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

Mohd Herwan, Sulaiman and Zuriani, Mustaffa (2024) Optimal power flow with renewable power generations using hyper-heuristic technique. In: Handbook of Whale Optimization Algorithm: Variants, Hybrids, Improvements, and Applications. Academic Press, Cambridge, 253 -264. ISBN 978-0-323-95365-8

Preview	Pdf Handbook of Whale Optimization Algorithm.pdf Download (542kB) | Preview
Preview	Pdf Optimal power flow with renewable power generations_ABST.pdf Download (47kB) | Preview
	Pdf Optimal power flow with renewable power generations.pdf Restricted to Repository staff only Download (1MB) | Request a copy

Abstract

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.

Item Type:	Book Chapter
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Hyper-heuristic; Loss minimization; Metaheuristic; Optimal power flow; Renewable energy integration
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Faculty of Computing Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	05 Aug 2024 07:15
Last Modified:	05 Aug 2024 07:15
URI:	http://umpir.ump.edu.my/id/eprint/42169
Download Statistic:	View Download Statistics

Actions (login required)

View Item