# Decentralized and Privacy-Preserving Learning of Approximate Stackelberg Solutions in Energy Trading Games with Demand Response Aggregators

arXiv: https://arxiv.org/abs/2304.02086 · HTML: https://mlsanity.com/paper/2304.02086/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 120 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications](https://mlsanity.com/paper/1705.06270/) (2017)
- [Stability and Generalization of Bilevel Programming in Hyperparameter Optimization](https://mlsanity.com/paper/2106.04188/) (2021)
- [Decentralized Bilevel Optimization](https://mlsanity.com/paper/2206.05670/) (2022)
- [Asynchronous Distributed Bilevel Optimization](https://mlsanity.com/paper/2212.10048/) (2022)
- [A Branch-and-Cut Algorithm for Mixed Integer Bilevel Linear Optimization Problems and Its Implementation](https://mlsanity.com/paper/2104.09010/) (2021)
- [A Stackelberg game for incentive-based demand response in energy markets](https://mlsanity.com/paper/2204.08730/) (2022)
- [Stackelberg Equilibria for Two-Player Network Routing Games on Parallel Networks](https://mlsanity.com/paper/2003.05882/) (2020)
- [Exponential convergence of recursive least squares with forgetting factor for multiple-output systems](https://mlsanity.com/paper/2003.07334/) (2020)
- [Bidirectional Pricing and Demand Response for Nanogrids with HVAC Systems](https://mlsanity.com/paper/2203.00270/) (2022)

## Abstract (arXiv)

In this work, a novel Stackelberg game theoretic framework is proposed for trading energy bidirectionally between the demand-response (DR) aggregator and the prosumers. This formulation allows for flexible energy arbitrage and additional monetary rewards while ensuring that the prosumers' desired daily energy demand is met. Then, a scalable (linear with the number of prosumers), decentralized, privacy-preserving algorithm is proposed to find approximate equilibria with online sampling and learning of the prosumers' cumulative best response, which finds applications beyond this energy game. Moreover, cost bounds are provided on the quality of the approximate equilibrium solution. Finally, real data from the California day-ahead market and the UC Davis campus building energy demands are utilized to demonstrate the efficacy of the proposed framework and algorithm.