The Sensitivity of the Fitch Wind Farm Parameterization to a Three-Dimensional Planetary Boundary Layer Scheme
Abstract. Wind plant wake impacts can be estimated with a number of simulation methodologies, each with its own fidelity and sensitivity to model inputs. In turbine-free mesoscale simulations, hub-height wind speeds can significantly vary with the choice of a planetary boundary layer (PBL) scheme. However, the sensitivity of wind plant wakes to a PBL scheme has not been explored because, until now, wake parameterizations were only compatible with one PBL scheme. We couple the Fitch wind farm parameterization with the new NCAR 3DPBL scheme and compare the resulting wakes to those simulated with a widely used PBL scheme. First, we simulate a wind plant in a pseudo-steady state under idealized stable, neutral, and unstable conditions with two PBL schemes: MYNN and the NCAR 3DPBL. For these idealized scenarios, MYNN consistently predicts internal wakes that are 0.25–1.5 m s−1 stronger than internal 3DPBL wakes. However, because MYNN predicts stronger inflow winds than the 3DPBL, MYNN predicts average capacity factors that are as large as 13 percentage points higher than with the 3DPBL, depending on the stability. To extend this sensitivity study, we conduct a month-long case study with both PBL schemes centered on the Vineyard Wind 1 lease area in the mid-Atlantic United States. Under stable and unstable conditions averaged across the month, MYNN again predicts stronger internal waking—by about 0.25 m s−1. However, again due to stronger plant inflow wind speeds in MYNN, the 3DPBL generates 4.7 %–7.8 % less power than MYNN in August 2020, depending on the turbine build-out scenario. Differences between PBL schemes can be even larger for individual instances in time. These simulations suggest that PBL schemes represent a meaningful source of modeled wind resource uncertainty; therefore, we recommend incorporating PBL variability into future wind plant planning sensitivity studies as well as wind forecasting studies.
