings. The study assumed clear, sunny conditions over all 16 regions, with each solar PV installation following an approximately logistic generation profile (as illustrated in Figure 3 Data processed and supplied by the Electric Power Engineering Centre (EPECentre), University of Canterbury. An estimation technique was used for the Bay of Plenty region, due to insufficient local data quality. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 10

19 Section 2 Study methodologies and assumptions 4). This was a conservative, worst-case assumption regarding system capacity for PV generation, as it results in the highest possible maximum rate of change in national net demand. Figure 4: Example of a PV generation profile at a selected site on a clear, sunny day (14 January 2005) It was assumed that this sunny-day scenario challenges generation balancing more than during midday system ramping on mixed-weather, variable days. The reason for this is the correlation in PV generation between various sites and regions on these days will be relatively low, resulting in the national net demand rate of change remaining modest. Also, high PV penetration scenarios will result in a significant amount of unused generating plant during daylight hours, which will be available to respond to rapid changes in net demand. Conversely, the evening ramp up towards the peak will see a high rate of change in net demand, coincident with reduced dispatchable generation availability. Variable weather is likely to affect system stability more than dispatch and generation ramping. This is covered in the other studies under the PV Generation Investigation Project. See subsection and Appendix A2.1 for more details of the data preparation process used to create the sunny-day scenarios Generation The generation mix can change significantly in the New Zealand power system between different periods. These changes are largely dependent on hydrology and the availability, Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 11

20 Section 2 Study methodologies and assumptions or relative value, of various generation types. As can be seen in Figure 5 below, significantly more thermal generation can be required in a dry year (2013) to meet demand compared to a wet year (2014). In this case, there was a 37% reduction in thermal generation needed during the winter period, from one year to the next. Figure 5: Comparison of winter period generation mix for 2013 and 2014 For the purpose of studying generation balancing in a high solar PV penetration scenario, generation inputs were modified to represent a conservative situation, as outlined below Hydro Hydro offers were modified such that this generation type was dispatched first, leaving it less available to respond to marginal changes in demand, as explained in subsection All existing market-offered hydro plants in both islands were considered available in the study, subject to transmission system capacity and outages. Treatment of hydro generation is critical in the determination of system solar PV limits, as it is the generation type that can quickly and easily respond to changes in net demand to balance the power system. Any issues with dispatching generation to meet demand in New Zealand will typically involve a localised or national lack of access to hydro ramping capacity at a particular point in time Thermal SPD solves do not account for the significant start-up and shutdown costs that are typical with large generating units. Consequently, dispatched running times throughout the day indicated by initial RTP solve sequences were checked for all thermal generators to avoid unrealistic outcomes. This was done for the following generator types when offered in Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 12

21 Section 2 Study methodologies and assumptions the market: closed-cycle gas turbines, open-cycle gas turbines, coal-fired units and diesel units offered. The Otahuhu B and Southdown stations are not included in the study. These stations were closed permanently in 2015, which reduced the amount of ramping capacity available. Resulting run time durations were evaluated regarding likely participant behaviour, leading to the contingent thermal generation availability assumptions. These are explained in subsection The justification for this adjustment is that market participants are unlikely to offer thermal generating capacity if forecast schedules indicate run times are insufficient to recoup operating costs incurred by starting up and shutting down. In practice, the assumption that one large closed-cycle gas turbine (CCGT) and one of the remaining Huntly Rankine units are both unavailable provides a suitable compromise between full and limited thermal plant availability. This assumption was applied to all study days. Even when thermal plant is offered, the SPD solve may not always clear these units. This can occur in situations where modelled switches between the units and the grid are seen as open, due to transmission or asset owner maintenance. In this study, it was impractical to ensure all thermal plant can be dispatched in every solve interval by manually revising the state of the network models represented in the RTP input case files. This is conservative, as it means ramping capacity that was physically available is occasionally left unused Other existing generation All other generation offers were left unchanged from the original RTP case inputs. These generation types are either typically invariant (geothermal), reflect weather conditions (wind), or coincide with industrial production schedules on the relevant study day (cogeneration). Changes to these generation types are not relevant to the PV Generation Investigation Project Generator ramp rates Ramp rates for generating plant were not altered from the initially offered ramp rates on the selected study days (these offered ramp rates are invariant in most cases). For example, a thermal generating unit may be able to increase power output by a limited fraction of its maximum rating per 5-minute dispatch interval, while a hydro unit may be practically unlimited (able to reach full output within a 5-minute period). In all cases, SPD uses a linear ramping function. The ramp rates of primary importance in the study were confirmed by the relevant asset owners, regarding their relevance for this study. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 13

22 Section 2 Study methodologies and assumptions Instantaneous Reserves Daytime synchronous generation in high solar PV penetration scenarios will be significantly lower than the current situation, due to a reduction in net demand. Instantaneous reserve requirements will change as less generation is dispatched, lowering power system inertia. This effect is partially balanced by possible reduction in large generator (risk) output that needs to be covered by instantaneous reserves. Net Free Reserve (NFR) values are very sensitive to system load and dispatched generation, so any significant change to these input conditions will invalidate the original values in the archive cases. Therefore, there is a need to adjust the NFR inputs for all selected study periods. NFR values are used to determine instantaneous reserves required to ensure the power system remains stable following an under-frequency event. NFR values are equivalent to the difference between the risk value and the amount of reserve procured for each risk class, in each trading period. In total, there are 16 risk classes based on all possible combinations of NI/SI, CE/ECE, AC/DC and FIR/SIR. The actual market system uses an iterative process to evaluate system inertia and the corresponding instantaneous reserve required. This process uses the Reserve Management Transient Stability Analysis Tool (RMTSAT). System inertia is represented by NFR values for each island, risk class and reserve type. This iterative process cannot be easily replicated at the present time in offline study tools, therefore a statistical model must be employed to estimate NFR values for the study. See Appendix A2.2 for technical details of the NFR estimation model used in this study. Instantaneous reserve offers assumed for hydro generating plant were modified to be consistent with the hydro generation energy offers. The availability of partially-loaded spinning reserve (PLSR) is also affected by the merit order of energy offers and PLSR constraints Other assumptions This study utilized other key assumptions, as explained in this section. In all cases, these represented either a speculative deduction based on current information in the case of unknown factors, or where multiple states are possible the assumption was generally conservative regarding generation balancing. Explicit assumptions included the following: Small residential and commercial roof-top solar PV systems will comprise the majority of installed solar PV capacity. As solar PV uptake proceeds, installed watts per capita will converge towards similar values within each council region, rather than continuing the highly localised patterns observed to date. Remaining differences in installed watts per capita will be largely due to differences in the regional solar resource. Socio-economic factors affecting solar PV uptake were not considered in this study. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 14

