Optimal power flow with renewable power generations using hyper-heuristic technique

• Main Authors: Mohd Herwan, Sulaiman, Zuriani, Mustaffa
• Format: Book Chapter
• Language: English; English; English
• Published: Academic Press 2024
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/42169/1/Handbook%20of%20Whale%20Optimization%20Algorithm.pdf; http://umpir.ump.edu.my/id/eprint/42169/2/Optimal%20power%20flow%20with%20renewable%20power%20generations_ABST.pdf; http://umpir.ump.edu.my/id/eprint/42169/3/Optimal%20power%20flow%20with%20renewable%20power%20generations.pdf; http://umpir.ump.edu.my/id/eprint/42169/; https://doi.org/10.1016/B978-0-32-395365-8.00025-7; https://doi.org/10.1016/B978-0-32-395365-8.00025-7

This paper presents a strategy to solve the Optimal Power Flow (OPF) problem solution, which considers the presence of renewable energy power generators such as wind, solar, and small hydro generation. The solution employs a high-level hyper-heuristic technique called Exponential Monte Carlo with counter (EMCQ) to solve the problem of loss minimization. The technique selects and integrates the strengths of three low-level meta-heuristics algorithms, including Grey Wolf Optimizer (GWO), Barnacles Mating Optimizer (BMO), and Whale Optimization Algorithm (WOA), to achieve the best possible results. The proposed strategy has been visualized and tested on a modified IEEE-57 bus system, and the outcomes of the hyper-heuristic technique have been compared to the effectiveness of the separate low-level meta-heuristics algorithms. The results demonstrate the effectiveness of the proposed approach in solving the loss minimization problem in the presence of renewable energy power sources.