Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System
Hybrid generating systems in power networks have emerged as a result of the rapid growth of renewable infrastructure and widespread support for green energy. One of the most significant problems in designing and operating an electric power generation system is the efficient scheduling of all power g...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2023/5395658 |
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| author | D. Blandina Miracle R. K. Viral P. M. Tiwari Mohit Bansal |
| author_facet | D. Blandina Miracle R. K. Viral P. M. Tiwari Mohit Bansal |
| author_sort | D. Blandina Miracle |
| collection | DOAJ |
| description | Hybrid generating systems in power networks have emerged as a result of the rapid growth of renewable infrastructure and widespread support for green energy. One of the most significant problems in designing and operating an electric power generation system is the efficient scheduling of all power generation facilities to meet the rising power demand. Economic load dispatch (ELD) is a generic procedure in the electrical power system, and the ELD in power system problems involves scheduling the power generating units to reduce cost and satisfy system constraints. Metaheuristic algorithms are gaining popularity for solving constrained ELD issues because of their larger global solution capacity, flexibility, and derivative-free construction. In this research, the ELD problem of integrated renewable resources is solved using a unique solution model based on hybrid optimization. Furthermore, this work considers multiobjectives such as total wind generation cost, total cost function of thermal units, and penalty cost function. The hybrid optimization model optimizes the power generation of thermal power plants within the maximum and minimum limitations. Additionally, the turbines are selected optimally by the hybrid optimization model to ensure the power generation of wind turbines based on the demands. The proposed hybrid optimization is a combination of particle swarm optimization (PSO) and cat swarm optimization (CSO), and the new algorithm is referred to as the particle oriented cat swarm optimization model (POCSO). Finally, the performance of the proposed work is compared to other conventional models. In particular, the cost function of POCSO is 6.25%, 6%, 11.7%, 36%, 27%, and 46.42% better than the cost function of whale optimization algorithm (WOA), elephant herd optimization (EHO), moth-flame optimization (MFO), dragonfly algorithm (DA), sealion optimization (SLnO), CSO, and PSO methods, respectively. Also, for IEEE-30 bus system, the best value of the proposed work is 7.46%, 5.41%, 16.30%, 14.88%, 17.60%, 13.86%, 15.21%, 17.49%, and 4.27% better than that of the PSO, CSO, SLnO, DA, MFO, EHO, WOA, multiagent glowworm swarm optimization (MAGSO), and Harris hawks optimization-based feed-forward neural network (HHO-FNN) methods, respectively. |
| format | Article |
| id | doaj-art-fe9cc9097d4e468db18ae304a1ad5696 |
| institution | OA Journals |
| issn | 2050-7038 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-fe9cc9097d4e468db18ae304a1ad56962025-08-20T02:22:32ZengWileyInternational Transactions on Electrical Energy Systems2050-70382023-01-01202310.1155/2023/5395658Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy SystemD. Blandina Miracle0R. K. Viral1P. M. Tiwari2Mohit Bansal3Department of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringHybrid generating systems in power networks have emerged as a result of the rapid growth of renewable infrastructure and widespread support for green energy. One of the most significant problems in designing and operating an electric power generation system is the efficient scheduling of all power generation facilities to meet the rising power demand. Economic load dispatch (ELD) is a generic procedure in the electrical power system, and the ELD in power system problems involves scheduling the power generating units to reduce cost and satisfy system constraints. Metaheuristic algorithms are gaining popularity for solving constrained ELD issues because of their larger global solution capacity, flexibility, and derivative-free construction. In this research, the ELD problem of integrated renewable resources is solved using a unique solution model based on hybrid optimization. Furthermore, this work considers multiobjectives such as total wind generation cost, total cost function of thermal units, and penalty cost function. The hybrid optimization model optimizes the power generation of thermal power plants within the maximum and minimum limitations. Additionally, the turbines are selected optimally by the hybrid optimization model to ensure the power generation of wind turbines based on the demands. The proposed hybrid optimization is a combination of particle swarm optimization (PSO) and cat swarm optimization (CSO), and the new algorithm is referred to as the particle oriented cat swarm optimization model (POCSO). Finally, the performance of the proposed work is compared to other conventional models. In particular, the cost function of POCSO is 6.25%, 6%, 11.7%, 36%, 27%, and 46.42% better than the cost function of whale optimization algorithm (WOA), elephant herd optimization (EHO), moth-flame optimization (MFO), dragonfly algorithm (DA), sealion optimization (SLnO), CSO, and PSO methods, respectively. Also, for IEEE-30 bus system, the best value of the proposed work is 7.46%, 5.41%, 16.30%, 14.88%, 17.60%, 13.86%, 15.21%, 17.49%, and 4.27% better than that of the PSO, CSO, SLnO, DA, MFO, EHO, WOA, multiagent glowworm swarm optimization (MAGSO), and Harris hawks optimization-based feed-forward neural network (HHO-FNN) methods, respectively.http://dx.doi.org/10.1155/2023/5395658 |
| spellingShingle | D. Blandina Miracle R. K. Viral P. M. Tiwari Mohit Bansal Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System International Transactions on Electrical Energy Systems |
| title | Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System |
| title_full | Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System |
| title_fullStr | Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System |
| title_full_unstemmed | Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System |
| title_short | Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System |
| title_sort | hybrid metaheuristic model for optimal economic load dispatch in renewable hybrid energy system |
| url | http://dx.doi.org/10.1155/2023/5395658 |
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