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首页> 外文期刊>Journal of Cleaner Production >Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach
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Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach

机译：插电式电动汽车的充电需求：基于一种新的粗糙人工神经网络方法预测行驶行为

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摘要

The market penetration of Plug-in Electric Vehicles (PEVs) is escalating due to their energy saving and environmental benefits. In order to address PEVs impact on the electric networks, the aggregators need to accurately predict the PEV Travel Behavior (PEV-TB) since the addition of a great number of PEVs to the current distribution network poses serious challenges to the power system. Forecasting PEV-TB is critical because of the high degree of uncertainties in drivers' behavior. Existing studies mostly simplified the PEV-TB by mapping travel behavior from conventional vehicles. This could cause bias in power estimation considering the differences in PEV-TB because of charging pattern which consequently could bungle economic analysis of aggregators. In this study, to forecast PEV-TB an artificial intelligence-based method-feedforward and recurrent Artificial Neural Networks (ANN) with Levenberg Marquardt (LM) training method based on Rough structure - is developed. The method is based on historical data including arrival time, departure time and trip length. In this study, the correlation among arrival time, departure time and trip length is also considered. The forecasted PEV-TB is then compared with Monte Carlo Simulation (MCS) which is the main benchmarking method in this field. The results comparison depicted the robustness of the proposed methodology. The proposed method reduces the aggregators' financial loss approximately by 16 $/PEV per year compared to the conventional methods. The findings underline the importance of applying more accurate methods to forecast PEV-TB to gain the most benefit of vehicle electrification in the years to come. (C) 2019 Elsevier Ltd. All rights reserved.
机译：插电式电动车（PEV）的节能和环保优势正在逐步扩大其市场渗透率。为了解决PEV对电网的影响，聚合器需要准确地预测PEV行驶行为（PEV-TB），因为在当前配电网络中添加大量PEV对电力系统提出了严峻挑战。由于驾驶员行为的高度不确定性，预测PEV-TB至关重要。现有研究大多通过绘制常规车辆的行驶行为来简化PEV-TB。考虑到PEV-TB的差异，这可能会导致功率估计出现偏差，这是因为充电方式会导致聚合器的经济分析混乱。在这项研究中，为了预测PEV-TB，开发了一种基于人工智能的方法-前馈和递归人工神经网络（ANN），并采用了基于粗糙结构的Levenberg Marquardt（LM）训练方法。该方法基于历史数据，包括到达时间，出发时间和行程长度。在这项研究中，还考虑了到达时间，出发时间和行程长度之间的相关性。然后将预测的PEV-TB与Monte Carlo Simulation（MCS）（该领域的主要基准测试方法）进行比较。结果比较描述了所提出方法的鲁棒性。与传统方法相比，本方法每年可减少聚合器的财务损失约16 $ / PEV。这些发现强调了应用更准确的方法来预测PEV-TB的重要性，以便在未来几年中最大程度地受益于车辆电气化。 （C）2019 Elsevier Ltd.保留所有权利。

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