基于MF-LSTM的城市电动汽车集中充电负荷可调潜力评估  

Evaluation of Adjustable Potential of Urban Electric Vehicle Centralized Charging Load Based on MF-LSTM

作　　者：潘玲玲 庄卫金 赵奇 田江 PAN Linging;ZHUANG Weijin;ZHAO Qi;TIAN Jiang(China Electric Power Research Institute Co.,Ltd.,Nanjing 210000,Jiangsu,China;Suzhou Power Supply Branch,State Grid Jiangsu Electric Power Co.,Ltd.,Suzhou 215000,Jiangsu,China)

机构地区：[1]中国电力科学研究院有限公司,江苏南京210000 [2]国网江苏省电力有限公司苏州供电分公司,江苏苏州215000

出　　处：《电气传动》2023年第8期59-69,共11页Electric Drive

基　　金：国家电网公司科技项目(5108-202118041A-0-0-00)。

摘　　要：在新型电力系统背景下,电网需求侧可调控资源对于系统稳定的重要性日益提升。电动汽车作为重要的可调度负荷资源,对其可调度潜力进行准确评估,能有效提升电网安全稳定运行能力。现有研究较少考虑电动汽车用户行为偏好对电网负荷调控的影响,因此,提出一种考虑用户充电偏好的电动汽车集中式电站可调潜力评估方法。考虑电动汽车充电时的外部条件与自身行为偏好因素,建立基于隶属度函数的用户充电行为模型,并结合长短期记忆神经网络算法对充电站的可调潜力进行评估。最后,通过实际充电站算例,分析了电动汽车用户与负荷可调度潜力之间的耦合关系,验证了所提方法对负荷可调控容量评估的有效性,为电动汽车可调负荷参与削峰填谷等需求响应服务提供了理论支撑。In the background of the new power system,the importance of demand-side dispatchable resources of the grid for system stability is increasing.As an important dispatchable load resource,an accurate assessment of electric vehicle(EV)dispatchable potential can effectively improve the safety and stability of the grid.Existing research has rarely considered the impact of EV user behavior preferences on grid load regulation.Therefore,a method for evaluating the adjustable potential of EV centralized power stations considering user charging preferences was proposed.The user charging behavior model based on the membership function(MF)was established considering external conditions and their own behavioral preferences when charging EVs.And the long short-term memory(LSTM)neural network algorithm was combined with MF to evaluate the adjustable potential of charging stations.Finally,the coupling relationship between EV users and load dispatchable potential was analyzed through actual charging station calculations,which verifies the effectiveness of the proposed method for load dispatchable capacity assessment and provides theoretical support for EV adjustable load participation in demand response services such as peak shaving and valley filling.

关 键 词：电动汽车 调度潜力 用户行为 隶属度函数 长短期记忆神经网络 

分 类 号：TM715[电气工程—电力系统及自动化]