NYISO 2015/2016 ICAP Demand Curve Reset

NYISO 2015/2016 ICAP Demand Curve Reset Business Issues Committee March 17, 2016 BOSTON CHICAGO DALLAS DENVER LOS ANGELES MENLO PARK MONTREAL NEW YORK SAN FRANCISCO WASHINGTON

Today s Presentation Provide a summary of the recommended changes to the ICAP Demand Curve reset (DCR) process, including changes to: DCR Periodicity Annual updating of ICAP Demand Curve values Provide a review and summary of issues discussed at the ICAPWG, including: Other proposals and issues evaluated throughout the process Summary of quantitative backcasting analysis Appendix includes additional details and numeric examples presented at the February 19, 2016 and March 3, 2016 ICAPWG meetings for the recommended DCR process changes Page 1

Summary of Proposed DCR Changes DCR Period: 4 years (currently, 3 years) Net EAS revenues Forecast net Energy and Ancillary Services (EAS) revenues using three-year average of historical net revenues (with no econometric adjustments) Adjust historical revenues to reflect market conditions at the tariff-specified level of excess (i.e., minimum installed Capacity Requirement, plus MW rating of peaking unit) through GE MAPS based scaling factors ( level of excess adjustment) Annual updates Net EAS revenues: Update annually based on updated historical net EAS revenues (new LBMPs and costs), applying fixed level of excess adjustment factors Gross cost of new entry (CONE): Adjust all zones based on single composite escalation factor, using finalized data at the time of reset/update, sourced from publicly-available indices Calculation of Reference Point Prices: Include annual updates to the winter-tosummer ratio (WSR) to reflect changes in system conditions Page 2

Decision Criteria Economic Principles: Each recommendation should be grounded in economic theory and reflect the structure of, and incentives in, the NYISO markets Accuracy: Choices should be made with the goal of providing accurate results that capture market expectations regarding net CONE Transparency: The DCR calculations and periodic updates to net CONE should be clear and transparent to Market Participants; calculation and update methods should be understandable and allow Market Participants to develop market expectations Feasibility: The DCR design and implementation should be practical and feasible from regulatory, administrative, and Market Participant perspectives Historical Precedence and Performance: DCR design decisions should where possible and relevant be informed by quantitative analysis based on historical data, and draw from lessons learned in the NYISO, ISO-NE and PJM wholesale capacity markets Page 3

Analysis Group ICAPWG Participation Analysis Group (AG) has worked with the NYISO and the ICAPWG to develop, review, and refine its initial proposal regarding changes to the DCR process. October 19, 2015: Introduction, initial scoping of key issues November 18, 2015: Initial discussion of key drivers to extend DCR period Initial discussion of alternative net EAS revenue approaches December 16, 2015: Detailed discussion of interrelated threshold issues January 26, 2016: Preliminary recommendations Detailed evaluation of key recommendations Initial backcasting of net EAS revenues, with annual updates February 19, 2016: Revisions to preliminary recommendations Additional details on annual update process Numeric examples, net EAS revenue estimation March 3, rd 2016: Updates to recommendations Additional backcasting of reference point prices, with annual updates Page 4

Benefits of Proposed Approach Increase in Periodicity Increases market certainty/stability vs three year reset Does not meaningfully increase risk that peaking unit technology will change between DCRs, compared to three years Provides an appropriate balance of accuracy, transparency, and feasibility, particularly if paired with annual updating and a more transparent net EAS revenue estimation strategy Annual Updates Create a more predictable and continuous evolution of reference point prices between and within DCRs, and reduce risk of step changes in reference point prices with DCRs Allow reference point prices to evolve between resets to capture changing market conditions that affect the true cost of new entry Reduce the need for one-time forecast adjustments at the time of each reset Benefits supported through qualitative assessment and quantitative backcasting analysis (next slide). Page 5

