Comprehensive Analysis of Multi-Objective Optimization Algorithms for Sustainable Hybrid Electric Vehicle Charging Systems

This study presents a multi-objective optimization approach for designing hybrid renewable energy systems for electric vehicle (EV) charging stations that considers both economic and reliability factors as well as seasonal variations in energy production and consumption. Four algorithms, MOPSO, NSGA...

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Vydáno v:Mathematics (Basel) Ročník 11; číslo 7; s. 1741
Hlavní autoři: Alshammari, Nahar F., Samy, Mohamed Mahmoud, Barakat, Shimaa
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.04.2023
Témata:
Algorithms
Convergence
Decomposition
Design factors
diversity
efficiency
Electric vehicle charging
Electric vehicles
Emissions
Energy storage
Genetic algorithms
Hybrid electric vehicles
hybrid EV charging system
Hybrid systems
Lithium-ion batteries
Mathematical optimization
Methods
multi-objective optimization
Multiple objective analysis
optimal design
Optimization
Rechargeable batteries
Reliability aspects
Renewable energy
Robustness
Seasonal variations
Sustainable development
Urban areas
Wind turbines
Saudi Arabia
ISSN:2227-7390, 2227-7390
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Shrnutí:This study presents a multi-objective optimization approach for designing hybrid renewable energy systems for electric vehicle (EV) charging stations that considers both economic and reliability factors as well as seasonal variations in energy production and consumption. Four algorithms, MOPSO, NSGA-II, NSGA-III, and MOEA/D, were evaluated in terms of their convergence, diversity, efficiency, and robustness. Unlike previous studies that focused on single-objective optimization or ignored seasonal variations, our approach results in a more comprehensive and sustainable design for EV charging systems. The proposed system includes a 223-kW photovoltaic system, an 80-kW wind turbine, and seven Lithium-Ion battery banks, achieving a total net present cost of USD 564,846, a levelized cost of electricity of 0.2521 USD/kWh, and a loss of power supply probability of 1.21%. NSGA-II outperforms the other algorithms in terms of convergence and diversity, while NSGA-III is the most efficient, and MOEA/D has the highest robustness. The findings contribute to the development of efficient and reliable renewable energy systems for urban areas, emphasizing the importance of considering both economic and reliability factors in the design process. Our study represents a significant advance in the field of hybrid renewable energy systems for EV charging stations.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math11071741