{"title":"Customized Mean Field Game Method of Virtual Power Plant for Real-Time Peak Regulation","authors":"Kuan Zhang;Yawen Xie;Nian Liu;Siqi Chen","doi":"10.1109/TSTE.2025.3533929","DOIUrl":null,"url":null,"abstract":"This paper proposes a customized incentive compatible mean field game (MFG) method for virtual power plant (VPP) with a large number of self-interest heterogeneous distributed energy resources (DERs) to participate in the real-time peak regulation. Firstly, an optimal chance-constrained peak-regulation bidding model of VPP considering the stochastic power flexibility is formulated, where inscribed pyramid approximation method is utilized to form a compact and concise dispatch region. Secondly, a customized MFG method with dynamic granulation division is proposed for encouraging very large-scale DERs to spontaneously respond to the peak regulation instructions from VPP while achieving dynamic allocation of peak-regulation revenue. Brouwer fixed-point theorem and contraction mapping theorem are used to prove the existence and uniqueness of the mean field equilibrium (MFE) of the formulated MFG, and ϵ-Nash property of MFE is validated based on the Lipschitz continuity condition. Furthermore, an accelerated decentralized solution algorithm is developed to rapidly search MFE, exhibiting good scalability. Comparative studies have validated the superiority of the proposed methodology on incentive compatibility and decomposition efficiency of the VPP's peak-regulation instructions.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1453-1466"},"PeriodicalIF":8.6000,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Sustainable Energy","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10854803/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper proposes a customized incentive compatible mean field game (MFG) method for virtual power plant (VPP) with a large number of self-interest heterogeneous distributed energy resources (DERs) to participate in the real-time peak regulation. Firstly, an optimal chance-constrained peak-regulation bidding model of VPP considering the stochastic power flexibility is formulated, where inscribed pyramid approximation method is utilized to form a compact and concise dispatch region. Secondly, a customized MFG method with dynamic granulation division is proposed for encouraging very large-scale DERs to spontaneously respond to the peak regulation instructions from VPP while achieving dynamic allocation of peak-regulation revenue. Brouwer fixed-point theorem and contraction mapping theorem are used to prove the existence and uniqueness of the mean field equilibrium (MFE) of the formulated MFG, and ϵ-Nash property of MFE is validated based on the Lipschitz continuity condition. Furthermore, an accelerated decentralized solution algorithm is developed to rapidly search MFE, exhibiting good scalability. Comparative studies have validated the superiority of the proposed methodology on incentive compatibility and decomposition efficiency of the VPP's peak-regulation instructions.
期刊介绍:
The IEEE Transactions on Sustainable Energy serves as a pivotal platform for sharing groundbreaking research findings on sustainable energy systems, with a focus on their seamless integration into power transmission and/or distribution grids. The journal showcases original research spanning the design, implementation, grid-integration, and control of sustainable energy technologies and systems. Additionally, the Transactions warmly welcomes manuscripts addressing the design, implementation, and evaluation of power systems influenced by sustainable energy systems and devices.