Incentive Design for Lowest Cost Aggregate Energy Demand Reduction

Abstract

We design an optimal incentive mechanism offered to energy customers at multiple network levels, e.g., distribution and feeder networks, with the aim of determining the lowest-cost aggregate energy demand reduction. Our model minimizes a utility's total cost for this mode of virtual demand generation, i.e., demand reduction, to achieve improvements in both total systemic costs and load reduction over existing mechanisms. We assume the utility can predict with reasonable accuracy the average load reduction response of end-users with respect to rebates by observing and learning from their past behavior. Within a single period formulation, we propose a heuristic policy that segments the customers according to their likelihood of reducing load. Within a multi-period formulation, we observe that customers who are more willing to reduce their aggregate demand over the entire horizon, rather than simply shifting their load to off-peak periods, tend to receive higher incentives, and vice versa.