Net Demand Variability (NDV) Summary

Transcription

1 Net Demand Variability (NDV) Summary Executive Summary: This document provides a summary of the Net Demand Variability (NDV) work presented to the Energy and Ancillary Service (EAS) workgroup in WG meetings 2, 6, 8 and 9. One reference case and two sensitivity scenarios based on potential generation mixes (Reference Case, High Cogeneration and High Coal to Gas based on the AESOs 2017 LTO) were simulated through 2030 to test possible directional impacts that may be expected for each year. Results on all scenarios indicate that, although system operations could be reliably managed, the lower flexibility of the system (as noted in the scenarios) may require the system controller to dispatch the Energy Market Merit Order (EMMO) more frequently (dispatch for ramp) and in larger volumes to manage future increases of NDV. The increased volume and frequency of dispatching the EMMO for ramp requirements may also have an operational (and cost) impact on some units as noted by both on/off cycling and load following. Further, the more frequent use of the EMMO to manage ramp requirements may result in: Price fluctuations (price fidelity); Supply surplus and in rare instances supply shortfall issues. With the completion of the impact assessment, the mitigation scenarios will be evaluated including; No Change / current rules Rule changes (i.e., dispatch tolerance rules) New products (i.e., Introduction of a specific ramp product); Consideration of how real time price signals can incent flexibility; Need for changes / value to different unit commitment models (ex. SCUC). As noted in the qualitative markets assessment, it is noted that a unit commitment model may not be able to mitigate against forecast error. See separate document on mitigation options for further discussion. Enter Footer Page 1 Public

2 1.0 Introduction In order to assess future views of the system and market, a Net Demand Variability (NDV) study was initiated. The AESO used system and market models to determine how future views of the fleet may impact system operations and market conditions. This summary report reflects the results of that impact assessment. This summary report references materials submitted separately to the Energy & AS (EAS) working group as noted in section Study Context: Net Demand Variability (NDV) is defined as the demand for electricity net of the contribution from variable generation (wind and solar) resources and any changes in load profile. This study evaluates expected changes to the generation mix discussed in the 2017 LTO, its impact on NDV, and resultant impacts on system operations and markets. The following summarizes the context for this study. a) As more variable generation is integrated into the Alberta Interconnected Electric System (AIES) between now and 2030, we can expect increased Net Demand Variability (NDV) and therefore a system that requires increased operational flexibility. b) The AESO currently manages NDV impacts from 1,445MW of variable wind generation using a combination of; Dispatching the EMMO to achieve the required ramp rate; Regulating reserve (AGC) and; Use of the Wind Power Management (WPM) process to manage extreme wind ramp events. c) One reference case and two sensitivity scenarios based on potential generation mixes (Reference Case, High Cogeneration and High Coal to Gas based on the AESOs 2017 LTO) were simulated through 2030 to test possible directional impacts that may be expected for each year 1. d) The early studies concluded that the addition of more than 5,000MW of variable generation by 2030 will result in increased NDV: i. As illustrated at the June 21, Session 2 meeting, in the maximum annual hourly NDV variation between maximum and minimum NDV was 4,720MW in 2015 (11,127MW- 6,407MW). This variation is expected to increase to approximately 9,905MW (12,918MW- 3,013MW) by 2030; 1 Please see slides 2 to 5 of the NDV presentation in WG6 for details on model and metrics Enter Footer Page 2 Public

3 ii. The 10 minute NDV increases between 2015 and 2030 range between approximately 50MW to 80MW. e) Further studies were conducted to provide more granularities to the merit order and test more detailed impact to the system. The results are attached. 2.0 Methodology: a) Two models to simulate market functions (Market Simulation Model) and operations functions (System Dispatch Simulation Model) were developed. b) Simulated merit orders from the Market Simulation Model were input into the System Dispatch Simulation Model to simulate operational behaviour. c) Historic behaviour observations formed the foundation of the models, where; i. Historic data was used to validate the models; ii. iii. iv. Assumptions of future changes to system were considered; The validated models were then used to simulate the future; Alternate sensitivities were simulated based on initial observations from the Reference Case results. d) Load forecasts and future generation scenarios for tested years were based on the 2017 Long Term Outlook (LTO). The modelling assumptions and fleet composition for each scenario could be found within each presentation referenced in section 5.0 below. e) Future variable generation (wind/solar) profiles and simulations were based on 200 sites extrapolated to the future. The profiles were also based on 2014, 2015 and 2016 weather years extrapolated to the future. f) Operations and market practices/behaviour were based on historic observations and related rules. The models were validated by performing a historic backcast. Enter Footer Page 3 Public

