Historical reconstruction dataset of hourly expected wind generation based on dynamically downscaled atmospheric reanalysis for assessing spatio-temporal impact of on-shore wind in Japan
Wind power is crucial for achieving carbon neutrality, but its output can vary due to local wind conditions. The spatio-temporal behavior of wind power generation connected to the power grid can have a significant impact on system operations. To assess this impact, the use of long-term reanalysis re...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-10-01
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| Series: | Big Earth Data |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/20964471.2024.2374044 |
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| Summary: | Wind power is crucial for achieving carbon neutrality, but its output can vary due to local wind conditions. The spatio-temporal behavior of wind power generation connected to the power grid can have a significant impact on system operations. To assess this impact, the use of long-term reanalysis results of wind data based on a numerical weather prediction (NWP) model is considered valid. However, in Japan, the behavior of on-shore wind power generation is influenced by diverse topographical and meteorological features (TMFs) of the installation site, making it challenging to assess possible operational impacts based solely on power curve-based estimates using a popular conversion equation. In this study, a nonparametric machine learning-based post-processing model that learns the statistical relationship between the TMFs at the target location and the actual wind farm (WF) output was developed to represent the expected per-unit output at each location. Focusing on historical reconstruction results and using this post-processing model to reproduce the real-world WF output behavior created a set of expected wind power generation profiles. The dataset includes hourly long term (1958–2012) wind power generation profiles expected under the WF installation assumptions at various on-shore locations in Japan with a 5 km spatial resolution and is expected to contribute to an accurate understanding of the impact of spatio-temporal wind power behavior. The dataset is publicly accessible at https://doi.org/10.5281/zenodo.11496867 (Fujimoto et al., 2024). |
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| ISSN: | 2096-4471 2574-5417 |