Improved mountain gazelle optimizer based interactive distributed strategy to solving large scale OPF

Abstract

this paper focuses on solving with accuracy the multi objective optimal power flow of large-scale power systems under critical loading margin stability using a novel improved mountain gazelle optimizer (IMGO)-based flexible distributed strategy. Multi-shunt compensator-based flexible AC transmission systems (FACTS), such as SVC and STATCOM devices, are integrated at specified locations to exchange reactive power with the network. It is well known that several metaheuristic methods are able to solve the standard OPF related to small and medium test systems with success. However, by considering large-scale electric systems based on FACTS devices and renewable energy and by considering the operation under loading margin stability, the majority of these techniques fail to achieve the near-global solution due to the high dimension and nonlinearity of the problem to be solved. In this study, a new planning strategy, namely the Multi-Objective OPF-Based Distributed Strategy (MO-OPFDS), is proposed to optimize individually and simultaneously various objective functions, in particular the total power loss (T∆P), and the total voltage deviation (T∆V). The mechanism search of the standard MGO is improved by dynamically adjusting exploration and exploitation during the search process. The robustness of the proposed variant was validated on the large electric test system, the IEEE 118-Bus, and on the Algerian Network 114-Bus under normal conditions and at critical loading margin stability. The obtained results compared to several recent techniques clearly confirm the high performance aspect of the proposed method in terms of solution accuracy and convergence behaviors.