Efficient economic operation based on load dispatch of power systems using a leader white shark optimization algorithm

被引：7
作者：
Hassan M.H. [1 ]
Kamel S. [2 ]
Selim A. [2 ]
Shaheen A. [3 ]
Yu J. [4 ]
El-Sehiemy R. [5 ]
机构：
[1] Ministry of Electricity and Renewable Energy, Cairo
[2] Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan
[3] Department of Electrical Power Engineering, Faculty of Engineering, Suez University, Suez
[4] State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing
[5] Department of Electrical Engineering, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh
来源：
Neural Computing and Applications | 2024年 / 36卷 / 18期
关键词：
Economic load dispatch; Leader strategy; Optimization algorithms; White shark optimizer;
D O I：
10.1007/s00521-024-09612-2
中图分类号：
学科分类号：
摘要：
This article proposes the use of a leader white shark optimizer (LWSO) with the aim of improving the exploitation of the conventional white shark optimizer (WSO) and solving the economic operation-based load dispatch (ELD) problem. The ELD problem is a crucial aspect of power system operation, involving the allocation of power generation resources to meet the demand while minimizing operational costs. The proposed approach aims to enhance the performance and efficiency of the WSO by introducing a leadership mechanism within the optimization process, which aids in more effectively navigating the complex ELD solution space. The LWSO achieves increased exploitation by utilizing a leader-based mutation selection throughout each generation of white sharks. The efficacy of the proposed algorithm is tested on 13 engineer benchmarks non-convex optimization problems from CEC 2020 and compared with recent metaheuristic algorithms such as dung beetle optimizer (DBO), conventional WSO, fox optimizer (FOX), and moth-flame optimization (MFO) algorithms. The LWSO is also used to address the ELD problem in different case studies (6 units, 10 units, 11 units, and 40 units), with 20 separate runs using the proposed LWSO and other competitive algorithms being statistically assessed to demonstrate its effectiveness. The results show that the LWSO outperforms other metaheuristic algorithms, achieving the best solution for the benchmarks and the minimum fuel cost for the ELD problem. Additionally, statistical tests are conducted to validate the competitiveness of the LWSO algorithm. © The Author(s) 2024.
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页码：10613 / 10635
页数：22
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