23 Section 2 Study methodologies and assumptions Solar PV is embedded entirely within distribution networks; no installed capacity will be large-scale and grid connected. Solar PV installations are homogeneously distributed in proximity to conforming GXPs. In this way, spatial diversity does not significantly affect the shape of the aggregate nodal generation profile. All PV generation is equivalent to negative load, offsetting the demand profile directly (none is stored for later use). PV generation is non-dispatchable, distributed, and not under any form of control by the system operator. No significant low-voltage distribution network congestion occurs as a result of high levels of PV generation, and therefore no curtailment (due to inverter voltage response) of installed solar PV generating capacity is expected at or near the solar irradiance peak. Where total aggregate PV generation exceeds gross national demand (net demand is negative), excess generation is curtailed via inverter frequency response, such that supply remains equal to demand. The 10 GW solar PV penetration was introduced to study the system limit. However, this scenario should not be considered a meaningful scenario in physical terms. See subsection 4.1 for more details. This investigation was purely a generation balancing and dispatch study. Detailed AC power system simulation is not necessary for this purpose. In practice, battery storage systems and other forms of energy storage have the potential to mitigate high solar PV penetration by shifting generation and flattening the demand curve. However, energy storage is currently at very low levels in New Zealand. This may change in the future, but the uptake of energy storage and its effect on the power system remains uncertain and will be covered in later studies. For this study, the assumption that no energy storage was present implied a worst-case scenario for generation balancing. 2.2 Methodology to investigate generation balancing This section introduces the process used to create PV generation profiles to be used in the study, and the software used to carry out the analysis. For technical details, refer to Appendix A Data preparation This section covers the data preparation carried out to develop GXP level PV generation profiles used in the generation balancing investigation. These profiles are necessary to simulate the effect of solar PV on net demand and investigate the potential high-level impacts on the New Zealand power system. A flow chart of the data preparation components and processes is presented in Figure 6 and explained further in the following subsections. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 15

24 Section 2 Study methodologies and assumptions Figure 6: Flow diagram for data preparation of sunny-day solar PV GXP generation profiles Normalised regional PV generation profiles The normalised PV generation profiles for each council region, introduced in subsection (generated watts per installed watt) were used to produce the sunny-day PV generation profile for each GXP. An example of the raw generation data for a selected site and time period is shown in Figure 7 below. Note that the normalised profiles do not reach a value of 1 over the five-day period. That is, the local irradiance is not typically sufficient for the output of a conventional solar PV panel to reach its maximum rated output. Figure 7: EPEC normalised Northland PV generation data for a selected five-day interval This data is the primary input to the final GXP level profiles. However, adequate management of certain features of the data is required, as follows: The data cover all seasons and a wide range of weather types. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 16

25 Section 2 Study methodologies and assumptions The regional profiles were normalised, so they did not reflect likely differences in the quantity of total installed solar PV capacity between regions. The regional profiles were not mapped to specific GXPs within the relevant council region in any way. The study required final sunny-day profiles valid for each of the four seasons, due to annual seasonal variance in solar irradiance (time of sunrise and sunset, and level of insolation). RRRRRRRRRRRRRRRR PPPP gggggg xx,zz (tt) Equation 1: Regional PV genx,z(t): the regional normalised sunny-day PV generation profile calculated for each region (x) and for each season (z). To develop the above representative sunny-day GXP profiles for each season, further processing of the raw generation data was required. This is described in Appendix A Regional distribution factors An assumed total national solar PV capacity installed (the primary study decision variable, as explained in subsection 2.2.3) is disaggregated to the level of council regions using static regional distribution factors. The justification for static factors is that they represent the proportion of total physical installed generating plant located within each region, and therefore should not vary by season. Regional population and dwellings data from the Statistics New Zealand 2015 census data [3] was used to calculate the regional distribution of solar PV using the following equation. The equation reflects an assumption that over time, installed solar PV watts per capita will converge and will be distributed proportionately to the population. RRRRRRRRRRRRRRRR PPPP xx = RRRRRRRRRRRRRRRR pppppppppppppppppppp xx 1 16 RRRRRRRRRRRRRRRR pppppppppppppppppppp xx Equation 2: NNNNNNNNNNNNNNNN PPPP Regional PV: the regional factor giving the proportion of total national solar PV capacity installed in each region (x). Regional population: the number of people normally resident in each council region (x). National PV: the assumed total national solar PV capacity installed GXP distribution factors The regional PV generation profiles were further disaggregated to GXPs lying within each council region s geographic boundaries. This allowed the detailed modification of GXP level demand for study purposes. To relate regional PV generation profiles to GXPs an approximate model to estimate installed capacity at GXPs within each region was developed. See Appendix A2.1 for details. Static factors were used for GXP distribution in a similar way to the regional distribution factors. Static factors represent the proportion of total physical installed generating plant Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 17

26 Section 2 Study methodologies and assumptions within each region. Being located in proximity (electrically) to each GXP within the region, they would not be expected to vary by season. Installed capacity was assumed to be approximately proportional to peak demand as measured at the transmission substation. This enabled the allocation of the regional solar PV generating capacity to GXPs within each region. Peak demand was used as a proxy for potential PV installations; indicating more buildings, residents and businesses. GXP gross demand data, consisting of the MV90 metering data set 4 with wind generation and identified embedded generation added back on, was used for this purpose. The peak value is the best available proxy for solar PV potential, because: Residential customers contribute to demand to a greater extent during peak demand periods, and are more likely to install solar PV systems (based on current trends). Commercial and industrial sites, with space for solar PV, will still be conservatively represented at their base load levels during the peak. The GXP distribution factors were calculated using the following equation, for n GXPs within each council region y GXP PV generation profiles GGGGGG dddddddddddddddddddddddd ffffffffffff yy = GGGGGG aaaaaaaaaaaaaa pppppppp dddddddddddd yy nn 1 GGGGGG aaaaaaaaaaaaaa ppeeaaaa dddddddddddd yy Equation 3: GXP distribution Factor The final GXP PV generation profiles, scaled by assumed total national solar PV capacity, allowed the derivation of net GXP demand with PV generation included for any selected study day. GXP PV generation profiles were subtracted from observed demand by GXP to represent the residual demand expected in a high PV penetration scenario. The product of the derived GXP distribution factors and the regional PV generation profiles provided the GXP PV generation profiles. This is expressed in the equation below, where GXP y lies within region x. GGGGGG PPPP gggggg yy,zz (tt) = GGGGGG dddddddddddddddddddddddd ffffffffffff yy RRRRRRRRRRRRRRRR PPPP xx RReegggggggggggg PPPP gggggg xx,zz (tt) Equation 4: GXP PV gen y,z(t): the final GXP level PV generation profile for each GXP (y) in season (z) RTP case preparation GXP nodal demand was modified for each 5-minute period in all case files, as per Equation 5. This modification determined the derived sunny-day residual load profile at each GXP y (using the calculated regional and GXP distribution factors). The calculation was valid for the relevant season z and the assumed total national solar PV installed. GGGGGG nnnnnn dddddddddddd yy,zz (tt) = GGGGGG oooooooooooooooo dddddddddddd yy,zz (tt) GGGGGG PPPP gggggg yy.zz (tt) Equation 5: GXP nodal demand modification determines the derived sunny-day residual load profile at each GXP y 4 Revenue metered 30-minute demand averages used for market pricing, reconciliation and settlement. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 18

