Comparative Analysis of Nature-Inspired Algorithms for Optimal Power Flow Problem: A Focus on Penalty-Vanishing Terms and Algorithm Performance
This study presents a comparative analysis of multiple nature-inspired algorithms for solving the non-polynomial Optimal Power Flow (OPF) problem. Through numerical evaluations, we assess their performance across diverse objective functions, addressing complexities such as multi-fuel sources, valve...
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2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10440616/ |
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| author | Gerardo Castanon Alberto F. Martinez-Herrera Ana Maria Sarmiento Alejandro Aragon-Zavala Fernando Lezama |
| author_facet | Gerardo Castanon Alberto F. Martinez-Herrera Ana Maria Sarmiento Alejandro Aragon-Zavala Fernando Lezama |
| author_sort | Gerardo Castanon |
| collection | DOAJ |
| description | This study presents a comparative analysis of multiple nature-inspired algorithms for solving the non-polynomial Optimal Power Flow (OPF) problem. Through numerical evaluations, we assess their performance across diverse objective functions, addressing complexities such as multi-fuel sources, valve point effects, and prohibited zones. The study involves the implementation of different nature-inspired heuristics and variants of the differential evolution algorithm to analyze their efficacy in solving the OPF problem within the context of large networks, specifically IEEE-30 and IEEE-57. The objectives of this research are threefold: (i) to determine the most effective nature-inspired algorithms for each case under consistent constraints, initial conditions, and using optimized parameters, (ii) to assess the success rate of penalty-vanishing terms concerning the penalized function versus the actual objective function, and (iii) to explore the impact of minor variations within a network on the behaviors, results, and profiles of penalty-vanishing terms. Utilizing a low-high sorting ranking method, considering mean, maximum, and minimum values for result computation and sorting, we identify the optimal algorithm among all those assessed for various objective functions, alongside assessing the success rate of penalty-vanishing terms. Our findings reveal that the differential evolution algorithm best version (DEAB) emerges as the most valuable solution. |
| format | Article |
| id | doaj-art-6107a2fe2f264d0da9962fe5f3882aec |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6107a2fe2f264d0da9962fe5f3882aec2025-01-18T00:00:11ZengIEEEIEEE Access2169-35362024-01-0112299402995810.1109/ACCESS.2024.336838310440616Comparative Analysis of Nature-Inspired Algorithms for Optimal Power Flow Problem: A Focus on Penalty-Vanishing Terms and Algorithm PerformanceGerardo Castanon0https://orcid.org/0000-0001-5208-5745Alberto F. Martinez-Herrera1https://orcid.org/0000-0002-7982-2255Ana Maria Sarmiento2https://orcid.org/0000-0002-9331-5954Alejandro Aragon-Zavala3https://orcid.org/0000-0003-3098-7275Fernando Lezama4https://orcid.org/0000-0001-8638-8373School of Engineering and Sciences, Tecnológico de Monterrey, Monterrey, Nuevo Leon, MexicoSchool of Engineering and Sciences, Tecnológico de Monterrey, Monterrey, Nuevo Leon, MexicoSchool of Engineering and Sciences, Tecnológico de Monterrey, Monterrey, Nuevo Leon, MexicoDepartment of Electronics and Mechatronics, Tecnológico de Monterrey, Querétaro Campus, Santiago de Querétaro, Querétaro, MexicoGECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, LASI—Intelligent Systems Associate Laboratory, Polytechnic of Porto, Porto, PortugalThis study presents a comparative analysis of multiple nature-inspired algorithms for solving the non-polynomial Optimal Power Flow (OPF) problem. Through numerical evaluations, we assess their performance across diverse objective functions, addressing complexities such as multi-fuel sources, valve point effects, and prohibited zones. The study involves the implementation of different nature-inspired heuristics and variants of the differential evolution algorithm to analyze their efficacy in solving the OPF problem within the context of large networks, specifically IEEE-30 and IEEE-57. The objectives of this research are threefold: (i) to determine the most effective nature-inspired algorithms for each case under consistent constraints, initial conditions, and using optimized parameters, (ii) to assess the success rate of penalty-vanishing terms concerning the penalized function versus the actual objective function, and (iii) to explore the impact of minor variations within a network on the behaviors, results, and profiles of penalty-vanishing terms. Utilizing a low-high sorting ranking method, considering mean, maximum, and minimum values for result computation and sorting, we identify the optimal algorithm among all those assessed for various objective functions, alongside assessing the success rate of penalty-vanishing terms. Our findings reveal that the differential evolution algorithm best version (DEAB) emerges as the most valuable solution.https://ieeexplore.ieee.org/document/10440616/Nature-inspired algorithmsoptimal power flowoptimizationpenalty-vanishing termssuccess rate |
| spellingShingle | Gerardo Castanon Alberto F. Martinez-Herrera Ana Maria Sarmiento Alejandro Aragon-Zavala Fernando Lezama Comparative Analysis of Nature-Inspired Algorithms for Optimal Power Flow Problem: A Focus on Penalty-Vanishing Terms and Algorithm Performance IEEE Access Nature-inspired algorithms optimal power flow optimization penalty-vanishing terms success rate |
| title | Comparative Analysis of Nature-Inspired Algorithms for Optimal Power Flow Problem: A Focus on Penalty-Vanishing Terms and Algorithm Performance |
| title_full | Comparative Analysis of Nature-Inspired Algorithms for Optimal Power Flow Problem: A Focus on Penalty-Vanishing Terms and Algorithm Performance |
| title_fullStr | Comparative Analysis of Nature-Inspired Algorithms for Optimal Power Flow Problem: A Focus on Penalty-Vanishing Terms and Algorithm Performance |
| title_full_unstemmed | Comparative Analysis of Nature-Inspired Algorithms for Optimal Power Flow Problem: A Focus on Penalty-Vanishing Terms and Algorithm Performance |
| title_short | Comparative Analysis of Nature-Inspired Algorithms for Optimal Power Flow Problem: A Focus on Penalty-Vanishing Terms and Algorithm Performance |
| title_sort | comparative analysis of nature inspired algorithms for optimal power flow problem a focus on penalty vanishing terms and algorithm performance |
| topic | Nature-inspired algorithms optimal power flow optimization penalty-vanishing terms success rate |
| url | https://ieeexplore.ieee.org/document/10440616/ |
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