Decentralized forecasting of wind energy generation with an adaptive machine learning approach to support ancillary grid services
<p>We report on an approach to distributed wind power forecasting, which supports wind energy integration in power grid operation during exceptional and critical situations. Forecasts are generated on-site the wind power plant (WPP) in order to provide blackout-robust data transmission directl...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-07-01
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| Series: | Advances in Science and Research |
| Online Access: | https://asr.copernicus.org/articles/20/81/2023/asr-20-81-2023.pdf |
| _version_ | 1797778315388387328 |
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| author | L. Holicki M. Dröse G. Schürmann M. Letzel |
| author_facet | L. Holicki M. Dröse G. Schürmann M. Letzel |
| author_sort | L. Holicki |
| collection | DOAJ |
| description | <p>We report on an approach to distributed wind power forecasting,
which supports wind energy integration in power grid operation during
exceptional and critical situations. Forecasts are generated on-site the
wind power plant (WPP) in order to provide blackout-robust data transmission
directly from the WPP to the grid operator. An adaptively trained
forecasting model uses locally available sensor data to predict the
available active power (AAP) signal in a probabilistic fashion. A forecast
generated off-site based on numerical weather prediction (NWP) is deposited
and combined on-site the WPP with the locally generated forecast. We
evaluate the performance of the method in a case study and find that the
locally generated forecast significantly improves forecast reliability for a
short-term horizon, which is highly relevant for enabling power reserve
provision from WPPs.</p> |
| first_indexed | 2024-03-12T23:15:27Z |
| format | Article |
| id | doaj.art-76737d5b2dd94264940d326ef7f6a961 |
| institution | Directory Open Access Journal |
| issn | 1992-0628 1992-0636 |
| language | English |
| last_indexed | 2024-03-12T23:15:27Z |
| publishDate | 2023-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Advances in Science and Research |
| spelling | doaj.art-76737d5b2dd94264940d326ef7f6a9612023-07-17T08:43:05ZengCopernicus PublicationsAdvances in Science and Research1992-06281992-06362023-07-0120818410.5194/asr-20-81-2023Decentralized forecasting of wind energy generation with an adaptive machine learning approach to support ancillary grid servicesL. HolickiM. DröseG. SchürmannM. Letzel<p>We report on an approach to distributed wind power forecasting, which supports wind energy integration in power grid operation during exceptional and critical situations. Forecasts are generated on-site the wind power plant (WPP) in order to provide blackout-robust data transmission directly from the WPP to the grid operator. An adaptively trained forecasting model uses locally available sensor data to predict the available active power (AAP) signal in a probabilistic fashion. A forecast generated off-site based on numerical weather prediction (NWP) is deposited and combined on-site the WPP with the locally generated forecast. We evaluate the performance of the method in a case study and find that the locally generated forecast significantly improves forecast reliability for a short-term horizon, which is highly relevant for enabling power reserve provision from WPPs.</p>https://asr.copernicus.org/articles/20/81/2023/asr-20-81-2023.pdf |
| spellingShingle | L. Holicki M. Dröse G. Schürmann M. Letzel Decentralized forecasting of wind energy generation with an adaptive machine learning approach to support ancillary grid services Advances in Science and Research |
| title | Decentralized forecasting of wind energy generation with an adaptive machine learning approach to support ancillary grid services |
| title_full | Decentralized forecasting of wind energy generation with an adaptive machine learning approach to support ancillary grid services |
| title_fullStr | Decentralized forecasting of wind energy generation with an adaptive machine learning approach to support ancillary grid services |
| title_full_unstemmed | Decentralized forecasting of wind energy generation with an adaptive machine learning approach to support ancillary grid services |
| title_short | Decentralized forecasting of wind energy generation with an adaptive machine learning approach to support ancillary grid services |
| title_sort | decentralized forecasting of wind energy generation with an adaptive machine learning approach to support ancillary grid services |
| url | https://asr.copernicus.org/articles/20/81/2023/asr-20-81-2023.pdf |
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