计及车主需求的电动汽车聚合商能量调度策略  

Energy Scheduling Strategy for Electric Vehicle Aggregators Considering Vehicle Owners Demands

作　　者：黄元清 刘迪迪[2] 覃光锋 贤燕华[2] 农丽萍[2] 卢虹兵 HUANG Yuanqing;LIU Didi;QIN Guangfeng;XIAN Yanhua;NONG liping;LU Hongbing(School of Electronic and Information engineering,Guangxi Normal University,Guilin,Guangxi 541001,China;Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips,Guangxi Normal University,Guilin,Guangxi 541001,China;Guangxi Science&Technology Normal University,Laibin,Guangxi 546199,China)

机构地区：[1]广西师范大学电子与信息工程学院,广西桂林541001 [2]广西类脑计算与智能芯片重点实验室(广西师范大学),广西桂林541001 [3]广西科技师范学院,广西来宾546199

出　　处：《南方电网技术》2024年第10期161-170,共10页Southern Power System Technology

基　　金：国家自然科学基金资助项目(62061006,12162005);广西科技计划项目(桂科AD23026225);广西类脑计算与智能芯片重点实验室基金(BCIC-23-Z7);大学生创新创业训练计划项目(202210602303)。

摘　　要：针对充电站聚合电动汽车充/放电调度问题,提出了一种计及车主需求的电动汽车聚合商(electric vehicle ag⁃gregator,EVA)能量优化调度策略,以最小化EVA长期购电成本。首先,充分考虑车主需求和外部电网电价的时变性,建立起EVA能量调度框架;其次,根据车主充电需求差异性设计了3种充电模式:双向调度模式、单向调度模式和快速充电模式,并分别建立负荷模型;然后,基于强化学习设计EVA的实时能量调度策略;最后,通过真实数据的仿真算例以及同其他贪婪算法的对比,验证了所提策略的合理性和有效性。结果表明,基于所提策略前两种调度模式较贪婪算法下的调度模式在一个月内可分别为EVA节省54.1%和47.5%的购电成本。Aiming at the charging/discharging scheduling problem for charging station aggregation of electric vehicles,an optimal energy scheduling strategy is proposed for a electric vehicle aggregator(EVA)that takes into account the demands of vehicle owners with the goal of minimizing the long-term power purchase cost of EVA.Firstly,adequate consideration of vehicle owners demands and the time-varying nature of external grid tariffs,an operational framework for EVA energy scheduling management is established.Secondly,the electric vehicles(EVs)are classified into three charging modes according to the difference of users'charging demands,that is,two-way-dispatch EVs,one-way-dispatch EVs and fast-dispatch EVs,and load models are established respectively.Then,based on reinforcement learning theory the real-time energy scheduling strategy is designed for EVA.Finally,the reasonableness and effectiveness of the proposed algorithm are verified by simulation examples of real data and comparing with other greedy algorithms.The results show that the first two scheduling modes based on the proposed strategy can save 54.1%and 47.5%of the cost of EVA in one month,compared with the scheduling mode under the greedy algorithms.

关 键 词：电动汽车聚合商 需求差异性 实时电价 强化学习 调度策略 

分 类 号：TM73[电气工程—电力系统及自动化]