Khairul Eahsun Fahim, Liyanage C. De Silva, Viknesh Andiappan, Sk. A. Shezan, Hayati Yassin
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To find comprehensive solutions to the ELD problem in power systems, this paper suggests a new method called the hybrid Jaya optimization algorithm, which uses the merits of the Jaya and teaching–learning-based optimization (TLBO) algorithms. This enhancement is proposed to improve the population variety, the balance between local and global search, and the early convergence of the original Jaya optimization method. A metaheuristic optimization technique called TLBO simulates the teaching–learning process in a classroom to optimize problems. The TLBO algorithm uses an exploration phase in which possible solutions are generated at random to discover the best solution. The algorithm then uses the exploitation phase to refine the search space-based parameter adjustments to enhance the quality of the best solution identified. On the other hand, the Jaya algorithm is a metaheuristic optimization algorithm motivated by the idea of social behavior in nature. Candidate solutions are improved repeatedly through cooperation and competition using a population-based approach, and each solution adjusts its position based on the best and worst answers in the population. By combining the advantages of both algorithms, hybrid Jaya (Jaya–TLBO) outperforms each method alone and minimizes the cost of power generation, improving convergence solution quality. To test its efficacy, the hybrid Jaya–TLBO algorithm is tested on four different test cases, such as an Institute of Electrical and Electronics Engineers (IEEE) 6-unit, 13-unit, 20-unit, 40-unit ELD system and an Indonesian 10-unit one. Simulation results show that the proposed algorithm is superior in cost minimization to other well-known algorithms that have been used recently. As a result, power system planners can utilize this technique to find the most economical load dispatch.</p>\n </div>","PeriodicalId":14051,"journal":{"name":"International Journal of Energy Research","volume":"2024 1","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/8420107","citationCount":"0","resultStr":"{\"title\":\"A Novel Hybrid Algorithm for Solving Economic Load Dispatch in Power Systems\",\"authors\":\"Khairul Eahsun Fahim, Liyanage C. De Silva, Viknesh Andiappan, Sk. A. Shezan, Hayati Yassin\",\"doi\":\"10.1155/2024/8420107\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n <p>Various algorithms have been created in the past to take economic load dispatch (ELD) into account. These algorithms, however, concentrate on multiple tuning parameters, necessitating hyperparameter adjustment. A unique parameterless hybrid is presented to explicitly evaluate ELD for test systems and real-world power plant systems matching the operational limitations. In addition, earlier algorithms could only offer estimates of the final cost of fuel based on the hyperparameter choices. This may prevent the global minimum values from being met. To find comprehensive solutions to the ELD problem in power systems, this paper suggests a new method called the hybrid Jaya optimization algorithm, which uses the merits of the Jaya and teaching–learning-based optimization (TLBO) algorithms. This enhancement is proposed to improve the population variety, the balance between local and global search, and the early convergence of the original Jaya optimization method. A metaheuristic optimization technique called TLBO simulates the teaching–learning process in a classroom to optimize problems. 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A Novel Hybrid Algorithm for Solving Economic Load Dispatch in Power Systems
Various algorithms have been created in the past to take economic load dispatch (ELD) into account. These algorithms, however, concentrate on multiple tuning parameters, necessitating hyperparameter adjustment. A unique parameterless hybrid is presented to explicitly evaluate ELD for test systems and real-world power plant systems matching the operational limitations. In addition, earlier algorithms could only offer estimates of the final cost of fuel based on the hyperparameter choices. This may prevent the global minimum values from being met. To find comprehensive solutions to the ELD problem in power systems, this paper suggests a new method called the hybrid Jaya optimization algorithm, which uses the merits of the Jaya and teaching–learning-based optimization (TLBO) algorithms. This enhancement is proposed to improve the population variety, the balance between local and global search, and the early convergence of the original Jaya optimization method. A metaheuristic optimization technique called TLBO simulates the teaching–learning process in a classroom to optimize problems. The TLBO algorithm uses an exploration phase in which possible solutions are generated at random to discover the best solution. The algorithm then uses the exploitation phase to refine the search space-based parameter adjustments to enhance the quality of the best solution identified. On the other hand, the Jaya algorithm is a metaheuristic optimization algorithm motivated by the idea of social behavior in nature. Candidate solutions are improved repeatedly through cooperation and competition using a population-based approach, and each solution adjusts its position based on the best and worst answers in the population. By combining the advantages of both algorithms, hybrid Jaya (Jaya–TLBO) outperforms each method alone and minimizes the cost of power generation, improving convergence solution quality. To test its efficacy, the hybrid Jaya–TLBO algorithm is tested on four different test cases, such as an Institute of Electrical and Electronics Engineers (IEEE) 6-unit, 13-unit, 20-unit, 40-unit ELD system and an Indonesian 10-unit one. Simulation results show that the proposed algorithm is superior in cost minimization to other well-known algorithms that have been used recently. As a result, power system planners can utilize this technique to find the most economical load dispatch.
期刊介绍:
The International Journal of Energy Research (IJER) is dedicated to providing a multidisciplinary, unique platform for researchers, scientists, engineers, technology developers, planners, and policy makers to present their research results and findings in a compelling manner on novel energy systems and applications. IJER covers the entire spectrum of energy from production to conversion, conservation, management, systems, technologies, etc. We encourage papers submissions aiming at better efficiency, cost improvements, more effective resource use, improved design and analysis, reduced environmental impact, and hence leading to better sustainability.
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