4 3.0 Operations Findings: The study tested four operational metrics to determine the impact of increased NDV on the system. a b c d Metric Impact/expected changes to Control Performance Standard 2 (CPS2) violations. System Operating Limit (SOL) violation. (TOP-007-AB-0). Area Control Error (ACE) events. This is related to impact on interties and neighboring systems. Variable energy spill due to power management tool activations. Description To measure the10 minute ACE (Area Control Error Balancing authority MW imbalance) performance at or above the 90% of the time within required threshold. To measure the 30 minute or longer ACE performance benchmark and resultant SOL violation. i.e. to measure the SOL violation that exceeds the tie line Total Transfer Capability (TTC) for more than 30 minutes or longer. To measure the 30 minute or longer ACE performance benchmark. i.e. to measure when the intertie flow exceeds the schedule by more than the Transmission Reliability Margin (TRM) of 65MW for more than 30 minutes, but not exceeding TTC. Potential spill of variable energy due to using the AESO s wind power management tool (how often and how much). The details provided below indicate that in all cases, the operational impact of additional NDV was manageable with the fleet and current dispatch protocol. a) CPS 2-Measure the 10 minute Area Control Error (ACE) or balancing authority MW imbalance performance at or above the minimum requirement of 90%. Conclusion: the operational metric was not violated in any study scenario. The results for the Least Proactive 2 approach The results for the Middle Ground approach indicate The results for the Middle Ground approach indicate 2 Least Proactive- Persistence level wind forecast is assumed and the system controller dispatches the EMMO (in 10 minute intervals) to meet the ramp requirement at the end of two consecutive intervals (dispatch now, only to achieve the required ramp at the end of the next interval). Enter Footer Page 4 Public

5 indicate CPS2 of 93-94%. The Middle Ground 3 and Most Proactive 4 CPS2 results are between 95-98% and 96-99% respectively. CPS2 results of 95-98%. CPS2 results of 97-98%. Impact No issue. The system controller is able to manage NDV using existing dispatch for ramp practice with no CPS2 violations. No issue. The system controller is able to manage NDV using existing dispatch for ramp practice with no CPS2 violations. No issue. The system controller is able to manage NDV using existing dispatch for ramp practice with no CPS2 violations. b) SOL violation-measure the 30 minute or longer ACE performance benchmark and the resultant SOL violation. Conclusion: The impact in the scenarios was the same indicating no issues related to the SOL violations. The 1 st SOL violation under the Least Proactive approach with the BC tie line in service is in 2022 (3,045MW of wind). Same as Reference Case Same as Reference Case The Most Proactive approach would result in no SOL violations when the BC tie line is in service. Impact Any impacts related to SOL violations can be avoided by adopting a Proactive dispatch approach (as done today). Any impacts related to SOL violations can be avoided by adopting a Proactive dispatch approach (as done today). Any impacts related to SOL violations can be avoided by adopting a Proactive dispatch approach (as done today). 3 Middle Ground- A 50% split between persistence and continued variable ramp (persistent ramp) within the past 10 minutes and the EMMO is dispatched to meet the ramp requirement between the current and next interval (10 minute). 4 Most Proactive-The variable ramp observed in the last interval (10 minute) is expected to continue (persistent ramp) to the next interval and the EMMO is dispatched to meet the required ramp requirement at the end of the interval. Enter Footer Page 5 Public

