PLANtoACT Task 2.1: Hourly profiles - Hydro power

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:水力発電の地域別プロファイルをモデリングに活用できる。

🏢実務担当者:地域の再生可能エネルギー計画や系統運用に利用可能なデータ。

🏛政策担当者:地域のエネルギー計画策定に役立つデータ基盤。

Regional Hydropower Profiles for the PLANtoACT Project This dataset contains normalized hourly hydropower generation profiles developed for the PLANtoACT project (Task 2.2), for two generation types: Run-of-River and Hydro Reservoir . The profiles represent the long-term hydropower generation behaviour of five European pilot regions and are intended for use in energy system modelling, renewable energy assessment, and regional energy planning. The dataset was generated by combining regional administrative boundaries, national hourly generation data from the ENTSOE Transparency Platform, and installed-capacity data from the JRC Hydropower Database. National hourly generation was downscaled to each region in proportion to its share of national installed capacity, aggregated into representative regional profiles per generation type, and normalized while preserving the long-term equivalent full-load hours (Heq). The dataset accompanies the scripts available in the corresponding GitLab repository. Study Regions The dataset contains hydropower generation profiles for the following regions: Country Region Italy Lombardia Romania Alba Germany Oberland France Auvergne-Rhône-Alpes Portugal Porto Metropolitan Area Not every region has capacity for both generation types: the Porto Metropolitan Area, for instance, has no significant hydro reservoir capacity, so only its Run-of-River profile is included. Dataset Structure Each regional folder contains: File Description <Region>_run_of_river_8760h.csv / .txt Final normalized hourly Run-of-River profile for a standard (8760-hour) year <Region>_hydro_reservoir_8760h.csv / .txt Final normalized hourly Hydro Reservoir profile for a standard (8760-hour) year <Region>_run_of_river.csv / .txt Aggregated multi-year normalized Run-of-River profile before correction <Region>_hydro_reservoir.csv / .txt Aggregated multi-year normalized Hydro Reservoir profile before correction heq_comparison_run_of_river.png Comparison of annual equivalent full-load hours, Run-of-River heq_comparison_hydro_reservoir.png Comparison of annual equivalent full-load hours, Hydro Reservoir profile_final_run_of_river_plot.png Visualization of the final normalized Run-of-River profile profile_final_hydro_reservoir_plot.png Visualization of the final normalized Hydro Reservoir profile plant_map.png Map of JRC hydropower plants used for the regional capacity scaling <Region>_jrc_summary.xlsx Plant-level and aggregated JRC capacity/characteristics summary (Run-of-River, Hydro Reservoir, Pumped Storage) raw/ Raw ENTSOE national hourly generation downloads and cached country-boundary data used for country detection The dataset also includes: Shapefiles/ , the regional administrative boundary polygons used to define each study region and to compute regional installed capacity; Normalized_Profiles_run_of_river_2023.png and Normalized_Profiles_hydro_reservoir_2023.png , which compare the normalized hydropower generation profiles across all study regions, for each generation type. Data Generation Methodology The regional hydropower profiles were generated according to the following workflow: Regional administrative boundaries were provided as GIS shapefiles. The country containing each regional polygon was identified against Natural Earth country boundaries. Installed capacity (MW) was extracted from the JRC Hydropower Database for Run-of-River and Hydro Reservoir plants, both for the whole country and for the plants located within the regional polygon. Hourly national generation was downloaded from the ENTSOE Transparency Platform for Run-of-River (PSR type B11) and Hydro Reservoir (PSR type B12), over a five-year period (2020–2024). National hourly time series were downscaled to each region in proportion to its share of national installed capacity, separately for Run-of-River and Hydro Reservoir. Annual equivalent full-load hours (Heq) were calculated for each generation type and each simulated year. The five-year regional profile was normalized using its own observed maximum generation. A non-linear correction factor was applied to the most recent year to preserve the five-year average equivalent full-load hours while maintaining the hourly variability of that year. Final normalized hourly profiles were exported for a standard (8760-hour) calendar year. Data Format The profile files contain a single column: Column Description normalised Hourly normalized hydropower generation (dimensionless, ranging from 0 to 1) Each row represents one hour of the year. The annual energy production can be reconstructed by multiplying the normalized profile by the corresponding regional maximum generation, or by the equivalent full-load hours (Heq) reported for each region and generation type. Intended Applications The dataset is intended for: Energy system modelling Renewable energy scenario analysis Regional energy planning Capacity expansion modelling Long-term electricity system simulations Sector coupling studies Academic research Software The dataset was generated using Python together with the following libraries: pandas NumPy GeoPandas Shapely SciPy Matplotlib Requests entsoe-py Hourly national hydropower generation data were obtained using the ENTSOE Transparency Platform API. Installed capacity data were obtained from the JRC Hydropower Database. Related Software The scripts used to generate this dataset are available from the associated GitLab repository: PLANtoACT / task_2_1 / Hydro Power Hourly Profiles · GitLab Funding This work was developed within the PLANtoACT project. The PLANtoACT project has received funding from the European Union's LIFE Programme under Grant Agreement No. 101214506 (LIFE-2024-CET), managed by the European Climate, Infrastructure and Environment Executive Agency (CINEA). Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or CINEA. Neither the European Union nor CINEA can be held responsible for them. Citation If you use this dataset in your work, please cite both the Zenodo record and the associated software repository: PLANtoACT / task_2_1 / Hydro Power Hourly Profiles · GitLab, https://gitlab.inf.unibz.it/plantoact/task_2_1/hydro-power-hourly-profiles.

• Zenodo https://zenodo.org/records/21834204first seen 2026-08-08 04:14:41