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Solving the Economic Load Dispatch Problem of Power System Based on Swarm Intelligence Optimization Algorithm.

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Názov: Solving the Economic Load Dispatch Problem of Power System Based on Swarm Intelligence Optimization Algorithm.
Autori: Sun, Yu-Feng1 sunyufeng@stu.ustl.edu.cn, Wang, Jie-Sheng2 wjs@ustl.edu.cn, Guan, Xin-Yi1 gxy@stu.ustl.edu.cn, Yin, Xue-Ru3 2393834225@qq.com
Zdroj: IAENG International Journal of Applied Mathematics. Feb2026, Vol. 56 Issue 2, p854-865. 12p.
Predmety: *SWARM intelligence, *OPTIMIZATION algorithms, *SIMULATION methods & models, *RESOURCE allocation, *LOAD dispatching in electric power systems, *FUEL costs, *ARTIFICIAL intelligence, *MATHEMATICAL optimization
Abstrakt: Economic Load Dispatch (ELD) in power systems represents a core challenge in optimizing power system operations. Practical dispatch models typically exhibit nonlinearity, multiple constraints and even non-convexity. To address the high computational complexity and susceptibility to local optima encountered by traditional optimization methods, this paper introduces six swarm-based optimization algorithms, such as Arithmetic Optimization Algorithm (AOA) and Whale Optimization Algorithm (WOA) and systematically evaluates their solution capabilities. A mathematical model for minimizing fuel costs is constructed. Comparative experiments are conducted by using simulation cases with 6 units (small-scale), 20 units (medium-scale) and 40 units (large-scale), focusing on analyzing the convergence speed, optimization accuracy and result stability of each algorithm. Simulation results demonstrate that MPA and WOA exhibit outstanding optimization capabilities in most scenarios, while AO A and RRTO show greater stability in high-dimensional scheduling problems. ZOA and SOA achieve a good balance between the global exploration and the local exploitation. This study confirms the significant advantages of swarm intelligence algorithms in complex ELD problems, providing theoretical and engineering practice support for subsequent integration with hybrid optimization strategies and machine learning methods. [ABSTRACT FROM AUTHOR]
Databáza: Academic Search Index
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Abstrakt:Economic Load Dispatch (ELD) in power systems represents a core challenge in optimizing power system operations. Practical dispatch models typically exhibit nonlinearity, multiple constraints and even non-convexity. To address the high computational complexity and susceptibility to local optima encountered by traditional optimization methods, this paper introduces six swarm-based optimization algorithms, such as Arithmetic Optimization Algorithm (AOA) and Whale Optimization Algorithm (WOA) and systematically evaluates their solution capabilities. A mathematical model for minimizing fuel costs is constructed. Comparative experiments are conducted by using simulation cases with 6 units (small-scale), 20 units (medium-scale) and 40 units (large-scale), focusing on analyzing the convergence speed, optimization accuracy and result stability of each algorithm. Simulation results demonstrate that MPA and WOA exhibit outstanding optimization capabilities in most scenarios, while AO A and RRTO show greater stability in high-dimensional scheduling problems. ZOA and SOA achieve a good balance between the global exploration and the local exploitation. This study confirms the significant advantages of swarm intelligence algorithms in complex ELD problems, providing theoretical and engineering practice support for subsequent integration with hybrid optimization strategies and machine learning methods. [ABSTRACT FROM AUTHOR]
ISSN:19929978