An intelligent ISWOA optimization approach for the best possible operation and control of hybrid power systems with facts devices and stochastic renewables

Renewable Energy Sources (RESs), such as solar and wind, pose major challenges for operation in conventional power systems because of their intermittency and uncertainty. Flexible AC Transmission System (FACTS) technologies like Thyristor-Controlled Series Capacitor (TCSC), Static Var Compensator (SVC), and Thyristor-Controlled Phase Shifter (TCPS) can be considered effective tools to improve reactive power compensation, voltage stability, and system flexibility in hybrid power systems. However, the traditional optimization techniques lack sufficient flexibility to deal with nonlinearity, multi-objective, and stochastic nature of modern power systems. This paper provides a solution in the form of an Improved Spider Wasp Optimization Algorithm (ISWOA) that exploits the exploring ability of Puma Optimization Algorithm and exploiting ability of Spider Wasp Optimizer. This ISWOA algorithm will be used to address the Optimal Power Flow (OPF) problem taking into account the uncertainties in RESs, reserve and penalty cost, limitations in operating range of generator, FACTS devices, and transformers taps. The performance of the proposed algorithm will be examined through MATLAB simulation on a modified IEEE 30-bus hybrid power system and the results will be compared to PSO, GEO, and BROA algorithms. The simulation results clearly indicate that ISWOA offers the lowest cost of generation and lower power losses while exhibiting better voltage stability under different loading and uncertainty conditions. The results prove that the ISWOA model presented above is indeed a powerful, efficient, and robust optimization algorithm for improving the operation of hybrid power systems.