Optimization of Renewable Energy Using Hybrid Multi-Objective Slime Mould and Simulated Annealing Algorithms

During the past few years, increase in population resulted in increase in energy demand all over the world. The increased energy demand resulting depletion of fossil fuels which are now being replaced with Renewable Energy (RE) systems. However, operating on different energy sources for cost management and energy demand are difficult to handle due to its uncertainties like varying weather, load demand. In order to address these issues, a Hybrid RE System (HRES) is proposed in this study with a Hybrid Multi-Objective Slime Mould Algorithm and Simulated Annealing (HMOSMASA) method. The Simulated Annealing (SA) with advanced local search ability improves the effectiveness of Multi-Objective Slime Mould (MOSMA) exhibiting decentralized behavior and finds optimal solutions efficiently. Primarily, the search space is explored by MOSMA then solutions are exploited for local search by SA to obtain best optimal solution. The HMOSMASA ensures optimal sizing of HRES, load demand effectively and which is trained on historically collected data. The HMOSMA method is evaluated by Annual System Cost, Net Present Cost, Levelized Cost of Energy that achieved optimized results of 6374${\$}$, 2109732${\$}$, 0.231${\$}$/kWh respectively. The HMOSMASA demonstrated best results compared to Particle Swarm Optimization (PSO) and Hybrid Grey Wolf Optimization (GWO).