Styliani I. Kampezidou;Justin Romberg;Kyriakos G. Vamvoudakis;Dimitri N. Mavris
{"title":"Decentralized and Privacy-Preserving Learning of Approximate Stackelberg Solutions in Energy Trading Games With Demand Response Aggregators","authors":"Styliani I. Kampezidou;Justin Romberg;Kyriakos G. Vamvoudakis;Dimitri N. Mavris","doi":"10.1109/TSMC.2024.3432000","DOIUrl":null,"url":null,"abstract":"In the pathway to 2030 electricity generation decarbonization and 2050 net-zero economies, scalable integration of distributed load can support environmental goals and also help alleviate smart grid operational issues through its electricity market participation. In this work, a novel Stackelberg game theoretic framework is proposed for trading the energy bidirectionally between the demand-response (DR) aggregator and the prosumers (distributed load). 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 and the number of learning samples), the 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, the 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 the algorithm.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Systems Man Cybernetics-Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10620646/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In the pathway to 2030 electricity generation decarbonization and 2050 net-zero economies, scalable integration of distributed load can support environmental goals and also help alleviate smart grid operational issues through its electricity market participation. In this work, a novel Stackelberg game theoretic framework is proposed for trading the energy bidirectionally between the demand-response (DR) aggregator and the prosumers (distributed load). 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 and the number of learning samples), the 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, the 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 the algorithm.
期刊介绍:
The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.