Trade-offs between aggregated and turbine-level representations of hydropower in optimization models

Artikel i vetenskaplig tidskrift, 2023

To model a future power system with high shares of variable renewables, it is essential to capture the flexibility of dispatchable technologies such as hydropower. However, the representation of hydropower is often oversimplified in energy system investment models, such that the flexibility of hydropower is significantly exaggerated. This suggests the need for improved representations of hydropower that capture physical river dynamics but are computationally efficient to maintain the tractability of large models. Here, we develop a series of hydropower optimization models for a single river with various levels of techno-physical detail to evaluate options for hydropower representations in energy system investment models. All models operate hourly over a full year with perfect foresight. We explore trade-offs between accuracy and computational time involved in including features such as the river network, head-dependent power production, and discharge-dependent turbine efficiencies. We find that the level of detail significantly affects the optimal production and confirm that a simplistic hydropower representation similar to those often used in investment models significantly overestimates the flexibility of hydropower. The most detailed nonconvex model includes a full river network, head-dependency, and turbine efficiencies and is solved in just one hour on a modern desktop computer. Furthermore, we linearize this detailed model, thereby reducing computation time to one minute while featuring production dynamics substantially more similar to the full nonconvex model than a naive linear network model. These contributions pave the way for improving hydropower representations in investment models to avoid overestimating the flexibility that hydropower may provide.