Lee Rachel, Brown Solomon
Department of Chemical and Biological Engineering, University of Sheffield, Sheffield S13JD, UK.
iScience. 2021 Jul 28;24(8):102914. doi: 10.1016/j.isci.2021.102914. eCollection 2021 Aug 20.
Understanding electric vehicle (EV) adoption rates and charging patterns is critical in enabling grid operators to maintain quality of supply and offers the potential to procure network services and avoid or postpone capital investments. Agent-based models have separately been shown to be useful in modeling EV adoption, policy options, behavioral influences, and grid impacts. In this work, we bring together these threads with real world travel data to present a multi-scale, behaviour-based EV adoption and use model able to replicate historical changes in vehicle fleets and match the most recent real world EV charging profile data. We have shown how our model can be used to simulate the impact of policies and consumer behavior on the rate of EV adoption across socio-economic groups and the locational grid impacts of EV charging, and as such we believe it to be of value to policy makers, grid operators, and demand response aggregators.
了解电动汽车（EV）的采用率和充电模式对于电网运营商维持供电质量至关重要，并且有可能获取网络服务并避免或推迟资本投资。基于代理的模型已分别被证明在模拟电动汽车采用情况、政策选择、行为影响和电网影响方面很有用。在这项工作中，我们将这些线索与实际出行数据结合起来，提出了一个多尺度、基于行为的电动汽车采用和使用模型，该模型能够复制车队的历史变化并匹配最新的实际电动汽车充电概况数据。我们已经展示了我们的模型如何用于模拟政策和消费者行为对不同社会经济群体电动汽车采用率的影响以及电动汽车充电的位置电网影响，因此我们认为它对政策制定者、电网运营商和需求响应聚合商具有价值。
