2016 ISO Flexible Capacity Needs Assessment: Study Methodology, Assumptions, and Preliminary Results

Transcription

1 2016 ISO Flexible Capacity Needs Assessment: Study Methodology, Assumptions, and Preliminary Results Presented at the CPUC RA Workshop April 15, 2015 Clyde Loutan Senior Advisor Renewable Energy Integration Karl Meeusen Market Design and Regulatory Policy Lead

2 Overview Data collected from LSEs Expected renewable build-out as of March 2015 Load forecast and wind/solar build-out for future years 3-hour flexible requirements for Maximum 3-hour upward ramps Contingency reserve Flexible capacity categories CPUC LSEs contribution to flexible capacity needs Slide 2

3 Disclaimers The results shown in the presentation are preliminary and should not be considered final The ISO is still in the process of validating the data submissions with the scheduling coordinators for the LSEs and following-up with those scheduling coordinators that did not reply to the data request The ISO will re-run the assessment if warranted The ISO will issue the final results of this study to each LRA with the Final Local Capacity Requirements study Slide 3

4 Summary of preliminary assessment results Flexible Capacity need is largest in the off-peak months Flexible capacity makes up a greater percentage of resource adequacy needs during the off-peak months Increase almost exclusively caused by 3-hour ramp, not increase in peak load Inclusion of incremental behind-the-meter solar PV contributes to the larger flexible capacity requirements Compared to last year s forecast of 2016 flexible capacity needs: Flexible capacity needs are larger in many months, Distribution of daily maximum three-hour net-load ramps are comparable Using the ISO flexible capacity contribution calculation majority of threehour net-load ramps are attributable to CPUC jurisdictional LSEs Flexible capacity categories demonstrate that there is ample opportunity for participation from various resource types Slide 4

5 Study Methodology and Assumptions Clyde Loutan Senior Advisor, Renewable energy Integration Slide 5

6 Analysis methodology assumptions Most of the external resources are firmed by another balancing authority and are treated as firm imports and are not included in the flexible capacity requirements assessment The ISO assumed no additional growth of VERs for the LSEs that did not submit a response to the ISO s data request Distributed Solar PV resources were included in the flexible capacity requirements assessment Slide 6

7 Summary of expected variable energy resources buildout collected from load serving entities Resource Type Existing MW (2014) 2015 MW 2016 MW 2017 MW 2018 MW ISO Solar PV 4,482 5,785 6,874 8,007 8,078 ISO Solar Thermal 1,214 1,214 1,200 1,130 1,095 ISO Wind 4,992 4,541 4,462 4,319 4,213 Incremental distributed PV 569 1,331 1,743 2,181 Total Variable Energy Resource Capacity in the 2014 Flexible Capacity Needs Assessment Non ISO VER Resources firmed by external BAA 10,687 12,110 13,867 15,198 15,567 1,784 2,200 2,168 1,978 1,978 Total internal and external VERs 12,471 14,310 16,035 17,176 17,545 Incremental New Additions in Each Year 1,839 1,726 1, Final estimated Variable Energy Resource Capacity used in the 2014 Flexible Capacity Needs Assessment (for comparison purposes) 13,659 15,212 16,099 Page 7

8 Expected RPS build-out through 2018 Page 8

9 The monthly flexibility capacity requirement is calculated using the most recent full year of the ISO s load, wind, and solar 1-minute data For new wind installation, develop minute-by-minute production profiles by scaling each 1-minute data by the expected installed capacity Solar profiles were created using both technology type and location of the new resources Generate net-load profiles for 2015 through 2018 Generate 1-minute load profiles for 2016 through 2018 Generate 1-minute solar profiles for 2016 through 2018 Generate 1-minute wind profiles for 2016 through 2018 Slide 9

10 The ISO s load forecast through 2018 was based on the CEC s 2014 IEPR forecast MW 50,000 45,000 40,000 35,000 30,000 CEC 2014 IEPR 1-in-2 Peak Load forecast 25,000 20,000 15,000 10,000 5,000 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 (Actual) 30,538 29,938 29,154 33,190 41,481 40,338 44,004 43,526 44,703 37,907 30,986 31, ,137 31,985 31,661 33,645 37,252 40,952 45,482 46,905 43,639 37,746 33,429 33, ,096 31,946 31,622 33,604 37,207 40,902 45,426 46,848 43,585 37,700 33,388 33, ,160 32,007 31,683 33,669 37,278 40,981 45,513 46,938 43,669 37,773 33,452 33, ,285 32,128 31,803 33,796 37,419 41,135 45,685 47,115 43,834 37,915 33,578 33,980 CEC 2014 IEPR 1-in-2 monthly peak load forecast (Mid Demand Scenario, with mid-additional achievable energy efficiency) Page 10