6 This requires the system controller to dispatch the EMMO in order to meet the required ramp requirement rather than to meet demand levels (See dispatch for ramp section below for more details). This result in the system controller dispatching the EMMO in order to meet the required ramp requirement (See dispatch for ramp section below for more details). This result in the system controller dispatching the EMMO in order to meet the required ramp requirement (See dispatch for ramp section below for more details). c) Big ACE event-measure the 30 minute or longer ACE performance benchmark when the intertie flow exceeds the 65MW Transmission Reliability Margin for more than 30 Minutes. Conclusion: The impact in the scenarios was the same indicating no issues. The Least Proactive approach indicates up to 150 instances per year with increased variable generation. Same as Reference Case Same as Reference Case The most Proactive approach indicates less than 10 instances. Impact Most Big ACE events can be avoided by adopting a Proactive dispatch approach (as done today). Most Big ACE events can be avoided by adopting a Proactive dispatch approach (as done today). Most Big ACE events can be avoided by adopting a Proactive dispatch approach (as done today). This result in the system controller dispatching the EMMO in order to meet the required ramp rate (See dispatch for ramp section below for more details). This result in the system controller dispatching the EMMO in order to meet the required ramp rate (See dispatch for ramp section below for more details). This result in the system controller dispatching the EMMO in order to meet the required ramp rate (See dispatch for ramp section below for more details). d) Variable spill due to WPM-Potential variable energy spill due to the use of the AESOs existing Wind Power Management (WPM) process. The variable energy was limited only as a result of WPM due to increased wind ramp. (Less than 0.1% of wind generation is observed).. Conclusion: The impact in the scenarios was the same indicating no issues. Enter Footer Page 6 Public

7 Volume (MWh) increase when moving from a Least Proactive to Most Proactive approach. However, as a percentage of lost variable energy, it is less than 0.1% of the variable generation in all approaches. This is even less when considering non supply surplus periods. As a percentage of lost variable energy, it is less than 0.1% of the variable generation in all approaches. This is even less when considering non supply surplus periods. As a percentage of lost variable energy, it is less than 0.1% of the variable generation in all approaches. This is even less when considering non supply surplus periods. Impact No issue No issue No issue e) Dispatch for ramp: Measure the change in dispatch for ramp with the addition of more variable generation. Conclusion: It is observed that more proactive dispatch of the EMMO is required to meet the required ramp requirement as you move from the Reference Case to the High Cogeneration or High Coal to Gas Scenarios. This proactive dispatch protocol is standard for system control and allows for management of the system based on the merit order submissions. However, there may be market impacts associated with such a practice discussed below. As more variable resources are added to the fleet and the generation mix change over time, the system controller is able to meet the operational ramp needs by dispatching the EMMO as done at present, but at a relatively higher degree. It is also observed that with changes to the generation fleet, ramp capability due to more gas generation replacing coal generation, the relative degree of dispatch for ramp reduce over time. The overall dispatch variance increased by approximately 20-30% over the Reference Case. The increase was similar to the Reference Case until about Between 2024 and 2028, the High Coal to Gas scenario shows a 20%- 30% increase when compared to the Reference Case and between 2029 and 2030, the variance increases by 50%-80%. Impact No operational concerns. However, more frequent use of the EMMO to Additional volume and frequency of dispatch for ramp is observed this may Additional volume and frequency of dispatch for ramp is observed this may Enter Footer Page 7 Public

