Abstract
Rivers in tropical climates are characterized by highly variable flows which are becoming more variable due to climate change. In tropical conditions, most hydropower plants are designed as run-of-river plants with limited water storage. The aim of this study is the selection and sizing of a hydropower plant for highly variable flows, using the Sebeya River as a case study. As is often the case, flow data was incomplete, and the study also demonstrated the use of machine learning to predict the Sebeya flow rate for 2019. Stochastic modeling was used to estimate the energy generation for multiple turbine types and the levelized cost of energy for all configurations, capturing the uncertainty in many of the input parameters. River flow varies between 1.3 m3/s and 5.5 m3/s in a year; the minimum LCOE occurs at the knee in the flow exceedance curve of river flow rate, near 1.8 m3/s. The optimal LCOE for the Sebeya river is around 0.08 $/kwh with an uncertainty of −0.011/+0.009 $/kWh. Additionally, certain turbine types—notably propeller turbines—perform poorly in this type of highly variable flow. The method and findings can be used to guide future investments in small- to mid-sized hydropower plants in similar climatic conditions.