Variability in Automated Responses of Commercial Buildings and Industrial Facilities to Dynamic Electricity Prices

Changes in the electricity consumption of commercial buildings and industrial facilities (C&I facilities) during Demand Response (DR) events are usually estimated using counterfactual baseline models. Model error makes it difficult to precisely quantify these changes in consumption and understand if C&I facilities exhibit event-to-event variability in their response to DR signals. This paper seeks to understand baseline model error and DR variability in C&I facilities facing dynamic electricity prices. Using a regression-based baseline model, we present a method to compute the error associated with estimates of several DR parameters. We also develop a metric to determine how much observed DR variability results from baseline model error rather than real variability in response. We analyze 38 C&I facilities participating in an automated DR program and find that DR parameter errors are large. Though some facilities exhibit real DR variability, most observed variability results from baseline model error. Therefore, facilities with variable DR parameters may actually respond consistently from event to event. Consequently, in DR programs in which repeatability is valued, individual buildings may be performing better than previously thought. In some cases, however, aggregations of C&I facilities exhibit real DR variability, which could create challenges for power system operation