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Real-time charging coordination of plug-in electric vehicles based on hybrid fuzzy discrete particle swarm optimization

机译：基于混合模糊离散粒子群算法的插电式电动汽车实时充电协调

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The main impact of uncoordinated plug-in electric vehicle (PEV) charging is adding new time-variant loads that can increase the strains on the generation units, transmission and distribution systems that may result in unacceptable voltage drops and poor power quality. This paper proposes two dynamic online approaches for coordination of PEV charging based on fuzzy genetic algorithm (FGA) and fuzzy discrete particle swarm optimization (FDPSO). The algorithms will minimize the costs associated with energy generation and grid losses while also maximizing the delivered power to PEVs considering distribution transformer loading, voltage regulation limits, initial and final battery state of charges (SOCs) based on consumers' preferences. The second algorithm relies on the quality and speed of DPSO solution for more accurate and faster online coordination of PEVs while also exploiting fuzzy reasoning for shifting charging demands to off-peak hours for a further reduction in overall cost and transformer loading. Simulation results for uncoordinated, DPSO, FGA and FDPSO coordinated charging are presented and compared for a 449-node network populated with PEVs. Results are also compared with the previously published PEV coordinated charging based on maximum sensitivity selections (MSS). Main contributions are formulating the PEVs charging coordination problem and applying different optimization methods including online FGA and FDPSO considering different driving patterns, battery sizes and charging rates, as well as initial SOCs and requested final SOCs. (C) 2015 Elsevier B.V. All rights reserved.
机译：不协调的插电式电动汽车（PEV）充电的主要影响是增加了新的随时间变化的负载，这会增加发电机组，输电和配电系统的压力，从而可能导致不可接受的电压降和不良的电能质量。本文提出了两种基于模糊遗传算法（FGA）和模糊离散粒子群优化（FDPSO）的动态在线PEV充电协调方法。该算法将最小化与能源生产和电网损失相关的成本，同时还可以根据用户的偏好，考虑配电变压器的负载，电压调节限制，初始和最终电池充电状态（SOC），最大限度地提高向PEV输送的功率。第二种算法依赖于DPSO解决方案的质量和速度来实现PEV的更准确和更快的在线协调，同时还利用模糊推理将充电需求转移到非高峰时段，从而进一步降低了总体成本和变压器负载。给出了不协调，DPSO，FGA和FDPSO协调充电的仿真结果，并比较了填充PEV的449节点网络的仿真结果。还将结果与基于最大灵敏度选择（MSS）的先前发布的PEV协调充电进行比较。主要贡献在于制定PEV的充电协调问题，并采用不同的优化方法，包括在线FGA和FDPSO，其中考虑了不同的驾驶模式，电池大小和充电率以及初始SOC和要求的最终SOC。 （C）2015 Elsevier B.V.保留所有权利。

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