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Enhanced Methodology for Evaluating Energy Efficiency Resources in ISO New England

This presentation outlines additional considerations for determining the performance of on-peak and seasonal peak energy efficiency resources (EERs) in all hours. It discusses the validity of assumptions, proposes a refined methodology based on system load, and provides examples and a summary of the proposal.

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Enhanced Methodology for Evaluating Energy Efficiency Resources in ISO New England

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  1. April 2019 | Holyoke, MA Doug Smith ISO New England Demand Resources Working Group Additional Considerations for Determining Performance of On-Peak and Seasonal Peak Energy Efficiency Resources (EERs) in all hours (revised)

  2. Presentation Outline • Background – Shaping Option A • The Validity of the Assumption that the Hourly Performance of a Broad Energy Efficiency Portfolio is Correlated with System Load • Basic Shaping Formula • The Confounding Impact of Behind-the-Meter Solar PV • Application of Proposed Refinements to Shaping Option A • Examples • Proposal Summary Comments

  3. Background: shaping currently known savings parameters (from 3/26/2019 presentation) Shaping optionA: • Where: • ACPee, On-Peak = The EER’s ACP had the CSC occurred in an On-Peak interval that is closest in time to the CSC that occurred • SL = System Load during CSC interval • PSL = projected seasonal peak load (the same peak load forecast used by the ISO to determine Demand Resource Seasonal Peak Hours) • 1.08 = gross-up for avoided T&D losses • This approach can be implemented immediately at low cost • But ACPee, off-peak is not calibrated to total off-peak energy savings estimated through M&V studies [where Off-Peak kWh Savings = kWh Savings x (1 - CF)], so summing ACPee, off-peak for every off-peak hour will probably not equal total off-peak energy savings

  4. Proposed Methodology - Assumption • Based on discussion at the DRWG meeting on March 26, Shaping Option A appeared to attract the most support from the working group and was the only option that could be implemented quickly and at relatively low cost • Implicit in that proposed methodology is the assumption that EE savings across a diverse portfolio of measures located throughout New England correlate to total system load • Intuitively, savings produced by a portfolio of Energy Efficiency measures should be greater during high-load periods, and lower during low-load periods • Individual Energy Efficiency measures produce savings during the operating hours of the affected end-use application, but a large portfolio of Energy Efficiency measures affecting a cross-section of end-use applications likely produce savings that follow the end-use load shape

  5. Is the Assumption Valid? • “Publicly available data on end-use load and energy savings shapes are limited, concentrated regionally, and should be expanded.” • Source: Berkeley Lab ‘Time Varying Value of Electrical Energy Efficiency’ July 10, 2017 Presentation and Report PDF links at https://emp.lbl.gov/publications/time-varying-value-electric-energy. • There is evidence that Energy Efficiency ‘Energy-to-Peak’ ratios of 0.55-0.59 do correlate well with ISO-NE load factor (0.54 in 2018) • The Brattle Group “Assessment of Load Factor as a System Efficiency Earning Adjustment Mechanism” February 10, 2017 pp 6-7 http://documents.dps.ny.gov/public/Common/ViewDoc.aspx?DocRefId=%7B92F01814-118C-4BCA-9B42-5A81B4205BAD%7D

  6. Is the Assumption Valid (Cont.)? • Review of Energy Efficiency Measure (EEM) data currently participating in ISO-NE FCM indicates wide diversity of measures and end use types though lighting is dominant

  7. Is the Assumption Valid (Cont.)? • According to EPRI research, lighting load shapes appear to correlate reasonably well with typical system load shapes • Source: http://loadshape.epri.com/enduse

  8. Is the Assumption Valid (Cont.)? • Based on currently available data, the assumption that EE performance correlates with total system load appears to be reasonable • The results of new studies, such as the NREL/LBNL 3-year study, should be monitored as results become available

  9. Shaping Formulas • On-Peak Resources: • Seasonal Peak Resources: • Where: • ACPee = Actual Capacity Provided by EE Resource • Perfee = The EER’s reported performance for the month • SL = System Load during CSC interval(s) • ASLs,w= Average System Load during On-Peak hours of most recently completed 3 summer or 2 winter performance months • SPLs,w = 90% of the 50/50 peak load forecast for the season • 1.08 = gross-up for avoided T&D losses • Two refinements are incorporated here: • Use hourly average System Load during On-Peak hours for On-Peak Resources, and 90% of the 50/50 peak load forecast for each season for Seasonal Peak Resources, in the denominator of the ratio • Shaping may be applied to determine ACP for all hours, not just ‘off-peak’ hours, eliminating the discontinuity between performance and non-performance hours

  10. What ‘System Load’ are EE Savings Correlated To? • A significant amount of load is masked by generators not participating in ISO-NE markets • Behind-the-Meter (BTM) generation appears to the ISO as load reduction • BTM includes all generation in New England that is not reporting generation output to ISO-NE • The largest component of BTM generation is PV, and this component continues to grow significantly • Reconstituted Load = ISO System Load plus PV Reconstitution • System Load is the sum of metered generation and metered net interchange, plus demand from pumped storage units and grossed up demand response.

