Optimal sizing of Hybrid energy Sources by Using Genetic Algorithm and Particle Swarm Optimization algorithms considering Life Cycle Cost

One of the critical challenges for power energy engineers and decision-makers is to select an optimum size for a renewable energy-based electrification system as a standalone microgrid with as minimum investment as possible. Several researchers have used genetic algorithm (GA) and Particle swarm optimization (PSO) tools for solving optimization problems in engineering. We used both algorithms here to select the optimum size of renewable energy (RE) generation units. In our system design, photovoltaic (PV) modules, wind turbines, and battery-banks are used as the primary power units whereas diesel generator serves as a backup. We have selected a village with 260 households in Ethiopia's eastern part, the Somali region, Darahtoleh village (7.31567N,45.52884E). Metrological data from Ethiopian Metrological Agency are used as a primary source to analyze the potential of solar and wind energy resources. We have run GA and PSO algorithms for twenty different numbers of runs (20, 30,40,…, 200) with 100 iterations for each considering life cycle cost (LCC) as an objective function and fulfilling the constraint function of peak energy demands. The results from both algorithms based on minimum LCC value are further analyzed. Results suggest an optimal combination of generation units with a minimum levelized cost of energy (LCOE) of 0.179$/kWhr and 65% of renewable energy (RE) penetration.