27 Section 2 Study methodologies and assumptions Aside from this demand adjustment, and the input data detailed in subsection 2.1, casespecific data for the selected study days (e.g. pertaining to wind generation, interruptible load offer quantities and transmission outages), was retained as it was on the study day in question vspd software Optimisation of generation dispatch and the balancing of supply and demand are performed in the market system, using SPD software. SPD is a linear optimisation programme. SPD obtains the least cost dispatch solution for each interval, subject to the transmission network configuration, offered generation capacity, ancillary service requirements and numerous other inputs. For this investigation, the market system real-time processes were simulated using an SPD variant developed specifically for offline study. This is a modification of vectorised SPD (vspd), a publicly available SPD version used for ex-post market analysis purposes using the General Algebraic Modelling System (GAMS) programming language. The modified version 5 sequentially solves for 5-minute intervals, with the dispatch solution from one interval supplying the initial conditions for the next. This process replicates the 5-minute dispatch process used in real-time. In this way, the behaviour of the power system can be analysed over all selected study periods with varying levels of total national solar PV capacity installed. The modified vspd version has the following features: Accepts RTP case types as inputs Offsets nodal demand values using specified GXP level PV generation profiles and an input total national solar PV installed Iterates solve sequences with increasing total national solar PV installed, consistent with pre-set search parameters, until a ramp rate deficit infeasibility is encountered (see below) Ignores ramp rates for the first 5-minute interval allowing generators and the HVDC to start from an unconstrained position, at their initial dispatch Uses offer overrides to represent wet year (cheap hydro) scenarios, as explained in subsection Uses modified Constraint Violation Penalty (CVP) values to achieve the correct sequence of outcomes for the study (a ramp rate deficit occurs first) - see Appendix A2.4 for the specific values used It is possible to use this software to determine system operation limits to the amount of PV generation than can be incorporated in the New Zealand power system. Solves were carried out for each selected study day, increasing total national solar PV installed (introduced in subsection ) until ramp rates became insufficient to meet a change in net demand within any 5-minute period. If the power system ramping ability was 5 Developed for Transpower by Scientia Consulting Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 19

28 Section 2 Study methodologies and assumptions incapable of matching the change in demand, the final feasible PV level was then taken as the power system capacity for PV generation. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 20

29 Section 3 Study Results 3 STUDY RESULTS The most notable result of the study was that no clear limitations to generation balancing under high solar PV penetration scenarios were identified via a ramp rate deficit. For this reason, results for all study scenarios were presented at two levels of total national solar PV installed; 4 GW and 10 GW. 10 GW is not considered a realistic amount of solar PV installed but was included as a stress test for dispatch in order to assess a very steep rate of change in net demand (discussed further in subsection 4.1. Several observations and potential issues are discussed in section 4. These considerations highlight aspects that require monitoring or further investigation, which will support power system planning and unearth longer-term issues facing the industry. All results referred to the early evening period (16:00 to 19:30). This is when the maximum rate of change of national net demand occurs in all seasons, and is the period of the day where the power system would be placed under the most stress by increased PV generation. Detailed scenario plots for all study days are included in Appendix A Ramp up margins Definition The ramp up margins 6 are the primary outputs used to determine how close the power system is to exhausting available generation ramping capacity for a given level of national solar PV installed. Put another way, the values represent the additional increment of ramping capacity available that may be required for dispatch in the interval. If either island ramp up margin falls to zero, this would coincide with a ramp rate deficit infeasibility occurring in the SPD interval solve. The ramp up margin shown in this section is for the North Island only, as explained in the following paragraph: Where both ramp up margins are reported, they are labelled separately. Note: Under normal circumstances, both North Island and South Island ramping capacity is available to meet an incremental increase in demand in either island, with HVDC transfer adjusting as necessary. The exception to this is when the HVDC link is at its maximum operational rating or is limited by the availability of instantaneous reserves in the receiving island. When this occurs, an incremental increase in demand in the receiving island can only be met by the available generation (and ramp up margin) in the same island. This scenario typically happens in the North Island during periods of high HVDC transfer (and would also be a risk during a bipole outage). 6 The ramp up margins are equal to the sum of uncleared offered generating capacity, available to each island, with each injection node quantity capped at the difference between the dispatch change in the current interval and the maximum offered 5-minute ramp up. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 21

30 Section 3 Study Results The magnitude of the ramp up margin can be benchmarked against generating unit capacity, either in terms of ramp rate or maximum continuous output (MCO) rating. See Table 3 for examples. For example, a ramp up margin of 30 MW would indicate that the unexpected loss of an offered OCGT unit could result in an inability for the system to ramp fast enough to meet changing demand. Unit type Ramp up max (MW/5 min) MCO rating (MW) Rankine CCGT OCGT Table 3: Example thermal unit ramp rates and MCO ratings Analysis No ramp rate deficit infeasibilities were observed in any season, regardless of the level of total national solar PV installed (up to 10 GW). Figure 8 shows that at the level of 4 GW solar PV installed, no ramp up margins drop to zero before the respective seasonal time of sunset (indicated by vertical bars). It is only system behaviour before the seasonal sunset time that is relevant when considering the impacts of PV generation. Figure 8: Comparison of scenario ramp up margins with 4 GW solar PV (vertical bars represent the seasonal time of sunset) Figure 9 shows that with 10 GW solar PV, the winter weekday ramp up margin does briefly fall to zero. As discussed in subsection 4.1.1, this occurs for reasons unrelated to PV generation and does coincide with a ramp rate deficit. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 22