Conclusions of Backcasting Analysis Analysis Group conducted a backcasting of annual updates and historical reference point prices. We concluded: Variability in reference point prices can be due to changes in either or both installed capital costs and net EAS revenues The net effect depends on the relative magnitude and importance of each component Between DCRs, annual updates have similar, or reduced variability, relative to the current approach Current approach tends to have greater variability between resets for each component (installed capital costs and net EAS revenues) individually Annual updates, by design, tend to have greater variability within each DCR period, with variability tracking actual changes in the cost of new entry This intra-reset variability reduces the potential for significant step changes with each reset Additional detail and results are included in the Appendix. Page 6

Reference Price ($/kw-month) Illustrative Backcast: Reference Prices Actual Reference Price 2007 LMS 2013 F-Class $25 $20 Actual Reference Price, with variability between resets and constant escalation within resets $15 $10 2013 F-Class Reference Price reflects: 2013 NERA demand curve model 2013 F-class installed capital cost; subsequent annual values based on updated annual escalation factors F-Class historical net EAS Revenues $5 2007 LMS Reference Price reflects: 2007 NERA demand curve model 2007 LMS installed capital cost; subsequent annual values based on updated annual escalation factors LMS historical net EAS Revenues The actual reference price technology at the time of each DCR LMS Tech LMS Tech F-Class Tech $0 2009 2010 2011 2012 2013 2014 2015 Page 7

Summary of Additional Considerations Throughout this process, Analysis Group also reviewed and considered: DCR Periodicity of 5 or 6 years Longer periods offer less frequent opportunity for Market Participant feedback and may potentially reduce accuracy over time Alternative net EAS revenue models, including econometric model or historic approach with futures adjustment A futures adjustment introduces potential for market manipulation, with concerns regarding zonal liquidity; also introduces potential overlap with other annual updates and adjustments Econometric approach requires forecasts of structural market changes and reduced transparency/predictability Annual updates to level of excess adjustment factors Fixed LOE adjustments factors are reasonable and provide for directionally and economically appropriate adjustment that seeks to reflect market conditions when at need Page 8

Today s Presentation Periodicity Initial ICAP Demand Curve Values Estimation of Gross CONE Estimation of Net EAS Revenues Determination of ICAP Demand Curve Parameters Annual Updates Updating of Gross CONE Updating of Net EAS Revenues Updating of ICAP Demand Curve Parameters Appendices Additional Backcasting Analysis Net EAS Revenues Numeric Examples Additional Exhibits Page 9

Recommendation: DCR Period Recommendation: switch from current three-year period between resets to a four-year period Five or six years: Deemed not prudent Forecast error increases with time Fewer opportunities to incorporate stakeholder feedback Higher risk that peaking unit technology and forecast elements deviate from actuals Three years: Status quo Accepted and understood by Market Participants Frequency of resets reduces market stability, particularly if/when peaking unit technology changes Four years: Recommended May increase market certainty/stability vs three year reset Does not meaningfully increase risk that peaking unit technology will change between DCRs, compared to three years Interaction with other recommendations: Move to longer DCR period increases the value of adopting a method that includes annual updates to gross CONE and net EAS revenues Annual updates must be sufficiently transparent to promote and ensure market stability Additional detail on annual updates are provided on the following slides and in greater detail starting on slide 16. Page 10

Demand Curve Calculations Over the DCR Period Capability Year 1 (2017/18) Capability Year 2 (2018/19) Capability Year 3 (2019/20) Capability Year 4 (2020/21) Capability Year 5 (2021/22) Assessment of peaking unit technology: Gross CONE Net EAS revenues ICAP Demand Curve Values for First Capability Year Timing: Data from 3- year period ending Aug. 31, 2016 Parameters in Nov. 2016 Annual Updates Gross CONE inflation adjustment Net EAS revenues Winter Summer ratio ICAP Demand Curve Values for Second Capability Year Timing: Data from 3- year period ending Aug. 31, 2017 Parameters in Nov. 2017 Annual Updates Gross CONE inflation adjustment Net EAS revenues Winter Summer ratio ICAP Demand Curve Values for Third Capability Year Timing: Data from 3- year period ending Aug. 31, 2018 Parameters in Nov. 2018 Annual Updates Gross CONE inflation adjustment Net EAS revenues Winter Summer ratio ICAP Demand Curve Values for Fourth Capability Year Timing: Data from 3- year period ending Aug. 31, 2019 Parameters in Nov. 2019 Assessment of peaking unit technology: Gross CONE Net EAS revenues ICAP Demand Curve Values for First Capability Year Timing: Data from 3- year period ending Aug. 31, 2020 Parameters in Nov. 2020 Additional detail on annual updates are provided on the following slides. Page 11

