在实现“双碳”目标的过程中，电动汽车的规模不断增大，其充放电对电网运行产生显著影响。考虑当前对单一电动汽车调度造成的控制指令下发数量较大的问题，分别从电动汽车侧与配电网侧2个角度，提出可调度能力评估指数与效益评估指数。构建电动汽车综合可调度潜力评估模型，对电动汽车群体划分优先调度区并下发指令，减少调度电动汽车的次数。为缓解电动汽车区域流动性给模型求解带来的困难，使用了一种以W-GAN以及 k-means聚类算法为基础的电动汽车分类方法。以3个削峰填谷算例为基础，分析了4个电动汽车群体的可调度潜力与调度效果。仿真结果表明，所提出的可调度能力评估指数、调度效益评估指数具有一定的合理性，且运用综合可调度潜力评估模型划分优先调度区可在较少调度电动汽车的情况下完成较好的削峰填谷效果。

The number of electric vehicles is mounting in the process of achieving "dual carbon" target. And their charging and discharging behaviours significantly impact the operation of power grid. In response to the numerous control instructions given to schedule every single electric vehicle（EV）， considering from EV side and distribution network side， a dispatchable capacity assessment index and a benefit assessment index are proposed. And a comprehensive dispatchable potential assessment model for EVs is established. Then， EVs can be divided into different groups and those in dispatch priority zones will be given schedule instructions preferentially. To alleviate the difficulty brought by EV mobility between regions to the model solving， an EV grouping method based on W-GAN and k-means clustering algorithm is used. Based on three peak load regulation cases， the dispatchable potential and dispatch performance of four EV groups are analysed. The simulation results show that the dispatchable capacity assessment index and the dispatching benefit assessment index of the proposed method are reasonable. Giving scheduling priority to the divided EV dispatch priority zones selected by the integrated dispatchable potential assessment model can accomplish peak load regulation with less dispatching times.

随着电动汽车逐步替代燃料汽车，电动汽车充电负荷对电网造成的影响也越来越大，为此提出了一种考虑出行需求与引导策略的电动汽车充电负荷时空分布预测方法。首先，基于路段通行时间模型建立划分功能区域的半动态交通网模型；进一步，建立电动汽车能耗模型，在分析电价、气候和季节等因素对车主出行需求影响的同时，对充电需求、半动态交通网模型、能耗模型以及传统出行链进行修正；然后，考虑外部因素影响下车主的有限理性，提出引导策略下私家车和出租车充电负荷预测方法；最后，在半动态交通网模型中用改进的出行链和起讫点(origin-destination, OD)矩阵分别模拟私家车和出租车研究时间内的出行行为，通过在划分区域的半动态交通网仿真，验证了所提出的电动汽车充电负荷时空分布预测方法的有效性。仿真结果也表明电动汽车充电负荷时空分布预测情况与对外部影响因素的分析相符，同时提出的引导策略能提升车主决策的满意程度。

 With electric vehicles (EVs) gradually replacing fueled vehicles, the impact of their charging load on the power grid is increasing. Therefore, this study proposes a spatial-temporal distribution prediction method for the charging load of EVs that considers travel demand and a guidance strategy. First, a semi-dynamic traffic network model that divides functional areas was developed based on a road travel time model. Furthermore, an energy-consumption model of EVs was established, and the charging demand, semi-dynamic transportation network model, energy consumption model, and traditional travel chain were revised according to the influence of the electricity price, climate, and season on the travel demand of vehicle owners. Considering the limited rationality of vehicle owners based on the influence of external factors, a charging load prediction method for private cars and taxis based on a guidance strategy is proposed. Finally, the modified trip chain and OD matrix were used to simulate the travel behavior of private cars and taxis, respectively, in the semi-dynamic traffic network model during the study period, and the validity of the proposed prediction method was verified through a simulation experiment of the semi-dynamic traffic network in the divided regions. The results show that the spatial-temporal distribution of the charging load for EVs is consistent with the analysis of external influencing factors, and the proposed guidance strategy can improve the satisfaction of vehicle owners. 

