| 含新能源发电的电动汽车充电站充电功率在线优化策略研究 |
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| 引用本文： | 周卓,芦翔,刘海涛,祁升龙,韩韬,王青.含新能源发电的电动汽车充电站充电功率在线优化策略研究[J].电测与仪表,2024,61(2):101-107. |
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| 作者姓名： | 周卓 芦翔 刘海涛 祁升龙 韩韬 王青 |
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| 作者单位： | 江苏金智科技股份有限公司,国网宁夏电力有限公司电力科学研究院,国网宁夏电力有限公司电力科学研究院,国网宁夏电力有限公司电力科学研究院,国网电力科学研究院有限公司,南昌大学信息工程学院 |
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| 基金项目： | 国家自然科学基金资助项目（51967013）；江西省自然科学基金资助项目（20212BAB214061） |
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| 摘 要： | 针对含新能源发电系统的电动汽车充电站充电功率优化问题,本文提出了一种充电功率在线实时优化策略,依据新能源发电出力及电动汽车当前充电状态动态调整未来24小时内充电站各充电桩充电功率。该在线优化策略依据电动汽车充电负荷特点,设计了基于状态依赖的决策变量分类方法,降低了每次优化待优化向量维度；改进了微分演化算法,分别对有效决策变量组合以及决策矩阵中有效元素进行优化,旨在快速、准确地对充电站中各充电桩未来24小时内的充电计划进行实时优化。该优化策略对降低充电站运行成本、改善因电动车充电引起的负荷波动具有一定的参考价值。
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| 关 键 词： | 电动汽车 电动汽车充电站 新能源发电 充电功率在线优化 决策变量分类 微分演化算法 |
| 收稿时间： | 2022/8/12 0:00:00 |
| 修稿时间： | 2022/8/22 0:00:00 |
On-Line Charging Power Optimization Strategy for EV Charging Stations with Uncertain Renewable Energy Generations |
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| ZHOU Zhuo,LU Xiang,LIU Haitao,QI Shenglong,HAN Tao and WANG Qing.On-Line Charging Power Optimization Strategy for EV Charging Stations with Uncertain Renewable Energy Generations[J].Electrical Measurement & Instrumentation,2024,61(2):101-107. |
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| Authors: | ZHOU Zhuo LU Xiang LIU Haitao QI Shenglong HAN Tao and WANG Qing |
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| Affiliation: | Wiscom System Co Ltd,Nanjing,Electric Power Research Institute,State Grid Ningxia Electric Power Co,Ltd,Yinchuan Ningxia,Electric Power Research Institute,State Grid Ningxia Electric Power Co,Ltd,Yinchuan Ningxia,Electric Power Research Institute,State Grid Ningxia Electric Power Co,Ltd,Yinchuan Ningxia,State Grid Electric Power Research Institute Co Ltd,Nanjing,School of Information Engineering,Nanchang University,Nanchang Jiangxi |
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| Abstract: | An on-line charging power optimization strategy is proposed for EV charging stations that integrated with renewable energy generations, by which the charging plan can be optimized on-line according to real-time conditions. During the optimization process, a state dependence based decision variables classification method is proposed to reduce the optimization dimensions and calculate quantity. The differential evolution algorithm (DEA) is improved for the optimization process, by which both the quickness and accuracy can be achieved in the optimization process. The proposed strategy is useful to reduce the charging cost and improve load fluctuation caused by EV charging. |
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| Keywords: | electric vehicles EV charging stations renewable energy generations on-line charging power optimization decision variables classification differential evolution algorithm |
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