Developing Optimal Reservoir Rule Curve for Hydropower Reservoir with an add-on Water Supply Function Using Improved Grey Wolf Optimizer

• Published in: Water resources management 2023-03, Vol.37 (5), p.2063-2082
• Main Authors: Choi, Youngje, Ji, Jungwon, Lee, Eunkyung, Lee, Sunmi, Yi, Sooyeon, Yi, Jaeeung
• Format: Article
• Language: English
• Subjects: Algorithms; Atmospheric Sciences; Civil Engineering; Climate change; Drought; Earth and Environmental Science; Earth Sciences; Environment; Genetic algorithms; Geotechnical Engineering & Applied Earth Sciences; Hydroelectric power; Hydrogeology; Hydrology/Water Resources; Optimization; Optimization techniques; Performance indices; Reliability; Reservoir operation; Reservoir storage; Supply & demand; Water demand; Water supply
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-023-03478-0

Climate change affects water demand and supply and causes more severe droughts and floods. To meet the increasing water demand and to prepare for the exacerbating climate change-fueled droughts, the South Korean government added a water supply function to the Hwacheon reservoir, built initially as a hydropower reservoir. However, it is missing the reservoir rule curve for water supply. The main objective is to develop a rule curve that maximizes water supply reliability and the operating objective of the Hwacheon reservoir. We develop the rule curve with a well-known optimization technique (Genetic Algorithms (GA)) and new optimization techniques (Grey Wolf Optimizer (GWO) and Improved Grey Wolf Optimizer (IGWO)). The novelty of this study is developing the most appropriate rule curve for hydropower reservoir with add-on water supply function. We evaluate and compare the performance of the developed rule curve to the firm supply method (FSM). We use the discrete hedging rule to build rule curve that provides a scheduled and rationing supply. The performance indices are time-based reliability, volumetric reliability, and the number of months when the reservoir storage is in each storage stage. Results showed that obtained rule curve with GA, GWO, and IGWO algorithms performed better than FSM. IGWO algorithm outperformed GA and GWO algorithms. We concluded that IGWO algorithm was an effective and powerful tool for developing reservoir rule curve. This research is a fundamental study demonstrating the effectiveness of IGWO algorithm as a promising alternative optimization algorithm for complex reservoir operation problems.