Enhanced particle swarm optimization with chaotic search for offshore micro-energy systems
Abstract As the global energy landscape shifts and sustainability becomes crucial, the offshore oil and gas sector confronts significant challenges and opportunities. This paper addresses the issues of energy efficiency and environmental impact of optimizing offshore micro-energy systems (OMIES) by...
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| Format: | Article |
| Language: | English |
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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/s41598-025-85557-3 |
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| author | Weiguo Huang Wen Li Xuewen Pan Qiming Liu Jie Yang |
| author_facet | Weiguo Huang Wen Li Xuewen Pan Qiming Liu Jie Yang |
| author_sort | Weiguo Huang |
| collection | DOAJ |
| description | Abstract As the global energy landscape shifts and sustainability becomes crucial, the offshore oil and gas sector confronts significant challenges and opportunities. This paper addresses the issues of energy efficiency and environmental impact of optimizing offshore micro-energy systems (OMIES) by proposing a multi-objective optimization model that integrates chaotic local search and particle swarm optimization (PSO). The model aims to achieve optimal scheduling of the energy system by comprehensively considering operational costs, carbon emissions, energy utilization efficiency, and energy fluctuation risks. The research results indicate that the optimization model can significantly improve energy utilization efficiency, reduce operational costs, and decrease environmental pollution. This study also explores the practicality of incorporating renewable energy into OMIES, tackling operational challenges to support low-carbon and secure energy operations on offshore platforms. These findings not only provide a new perspective on energy management for offshore oil and gas platforms but also contribute valuable strategies to the sustainable development of global energy. |
| format | Article |
| id | doaj-art-ff930c8e796f4887a849d9eec09a4b36 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ff930c8e796f4887a849d9eec09a4b362025-01-12T12:15:52ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-025-85557-3Enhanced particle swarm optimization with chaotic search for offshore micro-energy systemsWeiguo Huang0Wen Li1Xuewen Pan2Qiming Liu3Jie Yang4School of Information Engineering, Hunan University of Science and EngineeringSchool of Information Engineering, Hunan University of Science and EngineeringSchool of Information Engineering, Hunan University of Science and EngineeringSchool of Information Engineering, Hunan University of Science and EngineeringSchool of Information Engineering, Hunan University of Science and EngineeringAbstract As the global energy landscape shifts and sustainability becomes crucial, the offshore oil and gas sector confronts significant challenges and opportunities. This paper addresses the issues of energy efficiency and environmental impact of optimizing offshore micro-energy systems (OMIES) by proposing a multi-objective optimization model that integrates chaotic local search and particle swarm optimization (PSO). The model aims to achieve optimal scheduling of the energy system by comprehensively considering operational costs, carbon emissions, energy utilization efficiency, and energy fluctuation risks. The research results indicate that the optimization model can significantly improve energy utilization efficiency, reduce operational costs, and decrease environmental pollution. This study also explores the practicality of incorporating renewable energy into OMIES, tackling operational challenges to support low-carbon and secure energy operations on offshore platforms. These findings not only provide a new perspective on energy management for offshore oil and gas platforms but also contribute valuable strategies to the sustainable development of global energy.https://doi.org/10.1038/s41598-025-85557-3Micro-energy systemsOffshore oil and gasLow-carbon operationsParticle swarm optimizationChaotic search |
| spellingShingle | Weiguo Huang Wen Li Xuewen Pan Qiming Liu Jie Yang Enhanced particle swarm optimization with chaotic search for offshore micro-energy systems Scientific Reports Micro-energy systems Offshore oil and gas Low-carbon operations Particle swarm optimization Chaotic search |
| title | Enhanced particle swarm optimization with chaotic search for offshore micro-energy systems |
| title_full | Enhanced particle swarm optimization with chaotic search for offshore micro-energy systems |
| title_fullStr | Enhanced particle swarm optimization with chaotic search for offshore micro-energy systems |
| title_full_unstemmed | Enhanced particle swarm optimization with chaotic search for offshore micro-energy systems |
| title_short | Enhanced particle swarm optimization with chaotic search for offshore micro-energy systems |
| title_sort | enhanced particle swarm optimization with chaotic search for offshore micro energy systems |
| topic | Micro-energy systems Offshore oil and gas Low-carbon operations Particle swarm optimization Chaotic search |
| url | https://doi.org/10.1038/s41598-025-85557-3 |
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