A comparison of methods for assessing power output in non‐uniform onshore wind farms

Abstract

Wind resource assessments are used to estimate a wind farm's power production during the planning process. It is important that these estimates are accurate, as they can impact financing agreements, transmission planning, and environmental targets. Here, we analyze the challenges in wind power estimation for onshore farms. Turbine wake effects are a strong determinant of farm power production. With given input wind conditions, wake losses typically cause downstream turbines to produce significantly less power than upstream turbines. These losses have been modeled extensively and are well understood under certain conditions. Most notably, validation of different model types has favored offshore farms. Models that capture the dynamics of offshore wind conditions do not necessarily perform equally as well for onshore wind farms. We analyze the capabilities of several different methods for estimating wind farm power production in 2 onshore farms with non-uniform layouts. We compare the Jensen model to a number of statistical models, to meteorological downscaling techniques, and to using no model at all. We show that the complexities of some onshore farms result in wind conditions that are not accurately modeled by the Jensen wake decay techniques and that statistical methods have some strong advantages in practice. KEYWORDSonshore wind farms, statistical modeling, turbine wakes, wind power assessment INTRODUCTIONWind energy is a fast-growing segment of the energy sector worldwide. As wind starts to play a larger role in our electricity systems, there is increasing interest in using it efficiently. Wind power is, for the most part, not a dispatchable form of energy; forecasts are not perfect, and an individual wind farm cannot be set to a desired production level unless the wind conditions are sufficient for that level of power. Managing a farm's variability and intermittency is still a big challenge, but recent research shows promising results in optimizing farm production. 1-4 Knowledge of the amount of power that a wind farm will produce in a given hour, day, or year will improve the way that the farm is operated and financed. Perfect knowledge of future wind power production is impossible, but recent research has made great strides in estimating these quantities. Power estimation and prediction is important for multiple applications within the timeline of a wind energy project. In the initial planning stages of a farm, one must develop accurate estimates of what the wind conditions will be once the farm is built. This typically involves the installation of meteorological towers (or met towers) for data collection. This information is used to determine the turbine layout and alignment within the farm that will best capture the available energy. Initial estimates of long-term wind conditions are used to assess the financial viability of a wind farm. Thus, the decision of farm design and resource assessment is critical, as the return on investment depends on the ability of the wind farm to generate enough en...

Sensitivity of Wind Turbine Array Downstream Effects to the Parameterization Used in WRF

“…Gross CF exceed observed net CF from operating wind farms because they assume 100% WT availability (i.e., no downtime for maintenance or curtailment of production) and neglect WT interactions within a grid cell (i.e., all WT in a WRF gridcell experience the same wind speed profile). These sources are estimated to reduce power production in the United States by ,4% (curtailment) (Pryor et al 2018b), ,2% (operations and maintenance) (Carroll et al 2017), and #5% (within wind farm wakes) (Staid et al 2018). Thus, the discrepancy between net and gross CF varies in time and space, but these factors are likely to be on the order of a few percent (i.e., in the absence of other errors, if estimated gross CF is 40%, net CF are likely to be in the range of 30%-36%).…”

Variability in Wind Energy Generation across the Contiguous United States

“…During 2018 across all ISOs wind energy curtailment was 2.2%, while in ERCOT it was 2.5% (Wiser and Bolinger 2019). Wake losses for onshore wind farms are typically # 5% (Staid et al 2018), and WT availability typically exceeds 98% (Carroll et al 2017). Thus, the expectation is that gross CF will exceed net CF by 4-10 percentage points.…”

“…Throughout this analysis capacity factors (CF) are used to describe the efficiency of electrical power production from wind turbines relative to that achievable if all WTs operated at their rated capacity. Observational estimates of power production efficiency from operating wind farms and or clusters of wind farms computed in this way are referred to as net CF because they include power losses that derive from WT curtailment (Bird et al 2014), WT wakes (Barthelmie et al 2013;Staid et al 2018), WT operations and maintenance (O&M) (Carroll et al 2017;Olauson et al 2017), and electrical losses (Lumbreras and Ramos 2013). Modeled estimates of power production are referred to as gross CF because they exclude these factors and are solely a function of the wind speed in each grid cell and the assumed WT power curve.…”

20% of US electricity from wind will have limited impacts on system efficiency and regional climate

“…Total observed wake losses within land-based WT arrays are estimated to decrease power production and CF by ≤5% (ref. 43 ).…”