Optimal Mechanisms for Demand Response: An Indifference Set Approach

TL;DR

This paper develops an optimal demand response framework using indifference sets in home energy management systems, enabling efficient renewable energy utilization while maintaining user comfort, especially effective in large markets.

Contribution

It introduces a novel indifference set approach for demand response, showing price mechanisms are asymptotically optimal in large markets and providing efficient computation methods.

Findings

Price-based mechanisms are asymptotically optimal in large markets.

Optimal dynamic prices can be efficiently computed using HEMS data.

The approach enables demand response without grid instability in simulations.

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. Using an OpenDSS-powered grid simulation for Phoenix, Arizona, we demonstrate that our approach enables meaningful demand response without creating grid instability.