The escalating human energy demand, particularly in the realm of electricity consumption, imposes a substantial burden on power system planning, resulting in the emergence of the optimal power flow (OPF) problem. To address this challenge, researchers have implemented a Flexible AC Transmission System (FACTS) in modern power systems to optimize network congestion. This paper presents a new application of the Colony Predation Algorithm (CPA) in the domain of Optimal Power Flow (OPF) through the introduction of an enhanced Colony Predation Algorithm (PNMCPA). PNMCPA incorporates the gradient pyramid mechanism and the Nelder-Mead simplex (NMs), marking the first utilization of CPA in the OPF field. The proposed approach presents a novel hierarchical gradient pyramid mechanism that combines the approximate gradient descent method with the construction of a pyramid fall rate, thereby optimizing the population as a whole within a framework that enhances population diversity. Additionally, the NMs algorithm is employed to optimize the optimal set of solutions for the population, thereby accelerating the convergence efficiency of the algorithm. The primary objective of this study is to optimize multiple factors, including the generation cost and transmission loss of the power system when multiple FACT components are present. Utilizing the Weibull probability density function, the model captures the uncertain and unstable nature of wind energy resources, encompassing direct, penalty, and standby costs. The proposed algorithm is applied to the IEEE30 bus test system, and the experimental results demonstrate that PNMCPA achieves a final optimization cost of 806.0132 $/h, surpassing other optimization algorithms. Concerning the power loss matter within the bus system, the PNMCPA algorithm significantly effectively mitigates total power loss, reducing from 1.44 MW to 1.41 MW instead of the initial CPA and substantially enhancing convergence speed. Empirical simulation outcomes clearly endorse PNMCPA as a highly efficient approach to tackling optimal power flow problems in the presence of multiple FACTS devices. The significant advancements presented in this paper mark a significant contribution to the field, offering a novel and superior solution to optimize power systems in the face of escalating energy demands.
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