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 impac...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(Fujimotoet al.,2024).展开更多
文摘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(Fujimotoet al.,2024).
