We aim to improve wind farm control for power output by building on the results from WFSim for the development of a dynamic wind farm model. This model will be part of a closed-loop, economic model-predictive control approach for wind farms. It is constructed from first principles using open-source tools to be suitable for adjoint-based optimisation of turbine yaw angles. In a steady-state inflow configuration with two turbines, the new control model matches power expectations from high fidelity simulations in SOWFA to within 15 %. Under time-varying wind directions, it shows time delays in wake direction as inflow changes propagate through the farm with the wind speed, although the dynamics still differ from the SOWFA reference. The model runs flow simulations for a wind farm with a 3 x 3 array of turbines at a real-time order of magnitude on a regular laptop computer. The new control model shows dynamic flow behaviour as wake changes propagate through the wind farm. Some further adjustments are necessary to accurately model three-dimensional flow in two dimensions. With more validation of the wake dynamics, it will be suitable for application in a new closed-loop wind farm controller.

Abstract. Wind farm flow control (WFFC) is a topic of interest at several research institutes and industry and certification agencies worldwide. For reliable performance assessment of the technology, the efficiency and the capability of the models applied to WFFC should be carefully evaluated. To address that, the FarmConners consortium has launched a common benchmark for code comparison under controlled operation to demonstrate its potential benefits, such as increased power production. The benchmark builds on available data sets from previous field campaigns, wind tunnel experiments, and high-fidelity simulations. Within that database, four blind tests are defined and 13 participants in total have submitted results for the analysis of single and multiple wakes under WFFC. Here, we present Part I of the FarmConners benchmark results, focusing on the blind tests with large-scale rotors. The observations and/or the model outcomes are evaluated via direct power comparisons at the upstream and downstream turbine(s), as well as the power gain at the wind farm level under wake steering control strategy. Additionally, wake loss reduction is also analysed to support the power performance comparison, where relevant. The majority of the participating models show good agreement with the observations or the reference high-fidelity simulations, especially for lower degrees of upstream misalignment and narrow wake sector. However, the benchmark clearly highlights the importance of the calibration procedure for control-oriented models. The potential effects of limited controlled operation data in calibration are particularly visible via frequent model mismatch for highly deflected wakes, as well as the power loss at the controlled turbine(s). In addition to the flow modelling, the sensitivity of the predicted WFFC benefits to the turbine representation and the implementation of the controller is also underlined. The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of farm flow control benefits. It forms an important basis for more detailed benchmarks in the future with extended control objectives to assess the true value of WFFC.

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