Abstract
Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured(R2.8) in
detailby the open models developed in the power and energy system communities, where such open models exist. To enable modeling such
a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission
network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic
characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years.(R2.9)
These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system.(R2.10) The spatial
coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecastingof renewable power generation.
detailby the open models developed in the power and energy system communities, where such open models exist. To enable modeling such
a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission
network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic
characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years.(R2.9)
These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system.(R2.10) The spatial
coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecastingof renewable power generation.
Original language | English |
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Article number | 170175 |
Journal | Scientific Data |
Volume | 4 |
Number of pages | 31 |
ISSN | 2052-4463 |
DOIs | |
Publication status | Published - 2017 |