31 Section 3 Study Results Figure 9: Comparison of scenario ramp up margins with 10 GW solar PV (vertical bars represent the seasonal time of sunset) North Island ramp up margin minima over the evening period is given for all study cases in Table 4. As expected, in all cases winter and autumn are much worse in terms of ramp up margins than spring and summer. Also, each season s weekday scenario is always worse than either the Saturday or Sunday from the same season. (MW/5 min) Autumn Spring Summer Winter 4 GW Saturday Sunday Weekday GW Saturday Sunday Weekday Table 4: Scenario results for minimum North Island ramp up margin over evening period (16:00 19:30) Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 23

32 Section 3 Study Results 3.2 Maximum rate of change of net demand Definition The maximum rate of change of national net demand (the steepest gradient of the demand curve) is a direct measure of the system s requirement for generation ramping capacity. This metric can be used to compare scenarios. However, the ability to meet this rate of change will depend not only on the maximum rate of change, but also on the level of demand at which it occurs. For example, an increase in demand of 300 MW in a given 5-minute interval may be possible to meet when it occurs at of 4000 MW demand, but not when it occurs at 6000 MW. This is because more generating plant will be fully dispatched in the latter case Analysis Figure 10 below shows significantly steeper weekday net demand curves in autumn and winter than in spring and summer for the 4 GW solar PV scenarios. PV generation varies by season, with lowest output in winter. However, the gradient of the net demand curve is determined primarily by the magnitude of the evening demand peak, which occurs after sunset. This observation agrees with the observed ramp up margin minima; ramping issues are much more pronounced in seasons with higher evening demand. Figure 10: Net system demand for weekday 4 GW solar PV scenarios Table 5 compares net demand rate of change maxima over the evening period for the 4 GW and 10 GW solar PV scenarios. In most cases the observed maxima are approximately doubled, as greater daytime PV generation offsets more demand and creates a greater rate of change leading up to the system peak. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 24

33 Section 3 Study Results (MW/5 min) Autumn Spring Summer Winter 4 GW Saturday Sunday Weekday GW Saturday Sunday Weekday Table 5: Scenario results for maximum rate of change of net demand over evening period (16:00 19:30) 3.3 Thermal plant utilisation Definition Another measure of system stress is the quantity of thermal generation required over the evening period to meet both the rate of change and magnitude of demand. In a wet year scenario, as assumed in this study, thermal generation essentially acts as peaking capacity. This measure is less direct than the maximum rate of change of net demand, but does provide an indication of the physical consequence in a particular scenario Analysis After considering the initial solve results, full availability of thermal generation was found to be unrealistic. This is because thermal units were dispatched on for very short periods over the morning and evening demand peaks. The short dispatch periods become more pronounced with higher solar PV penetration, as net daytime demand reduces. For this reason, subsequent solves used a contingent assumption; modelling one of the remaining Huntly Rankine units and one of the remaining large CCGT as unavailable. Figure 11 and Figure 12 below demonstrate that spring and summer did not require significant thermal generation, even with high solar PV. Detailed generation mix graphs by time for all study scenarios are given in Appendix A1. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 25

34 Section 3 Study Results Figure 11: Comparison of average thermal generation over the evening period by season and day with 4 GW solar PV installed Figure 12: Comparison of average thermal generation over the evening period by season and day with 10 GW solar PV installed With high solar PV penetration, thermal generation over the evening period remained significantly higher in winter and autumn than in spring and summer (this is the same as the current low solar PV penetration). This again confirmed that evening peak demand is the key driver of system generation capacity requirements. Note: The reason autumn showed a higher total thermal generation than winter was due to a Huntly Rankine unit being available for dispatch in autumn. The unit was not dispatched in winter due to differences in the network model making Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 26

35 Section 3 Study Results the unit effectively unavailable. This discrepancy is not important to the overall study and can be disregarded. Table 6 shows that increasing installed solar PV from 4 GW to 10 GW does not substantially change the quantity of thermal generation supplied over the evening period. This implies that the existing thermal generation meets the approximately doubled rate of change of net demand, shown in subsection 3.2.2, while producing around the same amount of energy in total. The implications of this change are discussed in subsection 4.3. (MWh) Autumn Spring Summer Winter 4 GW Saturday Sunday Weekday GW Saturday Sunday Weekday Table 6: Scenario results for thermal generation over evening period (16:00 19:30) Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 27

36 Section 4 Key findings and conclusions 4 KEY FINDINGS AND CONCLUSIONS 4.1 Generation balancing The 4 GW solar PV capacity installed scenario represents a significant amount of solar PV capacity installed. With current population, this would equate to almost 900 watts installed per person. For reference, Germany, the world leader in solar PV, currently has a total of GW installed [4], or around 500 watts per person. Considering this, and the fact that New Zealand has a greater solar resource than Germany [5], an eventual total of 4 GW is feasible if installed capacity continues to grow rapidly. 10 GW solar PV capacity installed is far more than the amount expected in New Zealand [6]. This capacity is likely higher than can be accommodated by available rooftop space and would almost certainly not be economically efficient unless solar PV technology becomes very cheap. In addition, the power output produced would exceed gross national demand for extended periods of time on sunny days 7. The 10 GW scenario is included in the analysis as a virtual stress test for rate of change related issues, and should not be considered a physically meaningful scenario. The 4 GW and 10 GW solar PV scenarios differ in some ways but showed some important similarities. As expected, and shown in Figure 13, the seasons with higher demand (winter and autumn) showed greater system stress than those with lower demand on all three key metrics. Comparing this with Figure 14 shows the same seasonal dynamic, but also reveals that the increase in solar PV capacity installed from 4 GW to 10 GW substantially increases the maximum demand ramp while having minimal impact on thermal generation or ramp up margin. The study indicated that the maximum demand ramp is inversely proportional to the resulting minimum ramp up margin. This makes sense, as a greater demand for generation ramping capacity will consequently leave less in reserve. The quantity of thermal generation is less sensitive to the rate of change and depends more on seasonal trends in demand magnitude. Refer to Table 2 for a benchmark which can be used to situate the minimum ramp up margin values in context. 7 In practice, where total aggregate solar PV generation exceeds gross national demand (net demand is negative), generation would be curtailed via inverter frequency response, such that supply remains equal to demand. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 28

37 Section 4 Key findings and conclusions Figure 13: Key scenario metrics for evening period (16:00 to 19:30) by season and day with 4 GW solar PV installed Figure 14: Key scenario metrics for evening period (16:00 to 19:30) by season and day with 10 GW solar PV installed Compounding of existing seasonal capacity issues Although ramp rate deficits did not occur in the standard scenarios considered, deficits do occur in winter and autumn when additional North Island generation or IL offers are excluded (exploratory analysis - results not shown). However, this is always coincident with supply shortfalls at the demand peak. Ramp rate deficits are observed at the demand peak (after sunset) more frequently than during the ramp up period. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 29

