Charging demand forecasting of electric vehicles considering uncertainties in a microgrid

Chuanshen Wu, Sufan Jiang, Shan Gao, Yu Liu and Haiteng Han

Energy, 2022, vol. 247, issue C

Abstract: The currently increasing integration of electric vehicles (EVs) in microgrids (MGs) has gained significant attention. However, affected by the high uncertainties of weather, traffic, and driver behavior, the charging demand of EVs is difficult to forecast accurately. In this study, an optimal parameter forecasting method is presented to improve the forecasting accuracy of charging demand of EVs in an MG. For the methods of forecasting of EV status by sampling from probability distributions, this study modifies the optimal parameter values of probability distributions within fuzzy sets based on the feedback of EVs that have arrived in an MG. Fuzzy sets are utilized to limit the modification ranges of parameter values for the consideration of robustness. Moreover, the average values of multiple sampling results are calculated to improve the stability of forecasting results. Combined with the forecasted results, this study is executed over a rolling time horizon for energy management of EVs, ensuring that acceptable charge levels are reached at the disconnection times. Simulation results show that, compared with other state-of-the-art forecasting methods, the proposed forecasting method is highly effective in reducing forecasting errors of EVs and, hence, has better performance in regulating the charging of EVs in an MG.

Keywords: Charging demand; Electric vehicle; Forecasting; Microgrid; Uncertainty (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:247:y:2022:i:c:s0360544222003784

DOI: 10.1016/j.energy.2022.123475

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