Multi-objective optimization of hybrid energy systems using gravitational search algorithm
Abstract The depletion of fossil fuel reserves, increasing environmental concerns, and energy demands of remote communities have increased the acceptance of using hybrid renewable energy systems (HRES). However, choosing an optimal HRES from economic, environmental, reliability, and sustainability a...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86476-z |
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| author | Sayyed Mostafa Mahmoudi Akbar Maleki Dariush Rezaei Ochbelagh |
| author_facet | Sayyed Mostafa Mahmoudi Akbar Maleki Dariush Rezaei Ochbelagh |
| author_sort | Sayyed Mostafa Mahmoudi |
| collection | DOAJ |
| description | Abstract The depletion of fossil fuel reserves, increasing environmental concerns, and energy demands of remote communities have increased the acceptance of using hybrid renewable energy systems (HRES). However, choosing an optimal HRES from economic, environmental, reliability, and sustainability aspects is still challenging. To solve this challenge, this study introduces a novel multi-objective optimization approach using the Gravitational Search Algorithm (GSA) and non-dominated sorting techniques. The proposed framework addresses four objectives: minimizing the loss of power supply probability, reducing total costs, increasing renewable energy fraction, and lowering CO2 emissions. A carbon tax sensitivity analysis evaluates the system’s economic performance under varying scenarios. Also, the amount of damage caused by the release of carbon dioxide on human health and the ecosystem is examined. In this way, an optimal configuration consisting of wind turbines, photovoltaic panels, and diesel generators is introduced to satisfy the above objectives. Results demonstrate that the GSA outperforms established methods, such as multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II in Pareto front diversity and convergence. In this work, the optimal system achieves an 18.4% increase in renewable energy share, reducing ecosystem and human health damage by 14.2%. Notably, with a 20% increase in the carbon tax, system costs increased by 3%. These findings underscore the potential of multi-objective optimization combined with carbon tax policies to enhance energy system sustainability and affordability. |
| format | Article |
| id | doaj-art-e86b9c618be541c58414e725fd35c325 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-e86b9c618be541c58414e725fd35c3252025-01-26T12:28:31ZengNature PortfolioScientific Reports2045-23222025-01-0115112010.1038/s41598-025-86476-zMulti-objective optimization of hybrid energy systems using gravitational search algorithmSayyed Mostafa Mahmoudi0Akbar Maleki1Dariush Rezaei Ochbelagh2Department of Energy Engineering & Physics, Amirkabir University of Technology (Tehran Polytechnic)Faculty of Mechanical Engineering, Shahrood University of TechnologyDepartment of Energy Engineering & Physics, Amirkabir University of Technology (Tehran Polytechnic)Abstract The depletion of fossil fuel reserves, increasing environmental concerns, and energy demands of remote communities have increased the acceptance of using hybrid renewable energy systems (HRES). However, choosing an optimal HRES from economic, environmental, reliability, and sustainability aspects is still challenging. To solve this challenge, this study introduces a novel multi-objective optimization approach using the Gravitational Search Algorithm (GSA) and non-dominated sorting techniques. The proposed framework addresses four objectives: minimizing the loss of power supply probability, reducing total costs, increasing renewable energy fraction, and lowering CO2 emissions. A carbon tax sensitivity analysis evaluates the system’s economic performance under varying scenarios. Also, the amount of damage caused by the release of carbon dioxide on human health and the ecosystem is examined. In this way, an optimal configuration consisting of wind turbines, photovoltaic panels, and diesel generators is introduced to satisfy the above objectives. Results demonstrate that the GSA outperforms established methods, such as multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II in Pareto front diversity and convergence. In this work, the optimal system achieves an 18.4% increase in renewable energy share, reducing ecosystem and human health damage by 14.2%. Notably, with a 20% increase in the carbon tax, system costs increased by 3%. These findings underscore the potential of multi-objective optimization combined with carbon tax policies to enhance energy system sustainability and affordability.https://doi.org/10.1038/s41598-025-86476-zHybrid renewable energy systemMulti-objective optimizationGravitational search algorithmSustainable energyCarbon tax |
| spellingShingle | Sayyed Mostafa Mahmoudi Akbar Maleki Dariush Rezaei Ochbelagh Multi-objective optimization of hybrid energy systems using gravitational search algorithm Scientific Reports Hybrid renewable energy system Multi-objective optimization Gravitational search algorithm Sustainable energy Carbon tax |
| title | Multi-objective optimization of hybrid energy systems using gravitational search algorithm |
| title_full | Multi-objective optimization of hybrid energy systems using gravitational search algorithm |
| title_fullStr | Multi-objective optimization of hybrid energy systems using gravitational search algorithm |
| title_full_unstemmed | Multi-objective optimization of hybrid energy systems using gravitational search algorithm |
| title_short | Multi-objective optimization of hybrid energy systems using gravitational search algorithm |
| title_sort | multi objective optimization of hybrid energy systems using gravitational search algorithm |
| topic | Hybrid renewable energy system Multi-objective optimization Gravitational search algorithm Sustainable energy Carbon tax |
| url | https://doi.org/10.1038/s41598-025-86476-z |
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