Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach. (20th August 2019)

• Record Type: Journal Article
• Authors: Jahangir, Hamidreza; Tayarani, Hanif; Ahmadian, Ali; Golkar, Masoud Aliakbar; Miret, Jaume; Tayarani, Mohammad; Gao, H. Oliver
• Is Part Of: Journal of cleaner production. Volume 229(2019)
• Page Start: 1029
• Page End: 1044
• Publication Date: 2019-08-20
• Subjects: Plug-in electric vehicle -- Travel behavior -- Artificial neural network -- Rough neuron -- Smart charging; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2019.04.345
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstarct: 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. Highlights: A novel approach is proposed to forecast travel behavior in Plug-in Electric Vehicle. The method improves the accuracy of travel behavior forecasts by about 10%. The aggregators' financial loss and load profile spikes reduced by 23% and 20%. The findings help to increase market penetration of EVs and lower emissions.