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Detailed Information on Changes in CELT 2019 Summer Demand Forecast

Learn about the changes impacting the CELT summer demand forecast for 2019 and how they affect the Installed Capacity Requirement. Get insights into the load forecast methodology and model specification changes.

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Detailed Information on Changes in CELT 2019 Summer Demand Forecast

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  1. September 10, 2019 Jon Black Manager, Load Forecasting NEPOOL Reliability Committee Supplemental Information on Changes in the CELT 2019 Summer Demand Forecast

  2. Supplemental Information on Changes in the CELT 2019 Summer Demand Forecast At the July 25, 2019 Power Supply Planning Committee (PSPC) meeting, the ISO reviewed the long-term load forecast as it relates to the Installed Capacity Requirement (ICR) At the August 9, 2019 PSPC and August 20, 2019 Reliability Committee (RC) meetings, the ISO presented the estimated impact on ICR due to the 2019 load forecast changes At both meetings, Market Participants requested additional information on the changes that caused a decrease in the ICR, specifically, the forecast cycle changes case, the addition of a second weather variable case, and using a the shorter history weather period case (see Appendix I next slide) This presentation provides additional details on each of those changes

  3. Detailed Information on the Load Forecast Methodology • At the August 9, 2019 PSPC and August 20, 2019 RC, Market Participants also asked for the location of the details of the load forecast methodology • In response, on August 22, 2019, on the ISO shared with members and alternates of the RC and PSPC the following files, which are published annually on the load forecast webpage: • The energy and demand modeling methodology is described in the Forecast Modeling Procedure (e.g., 2019 Forecast Modeling Procedure). • All final forecast values are published in a Forecast Data spreadsheet containing a number of worksheets (e.g., 2019 Forecast Data). • All resulting energy and peak models are documented in a Model Details spreadsheet (e.g., 2019 Energy & Peak Model Details). • Ten year hourly forecasts in EEI format (e.g., hourly 2019 forecasts for the Region, RSP Subareas, and SMD Load Zones). • Additionally, materials associated with the Load Forecast Committee stakeholder discussions can be found at: https://www.iso-ne.com/committees/reliability/load-forecast/

  4. Forecast Cycle Change

  5. Background • Each year, the ISO updates the macroeconomic, load and weather data used to develop its peak demand forecast • The 2019 forecast changes caused by the updated macroeconomic forecast are not significant • Since CELT 2016, ISO has used 15 years of load and weather data to estimate peak demand forecast models • The result has been that, for each forecast cycle, the newly available year of data replaces the first year used in the previous forecast • Over the past two decades, there has been a decline in the overall electric energy intensity of the New England economy, in part due to the increased end-use efficiency improvement driven by federal standards • For the past few years, a result of these trends has been a decrease in the summer demand forecast as data are updated for each successive forecast cycle

  6. End-Use Efficiency Driven by Federal StandardsDepartment of Energy Appliance Standards * Source: Bonneville Power Administration, Appliance Standards – How they interact with energy efficiency programs, 2015. A significant share of building end-use electricity consumption is subject to Department of Energy (DOE) standards Since 2005, 45 mandatory DOE efficiency standards have taken effect (refer to graph)* Evolution of these standards drive out-of-market, end-use efficiency improvements The resulting electricity savings occur after the implementation of standards as appliance stock turns over

  7. Electric Energy Intensity of Regional Economy1991-2018 • The electric energy intensity of the regional economy has been declining for the past few decades • The graph illustrates the long-term trend in the relationship between annual electric gigawatt-hours and regional gross state product • The brown line is based on net load energy • The blue line is based on gross load energy after reconstituting for the energy savings from energy efficiency (EE) and behind-the-meter photovoltaics (BTM PV) • Based on the difference between the blue and brown lines, the effects of market-facing EE and BTM PV have been responsible for most, but not all, of this decline in intensity since 2006

  8. Observations *CAGR = Compound Annual Growth Rate These trends of less energy-intensive economic growth and greater out-of-market end-use efficiency are captured as new data are added to the historical period used to estimate ISO’s econometric load forecast model and the earlier data rolls off The decrease in the load forecast due to this standard data refresh has also been observed for the previous two CELT forecasts without any methodological changes

  9. Model Specification Change

  10. Background • As described in the July 25, 2019 PSPC presentation (refer to slides 1-14): • The model specification changes incorporated in the summer peak demand modeling used in CELT 2019 were made to mitigate forecast performance issues identified during extreme weather conditions in the summer of 2018 • The new model specification results in significant improvements in forecast performance, especially during extreme weather (i.e., peak load) conditions • A total of fifteen years of summer data were used to validate performance improvements • By way of additional background, the results of additional model validation work is shown on the following two slides: • The results are from a simulation of the CELT 2011 forecast with the CELT 2018 and CELT 2019 summer demand models, so that 8 years of out-of-sample forecast performance could be evaluated (2011-2018)

  11. Performance of Summer Peak Demand ModelHistorical out-of-sample performance Out-of-Sample Model Performance • To test for out-of-sample performance, the period used for model estimation ended in 2010 for both the CELT 2018 and CELT 2019 models • Note that actual energy was an input to the demand models to remove the effects of recession-driven, macroeconomic forecast uncertainty and, thus, to isolate the performance of the demand models • A comparison of out-of-sample forecast mean absolute percent error (MAPE) and mean error (ME) during 2011-2018 summer days (July non-holiday weekdays) is tabulated below • Based on out-of-sample performance, the 2019 summer peak demand model performs much better than the 2018 model

