轨迹数据驱动的电动汽车充电需求及V2G可调控容量估计

Trajectory-data-driven Estimation of Electric Vehicle Charging Demand and Vechicle-to-Grid Regulable Capacity

电动汽车(EV)充电需求估计是研究电动汽车与电网互动(V2G)的重要前提。为此,提出一种行驶轨迹数据驱动的EV充电需求预测模型,并进一步考虑用户多维效益,构建用户选择参与V2G响应的用户决策模型,分析区域V2G响应能力的调控潜力。首先,对行车轨迹大数据集进行清洗与挖掘,基于动态能耗理论构建了EV充电需求时空分布预估模型。其次,基于社会行为学理论并综合考虑用电需求效用、经济效用、环保效用以及社会效用,构建了EV用户选择参与V2G响应的概率选择模型。该模型不仅考虑了EV用户的异质性,而且体现了用户决策的交互影响。最后,建立V2G可响应容量调度模型,分析V2G响应资源对区域负荷的调节效果。结果表明,所提模型不仅能有效估计某城市区域的EV充电需求时空分布特性,而且能挖掘该区域选择参与V2G响应的EV潜在用户数量,为研究V2G响应资源对区域负荷的调控潜力提供了支撑。

Electric vehicle(EV)charging demand estimation is an important precondition for studying the vehicle-to-grid(V2 G)interaction.Therefore,this paper proposes a charging demand prediction model of EVs driven by driving trajectory data,constructs a decision-making model of users to choose to participate in V2 G response by further considering the multi-dimensional benefits of users,and analyzes the regulation potential of regional V2 G response capabilities.Firstly,the big data set of driving trajectory is cleaned and mined,and a prediction model for the spatio-temporal distribution of EV charging demand is constructed based on the dynamic energy consumption theory.Secondly,based on the social behavior theory and considering the electricity demand utility,economic utility,environmental protection utility and social utility,the probabilistic selection model of EV users participating in V2 G response is constructed.The model not only considers the heterogeneity of EV users,but also reflects the interactive influence of user decisions.Finally,a V2 G responsive capacity regulation model is established to analyze the adjustment effect of V2 G responsive resources on the regional load.The results show that the proposed model can not only effectively estimate the spatio-temporal distribution characteristics of EV charging demand in a certain urban area,but also obtain the number of potential EV users who choose to participate in V2 G response in this area,which provides support for studying the regulation potential of V2 G responsive resources on the regional load.