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Decentralized Plug-in Electric Vehicle Charging Selection Algorithm in Power Systems

机译：电力系统中的分散式插电式电动汽车充电选择算法

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摘要

This paper uses a charging selection concept for plug-in electric vehicles (PEVs) to maximize user convenience levels while meeting predefined circuit-level demand limits. The optimal PEV-charging selection problem requires an exhaustive search for all possible combinations of PEVs in a power system, which cannot be solved for the practical number of PEVs. Inspired by the efficiency of the convex relaxation optimization tool in finding close-to-optimal results in huge search spaces, this paper proposes the application of the convex relaxation optimization method to solve the PEV-charging selection problem. Compared with the results of the uncontrolled case, the simulated results indicate that the proposed PEV-charging selection algorithm only slightly reduces user convenience levels, but significantly mitigates the impact of the PEV-charging on the power system. We also develop a distributed optimization algorithm to solve the PEV-charging selection problem in a decentralized manner, i.e., the binary charging decisions (charged or not charged) are made locally by each vehicle. Using the proposed distributed optimization algorithm, each vehicle is only required to report its power demand rather than report several of its private user state information, mitigating the security problems inherent in such problem. The proposed decentralized algorithm only requires low-speed communication capability, making it suitable for real-time implementation.
机译：本文针对插电式电动汽车（PEV）使用充电选择概念，以在满足预定义的电路级需求限制的同时，最大限度地提高用户的便利程度。最佳的PEV充电选择问题需要穷举搜索电力系统中所有可能的PEV组合，而这对于实际数量的PEV来说是无法解决的。受到凸松弛优化工具在巨大的搜索空间中寻找接近最佳结果的效率的启发，提出了凸松弛优化方法在解决PEV充电选择问题中的应用。与非控制情况的结果相比，仿真结果表明，提出的PEV充电选择算法仅稍微降低了用户的便利程度，但是大大减轻了PEV充电对电力系统的影响。我们还开发了一种分布式优化算法，以分散的方式解决PEV充电选择问题，即每辆车都在本地做出二进制充电决策（充电或不充电）。使用提出的分布式优化算法，仅需要每辆车报告其功率需求，而无需报告其一些私人用户状态信息，从而减轻了此类问题中固有的安全性问题。所提出的分散算法仅需要低速通信能力，使其适合于实时实现。

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