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Optimal Day-Time Charging Strategies for Electric Vehicles considering Photovoltaic Power System and Distribution Grid Constraints

机译：考虑光伏发电系统和配电网约束的电动汽车最佳日间充电策略

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摘要

Electric vehicles (EVs) charging stations with a photovoltaic (PV) system for day-time charging have been studied. This paper investigates the issues such as how to coordinate the EVs customers for coordinated charging, maximize photovoltaic utilization, and reduce customers cost of EVs charging and operator electricity. Firstly, an ideal charging load curve was built through using the linear programming algorithm. This optimal curve, which realized maximum photovoltaic power and minimum electricity cost, was used as the objective curve. Secondly, a customer response model was utilized, to propose an optimization method and strategy for charging service tariffs. Particle swarm optimization algorithm was used for time-of-use tariffs and peak-flat-valley time division so that the charging load after price regulation was adjusted to best fit the objective curve, and both the EVs customers and the operator benefit from this. Finally, the proposed model and method have been verified by two cases.
机译：已经研究了具有用于日间充电的光伏（PV）系统的电动汽车（EV）充电站。本文探讨了诸如如何协调电动汽车客户进行协调充电，最大限度地利用光伏发电以及降低电动汽车充电和运营商用电成本等问题。首先，利用线性规划算法建立了理想的充电负荷曲线。该最佳曲线实现了最大的光伏发电量和最低的电费成本，被用作目标曲线。其次，利用客户响应模型，提出了一种优化服务收费的方法和策略。使用粒子群优化算法进行分时电价和峰谷平时分，以调整价格调节后的充电负荷，使其最适合目标曲线，从而使电动汽车用户和运营商都从中受益。最后，通过两种情况对提出的模型和方法进行了验证。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第5期|765362.1-765362.9|共9页
  • 作者

    Zhang Weige; Ge Wenjie; Huang Mei; Jiang Jiuchun;

  • 作者单位

    Beijing Jiaotong Univ, Natl Act Distribut Network Technol Res Ctr, Sch Elect Engn, Beijing 100044, Peoples R China.;

    Beijing Jiaotong Univ, Natl Act Distribut Network Technol Res Ctr, Sch Elect Engn, Beijing 100044, Peoples R China.;

    Beijing Jiaotong Univ, Natl Act Distribut Network Technol Res Ctr, Sch Elect Engn, Beijing 100044, Peoples R China.;

    Beijing Jiaotong Univ, Natl Act Distribut Network Technol Res Ctr, Sch Elect Engn, Beijing 100044, Peoples R China.;

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