Proposal : Net EAS Revenue Estimation Net EAS revenues earned by the peaking plant would be estimated as follows: Net EAS revenues estimated as the average net revenues earned by each peaking plant over a 3-year rolling historical period A level of excess adjustment will be made to historical prices based on scaling factors developed through a GE MAPS analysis, determined as part of the DCR Net EAS revenues for the 2017/18 Capability Year will be estimated using the 3- year period September 2013 to August 2016 Initial consultant draft report will reflect data from May 2013 to April 2016; data will be updated in October for the initial filing in November Rationale: Improve transparency and predictability of net EAS revenue calculations; Enable the periodic updating of net EAS revenue values to reduce forecast uncertainty; and Increase viability of longer (i.e., 4-year) DCR period Page 13

Proposal: Level of Excess Adjustment Retain adjustment of net EAS revenues to reflect tariffprescribed level of excess conditions Purpose: adjust historic prices (Energy and Operating Reserves) to approximate net EAS revenues at system conditions based on the minimum Installed Capacity Requirement plus the capacity of the peaking plant appropriate adjustments can improve the accuracy of results in approximating net CONE Level of Excess ( LOE ) adjustment to be estimated using GE MAPS model run(s) based on most recent CARIS database to identify adjustment factors for net EAS revenue estimation LOE adjustment factors based on ratio of (1) LBMP at tariff-prescribed excess conditions and (2) LBMP under current resource conditions Adjustment factors calculated by zone, by month, and by intra-month period (e.g., off-peak, on-peak, high on-peak) Adjustment would be estimated through adjustment factors established at the time of the DCR and would remain fixed until the next DCR Page 14

Net EAS Revenue Model Description Net EAS revenues model features: Hourly net revenues reflect the maximum of: day-ahead commitment, and real time dispatch, conditional on the unit s day ahead commitment that is, unit operations in real-time may reflect a change in operating status if more profitable given day-ahead commitment o For example, unit may buy-out of DAM reserves commitment if more profitable to supply energy in real-time; or, unit may supply in real-time, if not committed day-ahead Hourly net revenues calculated to ensure that fixed startup fuel and costs are recovered Reserve revenues reflect operating capability of the peaking plant Peaking plant supplies full or no output (no partial unit supply) Dual fuel capability accounted for through the option to generate on natural gas or ULSD based only on day-ahead fuel prices Detail and numerical examples for the net EAS model are provided in the Appendix. Page 15

Net EAS Revenue Model Page 16

Today s Presentation Periodicity Initial ICAP Demand Curve Values Estimation of Gross CONE Estimation of Net EAS Revenues Determination of ICAP Demand Curve Parameters Annual Updates Updating of Gross CONE Updating of Net EAS Revenues Updating of ICAP Demand Curve Parameters Appendices Net EAS Revenues Numeric Examples Additional Exhibits Page 17

