Evolutionary Oppositional Moth Flame Optimization for Renewable and Sustainable Wind Energy Based Economic Dispatch

Abstract

Fossil fuel power has limited its penetration into the power system network for the intermittency and unpredictability coordination. That's why, renewable wind energy incorporating load dispatch becomes a promising system. In this regard, this article proposes an economic load dispatch (ELD) in the existence of renewable wind technology for consuming less fossil fuel energy. For the stochastic scenery of wind speed, the Weibull probability density function (PDF) is used. To boost up the convergence swiftness and advance the simulation results, opposition-based learning (OBL) is integrated with the basic moth flame optimization (MFO) technique, which depends on the social dealings of the moth in nature. The performance of OMFO is evaluated through four cases and each case consists of three different load demands. The simulation results by these methods along with various other existing algorithms in the literature are presented to demonstrate the validity and usefulness of the proposed OMFO.