1.河海大学电气与动力工程学院,江苏省南京市 211100
2.国网安徽省电力有限公司,安徽省合肥市 230061
3.合肥工业大学计算机与信息学院,安徽省合肥市 230009
4.国家电网有限公司华东分部,上海市 200120
王珂(1980—),女,通信作者,博士,教授,主要研究方向:市场环境下柔性负荷调控、人工智能电网辅助决策技术。E-mail:41957502@qq.com
李玮桁(2001—),男,硕士研究生,主要研究方向:分布式资源聚合。E-mail:876081351@qq.com
王正风(1976—),男,正高级工程师,主要研究方向:电力系统运行和电力市场。E-mail:wangzf0016@ah.sgcc.com.cn
收稿:2025-04-09,
修回:2025-08-06,
网络首发:2025-10-10,
纸质出版:2025-12-25
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王珂,李玮桁,王正风等.基于两阶段鲁棒优化的电动汽车可调容量聚合及解聚方法[J].电力系统自动化,2025,49(24):115-124.
WANG Ke,LI Weiheng,WANG Zhengfeng,et al.Adjustable Capacity Aggregation and Disaggregation Method for Electric Vehicles Based on Two-stage Robust Optimization[J].Automation of Electric Power Systems,2025,49(24):115-124.
王珂,李玮桁,王正风等.基于两阶段鲁棒优化的电动汽车可调容量聚合及解聚方法[J].电力系统自动化,2025,49(24):115-124. DOI: 10.7500/AEPS20250409005.
WANG Ke,LI Weiheng,WANG Zhengfeng,et al.Adjustable Capacity Aggregation and Disaggregation Method for Electric Vehicles Based on Two-stage Robust Optimization[J].Automation of Electric Power Systems,2025,49(24):115-124. DOI: 10.7500/AEPS20250409005.
电动汽车响应速度快、灵活性高,是一种优质的需求侧灵活响应资源。然而,因其单体容量小、并网时间不确定、充电模式多样,易引发电网功率波动。因此,如何实现电动汽车集群的灵活聚合和平滑管控,挖掘电动汽车调节潜力,具有重要的理论意义和实际价值。首先,基于延迟充电、灵活充电和灵活充/放电3种典型模式,构建了电动汽车的荷电状态轨迹集合,系统刻画了不同模式下电动汽车的充/放电行为和灵活性表现;其次,以最大化调控时段内电动汽车集群聚合功率区间为目标,计及聚合功率解聚可行性,设计了基于两阶段鲁棒优化的电动汽车集群功率聚合模型;最后,从充/放电功率波动和车主满意度两个维度构建电动汽车集群聚合功率的解聚模型。算例验证结果表明,所提方法能够给出电动汽车集群功率的时序动态最优可调范围,并可确保聚合功率的可靠解聚。
Electric vehicles (EVs)
characterized by rapid response and high flexibility
represent a high-quality flexible response resource on the demand side. However
their small individual capacity
uncertain grid-connection time
and diverse charging modes tend to cause power fluctuations in the grid. Therefore
how to achieve flexible aggregation and smooth control of EV clusters and tap into the potential of EV regulation
has important theoretical significance and practical value. Firstly
based on three typical modes of delayed charging
flexible charging
and flexible discharging
an assemblage of state of charge (SOC) trajectories for EVs is constructed
systematically characterizing the discharging behaviors and flexibility performance of EVs in various modes. Secondly
with the objective of maximizing the aggregated power range of EV clusters during the regulation period
considering the disaggregating feasibility of aggregated power
a power aggregation model for EV clusters based on two-stage robust optimization is designed. Finally
a disaggregation model for the aggregated power of EV clusters is established from two dimensions of charging/discharging power fluctuations and user satisfaction index. Case study results demonstrate that the proposed method can provide the temporally dynamic optimal adjustable range for EV cluster power and ensure the reliable disaggregation of the aggregated power.
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