| 计及电动汽车充电模式的主动配电网多目标优化重构 |
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| 引用本文： | 张涛,张东方,王凌云,周远化,张晓林.计及电动汽车充电模式的主动配电网多目标优化重构[J].电力系统保护与控制,2018,46(8):1-9. |
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| 作者姓名： | 张涛 张东方 王凌云 周远化 张晓林 |
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| 作者单位： | 新能源微电网湖北省协同创新中心(三峡大学) |
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| 基金项目： | 国家自然科学基金项目资助(51407104);三峡大学学位论文培优基金项目资助(2017YPY036) |
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| 摘 要： | 电动汽车的入网会影响到电网的经济性和安全性,而配电网重构是电网优化运行的有效措施。根据主动配电网(ADN)的特点,提出了含分布式电源(DG)和电动汽车充电的优化重构模型。通过有功网损灵敏度确定DG的安装位置和容量,构造出DG出力和EV充电的多时段概率模型。建立有功网损、电压偏移指标(VSI)和开关操作次数的多目标优化数学模型以确定系统的最佳重构方案,并在IEEE33节点标准配电系统中,采用引入小生境技术的改进多目标粒子群算法(IMPSO)进行计算,提高了算法的全局寻优能力。考虑了电动汽车无序充电和智能充电两种模式,对比不同场景下得出的结果,验证了该方法的实用性和有效性。
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| 关 键 词： | 分布式电源 电动汽车充电 主动配电网重构 多目标优化 |
| 收稿时间： | 2017/4/17 0:00:00 |
| 修稿时间： | 2017/5/16 0:00:00 |
Multi-objective optimization of active distribution network reconfiguration considering electric vehicle charging mode |
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| ZHANG Tao,ZHANG Dongfang,WANG Lingyun,ZHOU Yuanhua and ZAHNG Xiaolin.Multi-objective optimization of active distribution network reconfiguration considering electric vehicle charging mode[J].Power System Protection and Control,2018,46(8):1-9. |
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| Authors: | ZHANG Tao ZHANG Dongfang WANG Lingyun ZHOU Yuanhua and ZAHNG Xiaolin |
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| Affiliation: | Hubei Provincial Collaborative Innovation Center for New Energy Microgrid China Three Gorges University, Yichang 443002, China,Hubei Provincial Collaborative Innovation Center for New Energy Microgrid China Three Gorges University, Yichang 443002, China,Hubei Provincial Collaborative Innovation Center for New Energy Microgrid China Three Gorges University, Yichang 443002, China,Hubei Provincial Collaborative Innovation Center for New Energy Microgrid China Three Gorges University, Yichang 443002, China and Hubei Provincial Collaborative Innovation Center for New Energy Microgrid China Three Gorges University, Yichang 443002, China |
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| Abstract: | Electric Vehicle (EV) will affect the economy and safety to the power grid, and distribution network reconfiguration is an effective measure for optimal operation of power grid. According to the characteristics of Active Distribution Network (ADN), this paper establishes an optimal reconstruction model with Distributed Generation (DG) and EV. The sitting and sizing of DG is determined by the network loss sensitivity, the multi time probability model is constructed with the output of DG and charging of EV. The network loss, Voltage Deviation Index (VSI) and the number of switching operation are set up as the multi-objective optimization functions to determine the best reconstruction scheme, and in the IEEE33 node standard distribution system, the Improved Multi-Objective Particle Swarm Optimization (IMPSO) algorithm with niche technology is used to improve the ability of global optimization for computing. The EV disordered charging and intelligent charging models are considered to verify the practicality and effectiveness of the proposed method under the comparison results of different scenarios. This work is supported by National Natural Science Foundation of China (No. 51407104) and Research Fund for Excellent Dissertation of China Three Gorges University (No. 2017YPY036). |
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| Keywords: | distributed generation electric vehicle charging active distribution network reconfiguration multi-objective optimization |
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