Algorithmic Assessment of Water Flow Potential for Sustainable Run-of-River Hydropower Plant Development

Abstract

Small-scale run-of-river hydropower plants typically exhibit a low Plant Capacity Factor (PCF) of only 14%, primarily due to variations in actual water flow rates. Meanwhile, the Ministry of Energy's renewable energy development plan emphasizes increasing electricity generation from small hydropower plants in areas with potential water resources nationwide. Therefore, developing techniques to assess hydropower potential and determine optimal installed capacity is critical. This study presents an algorithm designed to use actual flow rate data and probability distribution functions to enhance computational accuracy and address missing data issues. Additionally, the algorithm incorporates turbine operation constraints, such as the minimum flow rate required for operation, to improve reliability. The results demonstrate that the proposed algorithm effectively assesses hydropower potential in terms of both optimal installed capacity and annual electricity generation. Moreover, the study highlights the relationship between water flow rates and the continuous operation of hydropower turbines, supporting year-round efficiency. The algorithm is adaptable for real-world application in run-of-river hydropower plants, enhancing water resource utilization and aligning with national renewable energy development goals.