38 Section 4 Key findings and conclusions The winter weekday 10 GW scenario was the only case where a zero ramp up margin was observed. This occurred immediately before sunset and did not coincide with a ramp rate deficit infeasibility, indicating that the zero was due to an overall supply shortfall rather than a lack of ramping capacity to meet the rate of change in net demand. Another observation supporting this conclusion was in the scenario where the zero ramp up margin occurred at 17:25 while the maximum demand ramp occurred 20 minutes earlier (see Figure 35). These observations support the conclusion that solar PV only complicates existing seasonal generation capacity constraints, rather than creating entirely new issues. That is, a supply shortfall at the time of peak demand occurs more often and always before a ramping deficit situation leading up to the peak. This is the case under a wide range of additional scenarios including; increased North Island demand, reduced thermal generation availability, reduced hydro generation availability, sudden reductions in wind output, and so on. Consequently, mitigating a supply shortfall by making more generation available will also address any ramp deficit issues. This means peak capacity remains the more pressing system planning consideration, rather than solar PV induced ramping concerns. It is likely that supply shortfalls during the winter peak will be the primary consequence of limited generation availability, rather than available ramping capacity leading up to the peak. A high solar PV penetration does not change this. Ultimately, it is apparent that under current conditions and provided enough generation is available to meet the evening demand peak, the power system has no clear ramping capacity limit to increasing PV generation. It is beyond the scope of this report to discuss whether generators can continue to offer all existing plant, considering the commercial changes that solar PV will introduce. For instance, it is not possible to predict the operational costs imposed by changes in the role of thermal generation. This is discussed briefly in subsection Sensitivity to input factors The North Island ramp up margin was shown to be sensitive to North Island generation availability and IL offers. South Island generation availability is less important for ramp up margins. Wind generation is considered a minor factor for system ramping outcomes as it does not change rapidly enough to affect the outcome significantly, and is not correlated with solar PV output. The daily demand profile and season did have an impact on the system ramping outcome. Weekdays tended to present lower ramp up margins than either Saturday or Sunday in the same season, due to higher peak evening load causing less ramping capacity to be available. The seasons showed a similar demand profile in which ramp up margins in autumn and winter were significantly worse than spring or summer, primarily due to higher demand. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 30

39 Section 4 Key findings and conclusions Autumn demand was not as high as in winter but had greater sunny-day PV generation output. Consequently, the resulting maximum rates of change in net demand for autumn and winter were similar, and ramp up margins fell to the same levels (see Figure 8 and Figure 9). The HVDC link is important to ramping outcomes in high solar PV penetration scenarios. The North Island ramp up margin can drop suddenly when the HVDC link becomes limited by a lack of North Island instantaneous reserves (revealed by spikes in reserve prices). Ramp rate deficits are much more likely when the transfer is limited, as the HVDC link cannot respond in the northward direction causing the island ramp up margins to decouple. This means the fast-ramping South Island generating capacity cannot be accessed to meet an incremental increase in demand in the North Island. The availability of South Island ramping capacity to the North Island can be quite discontinuous between one interval and the next. This is because the HVDC can more easily become limited when dispatch is near the top of the reserve offer stack, which is where there are typically large price jumps between adjacent offer tranches. Therefore, the result is highly sensitive to small changes in demand. In effect, as the availability of ramping capacity becomes more critical, high solar PV penetration increases the reliance of the power system on the operation of the HVDC link. 4.2 Study limitations The results of this study should be considered in the context of the limitations present in the methodology. These limitations generally relate to the SPD formulation as a linear programme, assumptions made in the study, and real-world conditions that cannot be adequately factored into the analysis. While an exhaustive list is not possible, some of these limitations are as follows: Linear ramping of generating plant in the SPD solves was bounded by offered ramp rates. There were no starting or warm-up times before ramping generation responded instantly. Thermal generating plant minimum operating levels were not considered. There was no governor response, causing some fast ramping plant to compensate for the slow action of others. All PV generation output materialised at grid level; that is, there was no curtailment due to power quality issues or low-voltage feeder congestion, or loss of output due to localised shading of PV panels. All PV generation was treated as if located at the NIWA measurement site used for each region s solar profile. In reality, the aggregate curve would be spread wider with additional fluctuations due to geographic diversity and clustering of solar PV sites. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 31

40 Section 4 Key findings and conclusions Energy storage and demand side management were not considered. In practice, these may be able to contribute to mitigating the impacts of high solar PV penetration. The main consequence of these limitations is that possible lower level issues and constraints were overlooked by this study: Operational issues related to must-run generation Real-world thermal generation behaviour Distribution network limits for solar PV export to the grid Stability limits Prediction errors in modelling of solar PV for market scheduling 4.3 Market considerations Changes in the way thermal generation is dispatched are likely to have longer-term market implications. The scenario results showed that with a high solar PV penetration, offered thermal generation will be brought on rapidly leading up to the winter and autumn peaks (for example, see Figure 37 in the appendices). Spring and summer do not require significant thermal generation, even with high solar PV penetration (demonstrated by Figure 11 and Figure 12). However, the results summarized in Figure 11 and Figure 12 show that the energy quantity provided by thermal generation during the evening period did not change substantially with increasing solar PV penetration, despite the increased rate of change in net demand that must be met. The operational costs imposed by this relative shift in the role of thermal generation, from baseload towards peaking, are not possible to estimate here. The changes may influence trading behaviour, market offers, and generating plant investment and retirement decisions in the longer term. These commercial considerations cannot be adequately anticipated or replicated in this study. It is likely that the offered energy and reserve stacks available will change substantially as solar PV penetration increases and the supply mix evolves. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 32

