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Modeling noise and lease soft costs improves wind farm design and cost-of-energy predictions

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  • Chen, Le
  • Harding, Chris
  • Sharma, Anupam
  • MacDonald, Erin

Abstract

The Department of Energy uses the metric Cost-of-Energy to assess the financial viability of wind farms. Non-hardware costs, termed soft costs, make up approximately 21% of total cost for a land-based farm, yet are only represented with general assumptions in models of Cost-of-Energy. This work replaces these assumptions with a probabilistic model of the costs of land lease and noise disturbance compensation, which is incorporated into a wind-farm-layout-optimization-under-uncertainty model. These realistic representations are applied to an Iowa land area with real land boundaries and house locations to accentuate the challenges of accommodating landowners. The paper also investigates and removes a common but unnecessary term that overestimates cost-savings from installing multiple turbines. These three contributions combine to produce COE estimates in-line with industry data, replacing “soft” assumptions with specific parameters, identify noise and risk concerns prohibitive to the development of profitable wind farm. The model predicts COEs remarkably close to real-world costs. Wind energy policy-makers can use this model to promote new areas of soft-cost-focused research.

Suggested Citation

  • Chen, Le & Harding, Chris & Sharma, Anupam & MacDonald, Erin, 2016. "Modeling noise and lease soft costs improves wind farm design and cost-of-energy predictions," Renewable Energy, Elsevier, vol. 97(C), pages 849-859.
    • Handle: RePEc:eee:renene:v:97:y:2016:i:c:p:849-859
    DOI: 10.1016/j.renene.2016.05.045
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    References listed on IDEAS

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    Cited by:

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    2. Guirguis, David & Romero, David A. & Amon, Cristina H., 2017. "Gradient-based multidisciplinary design of wind farms with continuous-variable formulations," Applied Energy, Elsevier, vol. 197(C), pages 279-291.
    3. Momeni, Farhang & Sabzpoushan, Seyedali & Valizadeh, Reza & Morad, Mohammad Reza & Liu, Xun & Ni, Jun, 2019. "Plant leaf-mimetic smart wind turbine blades by 4D printing," Renewable Energy, Elsevier, vol. 130(C), pages 329-351.
    4. Yildiz, Anil & Mern, John & Kochenderfer, Mykel J. & Howland, Michael F., 2023. "Towards sequential sensor placements on a wind farm to maximize lifetime energy and profit," Renewable Energy, Elsevier, vol. 216(C).
    5. Copena, Damián & Simón, Xavier, 2018. "Wind farms and payments to landowners: Opportunities for rural development for the case of Galicia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 95(C), pages 38-47.
    6. Ti, Zilong & Deng, Xiao Wei & Zhang, Mingming, 2021. "Artificial Neural Networks based wake model for power prediction of wind farm," Renewable Energy, Elsevier, vol. 172(C), pages 618-631.
    7. Vasel-Be-Hagh, Ahmadreza & Archer, Cristina L., 2017. "Wind farm hub height optimization," Applied Energy, Elsevier, vol. 195(C), pages 905-921.

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