Capturing Dynamic Hydropower Performance in Long-Term Energy Planning

Hydropower is a reliable renewable energy source that plays a central role in the water–energy nexus and in integrating emerging energy loads and generation technologies. Hydropower plants (HPs) are designed to operate efficiently within defined ranges of reservoir releases and water levels. Deviations from these conditions, driven by changes in water availability, regulatory constraints, and evolving energy grid demands, reduce operational efficiency. As a result, less energy may be produced per unit of water in a future where hydrology and water operations are meaningfully different from what the HPs were designed for.

However, current state of the art large-scale, long-term energy planning models assume constant, near-optimal turbine efficiency or even constant hydraulic head, ignoring variability in HP efficiency and losses. These simplifications lead to systematic overestimation in our future projections of hydropower generation and capacity. They can also lead to underestimation of future resource adequacy needs, and consequent underinvestment in complementary energy infrastructure, increasing risks to future grid reliability. Models and approaches that provide more accurate, temporally and regionally resolved assessments of hydropower potential are therefore needed to support informed planning decisions.

Here we introduce HEADFIT (Hydraulic-Energy Analysis and Dynamic Fitting), a physics-informed framework for analyzing and calibrating hydropower system performance in long-term water–energy planning models. HEADFIT integrates plant hydraulics, including frictional and minor head losses, tailwater dynamics, and operational limits, with turbine efficiency curves for a high-fidelity estimation of how hydropower generation is expected to change in a changing climate. These relationships are approximated at the plant level using physics-informed relationships calibrated with observed hydrological and operational data. The calibrated plant-level models are then used to project hydropower generation under future hydrological scenarios. Lastly, we employ a Western United States power system model to propagate refined hydropower projections into more accurate grid performance assessments across time scales.

Preliminary analysis for 15 major hydropower plants across the Colorado and Columbia River basins shows that constant-efficiency assumptions overestimate annual hydropower production by an average of five percent, with larger biases during periods of high releases combined with low reservoir levels. These discrepancies reduce the accuracy of capacity and flexibility estimates that support essential grid services. They can also misguide investment and design decisions, increasing risks to grid reliability as climate and demand variability intensify.