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文章摘要
基于引入模拟退火思想的改进粒子群算法的电动汽车充电站最优规划*
Optimal Planning of Electric Vehicle Charging Station Based On PSOSA Algorithm
Received:December 09, 2015  Revised:March 09, 2016
DOI:
中文关键词: 充电站  最优规划  车流信息  用户成本  加权Voronoi图  改进粒子群算法
英文关键词: charging station  optimal planning  traffic flow  customer cost  weighted Voronoi diagram  improved particle swarm optimization algorithm
基金项目:四川省科技支撑项目:“主动配电网优化规划及协调运行关键技术研究”(2014JY0191);
Author NameAffiliationE-mail
Yan Tianze* Intelligent Electric Power Grid Key Laboratory of Sichuan Province,Sichuan University yantianze1991@hotmail.com 
Qiu Xiaoyan Intelligent Electric Power Grid Key Laboratory of Sichuan Province,Sichuan University 609296963@qq.com 
LIU Yanbo Intelligent Electric Power Grid Key Laboratory of Sichuan Province,Sichuan University 609296963@qq.com 
Tang Ke Intelligent Electric Power Grid Key Laboratory of Sichuan Province,Sichuan University 609296963@qq.com 
Wan Chengjiang Intelligent Electric Power Grid Key Laboratory of Sichuan Province,Sichuan University 609296963@qq.com 
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中文摘要:
      未来电动汽车的大规模发展,需要建设大量的充电站,充电站的合理布局对充电站投资者和用户具有非常重要的意义。本文提出了一种计及交通道路结构、车流信息及用户成本的电动汽车充电站最优规划模型。采用加权Voronoi图对待规划区进行服务区域划分,对传统粒子群算法引入模拟退火思想,并且对惯性权重更新机制做出改进。使用加权Voronoi图和引入模拟退火思想的改进粒子群算法相结合优化充电站的建设位置和容量配置。算例分析验证了规划模型和算法的正确性和实用性。
英文摘要:
      With the rapid growth of electric vehicles in the future, large amount of charging stations need to be constructed. Reasonable layout of charging stations has important significance to the investors of charging stations and the clients. This paper introduce an optimal planning model of charging stations, which considers the traffic structure, traffic flow and the cost of customers. Using weighted Voronoi diagram divides the area which need to be planned. Ameliorate the updating mechanism of inertia weight in PSO, and lead the concept of simulated annealing in it. By using weighted Voronoi diagram and PSOSA, the problem of optimal location and capacity of charging stations is solved. Finally, the example analysis verifies the practicality and effectiveness of the planning method and the algorithm.
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