UC Berkeley

Demand Response (DR) serves to reduce the demand for electricity especially during times when supply is scarce or expensive. The program entails calling the recruited consumers to reduce their energy consumption from an established baseline. Baseline is an estimate of the counter-factual against which load reductions are measured to determine payments to consumers. This creates an incentive for consumers to inflate their baseline so that their payments are inflated. There have been reported cases where consumers have inflated their baseline to increase their payments. To address this, we propose a novel self-reported baseline mechanism for DR programs in both retail and wholesale electricity markets. Also by comparing with mechanisms used in practice by System Operator's like CAISO (California Independent System Operator) we show that the proposed mechanism is either reliable or requires less payments for the same level of load curtailment provided by consumers.

Finally we consider a repeated DR setting where the DR events repeat. We propose a baseline DR mechanism and a pricing policy that learns consumer's response to payments and exploits the learnt information such that the cumulative losses in savings, compared to the oracle determined optimal pricing mechanism, does not grow at an uncontrolled rate. In particular we show that it grows at a sub-linear rate.