Lawrence Berkeley National Laboratory

Both Federal and California state policymakers are increasingly interested in developing more standardized and consistent approaches to estimate and verify the load impacts of demand response programs and dynamic pricing tariffs. This study describes a statistical analysis of the performance of different models used to calculate the baseline electric load for commercial buildings participating in a demand-response (DR) program, with emphasis on the importance of weather effects. During a DR event, a variety of adjustments may be made to building operation, with the goal of reducing the building peak electric load. In order to determine the actual peak load reduction, an estimate of what the load would have been on the day of the event without any DR actions is needed. This baseline load profile (BLP) is key to accurately assessing the load impacts from event-based DR programs and may also impact payment settlements for certain types of DR programs. We tested seven baseline models on a sample of 33 buildings located in California. These models can be loosely categorized into two groups: (1) averaging methods, which use some linear combination of hourly load values from previous days to predict the load on the event, and (2) explicit weather models, which use a formula based on local hourly temperature to predict the load. The models were tested both with and without morning adjustments, which use data from the day of the event to adjust the estimated BLP up or down.Key findings from this study are: - The accuracy of the BLP model currently used by California utilities to estimate load reductions in several DR programs (i.e., hourly usage in highest 3 out of 10 previous days) could be improved substantially if a morning adjustment factor were applied for weather-sensitive commercial and institutional buildings. - Applying a morning adjustment factor significantly reduces the bias and improves the accuracy of all BLP models examined in our sample of buildings. - For buildings with low load variability, all BLP models perform reasonably well in accuracy. - For customer accounts with highly variable loads, we found that no BLP model produced satisfactory results, although averaging methods perform best in accuracy (but not bias). These types of customers are difficult to characterize with standard BLP models that rely on historic loads and weather data. Implications of these results for DR program administrators and policymakers are: - Most DR programs apply similar DR BLP methods to commercial and industrial sector customers. The results of our study when combined with other recent studies (Quantum 2004 and 2006, Buege et al., 2006) suggests that DR program administrators should have flexibility and multiple options for suggesting the most appropriate BLP method for specific types of customers.