基于出行模拟的电动汽车充电负荷预测模型及V2G评估

提出一种交通路网约束下用户出行模拟的电动汽车充电负荷时空预测模型。首先,建立计及交通道路网络拓扑,道路—阻抗函数关系和区域功能特性的交通道路模型。其次,构建不同复杂程度的出行链模型模拟用户出行特性,运用Dijkstra算法选择耗时最短的行驶路径,进而采用蒙特卡洛方法模拟区域交通路网和出行链双重约束下,家用电动汽车充电负荷工作日1 d内的时空分布特性;然后,基于电动汽车负荷时空分布结果、综合荷电状态、停驻时间和电价3种特征因素,利用模糊算法计算电动汽车入网(V2G)可响应的功率和容量,并分析荷电状态对响应结果的影响。最后,以某区域为例,仿真获取电动汽车充电需求时空分布,进行V2G响应评估,结果验证了所提模型和方法的有效性。

Trip Simulation Based Charging Load Forecasting Model and Vehicle-to-Grid Evaluation of Electric Vehicles

A charging load forecasting model of electric vehicles(EVs)for trip simulation of users is proposed considering both traffic network and different trip chains. Firstly, a traffic road model is established that takes into account the network topology of traffic road, relationship and regional characteristics of road-impedance function. Then, different trip chain models with various complexities are constructed to simulate the trip characteristics of users. The Dijkstra algorithm is adopted to select the shortest time-consuming driving path. Moreover, Monte Carlo method is applied to simulate the spatial-temporal characteristics of charging load for household EVs which is attached to the combination of regional traffic road network and trip chains. The power and capacity affected by vehicle-to-grid(V2G)are calculated by fuzzy algorithm according to three characteristic factors such as spatial-temporal distribution of load for EVs, comprehensive state of charge, stopping time and electricity price based on spatial-temporal distribution of load for EVs. The effect of state of charge on response results is analyzed as well. Finally, taking a certain area as an example, the spatial-temporal distribution of charging demand for EVs is obtained by simulation, and the V2G response is evaluated. The results verify the effectiveness of the proposed model and method.