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Game Theoretic-Based Distributed Charging Strategy for PEVs in a Smart Charging Station

机译:智能充电站PEV的基于游戏的分布式充电策略

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This article investigates the charging problem of plug-in electric vehicles (PEVs) in a smart charging station (SCS) under a new interaction mechanism that allows the interactions among PEVs. The target is to coordinate the charging strategies of all PEVs such that the energy cost of SCS is minimized without compromising a set of constraints for PEVs and SCS. To this end, we first construct a non-cooperative game framework, in which each player (i.e., PEV) expects to minimize its cost by choosing the optimal charging strategy over the entire charging horizon. Then, the existence and optimality of Nash equilibrium (NE) for the formulated non-cooperative game is provided. Moreover, to find the unique generalized Nash equilibrium (GNE), we propose a distributed GNE-seeking algorithm based on the Newton fixed-point method. And a fast alternating direction multiplier method (fast-ADMM) framework is applied to determine the best response of PEVs. The convergence of the proposed distributed GNE-seeking algorithm and PEVs’ best response are also provided with theoretical analysis. Simulations are presented at last to validate the effectiveness of the proposed algorithm.
机译:本文在新的交互机制下调查智能充电站(SCS)中的插入电动车辆(PEV)的充电问题,该机制允许PEV之间的相互作用。目标是协调所有PEV的充电策略,使得SCS的能量成本最小化,而不会影响PEV和SCS的一组约束。为此,我们首先构建一个非合作游戏框架,其中每个玩家(即,PEV)期望通过在整个充电地平线上选择最佳充电策略来最小化其成本。然后,提供了制定的非协作游戏的纳什平衡(NE)的存在和最优性。此外,为了找到独特的广义纳什均衡(GNE),我们提出了一种基于牛顿定点法的分布式GNE寻求算法。应用快速交替方向乘法器方法(FAST-ADMM)框架来确定PEV的最佳响应。建议的分布式GNE算法和PEVS最佳响应的收敛性也具有理论分析。终结仿真以验证所提出的算法的有效性。

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