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This content will become publicly available on October 2, 2018

Title: A comparison of methods for assessing power output in non-uniform onshore wind farms

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. In conclusion, 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.
ORCiD logo [1] ;  [2] ;  [3]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Johns Hopkins Univ., Baltimore, MD (United States)
  3. Univ. of Michigan, Ann Arbor, MI (United States)
Publication Date:
Report Number(s):
SAND-2017-9660J; SAND-2016-6827J
Journal ID: ISSN 1095-4244; 656860
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Wind Energy
Additional Journal Information:
Journal Volume: 21; Journal Issue: 1; Journal ID: ISSN 1095-4244
Research Org:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
National Science Foundation (pre Sandia employment); USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
17 WIND ENERGY; onshore wind farms; statistical modeling; turbine wakes; wind power assessment
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1399873