Optimal Sizing in Hybrid Renewable Energy System with the Aid of Opposition Based Social Spider Optimization

Abstract

Rely on fossil fuels for several decades deplete the source and pollute environment drastically, these significant impact contemplates and urge seeking alternate energy. Renewable energy plays a vital role in compensating power demands and ecofriendly source of alternate energies. Despite, individual standalone source of renewable energy is inadequate to fulfill power demand and this urge hybrid renewable energy power generation. This research work incorporate solar photovoltaic and wind energy power generations as hybrid form of renewable energy to compensate power demands. This objective pursue with optimal sizing of enroll equipment responsible to compensate power demand. To pursue the research objective through manual take a long time to compute which pave the path of incorporating optimization techniques in the context of configuring appropriate generating and backup units to satisfy power demand in economic cost. The techniques intend to employ in this research are particle swarm optimization, artificial fish swarm optimization, adaptive genetic algorithm with Cauchy mutation (AGA-Cauchy), social spider optimization and opposition based social spider optimization (OSSO). It is apparent in the following results that OSSO a swarm intelligence techniques unveil proficient performance over contest techniques.