The optimal power flow is a pragmatic problem in a power system with complex behavior that includes many control parameters. Many metaheuristic algorithms with different search methodologies have been proposed for solving the OPF problem. However, existing algorithm faces challenges such as stagnation, premature convergence, and local optima trapping during the optimization process, which provide low-quality and misleading results for real-world problems. In this paper, a novel algorithm which is inspired by the natural foraging phenomenon of the flying squirrel named as Squirrel Search Algorithm is used and it is hybridized with arithmetic crossover operation to enhance its effectiveness and be used for solving the OPF problem. So, the proposed algorithm is named the Hybrid Flying Squirrel Search Algorithm (HFSSA). The capability and performance of the proposed algorithm are observed on benchmark test functions and on the IEEE-30 bus system. Generation fuel cost, emission, and transmission losses are considered objectives of an optimal power flow problem. We got optimal values by handling the control parameters; Generation fuel cost as 799.86 $/h, Emission as 0.20374 ton/h, and transmission loss as 3.1687 MW. The obtained results corroborate that the proposed algorithm outperforms the existing algorithms for solving the OPF problem.
Key words: Emission; Generation fuel cost; Hybrid Flying Squirrel Search Algorithm (HFSSA); Optimal Power Flow (OPF); Power Injection modeling (PIM); Transmission losses.
Copyright © 2023 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0