Enable Microgrid Energy Management: A Graph Based Aggregative Game Approach

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-16 DOI:10.1109/TNSE.2024.3429393

Gehui Xu;Yaoyu Zhang;Jian Sun;Guanpu Chen;Chenye Wu

引用次数: 0

Abstract

With the rising adoption of distributed and intermittent renewable energy sources, microgrids have emerged as a promising solution to the resulting challenges. Specifically, microgrids could rely on energy storage systems (ESSs) to balance power generation and varying loads. However, an increased number of ESSs, if not well coordinated, can lead to an increase in system operation costs. To overcome this issue, we adopt a graph-based aggregative game to regulate the charging and discharging strategies of multiple ESSs. We show the existence and uniqueness of the Nash equilibrium (NE) and propose the corresponding Graph-based Aggregative Charging Tracking (GACT) distributed algorithm to compute NE with a linear convergence rate. Our algorithm leverages existing communication resources efficiently and protects the private charging information of ESSs. Numerical experiments demonstrate the effectiveness of our proposed algorithm.

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实现微电网能源管理：基于图的聚合博弈方法

随着越来越多地采用分布式和间歇性可再生能源，微电网已成为应对由此带来的挑战的一种前景广阔的解决方案。具体来说，微电网可以依靠储能系统（ESS）来平衡发电和不同的负载。然而，如果不能很好地协调，ESS 数量的增加会导致系统运营成本的增加。为了解决这个问题，我们采用基于图的聚合博弈来调节多个 ESS 的充放电策略。我们证明了纳什均衡（NE）的存在性和唯一性，并提出了相应的基于图的聚合充电跟踪（GACT）分布式算法，以线性收敛速度计算纳什均衡。我们的算法有效地利用了现有通信资源，并保护了 ESS 的私有计费信息。数值实验证明了我们提出的算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.