An Optimal Sizing of Small Hydro/PV/Diesel Generator Hybrid System for Sustainable Power Generation.

Concerns surrounding the environment along with inadequate energy supply and high cost of same are responsible for pollution, high energy demand, unpredictable and uneconomical power generation. These have contributed to the widespread agreement that sustainable renewable energy sources (RES) must be developed, particularly in isolated villages where expanding the grid may be challenging and financially unviable for power corporations. As a result, in order to effectively and cheaply utilize the plentiful renewable energy resources, an optimal sizing approach is required. This study is aimed at investigating the economic performance of the hybrid system of a stand-alone Small Hydropower/PV/Diesel generator with battery electricity production. The cost function was minimized using Dragonfly Algorithm (DA) in order to minimize the Cost of Energy (COE) generation. The decision variables are the number of small hydro turbines (NSHP), number of solar panels (NPV), number of batteries (NBATT) and the capacity of Diesel generator (PDG). The developed method is applied to a typical Kiri village in Shelleng Local Government area of Adamawa State. For uniformity, the hourly solar irradiance data were created by converting the monthly average solar irradiance data. A dragonfly optimization technique was utilized to reach an optimum solution for the hybrid system. The result obtained showed that the system components: small Hydropower, solar PV and Diesel generator were able to generate electrical power of 5,783,600 W, 56,259 W and 5.2941e-05 W respectively to meet the energy demand. Results obtained from the developed scheme were compared with those obtained when TORSCHE algorithm was used in optimizing the hybrid system. It was observed that a total energy cost of $5,224,500 was obtained for the developed technique while $5,839,600 was obtained as the total cost for the TORSCHE model. This showed that the developed scheme outperformed the system output from the TORSCHE algorithm in terms of cost of energy by 89.46%. [ABSTRACT FROM AUTHOR]