The electric vehicles (EV) market is projected to continue its rapid growth, which will profoundly impact the demand on the electricity network requiring costly network reinforcements unless EV charging is properly managed. However, as well as importing electricity from the grid, EVs also have the potential to export electricity through vehicle-to-grid (V2G) technology, which can help balance supply and demand and stabilise the grid through participation in flexibility markets. Such a scenario requires a population of EVs to be pooled to provide a larger storage resource. Key to doing so effectively however is knowledge of the users, as they ultimately determine the availability of a vehicle. In this paper we introduce a machine learning model that aims to learn both a) the criteria influencing users when they decided whether to make their vehicle available and b) their reliability in following through on those decisions, with a view to more accurately predicting total available capacity from the pool of vehicles at a given time. Using a series of simplified simulations, we demonstrate that the learning model is able to adapt to both these factors, which allows the required capacity of a market event to be satisfied more reliably and using a smaller number of vehicles than would otherwise be the case. This in turn has the potential to support participation in larger and more numerous market events for the same user base and use of the technology for smaller groups of users such as individual communities.

中文翻译：

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学习能力：预测车辆到网格服务的用户决策

电动汽车（EV）市场预计将继续快速增长，除非对EV充电进行适当管理，否则这将对需要昂贵的网络加强的电网需求产生深远影响。然而，除了从电网进口电力外，电动汽车还具有通过车到网（V2G）技术出口电力的潜力，这可以帮助平衡供需并通过参与灵活市场来稳定电网。这种情况需要将大量的电动汽车集中起来以提供更大的存储资源。然而，有效做到这一点的关键是用户的知识，因为他们最终决定了车辆的可用性。在本文中，我们介绍了一种机器学习模型，旨在学习a）影响用户决定是否提供车辆的标准，以及b）遵循这些决策的可靠性，以更准确地预测总可用量在给定时间的车辆数量。使用一系列简化的模拟，我们证明了学习模型能够同时适应这两个因素，从而可以更可靠地满足市场活动所需的容量，并且使用较少的车辆。反过来，这有可能支持同一用户群参与更大，数量更多的市场活动，以及为较小的用户群体（例如单个社区）使用该技术。