  11. BTM Generation Impacts - PV • PV represents a significant portion of BTM generators, and its performance follows a specific and near coincident pattern • The ISO currently produces a daily operational estimate of BTM PV hourly output • At current levels of BTM PV installations, output ranges from zero to about 2000 MW • The unique performance characteristics of PV can cause both positive and negative bias in EE performance calculations if not accounted for • Given the significant and growing quantity of BTM PV as well as the availability of actual BTM PV performance data, System Load should be reconstituted for it prior to calibrating EE performance levels • Hourly EE performance is correlated to actual total system load (i.e., overall energy consumption levels), not observed System Load which is net of BTM PV production • To establish an accurate hourly ACP estimate for EE, BTM PV production must be added back to observed System Load to establish hourly EE performance

  12. Average BTM PV On-Peak Hours, Summer 2018 On-Peak Hours Average BTM PV output over On-Peak Hours = 954 MW Source: ISO estimates of actual BTM PV, from June 1, 2018 to August 31, 2018

  13. Historical PV Performance Data • ISO has contracted with a third-party vendor for PV production data services • Includes data from more than 9,000 PV installations • Data reported at 5-minute intervals, at the town level • Broad geographic coverage • Data provided begins in 2014 • An example snapshot of regional data is plotted to the right • Data are for February 8, 2019 at 1:00 pm • Yellow/red coloring shows level of PV production • No data available in towns colored gray • Data not requested in towns colored blue • This data is available the day following each operating day

  14. Effects of BTM PV on EE ACP • Without accounting for BTM PV, calculated EE performance could be biased • Bias occurs if BTM PV output is different during a scarcity condition compared with its average during On-Peak or Seasonal Peak performance hours • EE ACP would be biased downwards during periods of higher BTM PV • EE ACP would be biased upwards during periods with lower or no BTM PV • Shaping Factor = • Where: • SL = ISO System Load during scarcity • PV = BTM PV during scarcity • = Average ISO System Load during Performance Hours in each season • = Average BTM PV during Performance Hours in each season

  15. Low BTM PV During Scarcity Example Capacity Scarcity Condition during period of low BTM PV • System Load, Scarcity Condition (SL) = 23,122 MW • Average System Load, On-Peak Hours (ASLs)= 19,204 MW • BTM PV, Scarcity Condition (PV) = 0 MW • Average BTM PV, On-Peak Hours = 954 MW Without PV Impacts • Shaping Factor: Accounting for PV in this scenario lowers the ACP provided by On-Peak Resources With PV Impacts • Shaping Factor:

  16. Other BTM Generation • We do not have an estimate of hourly output of non-PV BTM generation • However, we do know that most non-PV systems are combined heat-and-power plants that do not have as much output fluctuation as PV from hour to hour • We would expect far less bias from ignoring the impact of these versus PV systems

  17. On-Peak Resource Shaping Proposal • Where: • ACPee= Actual Capacity Provided by the EER • Perfee, On-Peak = The EER’s reported On-Peak performance for the month • SL= System load during CSC interval(s) • PV = BTM PV during CSC interval(s) • ASLs,w = Average System Load during On-Peak hours of most recently completed 3 summer or 2 winter performance months • PVs,w= Average BTM PV output during On-Peak hours of most recently completed 3 summer or 2 winter performance months • 1.08 = gross-up for avoided T&D losses

  18. On-Peak Example 1 – Labor Day 2018 • ACP is higher than reported On-peak reported performance x 1.08 • System Load during CSC on Labor Day were 3,918 MW higher than the average load during On-peak hours • BTM PV output was 486 MW lower during the CSC vs. its average during On-peak hours • The higher load during scarcity on Labor day more than offset the lower PV output

  19. On-Peak Example 2 – CSC during Low Load • ACP reduced to about 60% of reported On-peak performance x 1.08 • System Load during CSC was 7,204 MW lower than the average load during On-peak hours • BTM PV output was 954 MW lower during the CSC vs. its average during On-peak hours • The lower load and BTM PV output during the CSC result in lower ACP

  20. Seasonal Peak Resource Shaping Proposal • Seasonal Peak hours are hours where load is 90% of the 50/50 peak load forecast for each season • This is the appropriate calibration point for Seasonal Peak Resources • Where: • ACPee = Actual Capacity Provided • Perfee, Seasonal Peak = The EER’s reported performance for the month • SL = System Load during CSC interval(s) • PV = BTM PV during CSC interval(s) • SPLs,w = 90% of the net 50/50 peak load forecast for the season • PVs,w = Forecasted effect of BTM PV during peak load for the season • 1.08 = gross-up for avoided T&D losses

  21. Seasonal Peak Example 1 – Labor Day 2018 • = 548 MW • ACP is slightly lower than Seasonal Peak reported performance x 1.08 • The ACP of Seasonal Peak resources is about the same as the Seasonal Peak performance during Seasonal Peak hours, as the reconstituted load during the CSC was about the same as the threshold for Seasonal Peak Hours

  22. Seasonal Peak Example 2 – CSC during Low Load • = 279 MW • ACP reduced to almost half of reported On-Peak performance • The lower load during the CSC results in lower ACP for Seasonal Peak resources

  23. Proposal Summary Comments • The ISO proposes for working group consideration the following approach to Shaping Option A: • The ACP of On-Peak Demand Resources consisting of Energy Efficiency measures: • The ACP of Seasonal Peak Demand Resources consisting of Energy Efficiency measures:

  24. Proposal Summary Comments (cont.) • Based on currently available data, the assumption that EE performance correlates with total system load appears to be reasonable • The results of new studies, such as the NREL/LBNL 3-year study, should be monitored as results become available • Three refinements to the approach presented on March 26, 2019 are proposed: • Use hourly average System Load during On-Peak Hours for On-Peak Resources, and 90% of the 50/50 peak load forecast for each season for Seasonal Peak Resources, in the denominator of the ratio • Apply the formula to determine ACP for all hours, not just ‘off-peak’ hours • Reconstitute observed System Load for BTM PV production • The resulting ACP could be used to determine a Capacity Performance Payment for EE resources • Actual incentives and penalties will depend on the individual resource’s ACP relative to its CSO as well as the Balancing Ratio • This approach can be implemented relatively quickly at low cost • All inputs are available immediately after a CSC occurs • Implementation cost and complexity are low compared with other options considered

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