The BEOWIND project

Enhancing the provision of ancillary services, the stability of the grid and the infrastructure lifetime for the Belgian offshore wind energy parks

Funded by the Belgian Energy Transition Fund

By the end of 2018, the worldwide installed wind power capacity reached 591 GW, growing with 51.3 GW in the last year alone. In the Belgian North Sea, 318 turbines accounting for a total power of 1556 MW, will be installed by the end of 2019. Moreover, the Belgian government has the ambition to reach a total of 4 GW offshore wind power by 2030. The shift from fossil fuels and nuclear energy to renewable energy sources brings more variability to the grid, leading to stronger grid frequency fluctuations and power imbalances. Therefore, ancillary services are expected to play a crucial role in the operation of the future power system. These services include:

Goals of the project

The BEOWIND project has the ambition to tackle the following fundamental questions:

  1. How can the North Sea wind farms contribute to the power system by providing ancillary services?
  2. What is the technical and economic potential of wind farms for ancillary service provision?
  3. What is the effect on mechanical loading, fatigue and infrastructure lifetime?

BEOWIND Methodology & concept

Methodology of the BEOWIND project - ancillary services by wind farms

The figure above shows the envisioned hierarchical control structure. A supervisory controller on the wind farm level determines the optimal farm response with regards to the condition of the grid and calculates the power set-points of the individual turbines, taking into account wind speed predictions, turbine dynamics, mechanical loading and wake effects. These power set-points are regulated by the individual turbine control systems on the lower control level. Two modelling concepts are envisioned to model the wind turbines and farm behaviour:

  1. Physics-based modelling, e.g., using the Blade-Element Method to model the wind turbines aerodynamics in the FAST software
  2. Data-driven modelling, e.g., using system identification techniques in ANFIS

Both modelling concepts will be combined with stochastic models for the prediction of:

  1. Wind speed
  2. Energy markets
  3. Power system imbalance
  4. Need for ancillary services


The BEOWIND project is a joint project between Ghent University and the University of Mons, funded by the Energy Transition Fund of the Belgian federal government (FPS Economy).

Project team



Articles and Conference Papers


For more information contact prof. Lieven Vandevelde.

logo Ghent University
University of Mons
FPS Economy