Optimal placement of <scp>FACTS</scp> devices and power?flow solutions for a power network system integrated with stochastic renewable energy resources using new metaheuristic optimization techniques

Flexible AC transmission systems (FACTS) and optimal power-flow (OPF) solutions play an important role in solving power operation problems. The volatile nature of the generation profiles from renewable energy sources, solar wind systems, determining locations sizes FACTS devices increase complexity OPF problems modern network models, such as loss, cost voltage deviation, a highly nonlinear-nonconvex optimization problem. Therefore, this article introduces employs four new independent, reliable efficient algorithms inspired by biological nature, namely: Slime Mould Algorithm (SMA), Artificial Ecosystem-based Optimization (AEO), Marine Predators (MPA) Jellyfish Search (JS), for both multi- single-OPF objective incorporating stochastic sources. proposed metaheuristic techniques are compared to common available alternatives literature, Particle Swarm (PSO), Moth Flame (MFO) Grey Wolf Optimizer (GWO), using IEEE 30-bus test system. To consider address challenges tested under different cases increasing load, with without FCTAS sources on network. result showed that MPA, SMA, JS AEO more effective solvers PSO, GWO MFO algorithms. For example, obtained 0.0844 p.u. case minimizing deviation 0.1155 which means algorithm improved term 27% PSO algorithm.