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Content archived on 2024-05-27

Development of a next generation wind resource forecasting system for the large-scale integration of onshore and offshore wind farms (ANEMOS)


Objectives and problems to be solved :Accurate forecasting of the wind resource up to two days ahead is recognised as a major contribution for reliable large-scale wind power integration. Especially, in a liberalised electricity market, prediction tools enhance the position of wind energy compared to other forms of dispatch able generation. The ANEMOS project aims to develop advanced forecasting models that will substantially outperform current methods. Emphasis is given to situations like complex terrain, extreme weather conditions, as well as to offshore prediction for which no specific tools currently exist. The prediction models are implemented in a software platform and installed for online operation at onshore and offshore wind farms by the end-users participating in the project. The project demonstrates the economic and technical benefits from accurate wind prediction at different levels: national, regional or at single wind farm level and for time horizons ranging from minutes up to several days ahead. Description of the Work: The project is structured into 10 work-packages, which address the technical objectives. Initially, the prediction requirements are defined in collaboration with end-users. The project develops prediction models based on both a physical approach and an alternative statistical approach. Research on physical models gives emphasis to techniques for use in complex terrain and the development of prediction tools based on CFD techniques, advanced model output statistics or high-resolution meteorological information. Statistical models (i.e. based on artificial intelligence) are developed for downscaling, power curve representation, up scaling for prediction at regional or national level, etc. A benchmarking process is set-up to evaluate the performance of the developed models and to compare them with existing ones using a number of case studies. The synergy between statistical and physical approaches is examined to identify promising areas for further improvement of forecasting accuracy. The performance of purely meteorological forecasts, but also long-term wind predictability up to 7 days ahead, is evaluated in detail. Appropriate physical and statistical prediction models are also developed for offshore wind farms taking into account advances in marine meteorology (interaction between wind and waves, coastal effects). The benefits from the use of satellite radar images for modelling local weather patterns are investigated. A next generation forecasting software, ANEMOS, is developed to integrate the various models. The tool is enhanced by advanced ICT functionality and can operate both in stand alone, or remote mode, or is interfaced with standard EMS/DMS systems. The software will be installed for on-line operation at a number of onshore and offshore wind farms. Finally, the benefits from wind prediction will be evaluated during on-line operation, while guidelines will be produced for the optimal use of wind forecasting systems. Milestones and expected results :The project provides an advanced technology for wind resource forecasting applicable in a large scale: at a single wind farm, regional or national level and for both interconnected and island systems. A major milestone of the project is the on-line operation of the developed software by the participating utilities for onshore and offshore wind farms. The outcome of the ANEMOS project will help consistently the increase of wind integration in two levels; in an operational level due to better management of wind farms, but also, it will contribute to increasing the installed capacity of wind farms. This is because accurate prediction of the resource reduces the risk of wind farm developers, who are then more willing to undertake new wind farm installations especially in a liberalised electricity market environment.

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Rue Claude Daunesse, Les Lucioles

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Participants (19)