摘要
采用电池租赁和建立集中型充电站是电动汽车产业具有竞争力的发展模式。根据规划区各类型电动汽车日行驶距离概率分布,建立了电动汽车日换电需求模型。以此为基础,将集中型充电站的规划与配电网调度相结合,建立了考虑削峰填谷作用的集中型充电站选址定容二层规划模型。利用改进遗传算法与自适应粒子群相结合的混合智能算法进行求解,并将加权伏罗诺伊图应用于集中型充电站服务区域的划分,实现了集中型充电站负载率的均衡。通过仿真算例验证了所提模型和方法的有效性和可行性。
Leasing batteries and establishing centralized charging stations are the future competitive development orientations in electric vehicle industry. Based on driving distance probability distribution of various electric vehicles obtained by statistical analysis, an uncertainty model of electric vehicle battery replacement demand is established. Considering the investment of centralized charging stations and lines, network loss and load shifting, a planning model for centralized charging stations is put forward. The hybrid intelligent optimization method, which combines the improved genetic algorithm with the self-adaptive particle swarm algorithm, is used to solve the above two layer programming models. The service area is divided by the weighted Voronoi diagram in order to balance the load rates of centralized charging stations. The simulation results show that the proposed model and method are effective and feasible for centralized charging station planning.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2014年第7期1052-1060,共9页
Proceedings of the CSEE
基金
国家自然科学基金项目(51377162)~~
关键词
集中型充电站规划
电动汽车换电需求
削峰填谷
改进遗传算法
自适应粒子群优化
二层规划
centralized charging station planning
electric vehicle battery replacement demand
load shifting
improved genetic algorithm
self-adaptive particle swarm optimization
bi-level programming model
