Projektdetaljer
Beskrivelse
Abstract:
The large-scale offshore wind farms play a significant role to accomplish today’s renewable energy needs of Europe, incorporating hundreds of wind turbines built. The growing renewable energy demand and development of power electronics pose new challenges to network stability, reliability, and power quality of the existing power grid. In order to identify and address these problems, the demand for a detailed investigation of interconnected large-scale offshore wind farms is increasing. However, the detailed modeling of the wind farms, including all wind turbines and passive components, will result in complex system dynamics, which makes it difficult to analyze and address grid interconnection problems. Therefore, a holistic modeling and analysis method for dynamic model reduction of offshore wind power plants is necessary.
Funding: WinGrid, an EU-H2020 Grant funded by the Marie Skłodowska CurieActions-Innovative Training Networks (MSCA-ITN)
The large-scale offshore wind farms play a significant role to accomplish today’s renewable energy needs of Europe, incorporating hundreds of wind turbines built. The growing renewable energy demand and development of power electronics pose new challenges to network stability, reliability, and power quality of the existing power grid. In order to identify and address these problems, the demand for a detailed investigation of interconnected large-scale offshore wind farms is increasing. However, the detailed modeling of the wind farms, including all wind turbines and passive components, will result in complex system dynamics, which makes it difficult to analyze and address grid interconnection problems. Therefore, a holistic modeling and analysis method for dynamic model reduction of offshore wind power plants is necessary.
Funding: WinGrid, an EU-H2020 Grant funded by the Marie Skłodowska CurieActions-Innovative Training Networks (MSCA-ITN)
| Status | Igangværende |
|---|---|
| Effektiv start/slut dato | 01/06/2021 → 31/05/2024 |
Samarbejdspartnere
- DNV GL Group
- Kiel University
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