The European Academy of Wind Energy (eawe), representing universities and institutes with a significant wind energy programme in 14 countries, has discussed the long-term research challenges in wind energy. In contrast to research agendas addressing short- to medium-term research activities, this eawe document takes a longer-term perspective, addressing the scientific knowledge base that is required to develop wind energy beyond the applications of today and tomorrow. In other words, this long-term research agenda is driven by problems and curiosity, addressing basic research and fundamental knowledge in 11 research areas, ranging from physics and design to environmental and societal aspects. Because of the very nature of this initiative, this document does not intend to be permanent or complete. It shows the vision of the experts of the eawe, but other views may be possible. We sincerely hope that it will spur an even more intensive discussion worldwide within the wind energy community.

The objective of this paper is to compare field data from a scanning lidar mounted on a turbine to control-oriented wind turbine wake models. The measurements were taken from the turbine nacelle looking downstream at the turbine wake. This field campaign was used to validate control-oriented tools used for wind plant control and optimization. The National Wind Technology Center in Golden, CO, conducted a demonstration of wake steering on a utility-scale turbine. In this campaign, the turbine was operated at various yaw misalignment set points, while a lidar mounted on the nacelle scanned five downstream distances. Primarily, this paper examines measurements taken at 2.35 diameters downstream of the turbine. The lidar measurements were combined with turbine data and measurements of the inflow made by a highly instrumented meteorological mast on-site. This paper presents a quantitative analysis of the lidar data compared to the control-oriented wake models used under different atmospheric conditions and turbine operation. These results show that good agreement is obtained between the lidar data and the models under these different conditions.

In this work a nonlinear model predictive controller with individual pitch control for a floating offshore wind turbine is presented. An aerodynamic model of the collective pitch control approach is extended by describing pitching and yawing moments based on rotor disk theory. This extension is implemented in a reduced nonlinear model of the floating wind turbine including disturbance preview of wind speed, linear vertical and horizontal wind shear, and wave height to compute optimal input trajectories for the individual pitch control inputs and the generator torque. An extended cost functional for individual pitch control is proposed based on the collective pitch control approach. The controller is evaluated in aero-servo-hydro-elastic simulations of a 5MW reference wind turbine disturbed by a three-dimensional stochastic turbulent wind field. Results show a significant blade fatigue load reduction compared to a baseline controller through minimizing yawing and pitching moments on the rotor hub while maintaining the advantages of the model predictive control approach with collective pitch control.

Profiling nacelle lidars probe the wind at several heights and several distances upstream of the rotor. The development of such lidar systems is relatively recent, and it is still unclear how to condense the lidar raw measurements into useful wind field characteristics such as speed, direction, vertical and longitudinal gradients (wind shear). In this paper, we demonstrate an innovative method to estimate wind field characteristics using nacelle lidar measurements taken within the induction zone. Model-fitting wind field reconstruction techniques are applied to nacelle lidar measurements taken at multiple distances close to the rotor, where a wind model is combined with a simple induction model. The method allows robust determination of free-stream wind characteristics. The method was applied to experimental data obtained with two different types of nacelle lidar (five-beam Demonstrator and ZephIR Dual Mode). The reconstructed wind speed was within 0.5 % of the wind speed measured with a mast-top-mounted cup anemometer at 2.5 rotor diameters upstream of the turbine. The technique described in this paper overcomes measurement range limitations of the currently available nacelle lidar technology.

The demand for minute-scale forecasts of wind power is continuously increasing with the growing penetration of renewable energy into the power grid, as grid operators need to ensure grid stability in the presence of variable power generation. For this reason, IEA Wind Tasks 32 and 36 together organized a workshop on “Very Short-Term Forecasting of Wind Power” in 2018 to discuss different approaches for the implementation of minute-scale forecasts into the power industry. IEA Wind is an international platform for the research community and industry. Task 32 tries to identify and mitigate barriers to the use of lidars in wind energy applications, while IEA Wind Task 36 focuses on improving the value of wind energy forecasts to the wind energy industry. The workshop identified three applications that need minute-scale forecasts: (1) wind turbine and wind farm control, (2) power grid balancing, (3) energy trading and ancillary services. The forecasting horizons for these applications range from around 1 s for turbine control to 60 min for energy market and grid control applications. The methods that can be applied to generate minute-scale forecasts rely on upstream data from remote sensing devices such as scanning lidars or radars, or are based on point measurements from met masts, turbines or profiling remote sensing devices. Upstream data needs to be propagated with advection models and point measurements can either be used in statistical time series models or assimilated into physical models. All methods have advantages but also shortcomings. The workshop’s main conclusions were that there is a need for further investigations into the minute-scale forecasting methods for different use cases, and a cross-disciplinary exchange of different method experts should be established. Additionally, more efforts should be directed towards enhancing quality and reliability of the input measurement data.