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Electric vehicle capacity forecasting model with application to load levelling

机译:电动汽车容量预测模型及其在负荷均衡中的应用

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There are many uncertainties associated with forecasting electric vehicle charging and discharging capacity due to the stochastic nature of human behavior surrounding usage and intermittent travel patterns. This uncertainty if unmanaged has the potential to radically change traditional load profiles. Therefore optimal capacity forecasting methods are important for large-scale electric vehicle integration in future power systems. This paper develops a capacity forecasting model considering eight particular uncertainties under three categories to overcome this issue. The model is then applied to a UK summer scenario in 2020. The results of this analysis demonstrate that the proposed model is accurate for charge and discharge prediction and a feasible basis for steady-state analysis required for large-scale electric vehicle integration.
机译:由于围绕使用和间歇性出行方式的人类行为的随机性,与预测电动汽车的充电和放电容量相关的不确定性很多。如果不加以管理,这种不确定性有可能从根本上改变传统的负荷曲线。因此,最佳容量预测方法对于未来电力系统中的大规模电动汽车集成至关重要。本文开发了一种容量预测模型,该模型考虑了三个类别下的八个特定不确定性以克服此问题。然后将该模型应用于2020年的英国夏季情景。分析结果表明,该模型对于充放电预测是准确的,并且是大规模电动汽车集成所需的稳态分析的可行基础。

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