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A Response Surface-Based Cost Model for Wind Farm Design

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  • Zhang, Jie
  • Chowdhury, Souma
  • Messac, Achille
  • Castillo, Luciano

Abstract

A Response Surface-Based Wind Farm Cost (RS-WFC) model is developed for the engineering planning of wind farms. The RS-WFC model is developed using Extended Radial Basis Functions (E-RBF) for onshore wind farms in the U.S. This model is then used to explore the influences of different design and economic parameters, including number of turbines, rotor diameter and labor cost, on the cost of a wind farm. The RS-WFC model is composed of three components that estimate the effects of engineering and economic factors on (i) the installation cost, (ii) the annual Operation and Maintenance (O&M) cost, and (iii) the total annual cost of a wind farm. The accuracy of the cost model is favorably established through comparison with pertinent commercial data. The final RS-WFC model provided interesting insights into cost variation with respect to critical engineering and economic parameters. In addition, a newly developed analytical wind farm engineering model is used to determine the power generated by the farm, and the subsequent Cost of Energy (COE). This COE is optimized for a unidirectional uniform “incoming wind speed” scenario using Particle Swarm Optimization (PSO). We found that the COE could be appreciably minimized through layout optimization, thereby yielding significant cost savings.

Suggested Citation

  • Zhang, Jie & Chowdhury, Souma & Messac, Achille & Castillo, Luciano, 2012. "A Response Surface-Based Cost Model for Wind Farm Design," Energy Policy, Elsevier, vol. 42(C), pages 538-550.
    • Handle: RePEc:eee:enepol:v:42:y:2012:i:c:p:538-550
    DOI: 10.1016/j.enpol.2011.12.021
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    References listed on IDEAS

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    8. Serrano González, Javier & Burgos Payán, Manuel & Santos, Jesús Manuel Riquelme & González-Longatt, Francisco, 2014. "A review and recent developments in the optimal wind-turbine micro-siting problem," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 133-144.
    9. Geyer, Philipp & Singaravel, Sundaravelpandian, 2018. "Component-based machine learning for performance prediction in building design," Applied Energy, Elsevier, vol. 228(C), pages 1439-1453.
    10. Newman, A. Jensen & Drew, Donald A. & Castillo, Luciano, 2014. "Pseudo spectral analysis of the energy entrainment in a scaled down wind farm," Renewable Energy, Elsevier, vol. 70(C), pages 129-141.
    11. Tautz-Weinert, Jannis & Yürüşen, Nurseda Y. & Melero, Julio J. & Watson, Simon J., 2019. "Sensitivity study of a wind farm maintenance decision - A performance and revenue analysis," Renewable Energy, Elsevier, vol. 132(C), pages 93-105.
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