A model predictive control approach in microgrid considering multi-uncertainty of electric vehicles. (January 2021)

• Record Type: Journal Article
• Authors: Wu, Chuanshen; Gao, Shan; Liu, Yu; Song, Tiancheng E.; Han, Haiteng
• Is Part Of: Renewable energy. Volume 163(2021)
• Page Start: 1385
• Page End: 1396
• Publication Date: 2021-01
• Subjects: Microgrid -- Electric vehicle (EV) -- Wind energy -- Multi-uncertainty -- Model predictive control (MPC); Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2020.08.137
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Managing uncertainty is key to enhancing robustness in microgrids. This study focuses on the uncertainties in aggregated electric vehicles (EVs) and establishes a two-layer model predictive control (MPC) strategy for charging EVs with a microgrid. The uncertainty in sampling results of EV connection times to the microgrid and the uncertainty in sampling results of state of charge (SOC) of each EV at the time of the initial connection are both taken into account through multi-uncertainty sampling. In order of consider the impact of extreme scenarios, as feedback from arriving EVs are combined with multi-uncertainty sampling for higher accuracy of sampling results during rolling optimization. Simulation results show that, compared to conventional strategies, the proposed strategy has better performance in reducing forecasting error and regulating the charging and discharging of aggregated EVs with uncertainties. Highlights: A model predictive control approach is proposed for coordination with wind energy. The uncertainty of sampling results of aggregated electric vehicles is handled. The impact of extreme scenarios of aggregated electric vehicles is considered. Feedback from aggregated electric vehicles is used for higher forecasting accuracy.