Enhanced skill optimization algorithm: Solution to the stochastic reactive power dispatch framework with optimal inclusion of renewable resources using large-scale network

Abstract

Optimal reactive power dispatch (ORPD) is taken as a vital problem related to electric power networks for economic and control operations. Nowadays, thermal generators are no longer utilized and renewable resources (RERs) have been integrated owing to their marvellous benefits. The integration of RERs into power networks is considered as a strenuous imposition due to their uncertainties. The objective is to determine the placement of four wind and four PV units into large-scale 118-bus network to reduce expected power losses. The normal, lognormal, and Weibull distributions are utilized to model system uncertainties, while Monte-Carlo simulation and reduction-based approaches are utilized to generate the novel set of optimal scenarios. To avoid stagnation problems in skilled optimization algorithm (SOA), three strategies such as fitness-distance balance selection, mutation, and gorilla troops-based approaches are utilized to improve overall strength of SOA. Effectiveness of ESOA is proved via statistical and non-parametric analysis using benchmark functions, the results are further compared with other optimization techniques. The proposed ESOA is also used to resolve the deterministic and stochastic ORPD frameworks to reduce power losses and expected power losses. By incorporation of RERs into the stochastic ORPD framework can saved the expected power losses around 24.01%.