41 Section 5 Recommendations 5 RECOMMENDATIONS 5.1 System solar PV capacity This study revealed no clear issues related to the ability of the New Zealand power system to balance supply and demand with increasing PV generation. The amount of generation ramping capacity in the power system was found to be likely sufficient to handle foreseeable quantities of solar PV in the near and mid-term. However, it will be necessary to carefully monitor North Island generation and reserve availability together with planned HVDC link outages, as system ramping outcomes were shown to be sensitive to these factors. It is prudent to proceed with the planned programme of work to investigate solar PV variable day effects and stability impacts, as these may set lower capacity limits for solar PV in the power system. Based on the results of this investigation, subsequent studies can safely assume multiple gigawatts of solar PV capacity installed. It will also be effective to use and refine methodologies developed in this study; for example, solar PV data preparation methods outlined in Figure 6 and the NFR estimation model detailed in Appendix A2.2. Transpower should also keep abreast of developments and experience in the wider world regarding energy storage and demand-side management as mitigation strategies for solar PV. 5.2 System planning Generating capacity at the winter peak remains the main driver for system planning in high solar PV penetration scenarios. Mitigating issues from a supply standpoint is likely to address any ramp rate related problems. It is advisable to continue to monitor and coordinate scheduled HVDC and generator outages, as these can affect the availability of ramping capacity to meet rapid changes in net demand that may be exacerbated by distributed PV generation. Where possible, outages should not be scheduled for the evening period on autumn and winter weekdays (outages at these times are currently avoided). 5.3 Longer term factors The results and conclusions of this study should be disseminated to interested parties, to provide context for discussions regarding the future of solar PV and its impacts on the power system. Methods for operationalising the stochastic forecasting of PV generation in market system scheduling and dispatch should also be investigated, before the growth of solar PV in New Zealand escalates to significant levels. Aggregation methodologies and weather dependencies for geographically distributed solar PV are mathematically complex. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 33

42 Section 5 Recommendations Existing tools for conforming load forecasting are unlikely to be sufficient to deal with the particular nature of solar PV. One promising avenue for this is the wavelet transformation based simulation model developed by Transpower representatives and participants during the Mathematics in Industry New Zealand forum, Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 34

43 Appendix A1: Additional scenario figures A1 ADDITIONAL SCENARIO FIGURES Appendix A1 provides detailed scenario plots for all study days, by season. A1.1 Autumn Autumn - Saturday Figure 15: Study scenario national PV generation and net demand over evening period for 18 April 2015 Figure 16: Scenario ramp up margins and actual system ramping over evening period for 18 April 2015 Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 35

44 Appendix A1: Additional scenario figures Figure 17: Scenario generation mix and HVDC transfer over evening period for 18 April 2015 with 4 GW solar PV Figure 18: Scenario generation mix and HVDC transfer over evening period for 18 April 2015 with 10 GW solar PV Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 36

45 Appendix A1: Additional scenario figures Autumn - Sunday Figure 19: Study scenario national PV generation and net demand over evening period for 19 April 2015 Figure 20: Scenario ramp up margins and actual system ramping over evening period for 19 April 2015 Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 37

46 Appendix A1: Additional scenario figures Figure 21: Scenario generation mix and HVDC transfer over evening period for 19 April 2015 with 4 GW solar PV Figure 22: Scenario generation mix and HVDC transfer over evening period for 19 April 2015 with 10 GW solar PV Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 38

47 Appendix A1: Additional scenario figures Autumn - Weekday Figure 23: Study scenario national PV generation and net demand over evening period for 14 April 2015 Figure 24: Scenario ramp up margins and actual system ramping over evening period for 14 April 2015 Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 39

48 Appendix A1: Additional scenario figures Figure 25: Scenario generation mix and HVDC transfer over evening period for 14 April 2015 with 4 GW solar PV Figure 26: Scenario generation mix and HVDC transfer over evening period for 14 April 2015 with 10 GW solar PV Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 40

49 Appendix A1: Additional scenario figures A1.2 Winter Winter - Saturday Figure 27: Study scenario national PV generation and net demand over evening period for 1 August 2015 Figure 28: Scenario ramp up margins and actual system ramping over evening period for 1 August 2015 Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 41

50 Appendix A1: Additional scenario figures Figure 29: Scenario generation mix and HVDC transfer over evening period for 1 August 2015with 4 GW solar PV Figure 30: Scenario generation mix and HVDC transfer over evening period for 1 August 2015 with 10 GW solar PV Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 42

51 Appendix A1: Additional scenario figures Winter - Sunday Figure 31: Study scenario national PV generation and net demand over evening period for 2 August 2015 Figure 32: Scenario ramp up margins and actual system ramping over evening period for 2 August 2015 Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 43

52 Appendix A1: Additional scenario figures Figure 33: Scenario generation mix and HVDC transfer over evening period for 2 August 2015 with 4 GW solar PV Figure 34: Scenario generation mix and HVDC transfer over evening period for 1 August 2015 with 10 GW solar PV Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 44

53 Appendix A1: Additional scenario figures Winter - Weekday Figure 35: Study scenario national PV generation and net demand over evening period for 28 July 2015 Figure 36: Scenario ramp up margins and actual system ramping over evening period for 28 July 2015 Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 45

54 Appendix A1: Additional scenario figures Figure 37: Scenario generation mix and HVDC transfer over evening period for 28 July 2015 with 4 GW solar PV Figure 38: Scenario generation mix and HVDC transfer over evening period for 28 July 2015 with 10 GW solar PV Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 46

55 Appendix A1: Additional scenario figures A1.3 Spring Spring - Saturday Figure 39: Study scenario national PV generation and net demand over evening period for 31 October 2015 Figure 40: Scenario ramp up margins and actual system ramping over evening period for 31 October 2015 Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 47

56 Appendix A1: Additional scenario figures Figure 41: Scenario generation mix and HVDC transfer over evening period for 31 October 2015 with 4 GW solar PV Figure 42: Scenario generation mix and HVDC transfer over evening period for 31 October 2015 with 10 GW solar PV Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 48

57 Appendix A1: Additional scenario figures Spring - Sunday Figure 43: Study scenario national PV generation and net demand over evening period for 1 November 2015 Figure 44: Scenario ramp up margins and actual system ramping over evening period for 1 November 2015 Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 49

58 Appendix A1: Additional scenario figures Figure 45: Scenario generation mix and HVDC transfer over evening period for 1 November 2015 with 4 GW solar PV Figure 46: Scenario generation mix and HVDC transfer over evening period for 1 November 2015 with 10 GW solar PV Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 50

59 Appendix A1: Additional scenario figures Spring - Weekday Figure 47: Study scenario national PV generation and net demand over evening period for 27 October 2015 Figure 48: Scenario ramp up margins and actual system ramping over evening period for 27 October 2015 Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 51

60 Appendix A1: Additional scenario figures Figure 49: Scenario generation mix and HVDC transfer over evening period for 27 October 2015 with 4 GW solar PV Figure 50: Scenario generation mix and HVDC transfer over evening period for 27 October 2015 with 10 GW solar PV Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 52

61 Appendix A1: Additional scenario figures A1.4 Summer Summer - Saturday Figure 51: Study scenario national PV generation and net demand over evening period for 9 January 2016 Figure 52: Scenario ramp up margins and actual system ramping over evening period for 9 January 2016 Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 53

