Optimal Design for Hybrid Renewable Energy System Using Particle Swarm Optimization

This paper proposes the Particle Swarm Optimization (PSO) to minimize the Hybrid Renewable Energy System (HRES) Cost of Energy (COE) on different sizing between wind turbines and solar PV modules subjecting to a particular investment constraint. The wind speed and solar irradiation data from a particular site are used to determine the annual energy production (AEP) following the HRES configuration. While the turbine power curve converts the wind speed range to wind power, solar power generation model transforms solar radiation incident on PV modules. The investment cost of the solar PV system is interpolated from a commercial supplier data. However, the wind turbine cost is derived from the turbine component cost model of the National Renewable Energy Laboratory (NREL) and discounted to the present value using the inflation rate. Once a wind turbine and PV modules are configured as HRES, the COE as the objective function would be determined. The minimum COE from this study at 0.1204 $/kWh is in the 95th percentile of the global renewable power generation cost database from IRENA ranging between 0.06 - 0.22 $/kWh for solar energy and 0.04 - 0.10 $/kWh for wind energy but higher than the average value because of the low potential on both solar radiation and wind speed data from the particular site. However, PSO as the preliminary design method could be determined the optimal HRES with minimized COE when compare with the conventional design.