The proposed work is organised in six WPs, of which four focus on scientific research. In addition, WP5 organises dissemination and communication activities, while project management is included in WP6. The project activities stretch over a 48-month period.
The TotalControl WPP control approach is built on a hierarchy of controllers, each reacting at different time scales and control time steps. At the slowest control level (WP2), the WPP is quasi-statically adapting its WT active and reactive power set points as well as WT yaw angles. For the power set points, delta control is used. This controller adapts the wind-farm to slowly changing environmental conditions and market elements (e.g. intraday market or 15 minute balancing intervals). A second control level is the WT controller (WP3). In TotalControl, this controller is included as part of the hierarchical concept. The WT controllers are developed in such a way that they are able to control the WPP themselves (though sub-optimally) with the possibility to e.g. adapt to market conditions, provide primary ancillary services, or reduce loads as needed to maintain target reliability of different turbine components under increased turbulence levels. At the same time, the WT controllers accept power set points from the quasi-steady control levels. Thus, using these two levels, the WPP can either sub-optimally operate using the WT controllers only, or reach a better optimum by the hierarchy of quasi-steady open-loop control and WT control.
Finally, a WPP controller (WP4) that responds dynamically to faster events (turbulent gusts, requests for ancillary services, etc.) and uses feedback from the WTs is being considered. This controller includes the use of model-predictive control for prediction of dynamic wake behavior and impacts on turbine loads, or for WPP optimized tracking of power signals in the context of secondary reserves. All communication with the WTs is based on dynamically changing power set points, or thrust set points, either using delta control or providing maximum power set points. Moreover, the dynamic WPP also contains a direct control level related to the WPP internal power grid (e.g. STATCOM devices, power converters). This third WPP control level aims at an integrated overall optimization of WPP operation considering both the slow-changing events considered in WP2 and the faster events to which the WT controller reacts. Thus, the dynamic feedback WPP controller combined with the WT controllers provides the best WPP performance, but the two other control configurations discussed above (WT only, or open-loop WPP + WT) provide redundancy and robust fall back options for WPP operation. Moreover, the open-loop WPP controller can be more easily retrofitted into existing WPPs.
In order to develop and test the different WPP controllers, a range of high-fidelity simulation models are used. These models are already available in the consortium, but will be thoroughly validated in WP1 against measurements in the Lillgrund WPP. Due to the complexity and multiscale nature of WPP flow dynamics, these models are very expensive in terms of simulation time, e.g. requiring supercomputing, and therefore not well suited as control design models. Therefore, WP1 further focuses on the formulation, verification and validation of fast physics-based WPP models that can be incorporated in the WP2 and WP4 control loops.