Climate-Optimized Rule Curves for Cascade Reservoirs in Lao PDR: Enhancing Hydropower Generation Under Future Uncertainty

Reservoir operation under climate change poses significant challenges for hydropower-dependent countries, particularly in cascade reservoir systems. This study aims to derive optimal future operating rule curves for the Nam Khan 2 and Nam Khan 3 cascade reservoirs in Lao PDR to maximize hydropower generation under climate change. Genetic Algorithm (GA), Invasive Weed Optimization (IWO), and Harmony Search (HS) were integrated with a reservoir simulation model to optimize monthly upper and lower rule curves. Future reservoir inflows were generated using climate projections from the INM-CM5-0 climate model’s SSP245 scenario for 2025–2050. The aim was to maximize average annual electricity generation for the entire cascade system while ensuring practicable reservoir operation. The optimized rule curves obtained from all three algorithms exhibited similar seasonal patterns, reflecting regional hydrological characteristics. The proposed rule curves significantly improved hydropower performance compared to the existing operating policies. For Nam Khan 2, average annual electricity generation increased from 324.089 GWh under current operations to 788.246, 787.100, and 786.561 GWh using GA, IWO, and HS. Similarly, Nam Khan 3 achieved substantial improvements, with average annual generation increasing from 156.029 GWh to 270.049, 266.840, and 266.547 GWh. The optimized rule curves also contributed to better storage regulation and reduced variability in energy production. The findings demonstrate that integrating metaheuristic optimization techniques with reservoir simulation models provides an effective framework for adaptive hydropower-oriented reservoir operation under future climate uncertainty.