| 引用本文: | 王育飞,蔡传高,薛花.基于改进NSGA-Ⅱ的社区电动汽车充电站优化充电策略[J].电力自动化设备,2017,37(12): |
| WANG Yufei,CAI Chuangao,XUE Hua.Optimized charging strategy of community electric vehicle charging station based on improved NSGA-Ⅱ[J].Electric Power Automation Equipment,2017,37(12): |
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| 摘要: |
| 提出基于改进的非支配排序遗传算法(NSGA-Ⅱ)的社区电动汽车充电站优化充电策略。首先，以电动汽车充电容量和配电变压器容量限制为约束条件，构建以单位电量充电费用最少、电网侧负荷方差最小为目标的电动汽车充电站多目标充电模型；然后，针对传统NSGA-Ⅱ存在的难以生成满足约束条件的初始种群、Pareto解集分布不均和最优解性能不高的缺点，提出改进初始种群生成和拥挤度比较算子相结合的NSGA-Ⅱ对模型进行求解，并采用基于信息熵的序数偏好法从最终Pareto解集中选择最优折中充电方案；最后，通过算例仿真验证了所提算法的有效性，表明改进NSGA-Ⅱ能在较大程度上提高电网侧的负荷水平和用户的充电性价比。 |
| 关键词: 电动汽车 社区充电站 NSGA-Ⅱ 多目标优化 充电策略 Pareto最优 |
| DOI：10.16081/j.issn.1006-6047.2017.12.015 |
| 分类号:TM73;U469.72 |
| 基金项目:国家自然科学基金资助项目(51407114)；上海市自然科学基金资助项目(15ZR1418000)；上海市科技创新行动计划项目(16DZ0503300) |
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| Optimized charging strategy of community electric vehicle charging station based on improved NSGA-Ⅱ |
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WANG Yufei, CAI Chuangao, XUE Hua
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College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
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| Abstract: |
| An optimized charging strategy for community electric vehicle charging station based on improved NSGA-Ⅱ(Nondominated Sorting Genetic Algorithm Ⅱ) is proposed. Firstly, the multi-objective charging model of electric vehicle charging station is established to minimize the charging cost of per unit electric energy and the load variance of grid side, with the capacity limitation of electric vehicle charging and distribution transformers as constraints. Then, aiming at the shortcomings of traditional NSGA-Ⅱ ,such as difficulties for generating the initial populations satisfying the constraints, uneven distribution of Pareto solution sets and low performance of optimal solution sets, an improved NSGA-Ⅱ,combining improved initial population generation method with comparison operator of crowding distance, is proposed to solve the model. The optimal compromise charging scheme is selected from the final Pareto solution sets by TOPSIS(Technique for Order Performance by Similarity to Ideal Solution) based on information entropy. Finally, simulative results of examples verify the effectiveness of the proposed algorithm and show that the improved NSGA-Ⅱ can improve the grid-side load level and charging cost performance of customers in large extent. |
| Key words: electric vehicles community charging station NSGA-Ⅱ multi-objective optimization charging strategy Pareto optimality |
