Learning to Price Electricity for Optimal Demand Response

Abstract:
The time at which renewable (e.g., solar or wind) energy resources produce electricity cannot generally be controlled. In many settings, however, consumers have some flexibility in their energy consumption needs, and there is growing interest in demand-response programs that leverage this flexibility to shift energy consumption to better match renewable production — thus enabling more efficient utilization of these resources. We study optimal demand response in a setting where consumers use home energy management systems (HEMS) to autonomously adjust their electricity consumption. Our core assumption is that HEMS operationalize flexibility by querying the consumer for their preferences and computing the “indifference set” of all energy consumption profiles that can be used to satisfy these preferences. Then, given an indifference set, HEMS can respond to grid signals while guaranteeing user-defined comfort and functionality; e.g., if a consumer sets a temperature range, a HEMS can precool and preheat to align with peak renewable production, thus improving efficiency without sacrificing comfort. We show that while price-based mechanisms are not generally optimal for demand response, they become asymptotically optimal in large markets under a mean-field limit. Furthermore, we show that optimal dynamic prices can be efficiently computed in large markets by only querying HEMS about their planned consumption under different price signals. We leverage this result to build an online contextual pricing algorithm, and show it to enable considerable reduction in peak system load in simulators calibrated to a number of major US cities.

Bio:
Stefan Wager is an associate professor of Operations, Information, and Technology at the Stanford Graduate School of Business, an associate professor of Statistics (by courtesy), and the Philip F. Maritz Faculty Scholar for 2025-26. His research lies at the intersection of causal inference, optimization, and statistical learning. He is particularly interested in developing new solutions to problems in statistics, economics and decision making that leverage recent advances in machine learning. He is currently serving as an associate editor for several publications including Biometrika, Management Science, Operations Research, and the Journal of the American Statistical Association. He has worked with or consulted for several Silicon Valley companies, including Dropbox, Facebook, Google, and Uber.