| 引用本文: | 臧海祥,傅雨婷,陈铭,沈海平,缪立恒,张思德,卫志农,孙国强.基于改进自适应遗传算法的EV充电站动态规划[J].电力自动化设备,2020,40(1): |
| ZANG Haixiang,FU Yuting,CHEN Ming,SHEN Haiping,MIAO Liheng,ZHANG Side,WEI Zhinong,SUN Guoqiang.Dynamic planning of EV charging stations based on improved adaptive genetic algorithm[J].Electric Power Automation Equipment,2020,40(1): |
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| 摘要: |
| 建立了综合充电站、电动汽车(EV)用户与配电网多方利益的快速充电站规划模型，考虑EV保有量增长的影响，同时计及EV增长率的不确定性，构建了2种EV充电站随机机会约束动态规划模型，并提出考虑充电需求空间分布的改进自适应遗传算法(IAGA)求解上述规划模型。通过一个实际算例验证了所提IAGA在求解充电站规划问题时的可行性与有效性，并对比分析了2种动态规划模型的规划结果。 |
| 关键词: 电动汽车 充电站 选址定容 动态规划 遗传算法 配电网 机会约束规划 |
| DOI：10.16081/j.epae.201912023 |
| 分类号:U469.72 |
| 基金项目:国家自然科学基金青年基金资助项目(51507052)；国网江苏省电力有限公司科技项目(J2017092) |
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| Dynamic planning of EV charging stations based on improved adaptive genetic algorithm |
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ZANG Haixiang1, FU Yuting1, CHEN Ming2, SHEN Haiping2, MIAO Liheng2, ZHANG Side1, WEI Zhinong1, SUN Guoqiang1
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1.College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China;2.Wuxi Power Supply Company, State Grid Jiangsu Electric Power Co.,Ltd.,Wuxi 214061, China
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| Abstract: |
| A fast charging station planning model is established, which integrates the multi-interests of charging station, EV(Electric Vehicle) users and distribution network. Considering the influence of EV ownership growth and the uncertainty of EV growth rate, two stochastic chance-constrained dynamic programming models for EV charging station are constructed. An IAGA(Improved Adaptive Genetic Algorithm) is proposed to solve the above planning models, which considers the spatial distribution of charging demand. The feasibility and effectiveness of the proposed IAGA in solving the charging station planning problem are verified by a practical example, and the planning results of two dynamic planning models are compared and analyzed. |
| Key words: electric vehicles charging station locating and sizing dynamic planning genetic algorithms distribution network chance-constrained programming |