电动汽车(electric vehicle, EV)是极具调节潜力的需求侧资源,针对现货市场环境下的电动汽车聚合商(electric vehicle aggregator, EVA)整合和调度EV负荷资源的问题进行研究。首先,考虑EV的车-桩充电负荷差异化建立了EV充电模型。其次,以EVA运营收益最大为目标,兼顾EV车主利益,基于主从博弈关系建立EVA日前优化模型;模型基于场景的随机优化方法考虑市场日前电价的不确定性,解得差异化EV充电服务费价格和EVA日前购电计划。随后,基于能量共享模式对各类EV进行日内滚动优化调度,减小EV接入不确定性给EVA差额交易带来的额外成本。最后,以某市区电动汽车聚合商为案例开展分析,结果表明所提两阶段调度策略能有效应对EV接入不确定性与市场电价不确定性,实现EVA与EV双赢。

Electric vehicles (EVs) are promising demand-side regulation resources. This study examines the integration and scheduling of EV load resources considering electric vehicle aggregators (EVAs) in the spot market. First, the EV vehicle-pile charging load differentiation was considered to establish the EV charging model. Second, to maximize the EVA operating income, taking into account the interests of EV users simultaneously, an EVA day-ahead optimization model based on the Stackelberg game is established, and a scenario-based stochastic approach is used to consider the uncertainty of the electricity market price, gaining differentiated pricing results of the EV charging service fee and EVA day-ahead energy purchase plan. Subsequently, based on the energy-sharing mode, all types of EVs are dispatched via rolling optimization scheduling in real time to reduce the unplanned EVA power purchase cost caused by real-time market transactions owing to EV access uncertainty. Finally, considering an EVA in the distribution area as an example, the calculation results prove the effectiveness of the two-stage scheduling strategy proposed in this study, which can cope with the uncertainty of EV access and market price as well as realize a win-win situation between EVA and EV users.

为深入挖掘电动汽车的可调度潜力,缓解含高比例新能源微电网的供能压力,结合多元需求响应技术,提出一种考虑电动汽车资源参与的微电网多时间尺度优化调度模型。在日前调度阶段,一部分电动汽车资源与价格型需求响应技术相结合,以用户的综合满意度为目标进行优化。微电网基于价格型电动汽车资源的调度计划,以经济低碳成本最低与灵活性满足度最大为目标进行优化,确定各侧可调资源的调度安排。在日内调度阶段,另一部分电动汽车资源与激励型需求响应技术相结合。微电网能量管理中心作为领导者,以运营成本最小为目标,激励型电动汽车群作为跟随者,以用电成本最小为目标,构建微电网日内主从博弈模型进行滚动优化,双方基于补贴价格与用能策略进行博弈。最后,基于某微电网场景进行仿真验证,结果表明所提模型能够降低用户用电成本,减少负荷曲线的峰谷差,实现新能源的全消纳。

To exploit the schedulable potential of electric vehicles (EVs) efficiently and relieve the energy supply pressure of microgrids with a high proportion of new energy sources, a multi-time-scale optimization scheduling model of the microgrid is proposed, considering the participation of EV resources combined with the multi-demand response technology. In the day-ahead scheduling stage, some EV resources are combined with the price-based demand response technology to optimize comprehensive user satisfaction. Based on the scheduling plan of the EV resources based on price, the microgrid is optimized focusing on minimizing economic cost, ensuring low-carbon expenditure, and maximizing flexibility satisfaction, and the scheduling arrangement of the adjustable resources on each side is determined. In the intra-day scheduling phase, another portion of the EV resources is combined with the incentive demand response technology. The microgrid energy management center, as the leader, aims to minimize the operating costs, and the incentive EV group, as the follower, aims to minimize the electricity costs. The intra-day master-slave game model of the microgrid was constructed for rolling optimization, and the two sides played the game based on a subsidized price and energy use strategy. Finally, a simulation verification was conducted based on a microgrid scenario, and the results show that the proposed model can reduce the electricity cost of users, reduce the peak-valley difference of the load curve, and realize the full absorption of new energy.