Monday , September 26 2022

A Multi-Objective Marine Predator Optimizer for Optimal Techno-Economic Operation of AC/DC Grids

Mosleh ALHARTHI1, Sherif GHONEIM1, Abdallah ELSAYED2, Ragab EL-SEHIEMY3,
Abdullah SHAHEEN4*, Ahmed GINIDI4

College of Engineering, Taif University, Taif 21944, Saudi Arabia,
2 Damietta University, Damietta 22052, Egypt
3 Kafrelshiekh University, Kafrelshiekh 33516, Egypt
4 Suez University, Suez 43518, Egypt (*Corresponding author),

Abstract: This article provides an improved Marine Predator Optimization (IMPO) for the optimized performance of combined alternating/direct current (AC/DC) electrical grids. The optimum performance of such AC/DC electrical grids is approached as a multi-objective issue with the goal of reducing the overall generated environmental emissions, fuel costs and the associated energy losses. The suggested IMPO includes an exterior repository which is meant to preserve nondominated individuals. The fuzzified decision procedure is often used to employed with a view to determining the correct acceptable operational solution for the combined AC/DC electricity grids. The suggested IMPO is created via the MATLAB environment and is employed on an updated standard power system of standard IEEE 57-bus. Besides, a comparative analysis is performed between the proposed IMPO algorithm, particle swarm optimization, bat optimization, dragonfly optimization, crow search optimization, grey wolf optimization, multi-verse optimization and salp swarm optimization. The simulation outputs demonstrate the effectiveness and preponderance of the proposed IMPO with capability in extracting well-diversified Pareto solutions.

Keywords: Marine predator optimizer, Multi-objective, AC/DC networks, Optimal management, Losses.


Mosleh ALHARTHI, Sherif GHONEIM, Abdallah ELSAYED, Ragab EL-SEHIEMY, Abdullah SHAHEEN, Ahmed GINIDI, A Multi-Objective Marine Predator Optimizer for Optimal Techno-Economic Operation of AC/DC Grids, Studies in Informatics and Control, ISSN 1220-1766, vol. 30(2), pp. 89-99, 2021.