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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| Published in: | Mathematics (Basel) Vol. 11; no. 7; p. 1741 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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01.04.2023
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| ISSN: | 2227-7390, 2227-7390 |
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| Abstract | 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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| AbstractList | 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. |
| Audience | Academic |
| Author | Samy, Mohamed Mahmoud Barakat, Shimaa Alshammari, Nahar F. |
| Author_xml | – sequence: 1 givenname: Nahar F. orcidid: 0000-0002-7650-6978 surname: Alshammari fullname: Alshammari, Nahar F. – sequence: 2 givenname: Mohamed Mahmoud orcidid: 0000-0002-3716-0564 surname: Samy fullname: Samy, Mohamed Mahmoud – sequence: 3 givenname: Shimaa surname: Barakat fullname: Barakat, Shimaa |
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| SubjectTerms | 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 |
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