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

Autor:	Kampezidou, Styliani I., Romberg, Justin, Vamvoudakis, Kyriakos G., Mavris, Dimitri N.
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Computer Science and Game Theory
Druh dokumentu:	Working Paper
DOI:	10.1109/TSMC.2024.3432000
Popis:	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. Comment: This work has been submitted to the IEEE for possible publication
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2304.02086 Zobrazit plný text záznamu View this record from Arxiv