近年来，电动汽车保有量快速增长，大规模电动汽车充电对电网的影响将逐步凸显。同时，电动汽车充电具有一定的时空灵活性和价格敏感性，合理的充电定价可优化充电负荷的时空分布，从而提升社会效益。本文对不同场景下大规模电动汽车充电定价引导问题开展研究，主要工作和成果如下：（1）针对目的地充电站，提出了充电运营商的最优日前充电定价策略。建立了电动汽车日前充电计划决策模型，并基于电动汽车个体间的充电聚合博弈模型，分析了给定充电定价下车辆集群的充电行为。在此基础上，基于充电运营商和电动汽车集群之间的主从博弈模型，提出了充电运营商的最优日前充电定价决策算法。所提方法可平滑充电站内充电负荷，且平衡充电运营商收益和电动汽车集群的充电成本。（2）针对城市内公共充电站，考虑电网-交通网耦合运行，提出了公共充电网络运营商的最优充电定价策略。分别建立了交通网多时段用户均衡模型和配电网多时段最优潮流模型以用于含大规模电动汽车的交通网和配电网的运行优化。在此基础上，依次建立了单个充电运营商的最优充电定价模型和多个充电运营商的充电定价博弈模型，并分别提出了基于不动点理论和最佳反应方法的求解算法。该工作能够提升充电运营商的收益，降低配电网运行成本和缓解交通网阻塞。（3）针对多日时间尺度的车网互动问题，提出了充电运营商的最优充放电定价引导方法。建立了多日时间尺度下电动汽车充电决策优化模型以更准确地评估车辆与电网互动的能力；针对配电网，提出了配电网车网互动指导功率曲线生成方法，以作为充电运营商的功率追踪目标；为辅助充电运营商分配车网互动指导功率曲线到车辆单体上，提出了一种基于充电价格信号的分布式迭代算法；最后，给出了运营商充电定价策略的参数优化方法，以提高运营商利润。相较于日前车网互动场景，所提多日时间尺度车网互动框架能有效增大车网互动容量，提高车主和运营商的车网互动收益。本文针对大规模电动汽车充电定价引导策略开展研究，并通过仿真对所提的模型和算法进行了验证分析。本文成果可以为充电运营商提供充电定价决策提供支持，提高充电设施运行效率和收益，同时也可以优化充电负荷的时空分布，减少配电网运行成本和交通路网的拥塞，助力电动汽车和电网协调发展。
As an important means of alleviating the energy crisis and achieving environmental protection, China‘s electric vehicle (EV) industry has rapidly developed, and the number of electric vehicles has increased rapidly. When making charging plans, EV owners may consider factors such as charging costs. In order to rationalize the charging behavior of vehicle clusters and reduce the negative impact of charging load on distribution network operation, it is necessary to formulate appropriate charging prices. This article conducts research on the pricing guidance of large-scale EV charging under different charging scenarios based on the spatiotemporal flexibility of charging load, and the main work and achievements are as follows:(1) For destination charging stations, the optimal day-ahead charging pricing strategy for charging aggregators is proposed. For electric vehicles, a day-ahead charging planning decision model is established, and based on a charging aggregate game model among individuals in the electric vehicle cluster, the charging behavior of the vehicle cluster is analyzed under a given charging pricing. Based on the charging leader-follower game model between the charging aggregator and the electric vehicle cluster, the optimal day-ahead charging pricing decision algorithm for the charging aggregator is proposed. This work can smooth the charging load within the charging station and balance the charging aggregator‘s revenue with the charging cost of the electric vehicle cluster.(2) For public charging stations within cities, considering the coupling operation of transportation and power, the optimal charging pricing strategy for public charging network operators is proposed. Multi-period traffic equilibrium models and multi-period optimal power flow models are established to describe the operation of transportation and distribution networks with a large number of electric vehicles, respectively. Based on this, the optimal charging pricing model for a single charging operator and the charging pricing game model for multiple charging operators are established, and solved using fixed-point theory and best response method, respectively. This work can improve the operating revenue of the charging operator, reduce the operating cost of the distribution network, and alleviate traffic congestion. (3) For the multi-day time scale of vehicle-grid interaction, the optimal charging and discharging pricing guidance method for electric vehicle aggregators is proposed. A vehicle charging decision optimization model is established for the multi-day time scale to assist vehicles in evaluating their own vehicle-grid interaction capacity. For distribution network operators, a power guidance curve generation method for vehicle-grid interaction is proposed. To assist the aggregator in allocating the power guidance curve among individual vehicles, a distributed iterative algorithm based on charging price signal is proposed. Finally, an optimization method for the charging pricing strategy of the aggregator is proposed to improve the aggregator‘s profit. The proposed multi-day time scale vehicle-grid interaction framework can effectively increase the vehicle-grid interaction capacity and improve the vehicle-grid interaction benefits for both vehicle owners and aggregators.This paper focuses on the research of large-scale electric vehicle charging pricing guidance strategies and validates the proposed models and algorithms through simulations. The results of this paper can provide charging pricing tools for urban charging operators, improve their operational efficiency and profit. This work can also optimize the spatial and temporal distribution of charging loads, reduce distribution network operating costs and traffic congestion, and promote the coordinated development of electric vehicles and the power grid.
