Comparative Analysis and Optimal Operation of an On-Grid and Off-Grid Solar Photovoltaic-Based Electric Vehicle Charging Station

Andu Dukpa (), Boguslaw Butrylo and Bala Venkatesh
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Andu Dukpa: Faculty of Engineering, University of New Brunswick, 15 Dineen Drive, Fredericton, NB E3B 5A3, Canada
Boguslaw Butrylo: Faculty of Electrical Engineering, Bialystok University of Technology, ul. Wiejska 45D, 15-351 Bialystok, Poland
Bala Venkatesh: Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria St., Toronto, ON M5B 2K3, Canada

Energies, 2023, vol. 16, issue 24, 1-17

Abstract: One of the key strategies for decarbonization and green transportation is using electric vehicles (EVs). However, challenges like limited charging infrastructure, EV battery characteristics, and grid integration complexities persist. This study proposes a mixed-integer linear programming (MILP) approach to optimize a grid-connected solar PV-based commercial EV charging station (SPEVCS) with a battery energy storage system (BESS) for profit maximization. The MILP model efficiently manages SPEVCS operations, considering solar power fluctuations, EV charging patterns, and BESS usage. By coordinating charging schedules, grid stability is reinforced, and excess solar power can be lucratively managed. Comparing grid-connected and off-grid SPEVCS scenarios highlights grid integration benefits. Solar power mismatches with optimal charging periods pose a challenge, addressed here by BESS utilization and import/export of deficit/surplus power from/to the grid. The proposed framework incorporates solar power forecasts and probabilistic EV arrival predictions, enhancing decision accuracy. This approach fosters viable commercial EV charging, promotes green transportation, and reinforces grid resilience.

Keywords: solar photovoltaic; electric vehicles; grid; profit maximization; mixed-integer linear programming (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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