Stable matching-enhanced MOEA/D for solving multi-objective optimal power flow problems
Optimal Power Flow (OPF) plays a fundamental role in the secure and efficient management of power systems, both in system design and real-time operation. Existing OPF approaches often struggle with the problem’s non-linearity, non-convexity, and mixed-variable characteristics, which hinder convergen...
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| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025025897 |
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| author | Ebrahim Akbari Amin Khodabakhshian Abolfazl Rahimnejad Stephen Andrew Gadsden |
| author_facet | Ebrahim Akbari Amin Khodabakhshian Abolfazl Rahimnejad Stephen Andrew Gadsden |
| author_sort | Ebrahim Akbari |
| collection | DOAJ |
| description | Optimal Power Flow (OPF) plays a fundamental role in the secure and efficient management of power systems, both in system design and real-time operation. Existing OPF approaches often struggle with the problem’s non-linearity, non-convexity, and mixed-variable characteristics, which hinder convergence and compromise solution diversity. This paper addresses these challenges by applying a multi-objective evolutionary algorithm based on decomposition (MOEA/D) enhanced with stable matching theory. The proposed method ensures a balanced and effective trade-off between solution accuracy and diversity in multi-objective optimization. Comparative evaluations against well-established algorithms demonstrate the superior performance of the proposed approach in approximating the Pareto front, improving computational efficiency, and maintaining solution diversity. The results highlight the effectiveness of the method in addressing OPF problems with conflicting objectives such as cost minimization, loss reduction, and voltage stability enhancement. This research provides a new perspective on applying stable matching mechanisms into evolutionary algorithms for power system optimization. |
| format | Article |
| id | doaj-art-8a8a11aad7484555b9633c648fb611b6 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-8a8a11aad7484555b9633c648fb611b62025-08-20T04:02:09ZengElsevierResults in Engineering2590-12302025-09-012710652010.1016/j.rineng.2025.106520Stable matching-enhanced MOEA/D for solving multi-objective optimal power flow problemsEbrahim Akbari0Amin Khodabakhshian1Abolfazl Rahimnejad2Stephen Andrew Gadsden3Department of Economics, University of Bergamo, Via dei Caniana 2 24127, Bergamo, BG, Italy; Corresponding author.Department of Electrical Engineering, University of Isfahan, Isfahan, IranDepartment of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, CanadaDepartment of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, CanadaOptimal Power Flow (OPF) plays a fundamental role in the secure and efficient management of power systems, both in system design and real-time operation. Existing OPF approaches often struggle with the problem’s non-linearity, non-convexity, and mixed-variable characteristics, which hinder convergence and compromise solution diversity. This paper addresses these challenges by applying a multi-objective evolutionary algorithm based on decomposition (MOEA/D) enhanced with stable matching theory. The proposed method ensures a balanced and effective trade-off between solution accuracy and diversity in multi-objective optimization. Comparative evaluations against well-established algorithms demonstrate the superior performance of the proposed approach in approximating the Pareto front, improving computational efficiency, and maintaining solution diversity. The results highlight the effectiveness of the method in addressing OPF problems with conflicting objectives such as cost minimization, loss reduction, and voltage stability enhancement. This research provides a new perspective on applying stable matching mechanisms into evolutionary algorithms for power system optimization.http://www.sciencedirect.com/science/article/pii/S2590123025025897Multi-objective optimizationMOEA/DOPFStable matchingContinuous and Discrete Variables |
| spellingShingle | Ebrahim Akbari Amin Khodabakhshian Abolfazl Rahimnejad Stephen Andrew Gadsden Stable matching-enhanced MOEA/D for solving multi-objective optimal power flow problems Results in Engineering Multi-objective optimization MOEA/D OPF Stable matching Continuous and Discrete Variables |
| title | Stable matching-enhanced MOEA/D for solving multi-objective optimal power flow problems |
| title_full | Stable matching-enhanced MOEA/D for solving multi-objective optimal power flow problems |
| title_fullStr | Stable matching-enhanced MOEA/D for solving multi-objective optimal power flow problems |
| title_full_unstemmed | Stable matching-enhanced MOEA/D for solving multi-objective optimal power flow problems |
| title_short | Stable matching-enhanced MOEA/D for solving multi-objective optimal power flow problems |
| title_sort | stable matching enhanced moea d for solving multi objective optimal power flow problems |
| topic | Multi-objective optimization MOEA/D OPF Stable matching Continuous and Discrete Variables |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025025897 |
| work_keys_str_mv | AT ebrahimakbari stablematchingenhancedmoeadforsolvingmultiobjectiveoptimalpowerflowproblems AT aminkhodabakhshian stablematchingenhancedmoeadforsolvingmultiobjectiveoptimalpowerflowproblems AT abolfazlrahimnejad stablematchingenhancedmoeadforsolvingmultiobjectiveoptimalpowerflowproblems AT stephenandrewgadsden stablematchingenhancedmoeadforsolvingmultiobjectiveoptimalpowerflowproblems |