A novel fuzzy hybrid red fox chimp for optimal power flow in FACTS devices

Due to the large-scale, multimodal, non-convex, and nonlinear characteristics of OPF, mitigation of OPF problems in power transmission systems becomes a more complicated task. Though there already exist numerous optimization approaches to manage OPF issues, they remained unconstrained (i.e., fall into local optima issues). Therefore, in this paper, we proposed a novel fuzzy hybrid red fox chimp (FHRC) algorithm for handling a high level of uncertainties that occurs in power systems on equipping FACTS devices and renewable energy sources. Achieving optimal system variables using the proposed FHRC algorithm optimizes the chosen objective function rather than fulfilling operational inequality constraints and power flow equations. The integrated functioning of the red fox algorithm and chimp optimization algorithm with the fuzzy logic system efficiently optimizes the OPF problems. The efficiency of the proposed FHRC algorithm is evaluated using the IEEE 30 bus system in terms of measures, namely power generation cost, power loss, cost function value, and voltage deviations, under the application of diverse load conditions. The experimentation results reveal that the FHRC algorithm optimizes the objective function of OPF problems more than other compared methods. In future, our proposed approach will be implemented in various other energy storage systems and real power network.