Objective and Algorithm Considerations When Optimizing the Number and Placement of Turbines in a Wind Power Plant

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11 Scopus Citations


Optimizing turbine layout is a challenging problem that has been extensively researched in the literature. However, optimizing the number of turbines within a given boundary has not been studied as extensively and is a difficult problem because it introduces discrete design variables and a discontinuous design space. An essential step in performing wind power plant layout optimization is to define the objective function, or value, that is used to express what is valuable to a wind power plant developer, such as annual energy production, cost of energy, or profit. In this paper, we demonstrate the importance of selecting the appropriate objective function when optimizing a wind power plant in a land-constrained site. We optimized several different wind power plants with different wind resources and boundary sizes. Results show that the optimal number of turbines varies drastically depending on the objective function. For a simple, one-dimensional, land-based scenario, we found that a wind power plant optimized for minimal cost of energy produced just 72% of the profit compared to the wind power plant optimized for maximum profit, which corresponded to a loss of about USD2 million each year. This paper also compares the performance of several different optimization algorithms, including a novel repeated-sweep algorithm that we developed. We found that the performance of each algorithm depended on the number of design variables in the problem as well as the objective function.

Original languageAmerican English
Pages (from-to)1143-1167
Number of pages25
JournalWind Energy Science
Issue number5
StatePublished - 2021

Bibliographical note

See NREL/JA-5000-78764 for article as published in Wind Energy Science Discussions

NREL Publication Number

  • NREL/JA-5000-81076


  • annual energy production
  • cost of energy
  • gradient-free optimization
  • layout optimization
  • objective function
  • profit
  • wind energy


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