In this study, we propose the use of model‐based receding horizon control to enable a wind farm to provide secondary frequency regulation for a power grid. The controller is built by first proposing a time‐varying one‐dimensional wake model, which is validated against large eddy simulations of a wind farm at startup. This wake model is then used as a plant model for a closed‐loop receding horizon controller that uses wind speed measurements at each turbine as feedback. The control method is tested in large eddy simulations with actuator disk wind turbine models representing an 84‐turbine wind farm that aims to track sample frequency regulation reference signals spanning 40 min time intervals. This type of control generally requires wind turbines to reduce their power set points or curtail wind power output (derate the power output) by the same amount as the maximum upward variation in power level required by the reference signal. However, our control approach provides good tracking performance in the test system considered with only a 4% derate for a regulation signal with an 8% maximum upward variation. This performance improvement has the potential to reduce the opportunity cost associated with lost revenue in the bulk power market that is typically associated with providing frequency regulation services. Copyright © 2017 John Wiley & Sons, Ltd.

Abstract

Abstract. Given a wind farm with known dimensions and number of wind turbines, we try to find the optimum positioning of wind turbines that maximises wind-farm energy production. In practice, given that optimisation has to be performed for many wind directions, and taking into account the yearly wind distribution, such an optimisation is computationally only feasible using fast engineering wake models such as the Jensen model. These models are known to have accuracy issues, in particular since their representation of wake interaction is very simple. In the present work, we propose an optimisation approach that is based on a hybrid combination of large-eddy simulation (LES) and the Jensen model; in this approach, optimisation is mainly performed using the Jensen model, and LES is used at a few points only during optimisation for online tuning of the wake-expansion coefficient in the Jensen model, as well as for validation of the results. An optimisation case study is considered, in which the placement of 30 turbines in a 4 km by 3 km rectangular domain is optimised in a neutral atmospheric boundary layer. Optimisation for both a single wind direction and multiple wind directions is discussed.

We investigate the feasibility of using large-eddy simulation (LES) for real-time forecasting of instantaneous turbulent velocity fluctuations in the atmospheric boundary layer. Although LES is generally considered computationally too expensive for real-time use, wall-clock time can be significantly reduced by using very coarse meshes. Here, we focus on forecasting errors arising on such coarse grids, and investigate the trade-off between computational speed and accuracy. We omit any aspects related to state estimation or model bias, but rather look at the size and evolution of restriction errors, subgridscale errors, and chaotic divergence, to obtain a first idea of the feasibility of LES as a forecasting tool. To this end, we set-up an idealized test scenario in which the forecasting error in a neutral atmospheric boundary layer is investigated based on a fine reference simulation, and a series of coarser LES grids. We find that errors only slowly increase with grid coarsening, related to restriction errors that increase. Unexpectedly, modelling errors slightly decrease with grid coarsening, as both chaotic divergence and subgrid-scale error sources decrease. A practical example, inspired by wind-energy applications, reveals that there is a range of forecasting horizons for which the variance of the forecasting error is significantly reduced compared to the turbulent background variance, while at the same time, associated LES wall times are up to 300 times smaller than simulated time.

Abstract. Given a wind-farm with known dimensions and number of wind-turbines, we try to find the optimum positioning of wind-turbines that maximises wind-farm energy production. In practise, given that optimisation has to be performed for many wind directions and taking into account the yearly wind distribution, such an optimisation is computationally only feasible using fast engineering wake models such as, e.g., the Jensen model. These models are known to have accuracy issues, in particular since their representation of wake interaction is very simple. In the present work, we propose an optimisation approach that is based on a hybrid combination of Large-Eddy Simulations (LES) and the Jensen model, in which optimisation is mainly performed using the Jensen model, and LES is used at a few points only during optimisation for online tuning of the wake-expansion coefficient in the Jensen model, and for validation of the results. An optimisation case study is considered, in which the placement of 30 turbines in a 4 by 3 km rectangular domain is optimised in a neutral atmospheric boundary layer. Both optimisation for single wind direction, and multiple wind directions are discussed.