Learning to Price Electricity for Optimal Demand Response

2026-09-30

AI Summary

Existing dynamic pricing methods struggle to effectively leverage contextual information, such as weather conditions, for aligning demand with renewable energy generation. This work proposes a neural network-based context-aware pricing algorithm that formulates the pricing process as a Stackelberg game, learning a mapping from multidimensional features to feasible prices. Furthermore, mean field theory is introduced to solve the formulation, enabling the efficient utilization of complex contextual features and the generation of optimal price signals under operational constraints. Experimental evaluations conducted through power grid simulations across multiple U.S. cities demonstrate that the proposed approach significantly enhances the overall value of demand response programs.

Abstract

There is considerable interest in using time-varying electricity prices to shape consumer demand response, and better align energy demand with renewable production. However, optimal prices generally vary over time in response to complex signals such as weather forecasts, sunrise/sunset times, and day-of-week patterns; and existing methods are not able to make efficient use of such rich contextual information. Here, we propose a neural-network-based algorithm for contextual energy pricing, modeling pricing as a Stackelberg game and leveraging a mean-field solution representation from Mehrabi et al.~(2024). The approach learns constrained mappings from contextual features to feasible price signals. We validate our approach by simulating the energy grid in several US cities, and show that incorporating contextual information can considerably increase the value of the demand response programs.

Problem

• Electricity Pricing
• Demand Response
• Contextual Information
• Renewable Energy
• Stackelberg Game

Innovation

• Contextual energy pricing
• Stackelberg game
• Neural network
• Demand response
• Mean-field solution