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Forecast of urban EV charging load and smart control concerning uncertainties

机译:城市电动汽车充电负荷预测与不确定性的智能控制

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Popularizing electric vehicles (EV) can be one of the most effective ways to deal with the ever severe air pollution. However, the accumulated charging power from the increasing integration of EVs could add large pressure to the peak of power grid. In this paper, a novel EV load forecasting model is formulated based on Markov chain, allowing for the stochasticity of user behavior, traffic and weather. The impact of EV integration is assessed in a typical medium voltage (MV) system while the resulting peak load and power loss are mitigated with charging control schemes. The proposed model could help to forecast the future charging demands with probabilistic uncertainties. The smart control schemes shall instruct the aggregator to make optimal charging plan concerning security and efficiency issues.
机译:普及电动汽车(EV)可能是应对日益严重的空气污染的最有效方法之一。但是,随着电动汽车集成度的提高,累积的充电功率可能会给电网的峰值增加很大的压力。本文基于马尔可夫链,提出了一种新颖的电动汽车负荷预测模型,该模型考虑了用户行为,交通和天气的随机性。在典型的中压(MV)系统中评估了EV集成的影响,同时通过充电控制方案减轻了产生的峰值负载和功率损耗。所提出的模型可以帮助预测具有概率不确定性的未来充电需求。智能控制方案应指示聚合器针对安全和效率问题制定最佳计费计划。

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