计及耦合因素的电动汽车充电负荷时空分布预测

实现电动汽车与电网互利共赢的基础问题之一是如何有效预测电动汽车的充电负荷，而电动汽车时空转移的随机性和转移过程中各因素的耦合性增加了充电负荷预测的难度，本文提出一种计及动态转移规划和耦合因素的电动汽车充电负荷时空分布预测方法。首先，基于出行链技术建立含多类型电动汽车的单体出行数学模型；在此基础上，考虑交通流量、行驶路况和温度，构建电动汽车的单位里程能耗数学模型。其次，基于马尔可夫决策过程理论，考虑剩余行程和路网拥堵信息，动态更新路网信息和随机规划电动汽车时空转移路径。最后，基于算例，对比分析电动汽车及其充电负荷在不同策略、职能区域和出行日情况下的时空分布。结果表明：本文所提方法能够全面反映电动汽车车主的出行决策，且预测结果能真实反应电动汽车类型和职能区域导致的其充电负荷幅值和分布上的差异。

Prediction of temporal and spatial distribution of electric vehicle charging load considering coupling factors

One of the component to realize the mutual benefit and win-win between electric vehicle and power grid is to effectively predict the charging load of electric vehicle while the difficulty of charging load prediction is in-creased because of the randomness of electric vehicle space-time transfer and a variety of coupling factors in the transfer process. This paper proposes a prediction method of space-time distribution of electric vehicle charging load considering dynamic transfer planning and coupling factors. Firstly, based on the travel chain technology, an individual travel mathematical model with multiple types of electric vehicles is established; Based on this, con-sidering the traffic flux, road conditions and temperature, the mathematical model of energy consumption per mileage of electric vehicle is constructed. Secondly, based on Markov decision process theory, considering the residual path and road network congestion information, the road network information is dynamically updated and the space-time transfer path of electric vehicles is randomly planned. Finally, based on an example, the space-time distribution of electric vehicle and its charging load under different strategies, functional areas and travel days are compared and analyzed. The results indicate that the proposed method can fully reflect the travel decision of electric vehicle owners, and the prediction results can truly reflect the differences in the amplitude and distribution of charging load caused by the type and functional area of electric vehicles.