Gross CONE Escalation Factor Gross CONE escalated annually by single state-wide composite index, reflecting most current, finalized data available at the time of the update Index Weight Source Data Period BLS, Quarterly Census of Employment and Wages; Utility System Construction, New York Statewide, Wages 25% Private Owner All Establishment Sizes, Average Annual Pay Annual Materials and Components (M&C) Turbines and Turbine Generator Sets (TGen) 30% 30% (Series ID: ENU360005052371) BLS Producer Price Index, Not Seasonally Adjusted, Final Demand-Intermediate Demand Goods Index, Materials and Components for Construction (Series ID: WPUID612) BLS Producer Price Index Commodity Data, Not Seasonally Adjusted, 11 Machinery and equipment, 97 Turbines and Turbine Generator sets (Series ID: WPU1197) Monthly (Year over Year change uses the 3-month average of finalized data, which is Feb/March/Apr for an October update) Other (GDP Deflator) 15% Bureau of Economic Analysis, Gross Domestic Product Implicit Price Deflator, Index 2009 = 100, Seasonally Adjusted Quarterly (Year over Year Change, Q2 and prior year Q2) Initial weights provided by Lummus are preliminary; final values will reflect selected peaking unit technology costs. FEBRUARY 29 2016 PRESENTATION TO NYISO ICAPWG Page 18

Additional Backcasting Analysis In response to stakeholder requests, new information was developed to inform decisions on proposed DCR process changes Additional backcasting analysis has been performed, including: Comparison of actual historical reference prices to estimated ( backcast ) reference prices that incorporate updating of two reference price elements: 1. Installed capital cost is adjusted annually across the entire 2009 to 2015 period based on the proposed composite escalation factor 2. Net EAS revenue is updated annually based on data from the prior three-years of historic market data Backcast reference prices hold constant all other elements of the reference price calculations; in particular, installed capital costs are not updated to new engineering estimates with each DCR Analysis provides a comparison of: Variability within reset periods due to annual updates Variability between resets due to methodology changes (i.e., new net EAS revenue calculations) Page 20

Additional Backcasting Analysis (cont d.) Backcasting results are limited to historical and available information Do not present gas price sensitivities (although sensitivity to gas prices is, in part, captured by historical variation in fuel prices and LBMPs) Do not present econometric estimates of net EAS revenues for alternative technologies Results presented are preliminary and do not reflect final net EAS revenue model and ICAP Demand Curve model: Net EAS revenues are based on preliminary results, using proposals and methodologies described in presentations to ICAPWG Backcasting results of reference point prices presented rely on previous NERA DCR models and assumptions Hold all assumptions (other than installed capital costs and net EAS revenues) constant Page 21

Methodology Backcasting analysis incorporates two elements of the updating proposal (within the NERA DCR model): Update of installed capital cost Apply annually updated composite escalation factor to installed capital cost estimates Update of Net EAS revenues Three-year rolling average of net EAS revenues based on historical prices (September to August period) Backcasting results do not include an adjustment to energy revenues for the tariff-prescribed level of excess Results do account for the capacity of the peaking plant when translating annual revenue requirements into monthly reference point prices, using the assumptions from the previous demand curve models developed by NERA Page 22

Methodology Results include three series of reference point prices: Actual past reference point prices Backcast of reference point prices, developed using the 2007 NERA model, installed capital cost estimate, and annual update values (red line) Reflects LMS technology for NYC and LI, F-class for NYCA Backcast of reference point prices, developed using the 2013 NERA model, installed capital cost estimate, and annual update values (green line) Reflects F-class for all zones Hypothetical results using the LMS for NYC and LI are included for comparison All results are presented in current year (nominal) dollars Page 23

Reference Price ($/kw-month) NYC Reference Price $25 Actual Reference Price 2007 LMS 2013 F-Class $20 $15 $10 $5 $0 LMS LMS F-Class 2009 2010 2011 2012 2013 2014 2015 Page 24

Reference Price ($/kw-month) NYCA (Zone F) Reference Price $14 Actual Reference Price 2007 F-Class 2013 F-Class $12 $10 $8 $6 $4 $2 F-Class F-Class F-Class $0 2009 2010 2011 2012 2013 2014 2015 Page 25

Reference Price ($/kw-month) Long Island Reference Price $16 Actual Reference Price 2007 LMS 2013 F-Class $14 $12 $10 $8 $6 $4 $2 $0 LMS LMS F-Class 2009 2010 2011 2012 2013 2014 2015 Page 26