城市EV时空充电负荷预测及充电站规划研究
摘要：针对目前城市电动汽车（electric vehicle, EV）充电站存在盲目建设、规划不合理导致的部分充电站利用率低、用户充电满意度低等问题，同时为适应“双碳”目标下发展大规模EV的充电站规划需求，提出一种基于蒙特卡洛模拟和回声状态网络（echo state network, ESN）拟合的城市EV时空充电负荷预测方法，进一步开展EV充电站规划研究。首先考虑城市交通路网结构和区域主要功能，将待规划区域进行网格划分并作为待建充电站备选位置；利用蒙特卡洛方法对各类EV进行多种模式的出行链模拟，获取各网格区域内的EV充电负荷数据集；为拟合各网格内EV充电负荷的多样化分布特征，建立基于回声状态网络 ESN学习算法的EV时空充电负荷预测模型，实现一定EV保有量下待规划区内EV时空充电负荷的预测。进一步考虑待规划网格区域内的最大充电预测负荷等约束条件﹑以充电站的建设和运维成本、EV用户充电出行成本以及配网损耗的综合成本最小为目标，建立EV充电站的规划模型，利用粒子群算法进行模型求解得到待规划区的充电站建设位置、数量及容量；最后以某城区EV充电负荷预测及充电站规划为例进行计算，验证了所提方法及模型的有效性。
Abstract:In allusion to the fact of low utilization rate of partial urban electric vehicle (abbr. EV) charging stations and low user satisfaction due to unreasonable construction and planning, to meet the needs of large-scale EVs charging stations planning under the dual carbon target, based on the fitting of Monte Carlo simulation with echo state network (abbr. ESN) a method to forecast urban EV space-time charging load was proposed to further conduct the research on EV charging station planning. Firstly, considering the structure of urban traffic network and the main functions of different regions, the area to be planned was divided into meshes as the alternative locations of charging stations to be built. The Monte Carlo method was utilized to simulate various modes of travel chains for all kinds of EVs to obtain EV charging load data set in each mesh. To fit the diversified distribution characteristics of EV charging load in each mesh, an ESN learning algorithm-based EV temporal and spatial charging load prediction model was established to realize the prediction of EV temporal and spatial charging loads within the region to be planned under a certain inventory of EV. Further taking the maximum predicted charging load and so on within the mesh to be planned as constraint conditions, and taking the minimized cost of construction, operation and maintenance, the charging and trip cost of EV users and the composite cost of the loss of distribution network as objective functions, an EV charging station planning model was constructed. The swarm clustering optimization was used to solve the constructed model to obtain the sites in the region to be planned for the construction of charging stations, the amount and the capacities of them. Finally, taking EV charging load prediction for a certain urban district and the planning of the charging station planning as example, the effectiveness of the proposed method and the constructed model is verified.
标题：城市EV时空充电负荷预测及充电站规划研究
英文标题：Research on Spatio-temporal Charging Load Prediction and Charging Station Planning of Urban Electrical Vehicles
作者：王宇飞, 张飞, 郭俊超, 孙鑫, 霍伟, 王冬生, 杨丽君,
关键词：EV, 时空负荷预测, 充电站规划, 回声状态网络, 粒子群算法,
发表日期：2023-04-10
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