A Modified Whale Optimizer for Single- and Multi-Objective OPF Frameworks

Mahmoud El-Dabah, Mohamed A. Ebrahim, Ragab A. El-Sehiemy, Z. Alaas and M. M. Ramadan
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Mahmoud El-Dabah: Electrical Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo 11651, Egypt
Mohamed A. Ebrahim: Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Benha 13511, Egypt
Ragab A. El-Sehiemy: Electrical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
Z. Alaas: Department of Electrical Engineering, Faculty of Engineering, Jazan University, Gizan 45142, Saudi Arabia
M. M. Ramadan: Faculty of Technology and Education, Helwan University, Helwan 11795, Egypt

Energies, 2022, vol. 15, issue 7, 1-18

Abstract: This paper is concerned with an imperative operational problem, called the optimal power flow (OPF), which has several technical and economic points of view with respect the environmental concerns. This paper proposes a multiple-objective optimizer NSWOA (non-dominated sorting whale optimization algorithm) for resolving single-objective OPFs, as well as multi-objective frameworks. With a variety of technical and economic power system objectives, the OPF can be formulated. These objectives are treated as single- and multi-objective OPF issues that are deployed with the aid of the proposed NSWOA to solve these OPF formulations. The proposed algorithm modifies the Pareto ranking and analyzes the optimum compromise solution based on the optimal Euclidian distances. This proposed strategy ensures high convergence speed and improves search capabilities. To achieve this study, an IEEE 30-bus (sixth-generation unit system) is investigated, with eight scenarios studied that highlight technical and environmental operational needs. When compared to previous optimization approaches, the suggested NSWOA achieves considerable techno-economic improvements. Additionally, the statical analyses are carried out for 20 separate runs. This analysis proves the high robustness of the proposed NSWOA at low levels of standard deviation.

Keywords: OPF; multi-objective optimization; Pareto optimal; NSWOA; techno-economic requirements (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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