8 achieve the required ramp may have price fidelity and unit cycling impacts discussed under Market Findings below. lead to further price fidelity and unit cycling impacts discussed under Market Findings below. lead to further price fidelity and unit cycling impacts discussed under Market Findings below. Description and example of dispatch for ramp: In the example below, the model assumes a 10 minute dispatch interval. In reality, there are no set intervals for system controller dispatch and therefore, the dispatch volumes and frequency could be different but, directionally in line with the observations. i. The standard deviation of overall dispatch variance MWs for 2015 was approximately 255MWs.The Reference Case shows that this gradually increases to approximately 330MW by 2020 and then gradually decreases to 2015 levels thereafter and ends up around 200MW by ii. iii. The High Cogeneration scenario shows that the standard deviation of overall dispatch variance MWs reaches a high of approximately 390MW in 2020 and gradually decreases to approximately 265MW by The High Coal to Gas scenario shows that the standard deviation of overall dispatch variance MWs reaches a high of approximately 330MW in 2020 and gradually decreases until However, it shows an increasing trend during to about 355MW likely due to an expected timing delay to new Combined Cycle unit development. 3.0 Market Findings: The study tested thee market metrics to determine the impact of increased NDV on the system. Metric Description a Supply surplus/shortfall situations. To Identify impact of unit commitment on supply surplus and shortfall situations. b Merit order / market asset characteristics. To identify merit order characteristics under different generation mixes such as quantity of $0, Minimum Stable Generation (MSG) and ramp capability. c Unit cycling. Quantify cycling of different types of thermal generation units (CC, SC, CTG and coal for some years). Enter Footer Page 8 Public

9 While the overall system operations are not impacted, the market metrics indicate that there may be potential impacts on the market pricing and asset cycling that must be considered. The details below show the summary. a) Supply Surplus/Shortfall-Identify impact of unit commitment on supply surplus/shortfall situations. Conclusion: Supply surplus events are impacted by increased NDV and a function of the expected fleet. Supply surplus events are expected to change from historic levels (58 hours for ) in the Reference Case and both scenarios. Due to the higher level of must run requirements, the high Cogeneration Scenario indicate the highest instances of supply surplus events. Due to expected fewer $0 blocks, the high Coal to Gas Scenario indicates lower levels of surplus events. Intertie availability and market driven intertie export capability may assists in supply surplus situations. An increase in the number of supply surplus/shortfall hours are also observed because of the over/under commitment of units based on a 24 hour ahead wind forecast error. Supply surplus hours increase as more variable generation is added. The historical benchmark of supply surplus events was exceeded between 2025 and The intertie can have a significant role in managing net demand variability. In 2030 the number of surplus hours increased from 153 to 1,087 by removing the intertie. Unit commitment based on a 24 hour forecast of wind creates a small increase in supply surplus hours and increases the risk of supply shortfall. In 2030, surplus hours increased by approximately 80 hours because of wind forecast error. Compared to the Reference Case, the High Cogeneration scenario was less flexible and resulted in more supply surplus events. Compared to the Reference Case, supply surplus hours increased from 153/1,087 to 367/1,454 Similar to the Reference Case, the intertie can have a large role in managing net demand variability. In 2030 the number of surplus hours increased from 367 to 1,454 by removing the intertie. Similar to the Reference Case, unit commitment based on a 24 hour forecast of wind created a small increase in supply surplus hours and increased the risk of supply shortfall. In 2030, surplus hours increased by approximately 40 hours Compared to the Reference Case, the High Coal to Gas scenario had lower $0 energy and resulted in less supply surplus events (This is similar to the sensitivity on the Reference Case which found if future CC units offer lower volumes of energy at $0, supply surplus events will decrease). Compared to the Reference Case, supply surplus hours decreased from 153/1,087 to 34/439 Similar to the Reference Case, the intertie can have a large role in managing net demand variability. In 2030 the number of surplus hours increased from 34 to 439 by removing the intertie. Similar to the Reference Enter Footer Page 9 Public

10 because of wind forecast error. Case, unit commitment based on a 24 hour forecast of wind created a small increase in supply surplus hours and increased the risk of supply shortfall. In 2030, surplus hours increased by approximately 30 hours because of wind forecast error. Impact Adding more variable generation will lead to more supply surplus events. The future operation of the intertie will have a large impact on the amount of supply surplus events. Higher levels of must run generation will decrease system flexibility and increase supply surplus events. Generation with lower zero dollar blocks will decrease the amount of supply surplus. Long lead time generation will lead to more supply surplus and shortfall events given a 24 hour forecast error in net demand. b) Merit order/market asset characteristic-identify merit order characteristics under different generation mixes such as quantity of $0, Minimum Stable Generation (MSG) and ramp capability.; and accordingly how many MWs could be reduced during supply surplus events. Conclusion: There is some degree of flexibility within the system to manage supply surplus events since some $0 blocks can be reduced to Minimum Stable Generation (MSG) blocks. The generation mix for the Reference Case appears to indicate the highest (fleet) ramp capability, while the lowest fleet ramp capability is observed in the high cogeneration scenario fleet. Results show that there is some degree of flexibility within the system to manage supply surplus events since some $0 Results show that there is some degree of flexibility within the system to manage supply surplus events since some $0 blocks can be Results show that there is some degree of flexibility within the system to manage supply surplus events since some $0 Enter Footer Page 10 Public

