| 考虑配网潮流约束的高速公路快速充电站校正规划方法 |
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| 引用本文： | 董晓红,穆云飞,于力,靳小龙,贾宏杰,余晓丹.考虑配网潮流约束的高速公路快速充电站校正规划方法[J].电力自动化设备,2017,37(6). |
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| 作者姓名： | 董晓红 穆云飞 于力 靳小龙 贾宏杰 余晓丹 |
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| 作者单位： | 天津大学 智能电网教育部重点实验室，天津 300072,天津大学 智能电网教育部重点实验室，天津 300072,南方电网科学研究院，广东 广州 510000,天津大学 智能电网教育部重点实验室，天津 300072,天津大学 智能电网教育部重点实验室，天津 300072,天津大学 智能电网教育部重点实验室，天津 300072 |
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| 基金项目： | 国家自然科学基金资助项目(51625702,51677124,51337005)；国家高技术研究发展计划(863计划)项目(2015AA-050403)；天津市应用基础与前沿技术研究计划(15JCQNJC43-500)；南方电网公司科技项目(KY2014-2-0024) |
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| 摘 要： | 提出了一种考虑配网潮流约束的高速公路快速充电站校正规划方法。利用电动汽车充电需求预测模型得到高速公路上电动汽车充电需求点的时空分布信息;在此基础上,基于共享型最近邻居聚类算法构建高速公路快速充电站的选址模型,以确定快速充电站的站址方案;进而基于排队论理论和充电需求的时空分布构建快速充电站定容模型,以确定快速充电站的容量配置。对该规划方案下配网潮流约束进行判断,若不满足,则利用所提出的校正原则对规划方案进行修正,直到满足配网的潮流约束为止,以得到最终的快速充电站规划方案。通过典型算例验证了所提规划方法的有效性。
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| 关 键 词： | 高速公路 快速充电站 电动汽车 聚类算法 选址定容模型 校正规划 潮流约束 配网 |
Freeway FCS planning and correction considering power-flow constraints of distribution network |
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| DONG Xiaohong,MU Yunfei,YU Li,JIN Xiaolong,JIA Hongjie and YU Xiaodan.Freeway FCS planning and correction considering power-flow constraints of distribution network[J].Electric Power Automation Equipment,2017,37(6). |
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| Authors: | DONG Xiaohong MU Yunfei YU Li JIN Xiaolong JIA Hongjie and YU Xiaodan |
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| Affiliation: | Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China,Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China,Electric Power Research Institute, China Southern Power Grid, Guangzhou 510000, China,Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China,Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China and Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China |
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| Abstract: | A method of freeway FCS(Fast Charging Station) planning and correction considering the power-flow constraints of distribution network is proposed. The EV(Electric Vehicle) charging demand prediction model is adopted to obtain the spatial-temporal distribution of freeway EV charging demand points, based on which, the SNN(Shared Nearest Neighbor) clustering algorithm is applied to build a location determination model for developing the freeway FCS location scheme and also the queuing theory is applied to build a capacity determination model for developing the freeway FCS capacity configuration. The obtained freeway FCS planning scheme is then judged by the power-flow constraints of distribution network and modified by the proposed correction principle until all these constraints are satisfied. Case study for a typical freeway verifies the validity of the proposed method. |
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| Keywords: | freeway fast charging station electric vehicles clustering algorithms location and capacity determination model correction planning power-flow constraints distribution network |
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