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文章摘要
含多类型充电设施的城市电动汽车充电站群协同规划方法
A Cooperative Planning Method of Urban Electric Vehicle ChargingStation Group With Multi-type Charging Facilities
Received:November 04, 2019  Revised:December 10, 2019
DOI:10.19753/j.issn1001-1390.2020.20.009
中文关键词: 电动汽车  充电站群  多类型充电设施  行为不确定性  混合整数二阶锥规划
英文关键词: electric vehicle  charging station group  multi-type charging facility  uncertainty of the electric vehicle behavior  mixed-integer second-order cone programming
基金项目:国家自然科学基金项目(51907122);上海市青年科技英才扬帆计划(19YF1423800)
Author NameAffiliationE-mail
Zhang Duanhong Key Laboratory of Control of Power Transmission and Conversion (SJTU),Ministry of Education 1018950495@qq.com 
Shi Yiwei Key Laboratory of Control of Power Transmission and Conversion(SJTU),Ministry of Education shiyiweisyw@gmail.com 
Feng Donghan Key Laboratory of Control of Power Transmission and Conversion (SJTU),Ministry of Education seed@sjtu.edu.cn 
Zhou Yun* Key Laboratory of Control of Power Transmission and Conversion (SJTU),Ministry of Education yun.zhou@sjtu.edu.cn 
LI Hengjie College of Electrical Engineering and Information Engineering, Lanzhou University of Technology 272692533@qq.com 
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中文摘要:
      为实现不同额定功率充电设施的有机组合配置,本文提出了一种含多类型充电设施的城市电动汽车充电站群选址定容规划方法。首先计及电动汽车用户的行为不确定性和需求多样性,考虑充电功率的站内分配,设计了站内多类型充电设施协同服务水平模型;在此基础上结合充电需求分配和配电网约束,提出了考虑社会综合成本的含多类型充电设施的充电站群协同规划方法。所提的方法能够建模成混合整数二阶锥模型进行有效求解。最后,通过耦合了地理信息及31节点配电网的某城市区域实际算例,验证了所提方法的合理性及有效性。
英文摘要:
      To organically configure charging facilities with different rated power, this paper proposes a siting and sizing planning method of urban EV charging station group, including multiple types of charging facilities. A cooperative service level model with multi-type charging facilities is designed in consideration of the uncertainty of EV users’ behavior and the diversity of their demand,and the intra-station charging power dispatch as well. On this basis, a cooperative planning method of charging station group with multi-type charging facilities is proposed. This model also considers comprehensive social cost, with constraints of charging demand allocation and distributed electricity network operation. The proposed optimization method can be modelled into a mixed integer second-order cone model, which could be solved effectively. In this paper, an actual urban area example, which combines geographic information with a 31-node distribution network, is used to verify the feasibility and effectiveness of the proposed method.
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