Learning to Price Electricity for Optimal Demand Response
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.
Related
- TRACE: A Reproducible Benchmark for Electricity Price Forecasting with Official Operational Text
- Day-Ahead Electricity Price Forecasting Using a Multivariate Group Lasso Method
- Quantile Regression Postprocessing with Negative Electricity Price Forecasts
- Online Conditional Vine Copulas: Forecasting Electricity Demand
- Tail reliability in probabilistic electricity price forecasts: comparing model combination and conformal prediction in the GB day-ahead market
BibTeX
@misc{shang2026learning,
title = {{Learning to Price Electricity for Optimal Demand Response}},
author = {Jing Shang and Mohammad Mehrabi and Xinyang Zhou and Mahmoud Saleh and Andrey Bernstein and Stefan Wager},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.00755},
eprint = {2610.00755},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.00755},
}
RIS
TY - UNPB
TI - Learning to Price Electricity for Optimal Demand Response
AU - Jing Shang
AU - Mohammad Mehrabi
AU - Xinyang Zhou
AU - Mahmoud Saleh
AU - Andrey Bernstein
AU - Stefan Wager
PY - 2026
DA - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2610.00755
UR - https://arxiv.org/abs/2610.00755
AB - 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.
ER -