4th International Workshop on Open Source Modelling and Simulation of Energy Systems - OSMSES 2026, Karlsruhe (Germany). 23-25 March 2026
Summary:
Hydropower plays a critical role in balancing highly renewable power systems, yet its representation in energy system optimization models is often constrained by limited availability of high-resolution inflow data. In practice, aggregated inflow time series are frequently used to reduce data requirements, potentially introducing hidden modeling biases. This paper analyzes the impact of inflow aggregation on generation expansion and operation decisions in energy system optimization models. Using an adapted NREL-118 bus test system and the open-source Low-carbon Expansion Generation Optimization (LEGO) model, we compare planning outcomes based on hourly inflow data against daily, weekly, monthly, and yearly aggregations. By computing expost regret with respect to hourly inflow operation, we quantify the cost and investment distortions caused by aggregation.Our results show that inflow aggregation leads to substantial cost increases and misallocation of generation capacity investments once hydropower becomes a significant share of the energy mix, as coarse aggregations systematically underestimate inflow variability. These findings demonstrate that high-resolution hydropower inflow data is essential for robust energy system planning and highlight the need for openly available, standardized inflow time series to support planning of future energy systems.
Spanish layman's summary:
El artículo muestra que agregar los datos de afluencia hidroeléctrica oculta variabilidad, distorsiona decisiones de inversión y operación, y aumenta los costes del sistema, sobre todo en sistemas con mucha hidráulica. Pide series horarias abiertas y estandarizadas.
English layman's summary:
The paper shows that aggregated hydropower inflow data hides variability, distorts investment and dispatch decisions, and increases system costs, especially in hydro-heavy systems. It calls for open, standardized hourly inflow data for robust planning.
Keywords: energy system optimization, hydropower modeling, renewable energy integration, time series, hydropower inflow
DOI: 
https://doi.org/10.1109/OSMSES69376.2026.11457210
Published in: OSMSES 2026: Conference proceedings, pp: 1-6, ISBN: 979-8-3315-4501-7
Publication date: 31-Mar-2026.
Citation:
F.C.A. Auer, R. Gaugl, T. Klatzer, D.A. Tejada, S. Wogrin, "Uncovering Hidden Biases in Hydropower: Why Detailed Inflow Data is Crucial for Energy System Optimization Models", presented at 4th International Workshop on Open Source Modelling and Simulation of Energy Systems - OSMSES 2026, Karlsruhe, Germany, 23-25 March 2026. In: OSMSES 2026: Conference proceedings, pp. 1-6, doi: 10.1109/OSMSES69376.2026.11457210
IIT-26-080C