11 The ISO used the CEC s 2014 IEPR 1-in-2 monthly peak load forecast with mid additional achievable energy efficiency to develop the load forecast Used 2014 actual 1-minute load data to build 1-minute load profiles for subsequent years Scaled the actual 1-minute load value of each month of 2014 to CEC forecasts for 2015, 2016, 2017 and 2018 using a load growth factor of monthly peak forecast divided by actual 2014 monthly peak 2015 Load Growth Assumptions Scale the actual 1-minute load value of each month of 2014 by the fraction (Monthly 2015_Peak_Load_Forecast /Monthly 2014_Actual_Peak_Load ) 2016 Load Growth Assumptions Scale each 1-minute load data point of 2015 by the fraction (Monthly 2016_Peak_Load_Forecast /Monthly 2015_Peak_Load ) 2017 Load Growth Assumptions Scale each 1-minute load data point of 2016 by the fraction (Monthly 2017_Peak_Load_Forecast /Monthly 2016_Peak_Load ) Slide 11

12 Wind growth assumptions Use actual 1-minute wind production data for 2014 was used to build minute data 1-minute wind profiles for projects installed in 2014 were created using 2014 actual data for the months the projects were not inservice (i.e. profiles for projects installed in May 2014 were created for January through April) Wind 1-minute profiles for 2015 were created by scaling the 1- minute wind data for 2014 based on installed capacity 2015 W 1-min = 2014 W Actual_1-min * 2015 W Installed Capacity /2014 W Installed Capacity Scaling maintained load/wind correlation Slide 12

13 Expected solar build-out as of March 2015 using LSE s data shows a significant increase in solar PV tracking MW Dec-14 Jan-15 Feb-15 Mar-15 Apr-15 May-15 Jun-15 Jul-15 Aug-15 Sep-15 Oct-15 Nov-15 Dec-15 Jan-16 Feb-16 Mar-16 Apr-16 May-16 Jun-16 Jul-16 Aug-16 Sep-16 Oct-16 Nov-16 Dec-16 Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17 Aug-17 Sep-17 Oct-17 Nov-17 Dec-17 Jan-18 Feb-18 Mar-18 Apr-18 May-18 Jun-18 Jul-18 Aug-18 Sep-18 Oct-18 Nov-18 Dec-18 10,000 Expected Solar Build-out through ,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1, PV Fixed PV Tracking PV Not yet decided Solar Thermal Slide 13

14 Solar growth assumptions Existing solar Use actual solar 1-minute production data for 2014 to develop 2015 profile 1-minute solar profiles for projects installed in 2014 were created for the months the projects were not in-service using 2014 actual solar data New solar installation Develop 1-minute solar production profiles for CREZs based on their technology (i.e. solar thermal, solar PV tracking & solar PV fixed) Solar 1-minute profiles for 2015 were created by scaling the 1-minute wind data for 2014 based on monthly installed capacity 2015 S 1-min = 2014 S Actual_1-min * 2015 S Installed Capacity /2014 S Installed Capacity Aggregate all new solar 1-minute production data by technology Slide 14

15 MW Forecasted maximum monthly three-hour net-load ramps 14,000 Maximum 3 Hour Max Net load Ramps* 12,000 10,000 8,000 6,000 4,000 2,000 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec ,974 9,421 9,284 8,850 6,498 5,876 6,392 6,412 7,784 9,066 10,858 11, ,570 9,843 9,709 9,322 6,817 6,125 6,476 6,337 8,118 9,396 11,163 11, ,614 10,135 10,021 9,592 7,076 6,380 6,544 6,383 8,348 9,669 11,448 12,151 * Excludes contingency reserves Slide 15