62 Appendix A1: Additional scenario figures Figure 53: Scenario generation mix and HVDC transfer over evening period for 9 January 2016 with 4 GW solar PV Figure 54: Scenario generation mix and HVDC transfer over evening period for 9 January 2016 with 10 GW solar PV Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 54

63 Appendix A1: Additional scenario figures Summer - Sunday Figure 55: Study scenario national PV generation and net demand over evening period for 10 January 2016 Figure 56: Scenario ramp up margins and actual system ramping over evening period for 10 January 2016 Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 55

64 Appendix A1: Additional scenario figures Figure 57: Scenario generation mix and HVDC transfer over evening period for 10 January 2016 with 4 GW solar PV Figure 58: Scenario generation mix and HVDC transfer over evening period for 10 January 2016 with 10 GW solar PV Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 56

65 Appendix A1: Additional scenario figures Summer - Weekday Figure 59: Study scenario national PV generation and net demand over evening period for 5 January 2016 Figure 60: Scenario ramp up margins and actual system ramping over evening period for 5 January 2016 Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 57

66 Appendix A1: Additional scenario figures Figure 61: Scenario generation mix and HVDC transfer over evening period for 5 January 2016 with 4 GW solar PV Figure 62: Scenario generation mix and HVDC transfer over evening period for 5 January 2016 with 10 GW solar PV Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 58

67 Appendix A2: Methodology detail A2 METHODOLOGY DETAIL A2.1 Data preparation Regional distribution factors The underlying assumption in the calculation of regional distribution factors was that installed solar PV watts per person will tend towards convergence between the various regions over time. This is considered a more likely end state than the current, highly localised uptake: Solar PV systems will become more economically competitive with grid supplied electricity as technology costs fall, prompting wider uptake. Electricity Distribution Business pricing regimes and distributed generation capabilities are likely to become more standardised over time, in response to increasing volumes of solar PV capacity. It is noted that differences in the solar resource and socio-economic factors will vary by region and will affect solar PV uptake. At the time of carrying out this analysis, no clear method had been identified to include these factors in regional distribution factors, and the relative importance of their effects is unknown. GXP distribution factors To determine GXP distribution factors, four years of cleaned gross electricity demand data ( ) for all conforming 8 GXPs was used to extract a single peak value (in MW) for each day per GXP. The peak values were then averaged and statistically validated to identify any data cleansing errors. Validation involved calculating the percentile value of all available demand data corresponding to the average peak daily demand for each GXP. Where the resulting percentile was not sufficiently high (>75th percentile) the demand profile was inspected manually to confirm data quality. Manual adjustments were made to GXP distribution factors where required, based on knowledge of the underlying load characteristics at each location. GXP distribution factors for all non-conforming [7], injection only, primarily industrial or intermittent demand GXPs were manually set to zero. Solar PV capacity is either not expected at these nodes, or cannot be predicted with a reasonable level of confidence. Load data processing This section describes the primary method for processing demand data to extract a single peak value for each day and for each GXP. The data was extracted from PI Historian using the TNL (Total Network Load) data set. An example of the unprocessed GXP demand data is displayed in Figure 63 below. 8 Conforming GXPs are defined in the Code, as per Clauses 13.27A to 13.27K. See [5] for details. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 59

68 Appendix A2: Methodology detail Figure 63: Example of unprocessed TNL data over 4 years for a selected GXP These data required processing to correct for the following: Zeros values which occur due to outages and load shifting by distributors Values representing a previous state of the network (e.g. Albany GXP prior to the commissioning of the Wairau road GXP) Decommissioned GXPs and associated data from affected neighbouring GXPs Reverse power-flows (negative values) due to unaccounted for embedded generation in the TNL data set GXPs that are identified as non-conforming in the market system An example of the final cleaned GXP data is displayed in Figure 64 below Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 60

69 Appendix A2: Methodology detail Figure 64: Example of processed TNL data for a selected GXP Figure 65: Probability distribution function for selected GXP daily peak values The Probability Distribution Function (PDF) of a random variable x has an area under the curve equal to one: PPPPPP(xx) dddd = 1 It was necessary to determine whether the mode, median or mean value was the appropriate measure of distribution centre, depending of the shape of the PDF curve. A Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 61

70 Appendix A2: Methodology detail high skew informs that the mean is less useful. For the available data set, the sample size was sufficient for using the median value. An example of several measures of daily peak demand values is given in Table 7. GXP 'ALB 110KV' Mean Maximum Median Standard deviation Variance Table 7: Summary statistics of daily peak demand values for an example GXP The appropriate measure was selected for each GXP depending on its characteristics. Data check: A final check was carried out to validate the result of the data cleaning method above. This was done to confirm accuracy and identify any need for further data cleaning. The method formed a PDF for all valid demand data for each GXP (not only daily peaks), excluding zeroes. The resulting average daily peak from the method above was then compared against these PDFs to find the percentile that the result corresponds to, for each GXP. Where percentile was not sufficiently high (> 75th), the data was inspected manually. A2.2 NFR estimation model In the real market system, NFR values are determined via non-linear power system simulation software, RMTSAT, which iterates with SPD solutions to converge to reliable final values for FIR NRF values (SIR NRF values are directly calculated). This process is not currently practical to emulate for off-line studies. In addition, the current version of RMTSAT is not designed to provide NFR values which are valid for high PV penetration scenarios. Estimation of NFR values is performed using total original (prior to solar PV) and final demand (electrical load), by island. HVDC transfer is not included; it would provide a better indication of island power supply but exhibits a dependency on NFR values via the SPD solves. NFR estimation using HVDC transfer would be circular, as the resulting NFR values could lead to a change in optimal HVDC transfer if re-solved. The primary assumption regarding NFR values for the purposes of this study was that they will typically exhibit a constant normal distribution standard score (z value) with varying levels of island demand. This implied that an original NFR value has an equivalent probability of occurrence at both its original and final island demand values, and that NFRs relative to island load will remain similar under future high PV penetration Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 62

71 Appendix A2: Methodology detail scenarios. This provided a means of calculating shifted NFR values for an altered island load: zz = xx ii μμ ii σσ ii = xx ff μμ ff σσ ff xx ff = μμ ff + σσ ff σσ ii (xx ii μμ ii ) The initial and final island loads are denoted i and f, and the term x indicates the relevant NFR observation and prediction values. Initial and final island loads will have different associated means and standard deviations. An example of this constant z value shift for the SI ACCE FIR NFR type is shown in Figure 66 and Figure 67 below. This is an approximate assumption only, as NFR values display a high level of non-linearity with varying input assumptions. At a minimum, it is expected that this adjustment will perform better than no adjustment, given a sufficiently large sample size. Figure 66: NFR values and island load for the SI ACCE FIR NFR type (Jan 2015 May 2016) Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 63

