采用拉格朗日松弛法的电动汽车分散优化充电策略  

Decentralized Optimization Charging Strategy Based on Lagrangian Relaxation Method

作　　者：许少伦[1] 严正[1] 张良[2] 冯冬涵[1] 赵小波[1] 

机构地区：[1]电力传输与功率变换控制教育部重点实验室(上海交通大学),上海市200240 [2]明尼苏达大学电气与计算机工程系,美国明尼阿波利斯市55455

出　　处：《电力建设》2015年第7期107-113,共7页Electric Power Construction

基　　金：国家自然科学基金项目(51377103);国家科技支撑计划资助项目(2013BAA01B04)~~

摘　　要：电动汽车聚合商作为电动汽车充电服务的提供商,是电网公司和电动汽车用户之间交互的重要协调者。从电动汽车聚合商的角度出发,在考虑电动汽车用户的电量需求、充电时间以及配电变压器的可用容量等约束条件下,以电动汽车聚合商充电收益最大化为目标,构建了基于拉格朗日松弛法的分散优化模型,研究了分散优化充电策略的执行机制和流程。采用蒙特卡洛方法模拟电动汽车的充电情况,通过仿真算例,对比分析了在无序充电、集中优化充电和分散优化充电模式下的负荷曲线、经济效益和计算效率。结果表明:基于拉格朗日松弛法的分散优化充电策略可得到近似于集中优化模式下的充电收益,同时具有更高的计算效率,适合实际应用。As the electric vehicle（ EV） charging service provider,aggregator is the important coordinator between the grid and EV users. In this paper,a decentralized optimization charging model based on the Lagrangian relaxation method was formulated from the perspective of EV aggregator,which took the maximum charging profit of the aggregator as target with considering constraints： users＇ electricity demand,charging time and available capacity of distribution transformers,etc. The implementation mechanism and process of the decentralized optimization charging strategy were also explained.Then,M onte Carlo method was used to simulate the charging situations of EVs,and based on this simulation,the load curve,economic benefits and computational efficiency under uncoordinated charging,centralized optimization charging and decentralized optimization charging modes were compared and analyzed. The results showthat the decentralized optimization charging using the Lagrangian relaxation method can get the approximate charging profit as the centralized optimization charging and the decentralized method possesses higher computing efficiency,so it is suitable for actual application.

关 键 词：电动汽车 充电策略 聚合商 经济效益 拉格朗日松弛法 

分 类 号：TM73[电气工程—电力系统及自动化] U469.72[机械工程—车辆工程]