Summary:
The continuous rise of renewable energy in the global energy mix highlights the need to analyze and enhance traditional energy plants’ flexibility to support integration. Hydropower, with its rapid response capabilities and significant energy storage, plays a vital role in this context. However, simplifications are required due to the complex interconnections among cascaded hydropower plants and the inherent uncertainty of water inflows. This study presents a data-driven methodology for representing hydropower plants physically and through equivalent energy models, accounting for inflow uncertainties implicitly. Using historical data, we apply analytical techniques – including auxiliary linear models, load-duration curves, and filtering methods in linear regressions – to configure key hydropower parameters such as water inflows, reservoir boundaries, and hydropower plant production limits. These methods can be applied across hydro systems of different scales. We have validated our approach for the Spanish system for 2019 and 2025, demonstrating its efficacy.
Spanish layman's summary:
Este estudio desarrolla un enfoque basado en datos para modelar centrales hidroeléctricas en modelos de mediano plazo. Utilizando datos históricos, aplicamos técnicas analíticas para definir parámetros clave. Validado en el sistema español para 2019 y 2025, nuestro método mejora la modelización hidroeléctrica para la integración de energías renovables.
English layman's summary:
This study develops a data-driven approach to model hydropower plants for medium-term models. Using historical data, we apply analytical techniques to define key parameters. Validated on the Spanish system for 2019 and 2025, our method enhances hydropower modeling for renewable integration.
Keywords: Equivalent hydropower plants; Mid-term planning models; K-means; Linear regression models; Fourier series filtering; Ridge regularization; Linear optimization models
JCR-JIF Impact Factor and WoS quartile: 9,100 - Q1 (2025)
DOI reference:
https://doi.org/10.1016/j.renene.2025.122730
Published on paper: June 2025.
Published on-line: March 2025.
Citation:
J.D. Gómez, F. Labora Gómez, J.M. Latorre, A. Ramos, "Efficient hydropower modeling for medium-term hydrothermal planning using data-driven approaches", Renewable Energy, Vol. 245, pp. 122730, June 2025. [Online: March 2025] doi: 10.1016/j.renene.2025.122730
