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Prediction of site specific wind energy value factors

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  • Capellaro, Mark

Abstract

This paper demonstrates a method to predict the value factor and market value of a wind turbine production. This will allow for the estimation of the economic viability of a turbine subject to pure market prices and also allow for prediction of the necessary financial support for turbines under subsidy schemes. The value factor is a common metric used to determine the value of variable energy sources. Research has shown that wind speed profiles that with a high value factor, or have a positive correlation with the market electricity price, have an economic advantage. However, previous research has determined overall value factors for wind in electricity networks at the macro level. This paper details a method at the micro level, since the wind conditions and therefore the value factor are themselves a site specific property. The data necessary to predict the value factor is the same as used to predict the energy production of the site. This allows for the prediction to be used before turbine installation and can be used as an optimization parameter for researchers and for turbine siting with regards to the electricity price/market.

Suggested Citation

  • Capellaro, Mark, 2016. "Prediction of site specific wind energy value factors," Renewable Energy, Elsevier, vol. 87(P1), pages 430-436.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p1:p:430-436
    DOI: 10.1016/j.renene.2015.10.019
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    References listed on IDEAS

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    1. Hirth, Lion, 2013. "The market value of variable renewables," Energy Economics, Elsevier, vol. 38(C), pages 218-236.
    2. MacCormack, John & Hollis, Aidan & Zareipour, Hamidreza & Rosehart, William, 2010. "The large-scale integration of wind generation: Impacts on price, reliability and dispatchable conventional suppliers," Energy Policy, Elsevier, vol. 38(7), pages 3837-3846, July.
    3. Katzenstein, Warren & Apt, Jay, 2012. "The cost of wind power variability," Energy Policy, Elsevier, vol. 51(C), pages 233-243.
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    Cited by:

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    3. Fabio Magrassi & Adriana Del Borghi & Michela Gallo & Carlo Strazza & Michela Robba, 2016. "Optimal Planning of Sustainable Buildings: Integration of Life Cycle Assessment and Optimization in a Decision Support System (DSS)," Energies, MDPI, vol. 9(7), pages 1-15, June.
    4. al Irsyad, Muhammad Indra & Halog, Anthony & Nepal, Rabindra, 2019. "Renewable energy projections for climate change mitigation: An analysis of uncertainty and errors," Renewable Energy, Elsevier, vol. 130(C), pages 536-546.
    5. Dragomir, George & Șerban, Alexandru & Năstase, Gabriel & Brezeanu, Alin Ionuț, 2016. "Wind energy in Romania: A review from 2009 to 2016," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 129-143.
    6. Burt, Michelle & Firestone, Jeremy & Madsen, John A. & Veron, Dana E. & Bowers, Richard, 2017. "Tall towers, long blades and manifest destiny: The migration of land-based wind from the Great Plains to the thirteen colonies," Applied Energy, Elsevier, vol. 206(C), pages 487-497.
    7. Samal, Rajat Kanti & Tripathy, M., 2019. "A novel distance metric for evaluating impact of wind integration on power systems," Renewable Energy, Elsevier, vol. 140(C), pages 722-736.
    8. Winkler, Jenny & Pudlik, Martin & Ragwitz, Mario & Pfluger, Benjamin, 2016. "The market value of renewable electricity – Which factors really matter?," Applied Energy, Elsevier, vol. 184(C), pages 464-481.

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