1. Load Impact Estimation for Demand Response Resources Nicole Hopper, Evaluation Manager July 14, 2009 National Town Meeting on Demand Response and Smart Grid Washington, DC

2. 2 Overview Context and uses for DR load impact estimation –Operations vs. settlement vs. resource planning –Ex post vs. ex ante load impacts Why establish load impact protocols for DR? –California was first –Ontario is well underway Estimation methods, key concepts and issues Conclusions

3. 3 Different Analytical Needs for Different Purposes Need to know the resources available under varying operating conditions (i.e., location, time of day, notification lead time, market prices, weather) Requires ex-ante estimation Estimates needed at the aggregate level (possibly by location) Prediction accuracy is critical SETTLEMENT OPERATIONS LONG-TERM RESOURCE PLANNING Need customer buy-in into method Participants want payment quickly Requires ex-post estimation Estimates needed at the individual participant level Prediction accuracy affects costs (overpayments /penalties) Ability to compare DR with other resources is critical— need to forecast load impacts under the conditions for which the system is designed Requires ex-ante estimation that reflects changes in participant mix Aggregate estimates required Prediction accuracy affects resource mix and reliability E.g., NAESB standards

4. 4 Load Impacts Can Be Used Throughout the Life Cycle of a DR Resource

5. 5 Estimating DR Resource Impacts Requires a Different Approach than Energy Efficiency Standardized methods for energy efficiency (EE) load impact estimation are not appropriate for DR, for a variety of reasons Energy EfficiencyDemand Response Tied to installation of more efficient devices that provide same service level Tied to behavioral actions & lower service levels Benefits accrue over lifetime of deviceBenefits tied to continuation of programs & customer participation “Always on”—benefits are in effect whenever the device is in use Benefits are event driven and there may be constraints on their frequency and timing The magnitude of resources is more-or-less constant Resources may vary significantly over time and conditions (e.g., weather) and may be largest when they are needed most Benefits mostly derive from avoided energy (MWh) Benefits mostly tied to avoided capacity (MW) Free-ridership rates must be quantifiedNot so relevant—customers typically don’t “do” DR in absence of a program or price incentive DR provides option value—insurance—even when it is not used

6. 6 The Role of Load Impact Protocols Set minimum requirements for estimating and reporting load impacts –Focus on what to provide, not how to do the job For ex post load impacts, streamline outputs to suit the program –Define program types based on features that affect analysis e.g., frequency of events, variation in event timing/duration/number of participants curtailed –For programs with few events, report impacts for each event day –For programs with frequent events, report results for representative event conditions e.g., average event day or each of top 5 system load days For ex ante load impacts, define common day types and event conditions –1-in-2 (normal) and 1-in-10 (extreme) weather year conditions Weather-sensitive DR, such as direct load control, has higher impacts under the extreme conditions for which the electric system is designed –E.g., report impacts for all 24 hours of monthly system peak day, and average of top ten peak days Future enrolment scenarios left to system planners in Ontario, but included in California protocols

7. 7 Load Impacts Involve Estimating what Customers Would Have Used in the Absence of DR Incentives Average Customer (KW) Load Impact DR Event Window Load Impact = Reference Load – Observed Load

8. 8 The Best Estimation Method Depends on the Context and Ultimate Use of Load Impacts For program settlement, baseline methods are typically used –Load on prior days, sometimes with same-day adjustments, used to predict load on event days in the absence of DR –May be subject to gaming and typically can’t incorporate key drivers of demand response into ex ante estimates For resource planning and operations, impact estimation is typically based on regressions developed from historical data over a longer period (i.e., a whole program year) –Statistically robust models allow quantification of key drivers of DR Variation in consumer activity across hours, days, months and seasons Variation in temperature Variation in incentives (e.g., prices, capacity payments, program availability) Changes in customer characteristics over time –If event data is limited, regression-based reference load estimation can be combined with assumptions about load drops or cycling strategies to produce valid impact estimates Engineering analysis, common in EE impact estimation, is typically not suitable for DR load impact estimation

9. Model Bias Should be Quantified No model will perfectly predict load, but you can assess the direction and degree of bias In this example, average AC loads are well predicted across a range of temperatures –Above 97 degrees, there is slightly more bias 9

10. 10 Model Bias can be Reduced by Estimating Loads with and without DR from the same Model Impact Based on Difference Between Regression-Based Predicted Load With and Without DR Impact Based on Difference Between Regression-Based Reference Load and Actual Load DR Event Window Subtracting actual observed load during events from a modeled reference load introduces bias This can be “netted out” by predicting both the baseline and the “actual” usage from the same model

11. 11 Conclusions Ex ante impact estimation that captures the option value of DR is the key to “operationalizing” DR resources –Long-term planning—giving full credit to DR’s ability to offset supply when it is needed most –Operations—providing predictive models to include DR in market operations We are making progress, but have a ways to go… –Convincing planners and operators will require building a track record of load impacts, based on empirical data, using sound methodologies that produce unbiased results An industry goal should be to develop a shared library of DR impact estimates, estimated under common conditions so that results can be compared and translated –DR load impact protocols are a key policy tool for achieving this goal

12. 12 For more information, please contact: Nicole Hopper Evaluation Manager Ontario Power Authority 120 Adelaide St. W., Suite 1600 Toronto, Ontario M5H 1T1 416-969-6274 nicole.hopper@powerauthority.on.ca

13. 13 California Utilities Are Developing an Extensive Library of Empirical Impact Estimates for a Variety of DR Options Each spring, California’s IOUs must produce hourly load impact estimates for all DR resources for selected day types and event conditions Ex post load impacts must be provided for each event day in the prior year Ex ante load impacts must be developed for a common set of weather, day type and event conditions for the average customer and for the program as a whole –Typical event days –Monthly system peak days –1-in-2 and 1-in-10 year weather conditions Load impacts must be provided for each of roughly 12 local capacity areas Many estimates are provided for different business categories and other customer segments A few examples are provided on the following slides (tens of thousands of similar tables have been filed with the CPUC in recent months)

14. 14 PG&E’s SmartAC Program, Typical Event Day Average Customer, 1-in-2 Year Weather Conditions

15. 15 PG&E’s SmartRate Tariff, Typical Event Day Average Customer, 1-in-2 Year Weather Conditions

16. 16 SCE’s Summer Discount Plan (A/C Cycling) Average Residential Customer, 1-in-10 Year Weather

17. 17 SCE’s Base Interruptible Tariff, Typical Event Day Average Customer, 1-in-2 Year Weather Conditions