Mechanism Design for Self-Reporting Baselines in Demand ResponseππSupported in Part by the National Science Foundation Under Grants EECS-1129061/9001, CPS-1239178; and by CERTS Under sub-award 09-206; PSERC S-52. Conference Paper uri icon

abstract

  • © 2016 American Automatic Control Council (AACC). Incentive-based Demand Response (DR) is a widely used tool to reduce the demand for electricity at times when the supply is scarce and expensive. In such DR programs, participating consumers are paid for reducing their energy consumption from an established baseline. This baseline is often based on the average historical consumption of a peer group on days that are similar to the upcoming DR event. In essence, baselines are estimates of the counter-factual consumption against which the aggregator measures load reductions and determines payments to the consumers in DR programs. Consumers have an incentive to inflate their baseline to increase the payments they receive. There are celebrated cases of consumers gaming this baseline to derive economic benefit. Several researchers have questioned the fairness of these baseline schemes used in current practice. We propose a novel DR mechanism to address gaming and fairness concerns. In our mechanism, each consumer forecasts their baseline consumption and reports their marginal utility to the aggregator who manages the DR program. Deviations in consumption from the self-reported baseline are penalized, providing an incentive for best-effort truthful estimation of baselines. The aggregator selects a set of consumers for each DR event to meet a load reduction requirement and are paid according to the observed reductions from their reported baseline. We show that truthful reporting of baseline and marginal utility is both incentive compatible and individually rational for every consumer. This establishes the correct baseline and the aggregator is able to meet any random load reduction requirement reliably.

author list (cited authors)

  • Muthirayan, D., Kalathil, D., Poolla, K., & Varaiya, P.

citation count

  • 7

publication date

  • July 2016

publisher