11 blocks can be reduced to Minimum Stable Generation (MSG) blocks. Of the generation mixes tested, this mix has the fastest ramping capability. reduced to Minimum Stable Generation (MSG) blocks. This generation mix had a slower ramping capability compared to the Reference Case. blocks can be reduced to Minimum Stable Generation (MSG) blocks. This generation mix had a slower ramping capability compared to the Reference Case. Impact n/a A fleet with more must run generation has a slower ramp capability than one with combined cycle units. A fleet with more coal to gas generation has a slower ramp capability than one with combined cycle units. c) Unit Cycling-Quantify unit cycling for different technologies. Conclusion: Unit cycling (on/off) as well has cycling for net demand (load cycling) increased in all scenarios. The technology impacted, is directly related to the generation mix in each scenario. Further sensitivities were tested related to a range of startup costs (related to on/off cycling) ranging from $20,000 to $120,000 and showed that startup costs had an impact on unit cycling 5. On/off cycles for large commitment units (300MW and larger) could increase to approximately 15 to 90 cycles per year. Load cycling by technology is greatest for CC and SC units. On/off cycles for large commitment units (300MW and larger) is similar to the Reference Case but increases by approximately 20 cycles per unit in Load cycling by technology is similar to the Reference Case with coal to gas units cycling slightly more. On/off cycles for large commitment units (300MW and larger) is similar to the Reference Case but increases by approximately 5 cycles per unit in Load cycling by technology shows that coal to gas units would be required to cycle more often compared to the Reference Case. 5 Please note that a revised on/off cycling metric result was provided following the discovery of a computational error (only impacted the Reference Case). The revised results indicate that although the overall upward-sloping trend remains the same, the revised calculation shifts down estimates by between 1 and 10 starts per unit. This information was sent as an Errata. Enter Footer Page 11 Public

12 Impact An increase in the frequency of on/off cycling demonstrates that units may be required to turn off more often than the existing practice. A fleet with more must run generation requires the remaining generators to cycle more often. With a large amount of converted coal units, these converted units will be required to cycle more to support the increased NDV. An increase in the frequency of load cycling demonstrates that units will need to be able to be more responsive to changes in net demand. 4.0 Alternate Options: The above impacts are directly related to the assumed generation mix. Should the market design incent the development of faster moving technologies, the above market impacts may be reduced. Further, intertie availability may also have a market/operational impact. With the completion of the impact assessment, the mitigation scenarios will be evaluated including; No Change / current rules Rule changes (i.e., dispatch tolerance rules) New products (i.e., Introduction of a specific ramp product); Consideration of how real time price signals can incent flexibility; Need for changes / value to different unit commitment models (ex. SCUC). As noted in the qualitative markets assessment, it is noted that a unit commitment model may not be able to mitigate against forecast error; See separate document on mitigation options for further discussion. Enter Footer Page 12 Public

13 5.0 References: All detailed presentations related to the information contained in this document could be found at the following locations; Date Session Link June Final.pdf September FINAL.pptx Sep-29-clean.pdf Material includes updated modelling on NDV impact for reference case. October Draft-9-for-WG.pdf Material includes NDV impact assessment and appendix on fleet assumptions for reference case and high cogeneration scenario. October 25 9 Meeting materials will be posted following the WG meeting including the NDV impact assessment for CTG plus an Errata to the reference case. Material includes NDV impact assessment and appendix on fleet assumptions details for the CTG scenario. Enter Footer Page 13 Public