Hybrid Decision Support Framework for Energy Scheduling Using Stochastic Optimization and Cooperative Game Theory
This study introduces a multi-criteria decision-making (MCDM) framework for optimizing multi-energy network scheduling (MENS). As energy systems become more complex, the need for adaptable solutions that balance consumer demand with environmental sustainability grows. The proposed approach integrate...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/24/6386 |
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| author | Peng Liu Tieyan Zhang Furui Tian Yun Teng Miaodong Yang |
| author_facet | Peng Liu Tieyan Zhang Furui Tian Yun Teng Miaodong Yang |
| author_sort | Peng Liu |
| collection | DOAJ |
| description | This study introduces a multi-criteria decision-making (MCDM) framework for optimizing multi-energy network scheduling (MENS). As energy systems become more complex, the need for adaptable solutions that balance consumer demand with environmental sustainability grows. The proposed approach integrates conventional and alternative energy sources, addressing uncertainties through fermatean fuzzy sets (FFS), which enhances decision-making flexibility and resilience. A key component of the framework is the use of stochastic optimization and cooperative game theory (CGT) to ensure efficiency and reliability in energy systems. To evaluate the importance of various scheduling criteria, the study applies the logarithmic percentage change-driven objective weighing (LOPCOW) method, offering a systematic way to assign weights. The weighted aggregated sum product assessment (WASPAS) method is then used to rank potential solutions. The hybrid scheduling alternative, combining distributed and centralized solutions, stands out as the best alternative, significantly improving resource optimization and system resilience. While implementation costs may increase, the hybrid approach balances flexibility and rigidity, optimizing resource use and ensuring system adaptability. This work provides a comprehensive framework that enhances the efficiency and sustainability of energy systems, helping decision-makers address fluctuating demands and renewable energy integration challenges. |
| format | Article |
| id | doaj-art-8623f654e6c342fd8f872c30583d4e1d |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-8623f654e6c342fd8f872c30583d4e1d2024-12-27T14:23:40ZengMDPI AGEnergies1996-10732024-12-011724638610.3390/en17246386Hybrid Decision Support Framework for Energy Scheduling Using Stochastic Optimization and Cooperative Game TheoryPeng Liu0Tieyan Zhang1Furui Tian2Yun Teng3Miaodong Yang4School of Electrical Engineering, Shenyang University of Technology, Shenyang 110000, ChinaSchool of Electrical Engineering, Shenyang University of Technology, Shenyang 110000, ChinaState Grid Zhejiang Electric Power Company, Ltd., Zhuji Power Supply Company, Zhuji 311800, ChinaSchool of Electrical Engineering, Shenyang University of Technology, Shenyang 110000, ChinaLiaoning Qinghe Power Generation Company Ltd., Tieling 112003, ChinaThis study introduces a multi-criteria decision-making (MCDM) framework for optimizing multi-energy network scheduling (MENS). As energy systems become more complex, the need for adaptable solutions that balance consumer demand with environmental sustainability grows. The proposed approach integrates conventional and alternative energy sources, addressing uncertainties through fermatean fuzzy sets (FFS), which enhances decision-making flexibility and resilience. A key component of the framework is the use of stochastic optimization and cooperative game theory (CGT) to ensure efficiency and reliability in energy systems. To evaluate the importance of various scheduling criteria, the study applies the logarithmic percentage change-driven objective weighing (LOPCOW) method, offering a systematic way to assign weights. The weighted aggregated sum product assessment (WASPAS) method is then used to rank potential solutions. The hybrid scheduling alternative, combining distributed and centralized solutions, stands out as the best alternative, significantly improving resource optimization and system resilience. While implementation costs may increase, the hybrid approach balances flexibility and rigidity, optimizing resource use and ensuring system adaptability. This work provides a comprehensive framework that enhances the efficiency and sustainability of energy systems, helping decision-makers address fluctuating demands and renewable energy integration challenges.https://www.mdpi.com/1996-1073/17/24/6386decision support frameworkenergy optimizationhybrid schedulingstochastic optimizationcooperative game theoryenergy resource management |
| spellingShingle | Peng Liu Tieyan Zhang Furui Tian Yun Teng Miaodong Yang Hybrid Decision Support Framework for Energy Scheduling Using Stochastic Optimization and Cooperative Game Theory Energies decision support framework energy optimization hybrid scheduling stochastic optimization cooperative game theory energy resource management |
| title | Hybrid Decision Support Framework for Energy Scheduling Using Stochastic Optimization and Cooperative Game Theory |
| title_full | Hybrid Decision Support Framework for Energy Scheduling Using Stochastic Optimization and Cooperative Game Theory |
| title_fullStr | Hybrid Decision Support Framework for Energy Scheduling Using Stochastic Optimization and Cooperative Game Theory |
| title_full_unstemmed | Hybrid Decision Support Framework for Energy Scheduling Using Stochastic Optimization and Cooperative Game Theory |
| title_short | Hybrid Decision Support Framework for Energy Scheduling Using Stochastic Optimization and Cooperative Game Theory |
| title_sort | hybrid decision support framework for energy scheduling using stochastic optimization and cooperative game theory |
| topic | decision support framework energy optimization hybrid scheduling stochastic optimization cooperative game theory energy resource management |
| url | https://www.mdpi.com/1996-1073/17/24/6386 |
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