Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach

JOURNAL OF CLEANER PRODUCTION

卷:229

Article

Jahangir, Hamidreza¹  Tayarani, Hanif¹  Ahmadian, Ali²  Golkar, Masoud Aliakbar¹  Miret, Jaume³  Tayarani, Mohammad⁴  Gao, H. Oliver^4,5 

[1] KN Toosi Univ Technol, Dept Elect Engn, Tehran, Iran

[2] Univ Bonab, Dept Elect Engn, Bonab, Iran

[3] Univ Politecn Cataluna, Dept Elect Engn, Barcelona 08034, Spain

[4] Cornell Univ, Sch Civil & Environm Engn, Ithaca, NY 14853 USA

[5] Jiaotong Univ, Antai Coll Econ & Management, Sino US Global Logist Inst, Shanghai 200030, Peoples R China

关键词: Plug-in electric vehicle;    Travel behavior;    Artificial neural network;    Rough neuron;    Smart charging;   

DOI  :  10.1016/j.jclepro.2019.04.345

来源: Elsevier

【 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.

【 授权许可】

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