Distributed wind resource assessment: Comparing measured annual energy production with predictions from computational fluid dynamics

Devon L. Martindale, Thomas L. Acker

Research output: Contribution to journalArticle

Abstract

The US Department of Energy’s Distributed Wind Resource Assessment Workshop identified predicting the annual energy production of a kilowatt-sized wind turbine as a key challenge. This article presents the methods and results for predicting the annual energy production of two 2.1 kW Skystream 3.7 wind turbines using computational fluid dynamics, in this case Meteodyn WT. When compared with actual production data, annual energy production values were uniformly underpredicted, with errors ranging from 1% to in excess of 30%, depending on the solver settings and boundary conditions. The most accurate of the simulations with errors consistently less than 10% were achieved when using recommended solver settings of neutral atmospheric stability, and roughness values derived from the US National Land Cover Database. The software was used to create an annual energy production map for the modeling domain, which could be a valuable tool in estimating the energy output and economic value of a proposed wind turbine.

Original languageEnglish (US)
Pages (from-to)657-672
Number of pages16
JournalWind Engineering
Volume43
Issue number6
DOIs
StatePublished - Dec 1 2019

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Computational fluid dynamics
Wind turbines
Surface roughness
Boundary conditions
Economics

Keywords

  • annual energy production
  • computational methods
  • distributed wind energy conversion system
  • Meteodyn WT
  • Wind energy
  • wind power prediction
  • wind resource assessment

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology

Cite this

Distributed wind resource assessment : Comparing measured annual energy production with predictions from computational fluid dynamics. / Martindale, Devon L.; Acker, Thomas L.

In: Wind Engineering, Vol. 43, No. 6, 01.12.2019, p. 657-672.

Research output: Contribution to journalArticle

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