Abstract
Abstract. The magnitude of wake interactions between individual wind turbines depends on the atmospheric stability. We investigate strategies for wake loss mitigation through the use of closed-loop wake steering using large eddy simulations of the diurnal cycle, in which variations in the surface heat flux in time modify the atmospheric stability, wind speed and direction, shear, turbulence, and other atmospheric boundary layer (ABL) flow features. The closed-loop wake steering control methodology developed in Part 1 (Howland et al., 2020c, https://doi.org/10.5194/wes-5-1315-2020) is implemented in an example eight turbine wind farm in large eddy simulations of the diurnal cycle. The optimal yaw misalignment set points depend on the wind direction, which varies in time during the diurnal cycle. To improve the application of wake steering control in transient ABL conditions with an evolving mean flow state, we develop a regression-based wind direction forecast method. We compare the closed-loop wake steering control methodology to baseline yaw-aligned control and open-loop lookup table control for various selections of the yaw misalignment set-point update frequency, which dictates the balance between wind direction tracking and yaw activity. In our diurnal cycle simulations of a representative wind farm geometry, closed-loop wake steering with set-point optimization under uncertainty results in higher collective energy production than both baseline yaw-aligned control and open-loop lookup table control. The increase in energy production for the simulated wind farm design for closed- and open-loop wake steering control, compared to baseline yaw-aligned control, is 4.0 %–4.1 % and 3.4 %–3.8 %, respectively, with the range indicating variations in the energy increase results depending on the set-point update frequency. The primary energy increases through wake steering occur during stable ABL conditions in our present diurnal cycle simulations. Open-loop lookup table control decreases energy production in the example wind farm in the convective ABL conditions simulated, compared to baseline yaw-aligned control, while closed-loop control increases energy production in the convective conditions simulated.
Highlights
Collective wind farm power maximization through wake steering control has demonstrated potential in large eddy simulations (LESs) of idealized atmospheric boundary layer (ABL) conditions (Gebraad et al, 2016), wind tunnel experiments (Campagnolo et al, 2020), and in initial field experiments (Fleming et al, 2019; Howland et al, 2019; Doekemeijer et al, 2021)
Several challenges arise in open-loop wake steering control, including time-varying ABL flow conditions with measurement uncertainty (Quick et al, 2017; Annoni et al, 2019) and wake model parameter uncertainty (Schreiber et al, 2020; Howland, 2021b), which may lead to a discrepancy between the optimal yaw misalignment set points in the steady-state wake model and the true optimal yaw misalignment values which vary in time
We investigate the performance of the closed-loop wake steering control methodology developed in Part 1 (Howland et al, 2020c) in the stratified ABL with time-varying wind direction and atmospheric stability
Summary
Collective wind farm power maximization through wake steering control has demonstrated potential in large eddy simulations (LESs) of idealized atmospheric boundary layer (ABL) conditions (Gebraad et al, 2016), wind tunnel experiments (Campagnolo et al, 2020), and in initial field experiments (Fleming et al, 2019; Howland et al, 2019; Doekemeijer et al, 2021). The primary approach of wake steering control has been open-loop, in which a lookup table of model-optimal yaw misalignment set points is constructed as a function of the incident wind direction, wind speed, and turbulence intensity (Fleming et al, 2019). We investigate the performance of the closed-loop wake steering control methodology developed in Part 1 (Howland et al, 2020c) in the stratified ABL with time-varying wind direction and atmospheric stability. Through closed-loop control, the yaw misalignment set-point optimization adapts to the estimated wake model parameters, which vary with atmospheric stability. The set of findings presented here demonstrate the utility of closed-loop wake steering control in more realistic ABL conditions, with time-varying wind direction, wind speed, and atmospheric stability. The lookup table construction, for openloop control, is discussed in Appendix E
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