Research project WindStore
Optimised system integration of offshore wind energy through intelligent linkage of various forecast concepts and anticipatory management of distributed cascade storage
The massive deployment of offshore wind energy plays a central role in the design of an energy system based on renewable energies. However, large offshore wind farms occasionally generate excessive amounts of powers within a small geographical area that often transmission lines of the power system are congested like a bottleneck. Conclusively, large amounts of generated energy cannot be used. Furthermore, forecast errors often lead to large deviations in scheduled energy delivery. These deviations need to be balanced on short notice by various actors such as grid operators and electricity traders. To reduce these fluctuations and errors, forecasts will play a central role in the planning and use of various flexibility options and storage concepts in the future. The joint project WindStore, funded by the Federal Ministry for Economic Affairs and Energy, aims to develop and analyse a novel concept for forecast-based storage management.
Research projekt WindStore | |
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Duration | January 2024 to December 2026 |
Funded by | Federal Ministry for Economic Affairs and Energy |
Project participants | - Institute of Networked Energy Systems
- Fraunhofer Institute for Energy Economics and Energy System Technology
- 4Cast Ltd & Co. KG (until December 2025)
- SEtrade Ltd
associated: - WEPROG Ltd
- EnBW Energie Baden-Württemberg AG
- Stadtwerk Haßfurt Ltd
- SWW Wunsiedel Ltd
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The WindStore project brings together various expertise from forecasting, satellite-based remote sensing, meteorology, Artificial Intelligence (AI), storage operation, operation of hydrogen electrolysers, and the electricity market. The goal is to develop an innovative storage management that can cope with large forecast errors and large-scale power fluctuations by using distributed battery storage and electrolysers. Forecast uncertainty shall also be used for optimal planning of usable flexibilities. Satellite observations and Artificial Intelligence are used to detect large-scale forecast errors and short-term changes in wind conditions, enabling optimal operation of battery storage and electrolysers. The project participants aim at an optimised design of a forecast-based distributed storage concept and the associated storage management. The operating principle of which is to be demonstrated in a field test.
The Institute of Networked Energy Systems is focusing on the satellite-based detection of wind field forecast deviations. In this context, two methods for detecting wind forecast errors using satellite data are researched: On the one hand, simulated satellite images from the European Centre for Medium-Range Weather Forecasts (ECMWF) are compared with observed satellite images in order to identify possible phase errors in cloud and wind field forecasting and to propagate them into the future. The second method compares wind fields derived from space-born radar systems with the predicted wind fields of an ensemble forecast system. The aim is to identify, via a so-called "best-member-selection", the forecast member that has the best match with the observation at a given time and to evaluate its future accuracy for wind forecasting.
