Performance Evaluation of PSO, GA, DOA, NRBO, and GGO for Static Optimal Power Flow: A Benchmarking Study

Abstract

The growing demand and power of modern power systems necessitates an economical and stable operation, and therefore, the Optimal Power Flow (OPF) problem is of heavy research interest. The OPF must be solved efficiently to lower operational costs and ensure system stability. A comprehensive comparative study in this paper compares the performance of five metaheuristic algorithms for solving the fuel cost minimization problem of the OPF problem. Two well-known algorithms, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), and three new ones, Dream Optimization Algorithm (DOA), Newton-Raphson-Based Optimizer (NRBO), and Greylag Goose Optimization (GGO), were tested on the benchmark IEEE 6-bus, 30-bus, and 57-bus systems. The performance of the algorithms was compared in terms of statistical measures of the best cost, mean cost, and standard deviation from ten independent runs. Numerical findings indicate that the performance of algorithms is highly dependent on system size. For the 6-bus case, NRBO had the lowest fuel cost, while PSO had better stability with the lowest standard deviation. In the 30-bus case, NRBO was the most effective algorithm to utilize, having better performance in all aspects measured. In the larger 57-bus system, PSO had the best solution overall but least consistent in performance. This study concludes that there isn't a single best algorithm for all OPF problem sizes, providing an authoritative benchmark that marks the strengths and weaknesses of classical and contemporary metaheuristics across different power system applications.

Keywords

• Metaheuristic
• Optimal Power Flow
• Optimization
• Fuel Cost

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