1. An Approach to Adjust ERCOT’s Long-Term Load Forecast for Utility Energy Efficiency Programs Jay Zarnikau Frontier Associates LLC June 10, 2013

2. The Statute 39.905 (b-3) Beginning not later than January 1, 2008, the commission, in consultation with the State Energy Conservation Office, annually for a period of five years shall compute and report to ERCOT the projected energy savings and demand impacts for each entity in the ERCOT region that administers standard offer programs, market transformation programs, combined heating and power technology, demand response programs, solar incentive programs, appliance efficiency standards, energy efficiency programs in public buildings, and any other relevant programs that are reasonably anticipated to reduce electricity energy or peak demand or that serve as substitutes for electric supply. (b-4) The commission and ERCOT shall develop a method to account for the projected efficiency impacts under Subsection (b-3) in ERCOT's annual forecasts of future capacity, demand, and reserves.

3. The Challenge A lot of energy efficiency program impacts are already reflected in the historical data used by ERCOT to develop its long-term load forecasts. It is not easy remove the historical effects of the programs. The programs have been around for a while. Double-counting would result if the total projected impacts of the programs were simply subtracted from ERCOT’s load forecast, since the existing programs have already moved historical usage downward and econometric models will tend to respond as though historical trends continue into the future. In other words, projections from the models tend to assume these programs will continue into the future (which they probably will). So, adjustments to the forecast should really reflect expected changes in the historical trend in program impacts.

4. My Proposal Treat the utility load management programs in the same manner as ERS or LRs. – Give them full credit. – Load management accounts for more than half of the total energy efficiency peak load reduction from the TDUs. – If a deployment occurred during a peak, an adjustment to the load management program impacts may be needed, to avoid any double-counting. Count a fraction of the (non-load management) energy efficiency program impacts forecast by the TDUs.

5. Calculation of the Appropriate Fraction Bottom-Up Approach: – One could use an end-use model, which considers the stock, usage, and efficiencies of all electricity-intensive equipment. – Isolate the projected changes in these three factors which are related to energy efficiency programs. – The ERCOT staff has mentioned this type of modeling in the past. But, it could be a big long-term project. Not viable in the near-term. – Sometimes these types of models are weak in capturing behavioral impacts (e.g., price elasticities, bounce-back, etc.) Top-Down Approach: – This may be the only viable way to do the calculation, in the near-term

6. Top Down Approach Compare the actual historical peak demand against ERCOT’s projection of the peak (before any consideration of energy efficiency programs). Calculate the difference, or average forecast error. Assume for the moment that the only reason for forecast error was failure to consider the impacts of the utility energy efficiency programs. Calculate the fraction as the forecast error over the reported energy efficiency peak demand reduction. In other words, the “adjustment” is the percentage of energy efficiency impacts reported by the TDUs that would minimize ERCOT’s load forecast error.

7. Accounting for Other Factors Obviously, energy efficiency programs impacts aren’t the only reason (or even the biggest reason) why there is load forecast error. So, we need to try to remove any other contributors to load forecast error before we calculate the fraction. – Since most forecast error is due to deviations in weather from typical peak conditions, use weather-adjusted actual historical peak demand data in the calculation. (Although, ERCOT has informed me this may not be readily available.) – Account for any error in economic/demographic forecasts, as well. – One approach would be to re-simulate ERCOT’s long-term forecasting models after each peak using actual data for all exogenous variables. This would remove the modeling error associated with imperfect forecasts of exogenous variables (e.g., weather and economic projections). Then, attribute the remaining error to energy efficiency. (EROT is now doing something like this, at least for recent forecasts.) – The use of averages over a number of years would also help.

8. Results I got a fraction of 10% when I did the math earlier. But I don’t have all the data and models necessary to do the calculations described in the previous slide is an accurate manner.

9. Comparison with ERCOT’s Proposal ERCOT’s approach would help us estimate the impacts of “energy efficiency” on the forecast. But, it won’t enable us to distinguish the energy efficiency attributable to programs from “other” energy efficiency impacts which create deviations from naturally-occurring trends in technology and markets. My proposal will get us closer to assigning impacts to programs, but it may have the same problem.

10. Limitations Despite our best efforts for identify and control for other sources of error, we’ll never be able to fully eliminate the forecast errors not associated with the energy efficiency programs. In addition to forecast error due to imperfect forecasts of exogenous variables, models are not perfect. The model is a source of error. This ignores the energy efficiency efforts of the NOIEs and state agencies, as well as changes in building codes and appliance standards.

11. Questions???