As the amount of wind energy is increasing, the balancing of electricity production and consumption becomes more and more challenging. There are already many instances where too much wind power is available, leading to low, or even negative, power market prices.

These financial incentives to reduce production are not always sufficient, thus grid operators can additionally demand such reduction (curtailment) when there is a danger of overloading the electrical network.

This poses a challenge for wind farm operators, since such curtailments (e.g., by keeping offshore turbines idling) can affect the fatigue lifetime of wind turbines. The main objective of this project is therefore to develop and investigate strategies for flexible and lifetime-conscious power curtailment, optimizing the available decisions (e.g., regarding the periods and length of idling, or the degree of de-rating) under uncertainty in future market prices and wind conditions.

To achieve this, the fatigue damage efficiency of different curtailment options will be established based on simulations and real-world data, and dynamic programming techniques and artificial intelligence will be used to optimise the economic value of the wind turbine asset.

The main novelties are the consideration of different wind turbine components and damage criteria (e.g., including pitch actuator wear), the use of real-world data, and the consideration of uncertainties.

A secondary objective is to improve the accuracy and reduce the uncertainty of remaining useful lifetime predictions, and to extend the approach to include the possibility of lifetime extension.

A first result will be the precise mathematical definition of the most relevant optimization problems for implementing flexible curtailment, in the most economical way.

A second result will be an innovative solution strategy to determine the optimal curtailment strategies. A third result will be the development of a relevant industrial application, using real-world wind turbine data, for the industrial partner.

Academic secondment at ETH Zurich (3 months, supervised by Prof. Eleni Chatzi, M13-15) to work on the theoretical foundations and techniques for solving the optimization problems. Industrial secondment at EnBW (3 months, supervised by Dr. Lisa Ziegler, M22-24) to work with real-world wind turbine data and develop a practical approach that can be used in industry.