FlexiWind

The overall objectives of FlexiWind consist of researching the potential and influence of flexible control strategies on wind turbines or wind farms. For this purpose, new and existing control methods, methods for estimating fatigue loads and new simulation approaches for modeling wind flow are combined to enable optimal wind farm operation. Furthermore, FlexiWind deals with the direct application of flexible control strategies to wind farms and the associated dynamic adaptation of the wind farm's operating state to respond to changing environmental conditions. This is to be implemented in a real-time capable simulation environment, with which the potentials of optimal control strategies can be raised.

The individual control of each wind turbine (WT) allows to flexibly adapt the power to the current demand and thus to offer important grid services such as the provision of balancing power. Furthermore, the load of individual WTs can be better controlled and thus a uniform lifetime consumption for the wind farm can be achieved. In particular, the aspect of lifetime modeling is considered in detail in FlexiWind in order to enable a more accurate calculation of the load on the WTs in interaction with an improved flow modeling. This allows more precise statements to be made with regard to upcoming maintenance measures, and the expected remaining service life can be estimated more accurately and even extended, if necessary, by deliberately reducing the loads.

The implementation of optimized control strategies for the flexible wind farm is to be carried out by means of a real-time capable simulation environment, which is to be developed and set up in FlexiWind. As a digital wind farm twin of an existing wind farm of the project partners, this environment allows to simulate the complex interaction of tracking, wind farm control, grid requirements and loads in real time or faster.

In the simulation environment, the respective transient and non-linear sub-models for the aeroelasticity of the WTs, the propagation of wakes and the wind farm control are combined into an overall simulation model. By additionally providing standard interfaces for wind farm controller hardware, wind farm controllers can be developed and tested on field-deployed controllers in a hardware-in-the-loop (HiL) environment.

The real-time simulation environment provides the load on the WTs and also at locations where no measured variables of the real wind farm are available. It also offers the possibility to estimate the effects of control interventions in the wind farm such as wake control and of dynamic results such as gusts on the load of individual WTs (fatigue tracking, condition monitoring). Thus, their lifetime (also for continued operation beyond the design lifetime) can be specifically influenced. In addition, it is possible to increase the yield in a targeted manner (at the expense of the service life). This increases the economic efficiency of the wind farm, since the wind farm control can be based on an optimum of increasing the lifetime and higher yield of the WTs due to the detailed analyses, which is not possible without such a simulation environment.

The accuracy of the real-time simulation environment will be improved during the project by using the results from the simulation models to improve the lifetime modeling and to develop optimized control algorithms. In addition, validation will be carried out with data that are available, among other things, as part of the RAVE research initiative. With FINO1 and the research wind farm alpha ventus (av), these offer the possibility of illuminating both the flow conditions and the system responses of the individual wind turbines. In addition, CFD calculations will be performed at IWES to calibrate the simulation environment. The real-time system will also be validated with measurement data from at least one wind farm of the project partners. The reduction of uncertainties in the modeling resulting from the work and project results and the uncertainties in the modeling and the involvement of relevant stakeholders (wind farm operators, WT manufacturers) to define different operating scenarios for flexible plant control results in a more realistic assessment of the financial and technical potential of flexible plant and farm control. Such a strategy has the potential to reduce the overall cost of operation and maintenance, and thus the cost of electricity, while contributing to grid stability, thus making wind energy more competitive.