16 Preliminary Results Karl Meeusen, Ph.D. Market Design and Regulatory Policy Lead Slide 16

17 The proposed interim flexible capacity methodology designed to provide the ISO with sufficient flexible capacity Methodology Flexibility Requirement MTHy = Max[(3RR HRx ) MTHy ] + Max(MSSC, 3.5%*E(PL MTHy )) + ε Where: Max[(3RR HRx ) MTHy ] = Largest three hour contiguous ramp starting in hour x for month y E(PL) = Expected peak load MTHy = Month y MSSC = Most Severe Single Contingency ε = Annually adjustable error term to account for load forecast errors and variability Methodology for 2017 and beyond needs to be developed as needs change Slide 17

18 Flexible capacity requirement is split into its two component parts to determine the allocation The largest 3-hour net-load ramp is decomposed into four components to determine the LRA s allocation Three hour net load ramp = Δ Load Δ Wind Output Δ Solar PV Δ Solar Thermal Maximum of the Most Severe Single Contingency or 3.5 percent of forecasted coincident peak Allocated to LRA based on peak-load ratio share Slide 18

19 Contributions to the three hour net load ramp: Load wind, solar PV, and solar thermal Average of Load contribution 2016 Average of solar thermal contribution 2016 Average of solar PV contribution 2016 Average of Wind contribution 2016 January 50% 4% 46% 1% February 52% 5% 41% 2% March 43% 8% 46% 2% April 41% 8% 53% -2% May 22% 10% 61% 8% June 18% 11% 78% -7% July 88% 3% 15% -6% August 55% 6% 45% -6% September 54% 7% 40% -1% October 29% 9% 60% 2% November 46% 7% 46% 1% December 53% 6% 39% 2% Page 19

20 Components of the flexible capacity needs Three Hour Net Load Ramp % Expected Peak Load 2016 Flexible Capacity Need 2016 Three Hour Net Load Ramp % Expected Peak Load 2016 Flexible Capacity Need 2017 January 9,974 1,129 11,103 10,570 1,113 11,684 February 9,421 1,086 10,507 9,843 1,070 10,913 March 9,284 1,078 10,362 9,709 1,062 10,772 April 8,850 1,139 9,989 9,322 1,123 10,446 May 6,498 1,233 7,731 6,817 1,218 8,034 June 5,876 1,368 7,244 6,125 1,353 7,478 July 6,392 1,543 7,935 6,476 1,528 8,004 August 6,412 1,587 7,998 6,337 1,572 7,909 September 7,784 1,475 9,259 8,118 1,460 9,577 October 9,066 1,265 10,331 9,396 1,250 10,645 November 10,858 1,147 12,005 11,163 1,132 12,295 December 11,662 1,155 12,817 11,992 1,139 13,132 Page 20

21 Forecasted monthly 2016 through 2018 ISO systemwide flexible capacity needs* MW 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec ,103 10,507 10,362 9,989 7,731 7,244 7,935 7,998 9,259 10,331 12,005 12, ,684 10,913 10,772 10,446 8,034 7,478 8,004 7,909 9,577 10,645 12,295 13, ,714 11,192 11,070 10,702 8,282 7,721 8,061 7,944 9,796 10,906 12,567 13,277 *Flexibility Requirement MTHy = Max[(3RR HRx ) MTHy ] + Max(MSSC, 3.5%*E(PL MTHy )) + ε ε = 0 Slide 21

22 The 2016 forecasted distribution range of daily maximum and secondary 3-hour net load ramps Distribution of daily max 3- hour net load ramps Distribution of daily max Secondary 3-hour net load ramps Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec bottom 10% 10% to 20% 20% to 30% 30% to 40% 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec bottom 10% 10% to 20% 20% to 30% 30% to 40% 40% to 50% 50% to 60% 60% to 70% 70% to 80% 40% to 50% 50% to 60% 60% to 70% 70% to 80% 80% to 90% Max 80% to 90% Max Slide 22

23 Flexible capacity categories allow a wide variety of resources to provide flexible capacity Category 1 (Base Flexibility): Operational needs determined by the magnitude of the largest 3-hour secondary net-load ramp Category 2 (Peak Flexibility): Operational need determined by the difference between 95 percent of the maximum 3-hour net-load ramp and the largest 3-hour secondary net-load ramp Category 3 (Super-Peak Flexibility): Operational need determined by five percent of the maximum 3-hour net-load ramp of the month Slide 23