72 Appendix A2: Methodology detail Figure 67: Resulting normal probability distribution function curves for initial and final island load values To determine the appropriate NFR population subgroups (all possible discrete values for i and f) final NFR and island load data was extracted for the period of January 2015 to March This provided a sample size of 22,174 for each of the 16 NFR types. The data was then binned by island load into consecutive sub-samples of size 50. Mean and standard deviation were then calculated for each bin. Initial and final bins were selected by proximity, with the closet bins being used for each i observation. Bins are more densely populated for more frequent island load values, due to the constant sample size grouping method used. The NFR data in each load bin may not be normally distributed. However, the use of normal distributions was considered to be adequate and had two distinct advantages: Distribution z values could be compared directly, allowing a simple analytical calculation of a final NFR value from an initial NFR observation. The distributions could be described using only mean and standard deviation, rather than a large set of calculated percentiles. This meant that the ultimate size of the approximation model data supplied to vspd was more manageable. Detailed statistical validation and detailed RMTSAT studies can be carried out in the future to confirm this approach if necessary. Approximated Net Free Reserve (NFR) information for each 30-minute trading period of the final vspd outputs can be checked using an offline study version of RMTSAT. This may identify instances where the NFR approximation is inaccurate, and where study periods need to be manually adjusted with new NFR values and re-run. In addition, approximate checking of FIR quantities relative to binding risk magnitude can be carried out for each vspd solve, as described in Appendix A2.3. Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 64

73 Appendix A2: Methodology detail Final processed values: Figure 68: Final processed estimation model NFR means and standard deviations by risk class for North Island FIR Figure 69: Final processed estimation model NFR means and standard deviations by risk class for North Island SIR Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 65

74 Appendix A2: Methodology detail Figure 70: Final processed estimation model NFR means and standard deviations by risk class for South Island FIR Figure 71: Final processed estimation model NFR means and standard deviations by risk class for South Island SIR Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 66

75 Appendix A2: Methodology detail A2.3 FIR validation check Given that input NFR values are estimated, a final post-solve check was performed for each interval to confirm that dispatched FIR quantities were approximately correct relative to binding risk plant values. This was done by reference to the observed relationships between FIR and risk for the period of 1 Jan 2015 to 31 March A check was not performed for SIR, since SIR procurement is usually much closer to a 1 to 1 ratio. Refer to subsection for details of the NFR estimation. A normal range was defined based on observed values for each risk class. This was given as a ± value around the least squares linear regression of each risk class sample (procured island FIR MW and binding risk MW). These regressions are displayed in Figure 72 and Figure 73 below. It was found to be more appropriate to define the normal range by altering the intercept, not the slope, because FIR variance did not seem to increase significantly with risk, i.e. the norm band remained at a relatively constant width. Approximate values were used for intercept_dn and intercept_up (intercept ± value) but could be altered to make the checks more or less sensitive. For each risk class the following lower bound check was performed: FIR Slope class Risk MW class + Intercept class InterceptDn class This confirmed that the FIR procured was approximately adequate to cover each risk class. For the binding risk, a second check was also carried out to confirm that FIR procured was not excessive: FIR Slope class Risk MW class + Intercept class + InterceptUp class The inequalities above represent the expected state. Dispatched FIR and the frequency keeping band were added onto the unit MW dispatch for the AC CE risk classes, if applicable. DC CE risk was the bipole transfer minus 528 MW (for the North direction only; South direction was not considered). island 9 reserve class risk class slope intercept intercept_dn intercept_up NI FIR ACCE NI FIR DCCE NI FIR DCECE SI FIR DCCE SI FIR DCECE SI FIR MANCE SI FIR MANECE Table 8: FIR validation check parameters 9 North Island or South Island Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 67

76 Appendix A2: Methodology detail The FIR check result was produced in the pvresults CSV file for each vspd run. It was reported by the columns titled NI_FIR_ACCE_CHKFAIL and similar for other risk classes. A result of 1 indicates a failed check. Figure 72: Linear regressions for DC risks classes, 1 Jan 2015 to 31 March 2016 Figure 73: Linear regressions for AC risks classes, 1 Jan 2015 to 31 March 2016 Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 68

77 Appendix A2: Methodology detail A2.4 CVP values The following CVP values were modified from their default values in the study: Constraint violation penalty Modified values i_deficit6sreserve_ce i_deficit60sreserve_ce i_deficitbranchgroupconstraint i_surplusbranchgroupconstraint i_deficitmnodeconstraint 5000 i_surplusmnodeconstraint 5000 i_deficitramprate This was done so that ramp rate deficit occurs first in the case of an inability to ramp generation fast enough to meet a change in net demand. Ordinarily, a 6 or 60 second reserve deficit would occur first, which would obscure the outcome that this study is investigating. Branch group constraint and market node constraint CVP were also relaxed to allow the full amount of PV generation to be available to the system (local constraints and congestion issues are disregarded). Effect of Solar PV on Generation Dispatch in New Zealand - Transpower New Zealand Limited. All rights reserved. 69

78 Appendix A3: Emerging Energy Programme: plan and outcome strategy A3 EMERGING ENERGY PROGRAMME: PLAN AND OUTCOME STRATEGY Emerging Energy Technologies: Programme Tranche Plan Historic Work 2016/ / /19 Wind Wind Capacity Assessment Solar PV Solar PV Variability Studies Solar PV System Stability Studies Training Battery and Storage Market System, Real Time Operations, Process and People Situational Intelligence Initial Work Battery Storage Trial Invex Stage 1 Situational Intelligence Programme Definition Battery Operations Impact Assessment Situational Intelligence Stage 1 Invex Battery Storage Next Steps Consideration of Economic Options for Investment Situational Intelligence Stage 1 Capex Situational Intelligence Future Phasing Monitoring Progress Against Transmission Tomorrow Future States (ongoing) Work Packages to be Executed with each Emerging Technology Lines Company Data Exchange Review Assessment of Capabilities Load Forecast Review Ancillary Services Review SO Tools Review Policy and Standard Review Effect of Solar PV on Generation Dispatch in New Zealand Transpower New Zealand Limited. All rights reserved. 70

79 Emerging Energy Programme: plan and outcome strategy A3.1 Emerging Energy Technologies Outcome Strategy Map Effect of Solar PV on Generation Dispatch in New Zealand Transpower New Zealand Limited. All rights reserved. 71