量化电动汽车充电站的聚合灵活性以提供可靠的拥堵管理产品——荷兰案例研究
Quantifying the Aggregate Flexibility of Electric Vehicles Charging Stations for Dependable Congestion Management Products -- A Dutch Case Study
AI总结：
本文基于荷兰50余万笔真实充电交易建立数据驱动模型，量化不同充电站和调度策略提供再调度与容量限制产品的概率，并结合机器学习预测日前用电及灵活性。
摘要：
电动汽车（EV）在向可持续交通模式转型的过程中发挥着关键作用，因此对能源转型至关重要。随着电动汽车数量的增长，管理电动汽车充电的聚合功率对于维持电网稳定和缓解拥堵至关重要。本研究分析了荷兰超过50万笔真实充电交易，以探讨充电需求增长和智能充电灵活性给能源系统带来的挑战与机遇。具体而言，研究根据荷兰相关服务的规范，量化了提供可靠拥堵管理服务的集体能力。本研究建立了一个数据驱动的充电行为模型，以探讨在不同聚合层级和服务类型下交付可靠拥堵管理服务的影响。研究针对不同类别的充电站（CS）和调度策略，评估了提供不同灵活性产品（即用于拥堵管理的再调度和容量限制）的概率能力。这些概率可帮助电动汽车聚合商（如充电点运营商）根据相关法规就是否提供拥堵缓解产品作出知情决策，并帮助配电系统运营商评估其潜力。此外，研究还展示了如何结合机器学习模型预测日前用电量，随后在运行层面预测再调度灵活性。研究结果表明，电动汽车到达、离开和连接的时间在决定产品提供的可行性方面起着关键作用，而且通常可以利用足够多的充电站来交付可靠服务。
英文摘要：
Electric vehicles (EVs) play a crucial role in the transition towards sustainable modes of transportation and thus are critical to the energy transition. As their number grows, managing the aggregate power of EV charging is crucial to maintain grid stability and mitigate congestion. This study analyses more than 500 thousand real charging transactions in the Netherlands to explore the challenge and opportunity for the energy system presented by increased charging needs and smart charging flexibility. Specifically, it quantifies the collective ability to provide dependable congestion management services according to the specifications of those services in the Netherlands. In this study, a data-driven model of charging behaviour is created to explore the implications of delivering dependable congestion management services at various aggregation levels and types of service. The probabilistic ability to offer different flexibility products, namely, redispatch and capacity limitation, for congestion management, is assessed for different categories of charging stations (CS) and dispatch strategies. These probabilities can help EV aggregators, such as charging point operators, make informed decisions about offering congestion mitigation products per relevant regulations and distribution system operators to assess their potential. Further, it is shown how machine learning models can be incorporated to predict the day-ahead consumption, followed by operationally predicting redispatch flexibility. The findings demonstrate that the timing of EV arrivals, departures, and connections plays a crucial role in determining the feasibility of product offerings, and dependable services can generally be delivered using a sufficiently large number of CSs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