24 Seasonal breakout of flexible capacity needs Actual Contributions Super-Peak Base Flexibility Peak Flexibility Flexibility Seasonal Contribution* Super-Peak Base Flexibility Peak Flexibility Flexibility Jan 68% 27% 5% 64% 31% 5% Feb 67% 28% 5% 64% 31% 5% Mar 68% 27% 5% 64% 31% 5% Apr 64% 31% 5% 64% 31% 5% May 82% 13% 5% 87% 8% 5% Jun 86% 9% 5% 87% 8% 5% Jul 93% 2% 5% 87% 8% 5% Aug 88% 7% 5% 87% 8% 5% Sep 86% 9% 5% 87% 8% 5% Oct 74% 21% 5% 64% 31% 5% Nov 49% 46% 5% 64% 31% 5% Dec 55% 40% 5% 64% 31% 5% * The non-summer months are corrected from the percentages released in the draft report Page 24

25 MW MW Flexible capacity needs by category Total Flexible Capacity Needed in Each Category 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Super-Peak Flexibility Peak Flexibility 4,510 4,282 4,251 4, ,166 4,898 5,400 Base Flexibility 6,031 5,726 5,685 5,500 6,879 6,490 6,978 6,897 7,910 5,571 6,549 7,221 Seasonal Flexible Capacity Needed in Each Category* 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Super-Peak Flexibility Peak Flexibility 3,493 3,316 3,292 3, ,226 3,793 4,182 Base Flexibility 7,048 6,692 6,644 6,428 6,879 6,490 6,978 6,897 7,910 6,511 7,654 8,439 * The non-summer months are corrected from the percentages released in the draft report Page 25

26 Must offer obligation hours for Peak and Super-Peak flexible capacity categories Month Starting Hour for monthly average net load ramp Jan 14 Feb 15 Mar 16 Apr 16 May 16 Jun 15 Jul 12 Aug 12 Sep 14 Oct 15 Nov 14 Dec 14 Seasons: Non-summer: Jan Apr, Oct Dec Summer May Sep Must offer obligations hours: Summer months 12:00 p.m. through 5:00 p.m. Non-summer months 3:00 p.m. through 8:00 p.m. Page 26

27 Flexible capacity needs are largely attributable to CPUC jurisdictional LSE s Δ Load Δ PV Fixed* Δ Solar Thermal Δ Wind Δ PV Fixed* Δ Solar Thermal Δ Wind January 92% 97% 100% 91% 94% 100% 91% February 96% 97% 100% 91% 94% 100% 91% March 95% 97% 100% 91% 94% 100% 91% April 90% 97% 100% 91% 94% 100% 91% May 98% 97% 100% 91% 94% 100% 91% June 95% 97% 100% 91% 94% 100% 91% July 96% 96% 100% 91% 94% 100% 91% August 95% 96% 100% 91% 94% 100% 91% September 95% 96% 100% 91% 94% 100% 91% October 96% 96% 100% 91% 94% 100% 91% November 95% 96% 100% 91% 94% 100% 91% December 94% 96% 100% 91% 94% 100% 91% [1] Includes contributions from behind the meter solar. Slide 27

28 MW CPUC jurisdictional LSE s contribution to need Preliminary Flexible capacity Requirements: CPUC Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2016 Total Total Slide 28

29 MW Three categories of flexibility allow a variety of resource types to help address flexible capacity need 14,000 CPUC Flexible Capacity Need by Category* 12,000 10,000 8,000 6,000 4,000 2,000 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Super-Peak Flexibility Peak Flexibility 3,161 3,027 2,885 2, ,841 3,367 3,630 Base Flexiblity 6,380 6,108 5,822 5,687 5,895 6,177 7,037 6,907 7,338 5,733 6,795 7,326 * The non-summer months are corrected from the percentages released in the draft report Slide 29

30 Review of preliminary assessment results Flexible Capacity need is largest in the off-peak months Flexible capacity makes up a greater percentage of resource adequacy needs during the off-peak months Increase almost exclusively caused by 3-hour ramp, not increase in peak load Inclusion of incremental behind-the-meter solar PV contributes to the larger flexible capacity requirements Compared to last year s forecast: Flexible capacity needs are high in many months, Distribution of daily maximum three-hour net-load ramps are comparable Using the ISO flexible capacity contribution calculation majority of threehour net-load ramps are attributable to CPUC jurisdictional LSEs Flexible capacity categories demonstrate that there is ample opportunity for participation from various resource types Slide 30

31 Next Steps Stakeholder comments Due April 22, 2015 Final report May 1, 2015 Page 31

32 Thank You! Questions Slide 32