Optimal sizing and design of renewable power plants in rural microgrids using multi-objective particle swarm optimization and branch and bound methods

Abstract

As energy prices rise, optimizing renewable power plant sizing is vital, especially in areas with unreliable electricity supply due to distant transmission lines. This study addresses this issue by optimizing a renewable power plant portfolio for a Spanish municipality facing such challenges. The presented approach involves a systematic method. Firstly, energy demand is thoroughly analyzed. Next, available renewable resources are explored and optimal plant placements are determined. A multi-objective particle swarm optimization algorithm is then used to size each plant, minimizing annualized costs and grid energy imports. The most suitable feasible optimum is selected from theoretical configurations using branch and bound techniques, prioritizing practicality. In the specific case analyzed, the results show a 20-year Internal Rate of Return of 8.33 %. This is achieved with the following capacities for each plant: 750 kW of photovoltaic solar energy, 160 kW of turbine-based generation, 180 kW of hydroelectric pumping, 160 kW for the biomass plant, and 200 kW from the wind turbine. This study offers an innovative solution to energy challenges, providing practical insights for cost-efficient, sustainable projects.