Abstract
This article presents a robust model predictive control (R-MPC)-based scheduling approach for electric vehicle (EV) charging service at a station with power supply from a photovoltaic (PV) system and grid. The designed algorithm aims to maximize the operational revenue while handling high power loads in the grid, intermittent solar power supply, charging demands uncertainties, and online computational burdens. To this end, the scheduling problem is formulated as a mixed-integer nonlinear program (MINLP) with solar power generation and charging demands models embedded. The time-of-use price is employed for cost calculation. The resulting optimization problem then can be further simplified as a mixed-integer linear program (MILP). The proposed R-MPC is evaluated in the IEEE-13 node test feeder with simulated power load profiles and real charging demands data for demonstrations. The results show that R-MPC achieves near-optimal profit and meets all charging requests even under high uncertainties.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.