Title: Optimality and Learning of Dynamic Pricing for Demand Response: an Indifference Set Approach
Speaker: Mohammad Mehrabi (Stanford University)
Time: 12 November 2026, 12:30-13:30
Place: EA 302
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 and 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. Building on this result, we propose an online contextual pricing algorithm and demonstrate significant reductions in peak system load using simulations calibrated to a range of major U.S. cities. This talk is based on the following two works: https://arxiv.org/abs/2409.07655 https://arxiv.org/pdf/2610.00755
Bio: Mohammad Mehrabi is an incoming Assistant Professor at the University of Toronto in the Department of Mechanical and Industrial Engineering (MIE), where he will be part of the Operations Research group. Previously, he was a Postdoctoral Scholar in Operations, Information, and Technology at the Stanford Graduate School of Business from 2023 to 2026. He received his Ph.D. in Statistics from the Department of Data Sciences and Operations at the USC Marshall School of Business in 2023. His research broadly spans machine learning, statistics, and optimization for data-driven decision making, with a special focus on dynamic systems. Methodologically, he is interested in the design and analysis of reliable, robust, and efficient statistical methods. In applied settings, he develops models and algorithms for pricing and incentive design, with applications to demand response programs in electricity markets.