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A Novel Hybrid Algorithm for Solving Economic Load Dispatch in Power Systems
Journal article   Open access   Peer reviewed

A Novel Hybrid Algorithm for Solving Economic Load Dispatch in Power Systems

Khairul Fahim, Liyanage De Silva, Viknesh Andiappan, Sk Shezan and Hayati Yassin
International journal of energy research
2024
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Power Systems2.20 MBDownloadView
CC BY V4.0 Open Access

Abstract

Algorithms Candidates Convergence Economics Exploitation Genetic algorithms Heuristic methods Machine learning Mutation Neural networks Optimization Optimization algorithms Parameter identification Power dispatch Power plants Social behavior Social behaviour Success
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.

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Collaboration types
Domestic collaboration
International collaboration
Citation topics
4 Electrical Engineering, Electronics & Computer Science
4.18 Power Systems & Electric Vehicles
4.18.296 Energy Forecasting
Web Of Science research areas
Energy & Fuels
Nuclear Science & Technology
ESI research areas
Engineering
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