| 引用本文: | 丁丹军,戴康,张新松,等.基于模糊多目标优化的电动汽车充电网络规划[J].电力系统保护与控制,2018,46(3):43-50.[点击复制] |
| DING Danjun,DAI Kang,ZHANG Xinsong,et al.Network planning for electric vehicle charging based on fuzzy multi-objective optimization[J].Power System Protection and Control,2018,46(3):43-50[点击复制] |
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| 摘要: |
| 电动汽车充电网络规划对电动汽车发展具有重要意义，直接影响了车辆使用的便利性与配电网络运行的经济性。为此，建立了同时考虑充电网络服务能力最大化与配电系统网络损耗最小化的电动汽车充电网络规划模型。该模型是典型的多目标优化问题，且两个优化目标具有不同维度，并可能互相冲突，很难在优化中互相协调。因此，通过定义目标隶属度函数对模型中的子优化目标进行模糊化，将原始优化问题转换为基于最大满意度的单目标优化问题，并采用遗传算法对其求解。以25节点交通网络以及IEEE33节点配电系统为例进行了仿真试验，验证了所提模型及求解方法的有效性。 |
| 关键词: 充电网络规划 服务能力最大化 网络损耗最小化 模糊多目标优化 遗传算法 |
| DOI：10.7667/PSPC170006 |
| 投稿时间：2017-01-02修订日期：2017-06-04 |
| 基金项目:国家自然科学基金(51607098, 61673226);国家电网公司科技项目(SGJSSZ00FZWT1601138) |
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| Network planning for electric vehicle charging based on fuzzy multi-objective optimization |
| DING Danjun,DAI Kang,ZHANG Xinsong,GU Juping,ZHOU Hui,QIAN Kejun |
| (Suzhou Power Supply Company, State Grid Jiangsu Electric Power Company, Suzhou 215004, China;School of Electrical Engineering, Nantong University, Nantong 226019, China) |
| Abstract: |
| Network planning for Electric Vehicle (EV) charging is of importance to the development of EV and has direct impacts on the convenience of EV owners and the economic performance of distribution systems. An EV charging network planning model is proposed to maximize charging service capacity and to minimize energy losses in distribution systems. The model proposed is a typical multi-objective decision-making model, and two optimization objects of which are of different dimensions and might be inherent conflicting each other. As a result, they can not get their optimal results simultaneously. The original planning model is transformed into a single objective optimization problem based on maximum satisfaction degree by fuzzy processing two optimization objects through defining objective membership functions. The single objective optimization problem is then solved by Genetic Algorithm (GA). In the end, a 25-node traffic network and IEEE33 node distribution system are utilized to justify the formulation and solving technique presented here. This work is supported by National Natural Science Foundation of China (No. 51607098 and No. 61673226) Science and Technology Project of State Grid Corporation of China (No. SGJSSZ00FZWT1601138). |
| Key words: charging network planning service capacity maximization energy losses minimization fuzzy multi-objective optimization genetic algorithm |
