Northern Goshawk Optimization Algorithm for Optimal Power Flow with FACTS Devices in Wind Power Integrated Electrical Networks

Abstract

Power-operational problems must be resolved with flexible AC transmission systems (FACTS) and optimum power flow (OPF). The complexity of power flow analysis in modern power network models is increased by the unpredictability of renewable energy generating sources in addition to the challenge of determining the appropriate locations and ratings for FACTS devices. FACTS devices can increase the thermal capacity of power transmission lines, enabling the network to accommodate more electricity from renewable sources. The major goals of this study are to lower production costs, decrease power losses, enhance the voltage profile of the system, and enhance load capability. To solve a variety of optimization issues optimally, optimization meta-heuristics approaches have been created in response to the increased emphasis on renewable energy sources, such as wind power. Wind energy is an important component of modern power systems due to its affordability and clean nature. This article presents an OPF method for efficiently scheduling energy from wind farms using the Northern Goshawk Optimization (NGO) meta-heuristic technique. The Weibull probability distribution function is used to represent wind speed and estimate the cost of electricity produced by wind turbines based on the planned power. To validate the NGO approach and its results compared with those of other meta-heuristic optimization techniques, such as Chef-Based Optimization Algorithm, Dingo Optimization Algorithm, and Chameleon Swarm Algorithm, the IEEE system 30-bus is used to solve the OPF problem.