  12. Scatter Plot of Out-of-Sample Model PerformanceJuly Non-Holiday Weekdays, 2011-2018 When peak loads are highest, the blue squares are closer to the actual peak loads (represented by the dotted black line) than the red circles

  13. Weather History Change

  14. Background • ISO uses historical weather to generate a weekly distribution of peaks loads for the 10 year forecast horizon • For CELT 2019, ISO shortened the weather history used to generate its probabilistic, weekly demand forecast from 40 to 25 years • This change was made primarily because wind speed data needed for the new winter demand model used for CELT 2019 was not available for all of the years of the former 40 year period, and to keep a consistent historical weather period for both summer and winter monthly forecasts • Upon considering this change, a survey of other ISO/RTO methodologies revealed that a shorter length of weather history used in demand modeling is more consistent with the length of weather history used by other North American ISO/RTOs (see table below) • Most other ISO/RTOs use 25 or fewer years in their selection of weather history

  15. Weather Variable Probability DistributionCurrent 25 Year History & Former 40 Year History • Below are the weekly 95th and 99th percentiles (for July and August weeks) of the two weather variables used in the summer demand models, cooling degree days (CDD) and 3-day weighted temperature-humidity index (WTHI) • A comparison of these values is indicative of the changes in the “peak-eliciting” portion of the resulting weekly load distribution that impacts ICR • While the CDD values are relatively consistent for both historical weather periods, the lower WTHI values across some weeks for the 25 year weather period results in slightly lower probabilities for higher loads in those weeks, which caused the decrease in ICR

  16. Acronyms BTM PV – Behind-the-meter photovoltaic CDD – Cooling degree days CELT – Capacity, Energy, Loads and Transmission DOE – Department of Energy EE – Energy efficiency ICR – Installed Capacity Requirement ISO – ISO New England MAPE – Mean absolute percent error ME – Mean error PSPC – Power Supply and Planning Committee PV – Photovoltaic RC – Reliability Committee WTHI – Weighted temperature-humidity index

  17. Appendix I Impact of changes to the load forecast methodology on ICR for FCA 14

  18. 2019 CELT Load Forecast Changes The 2019 CELT load forecast* was reviewed in detail at the July 25, 2019 PSPC meeting. The 2019 CELT load forecast has incorporated the following component changes to the load forecast methodology in addition to the standard update to the underlying macro economic assumption and model estimation period: • Forecast Cycle Change (Standard Update) • This would be the normal forecast change as done in the past • Underlying input macro economic assumptions seen in 2019 vs 2018 • Model estimation period – daily peak load and weather for the historical period covering 2004-2018 (2003-2017 used last year) • 2019 Changes to the methodology of the load forecast model • Added a Second Weather Variable (Modeling Specification) • Incorporated a second weather variable, cooling degree days (CDD), in addition to weighted temperature-humidity index (WTHI) • Separated July and August Peak Modeling (Monthly Peak Demand Modeling) • Developed separate July and August monthly models rather than a combined July/August summer seasonal peak model • Shortened Weather History (Weather History Change) • Historical weather period used to generate probabilistic forecast shortened from 40 years to 25 years • New 25-year period covers 1991-2015 (1975-2014 used last year) *For details of the 2019 load forecast, please see Review of the 2019 Long-Term Load Forecast presentation at: https://www.iso-ne.com/static-assets/documents/2019/07/20190725_a03_2019_longterm_forecasts_icr.pptx For details on the load forecast methodologies and assumptions, please see: https://www.iso-ne.com/committees/reliability/load-forecast/

  19. Analysis to Identify Impact on ICR associated with the Changes in Load Forecast Methodology The following simulations were conducted using a common set of resource assumptions developed for FCA 14 ICR calculations for scenario that includes Mystic Units 8 & 9 to identify the impact of each of the methodology changes to the gross load forecast model for the CCP 2023-2024: 2018 Case • Calculate the ICR using the 2018 CELT gross load forecast for the CCP 2023-2024 Forecast Cycle Case • Calculate the ICR with the same load forecast modeling methodology as used to develop the 2018 CELT gross load forecast • Updated underlying input assumptions associated with the 2019 CELT forecast • Revised daily peak load and weather model estimation period • Used 2004-2018 instead of the 2003-2017 period used in the 2018 CELT load forecast

  20. Analysis to Identify Impact on ICR associated with the Changes in Load Forecast Methodology, cont. Second Weather Variable Case • Using the Forecast Cycle Case input assumptions modify the gross load forecast based on incorporating a second weather variable, cooling degree days (CDD), in addition to the weighted temperature-humidity index (WTHI) Separate July and August Peak Load Model Case • Using the Forecast Cycle Case input assumptions modify the gross load forecast developed by using separate July and August monthly peak models rather than a combined July/August summer seasonal peak model Shorter History Weather Period Case • Using the Forecast Cycle Case input assumptions modify the gross load forecast developed based on a shorter historical weather period, from 40 years to 25 years, to generate the probabilistic forecast. The new 25-year period covers 1991 through 2015 instead of the 40-year (1975 through 2014) period used in the 2018 CELT load forecast

  21. Analysis to Identify Impact on ICR associated with the Changes in Load Forecast Methodology, cont. Methodology to Obtain Impact of Individual Changes • The impact of the forecast cycle change is obtained by comparing the ICR of the 2018 Case with the ICR of the Forecast Cycle Case • The impact of each component change to the load forecast methodology is obtained by comparing the ICR of each case with and without the change

  22. Estimated Impacts on ICR due to 2019 Gross Load Forecast Changes

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