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Understanding controlled EV charging impacts using scenario-based forecasting models

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Document pages: 22 pages

Abstract: Electrification of transport is a key strategy in reducing carbon emissions.Many countries have adopted policies of complete but gradual transformation toelectric vehicles (EVs). However, mass EV adoption also means a spike in load(kW), which in turn can disrupt existing electricity infrastructure. Smart orcontrolled charging is widely seen as a potential solution to alleviate thisstress on existing networks. Learning from the recent EV trials in the UK andelsewhere we take into account two key aspects which are largely ignored incurrent research: EVs actually charging at any given time and wide range of EVtypes, especially battery capacity-wise. Taking a minimalistic scenario-basedapproach, we study forecasting models for mean number of active chargers andmean EV consumption for distinct scenarios. Focusing on residential chargingthe models we consider range from simple regression models to more advancedmachine and deep learning models such as XGBoost and LSTMs. We then use thesemodels to evaluate the impacts of different levels of future EV penetration ona specimen distribution transformer that captures typical real-world scenarios.In doing so, we also initiate the study of different types of controlledcharging when fully controlled charging is not possible. This aligns with theoutcomes from recent trials which show that a sizeable proportion of EV ownersmay not prefer fully controlled centralized charging. We study two possiblecontrol regimes and show that one is more beneficial from load-on-transformerpoint of view, while the other may be preferred for other objectives. We showthat a minimum of 60 control is required to ensure that transformers are notoverloaded during peak hours.

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