车联网中多主体参与的电动汽车预充电路径规划

Electric vehicle pre-charging path planning with multi-agent participation in the Internet of Vehicles

中文关键词  电动汽车  车网互动  路电耦合  路径规划  多主体决策

英文关键词  electric vehicles  vehicle to grid  transportation-energy coupling  path planning  multi-subject decision making

中文摘要

      随着电动汽车的发展和普及, 能源网、交通网以及信息网之间的耦合程度不断加深, 以单一路网交通系统 为背景的传统路径规划模型已不能适应路电耦合场景中电动汽车充电导航决策的需求. 针对此问题, 本文计及能 源侧负荷需求响应和交通侧流量分布限制, 建立了一种基于车–路–站–网系统信息交互的多主体参与的电动汽车预 充电路径规划模型; 然后, 在综合评估用户侧出行成本、配电网负荷波动以及聚合商运营利益的基础上, 利用启发 式的A*算法对规划区域内的最优充电桩及其预约时段进行求解; 最后, 以随机性和灵活性较大电动私家车为建模 对象, 结合长沙市某地区路网验证了该模型在短时间尺度调度中的有效性. 结果表明, 文中所提模型能有效减少用 户侧开销, 促进电网的安全稳定运行, 同时能在一定程度上均衡充电桩的区域使用差异, 实现统筹多元主体目标的 综合最优路径规划

英文摘要

      With the development and popularization of electric vehicles, the coupling between energy network, traffic network and information network have been deepened, and the traditional path planning model based on a single traffic system on the road side can no longer adapt to the demand of electric vehicle charging navigation decision in the roadelectric coupling scenario. In this paper, a multi-participant EV pre-charging path planning model based on the interaction of vehicle-road-station-grid system information is established to address this problem, taking into account the energy-side load demand response and traffic-side traffic distribution constraints. Then, based on the comprehensive evaluation of userside travel cost, distribution network load fluctuation and aggregator’s operation benefit, the optimal charging pile and its reservation time in the planning area are solved by using the heuristic A* algorithm. Finally, the effectiveness of the model in short time scale scheduling is verified by combining the road network in a certain area of Changsha city with a random and flexible electric private car as the modeling object. The results show that the proposed model can effectively reduce the user-side costs, promote the safe and stable operation of the grid. Meanwhile, it also can balance the regional differences in the use of charging piles to a certain extent. In a word, this model can realize the comprehensive optimal path planning that integrates the objectives of multiple subjects.