Fully Distributed Game Strategy for Second-Order Players and Its Application to Networked Electricity Markets

IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-02-05 DOI:10.1109/TSIPN.2025.3538996
Zhenhua Deng;Minghuan Ye;Xiang-Peng Xie;Xiaojun Yang
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Abstract

In this paper, we study the noncooperative games (NGs) of multi-agent systems. In our problem, the players have private payoff functions, and their decisions are subject to local and coupling nonlinear inequality constraints. Moreover, our problem contains second-order dynamical systems of players. To control these second-order players to autonomously participate in the games, a distributed adaptive strategy is proposed based on state feedback and primal-dual methods. With our method, the updates of the control inputs of all players depend only on their own and neighbors' information, and are independent of global parameters or variables, different from other related methods. By virtue of variational analysis and LaSalle invariance principle, it is proved that our strategy converges to the variational Generalized Nash Equilibrium (v-GNE) of the games. Finally, the proposed method is applied to networked electricity market games of smart grids.
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二阶参与者的全分布博弈策略及其在网络电力市场中的应用
本文研究的是多代理系统的非合作博弈(NGs)。在我们的问题中,参与者都有私人报酬函数,他们的决策受局部和耦合非线性不等式约束的限制。此外,我们的问题还包含参与者的二阶动态系统。为了控制这些二阶参与者自主参与博弈,我们提出了一种基于状态反馈和原始二元方法的分布式自适应策略。与其他相关方法不同的是,在我们的方法中,所有玩家的控制输入更新只取决于他们自己和邻居的信息,而与全局参数或变量无关。通过变分分析和拉萨尔不变性原理,证明了我们的策略收敛于博弈的变分广义纳什均衡(v-GNE)。最后,提出的方法被应用于智能电网的网络化电力市场博弈。
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来源期刊
IEEE Transactions on Signal and Information Processing over Networks
IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications
CiteScore
5.80
自引率
12.50%
发文量
56
期刊介绍: The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.
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