1 Department of Wind Energy, Technical University of Denmark2 Wind Energy Systems, Department of Wind Energy, Technical University of Denmark3 Department of Applied Mathematics and Computer Science, Technical University of Denmark4 Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark5 Center for Electric Power and Energy, Department of Electrical Engineering, Technical University of Denmark6 Risø National Laboratory for Sustainable Energy, Technical University of Denmark7 Department of Informatics and Mathematical Modeling, Technical University of Denmark8 Department of Electrical Engineering, Technical University of Denmark
Wind power fluctuations, especially offshore, can pose challenges in the secure and stable operation of the power system. In modern large offshore wind farms, there are supervisory controls designed to reduce the power fluctuations. Their operation is limited due to the fact that they imply loss of production, hence revenue for the wind farm operator. On the other hand, progresses in short term forecasting, together with the increasing use of probabilistic forecasting can help in achieving efficient power fluctuations reduction with minimum lost production. Here we present supervisory control concepts that consider different wind power regimes to derive control setpoints by using a Markov-Switching AutoRegressive model. We evaluate the performance versus measured data in terms of power ramp characteristics and energy efficiency.