摘要
在分析城市电动汽车充电站特性的基础上,建立以充电站年运行费用最小、投资成本最小以及充电成本最小的多目标优化模型。在确定目标函数的基础上提出一种新颖的智能算法—细菌菌落算法。细菌菌落算法根据单个细菌的生长方式及其群体菌落生长演化过程来寻找最优解,建立细菌菌落的生成和死亡的寻优机制。算例验证了所提算法具有良好实用性和适应性,并且也验证所提模型的实际意义。
Based on the analysis of the characteristics of charging stations for urban electric vehicles, a muhi-objective optimization model is established, with the minimum annual operation cost, minimum investment cost and minimum charging cost as objectives. Accordingly, a new intelligent algorithm, i.e., bacterial colony optimization （BCO） algorithm, is presented, which searches for the optimal solution according to the way of single bacterial growth and its group evolution, and sets an optimization-searching mechanism to describe the growth and death of bacteria colonies. Experiments verify the practicability and adaptability of the proposed algorithm, as well as the practical significance of the proposed model.
作者
李渊博
蒋铁铮
陈家俊
LI Yuanbo JIANG Tiezheng CHEN Jiajun(College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410004, China)
出处
《电力系统及其自动化学报》
CSCD
北大核心
2017年第7期112-117,共6页
Proceedings of the CSU-EPSA
关键词
充电站
多目标优化
细菌菌落优化算法
电动汽车
交通流量
charging station
multi-objective optimization
bacterial colony optimization algorithm
electric vehicle
traffic flow
