# Learning to Price Electricity for Optimal Demand Response

arXiv: https://arxiv.org/abs/2610.00755 · HTML: https://mlsanity.com/paper/2610.00755/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 46 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Builds on (most cited first, 11 of 11)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [OptNet: Differentiable Optimization as a Layer in Neural Networks](https://mlsanity.com/paper/1703.00443/) (2017)
- [Differentiable Convex Optimization Layers](https://mlsanity.com/paper/1910.12430/) (2019)
- [On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems](https://mlsanity.com/paper/1906.00331/) (2019)
- [Optimal rates for zero-order convex optimization: the power of two function evaluations](https://mlsanity.com/paper/1312.2139/) (2013)
- [Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions](https://mlsanity.com/paper/1904.04205/) (2019)
- [On Approximation Capabilities of ReLU Activation and Softmax Output Layer in Neural Networks](https://mlsanity.com/paper/2002.04060/) (2020)
- [Online Stochastic Optimization of Networked Distributed Energy Resources](https://mlsanity.com/paper/1711.09953/) (2017)
- [An Incentive-Based Online Optimization Framework for Distribution Grids](https://mlsanity.com/paper/1705.01482/) (2017)
- [Optimal Mechanisms for Demand Response: An Indifference Set Approach](https://mlsanity.com/paper/2409.07655/) (2024)
- [Unlocking Deep Demand Flexibility via Dynamic Signals](https://mlsanity.com/paper/2605.06960/) (2026)

## Abstract (arXiv)

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.