A high-performance crisscross search based grey wolf optimizer for solving optimal power flow problem. (15th June 2021)

• Record Type: Journal Article
• Authors: Meng, Anbo; Zeng, Cong; Wang, Peng; Chen, De; Zhou, Tianmin; Zheng, Xiaoying; Yin, Hao
• Is Part Of: Energy. Volume 225(2021)
• Publication Date: 2021-06-15
• Subjects: Optimal power flow -- Grey wolf optimizer -- Horizontal crossover -- Vertical crossover -- Crisscross search; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.120211
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This paper formulates the optimal power flow (OPF) problem with the consideration of minimizing many objective functions including the basic fuel cost, fuel cost with valve-point effects, transmission active power loss, basic fuel cost with transmission active power loss as well as basic fuel cost with voltage deviation. To solve the OPF problem, a novel crisscross search based grey wolf optimizer (CS-GWO) is proposed, in which the hunting operation in GWO is firstly modified by introducing a greedy mechanism and then the horizontal crossover operator is added to refine the first three ranking wolves. In addition, the vertical crossover operator is applied to maintain the population diversity so as to prevent the premature convergence, which provides a unique mechanism for GWO to get rid of dimensional local optimum. The cooperation of last two operators can accelerate convergence speed and avoid falling into dimensional local optimum of hunting process. The proposed CS-GWO is validated on IEEE 30-bus system and IEEE 118-bus system. The experimental results demonstrate the CS-GWO has obvious advantage over the original GWO and the other state-of-art heuristic algorithms, especially in large-scale system. Highlights: A crisscross search based grey wolf optimizer is proposed for OPF problem. The horizontal crossover is used to improve the global search ability of GWO. The vertical crossover helps for GWO to overcome the premature convergence. The CS-GWO has superiority over other methods in terms of solution quality.