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Article

Dynamic Charging Optimization Algorithm for Electric Vehicles to Mitigate Grid Power Peaks

1
Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski (UQAR), Rimouski, QC G5L 3A1, Canada
2
Département de Génie Mécanique, Ecole de Technologie Supérieure (ETS), Montréal, QC H3C 1K3, Canada
3
Faculty of Engineering, Lebanese University, Beirut 1003, Lebanon
4
Centre National Intégré du Manufacturier Intelligent (CNIMI), Université du Québec à Trois-Rivières (UQTR), Drummondville, QC J2C 0R5, Canada
*
Authors to whom correspondence should be addressed.
World Electr. Veh. J. 2024, 15(7), 324; https://doi.org/10.3390/wevj15070324 (registering DOI)
Submission received: 25 June 2024 / Revised: 11 July 2024 / Accepted: 17 July 2024 / Published: 21 July 2024
(This article belongs to the Topic Electric Vehicles Energy Management, 2nd Volume)

Abstract

The rapid proliferation of electric vehicles (EVs) presents both opportunities and challenges for the electrical grid. While EVs offer a promising avenue for reducing greenhouse gas emissions and dependence on fossil fuels, their uncoordinated charging behavior can strain grid infrastructure, thus creating new challenges for grid operators and EV owners equally. The uncoordinated nature of electric vehicle charging may lead to the emergence of new peak loads. Grid operators typically plan for peak demand periods and deploy resources accordingly to ensure grid stability. Uncoordinated EV charging can introduce unpredictability and variability into peak load patterns, making it more challenging for operators to manage peak loads effectively. This paper examines the implications of uncoordinated EV charging on the electric grid to address this challenge and proposes a novel dynamic optimization algorithm tailored to manage EV charging schedules efficiently, mitigating grid power peaks while ensuring user satisfaction and vehicle charging requirements. The proposed “Proof of Need” (PoN) charging algorithm aims to schedule the charging of EVs based on collected data such as the state of charge (SoC) of the EV’s battery, the charger power, the number of connected vehicles per household, the end-user’s preferences, and the local distribution substation’s capacity. The PoN algorithm calculates a priority index for each EV and coordinates the charging of all connected EVs at all times in a way that does not exceed the maximum allocated power capacity. The algorithm was tested under different scenarios, and the results offer a comparison of the charging power demand between an uncoordinated EV charging baseline scenario and the proposed coordinated charging model, proving the efficiency of our proposed algorithm, thus reducing the charging demand by 40.8% with no impact on the overall total charging time.
Keywords: energy management; electric vehicle; charging optimization; smart charging; dynamic optimization algorithm; state of charge; efficiency; peak load management; grid modernization energy management; electric vehicle; charging optimization; smart charging; dynamic optimization algorithm; state of charge; efficiency; peak load management; grid modernization

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MDPI and ACS Style

Aoun, A.; Adda, M.; Ilinca, A.; Ghandour, M.; Ibrahim, H. Dynamic Charging Optimization Algorithm for Electric Vehicles to Mitigate Grid Power Peaks. World Electr. Veh. J. 2024, 15, 324. https://doi.org/10.3390/wevj15070324

AMA Style

Aoun A, Adda M, Ilinca A, Ghandour M, Ibrahim H. Dynamic Charging Optimization Algorithm for Electric Vehicles to Mitigate Grid Power Peaks. World Electric Vehicle Journal. 2024; 15(7):324. https://doi.org/10.3390/wevj15070324

Chicago/Turabian Style

Aoun, Alain, Mehdi Adda, Adrian Ilinca, Mazen Ghandour, and Hussein Ibrahim. 2024. "Dynamic Charging Optimization Algorithm for Electric Vehicles to Mitigate Grid Power Peaks" World Electric Vehicle Journal 15, no. 7: 324. https://doi.org/10.3390/wevj15070324

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