Abstract
In-cloud ice mass accretion on wind turbines is a common challenge that is faced by energy companies operating in cold climates. On-shore wind farms in Scandinavia are often located in regions near patches of forest, the heterogeneity length scales of which are often less than the resolution of many numerical weather prediction (NWP) models. The representation of these forests—including the cloud water response to surface roughness and albedo effects that are related to them—must therefore be parameterized in NWP models used as meteorological input in ice prediction systems, resulting in an uncertainty that is poorly understood and, to the present date, not quantified. The sensitivity of ice accretion forecasts to the subgrid representation of forests is examined in this study. A single column version of the HARMONIE-AROME three-dimensional (3D) NWP model is used to determine the sensitivity of the forecast of ice accretion on wind turbines to the subgrid forest fraction. Single column simulations of a variety of icing cases at a location in northern Sweden were examined in order to investigate the impact of vegetation cover on ice accretion in varying levels of solar insolation and wind magnitudes. In mid-winter cases, the wind speed response to surface roughness was the primary driver of the vegetation effect on ice accretion. In autumn cases, the cloud water response to surface albedo effects plays a secondary role in the impact of in-cloud ice accretion, with the wind response to surface roughness remaining the primary driver for the surface vegetation impact on icing. Two different surface boundary layer (SBL) forest canopy subgrid parameterizations were tested in this study that feature different methods for calculating near-surface profiles of wind, temperature, and moisture, with the ice mass accretion again following the wind response to surface vegetation between both of these schemes.
Highlights
In the past several decades, commercial wind power has grown from humble beginnings in small research wind farms to an important energy resource worldwide
The model used in this study—Modèle Unifié Simple Colonne (MUSC) [11]—is a single column version of the HARMONIE-AROME configuration of the ALADIN-HIRLAM 3D numerical weather prediction (NWP) system [12] developed by Météo France as a testbed for NWP
This is combined with the condensation parameterization that is described in Section 2b of Müller et al [15], which addresses a challenge with the microphysics scheme in which ice formed too quickly at temperatures above −20 ◦ C [12] MUSC contains the same physics and parameterization packages as HARMONIE-AROME with the exception of horizontal advection being ignored in the simulation; MUSC contains an added capability to implement large scale forcing based on a horizontal thermal gradient
Summary
In the past several decades, commercial wind power has grown from humble beginnings in small research wind farms to an important energy resource worldwide. Independent power producers, and investors have especially taken advantage of regions in cold climates, such as those in Scandinavia, where sparse human population and high air density combined with terrain-induced wind flows make for an excellent region for wind farm development. According to IEA [1], 69 GW of wind energy were located in cold climates in 2012, with 10 GW/Year being built from 2013–2017. A challenging aspect of operating wind farms in cold climate regions is that of ice accretion on the wind turbine blades. The accretion of ice affects the aerodynamic efficiency of the turbine, yielding lower power output. Increased loads and vibration can decrease the life of Energies 2020, 13, 4258; doi:10.3390/en13164258 www.mdpi.com/journal/energies
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