Hydrology + Power Generation + Irradiance - Kabalega/Buseruka

This paper presents a stochastic-based approach for the capacity planning of Variable Renewable Energy (VRE) Systems.  The optimization problem is construed as a multi-objective and single objective problem. The Non-dominated Genetic Sorting Algorithm II (NSGA-II) was used to solve the multi-objective problem, considering the two objectives of maximizing the energy production and minimizing the energy production cost over the plant’s design life. The single-objective function was solved using Genetic Algorithm (GA) with the goal of minimizing the Levelized Cost of Electricity (LCOE). The Hydromax Buseruka 9 MW plant site, in Uganda was used as a case study. Our stochastic optimization approach utilized Monte Carlo simulations with 100 scenarios, each evaluated over a plant’s design life of 25 years. The optimal installed capacity of the hybridized power plant is 18.99 MW of which 8.99 MW is for the small hydropower plant and 10MWp for the solar photovoltaic plant. The capacity factor of the hybridized plant is approximately 0.529 and the LCOE is 7.58 US cents per kWh based on NSGA-II. The capacity factor and LCOE were 0.506 and 0.861 US cents per kWh, respectively, when GA was used for the optimization process. It is imperative to note that the resultant LCOE is heavily affected by the economic opportunity cost of capital which for Uganda, it is quite high at 11%. Considering the need to ensure that the capacity of VRE based power plants is sized appropriately to maximize the plant factor while minimizing the LCOE, this capacity planning methodology is a valuable tool for project developers, policymakers and other stakeholders in the sustainable deployment and grid-integration of Variable Renewable Energy Systems