IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i8p1486-d224239.html
   My bibliography  Save this article

Spatiotemporal Correlations in the Power Output of Wind Farms: On the Impact of Atmospheric Stability

Author

Listed:
  • Nicolas Tobin

    (Department of Mechanical Science and Engineering, University of Illinois, Urbana, IL 61801, USA)

  • Adam Lavely

    (Department of Aerospace Engineering, Pennsylvania State University, University Park, PA 16802, USA)

  • Sven Schmitz

    (Department of Aerospace Engineering, Pennsylvania State University, University Park, PA 16802, USA)

  • Leonardo P. Chamorro

    (Department of Mechanical Science and Engineering, University of Illinois, Urbana, IL 61801, USA
    Department of Civil and Environmental Engineering, University of Illinois, Urbana, IL 61801, USA
    Department of Aerospace Engineering, University of Illinois, Urbana, IL 61801, USA)

Abstract

The dependence of temporal correlations in the power output of wind-turbine pairs on atmospheric stability is explored using theoretical arguments and wind-farm large-eddy simulations. For this purpose, a range of five distinct stability regimes, ranging from weakly stable to moderately convective, were investigated with the same aligned wind-farm layout used among simulations. The coherence spectrum between turbine pairs in each simulation was compared to theoretical predictions. We found with high statistical significance ( p < 0.01) that higher levels of atmospheric instability lead to higher coherence between turbines, with wake motions reducing correlations up to 40%. This is attributed to higher dominance of atmospheric motions over wakes in strongly unstable flows. Good agreement resulted with the use of an empirical model for wake-added turbulence to predict the variation of turbine power coherence with ambient turbulence intensity (R 2 = 0.82), though other empirical relations may be applicable. It was shown that improperly accounting for turbine–turbine correlations can substantially impact power variance estimates on the order of a factor of 4.

Suggested Citation

  • Nicolas Tobin & Adam Lavely & Sven Schmitz & Leonardo P. Chamorro, 2019. "Spatiotemporal Correlations in the Power Output of Wind Farms: On the Impact of Atmospheric Stability," Energies, MDPI, vol. 12(8), pages 1-12, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1486-:d:224239
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/8/1486/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/8/1486/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pankaj K. Jha & Earl P. N. Duque & Jessica L. Bashioum & Sven Schmitz, 2015. "Unraveling the Mysteries of Turbulence Transport in a Wind Farm," Energies, MDPI, vol. 8(7), pages 1-29, June.
    2. Katzenstein, Warren & Fertig, Emily & Apt, Jay, 2010. "The variability of interconnected wind plants," Energy Policy, Elsevier, vol. 38(8), pages 4400-4410, August.
    3. Huiwen Liu & Imran Hayat & Yaqing Jin & Leonardo P. Chamorro, 2018. "On the Evolution of the Integral Time Scale within Wind Farms," Energies, MDPI, vol. 11(1), pages 1-11, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kruyt, Bert & Lehning, Michael & Kahl, Annelen, 2017. "Potential contributions of wind power to a stable and highly renewable Swiss power supply," Applied Energy, Elsevier, vol. 192(C), pages 1-11.
    2. Fei Zhao & Yihan Gao & Tengyuan Wang & Jinsha Yuan & Xiaoxia Gao, 2020. "Experimental Study on Wake Evolution of a 1.5 MW Wind Turbine in a Complex Terrain Wind Farm Based on LiDAR Measurements," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    3. Lenzen, Manfred & McBain, Bonnie & Trainer, Ted & Jütte, Silke & Rey-Lescure, Olivier & Huang, Jing, 2016. "Simulating low-carbon electricity supply for Australia," Applied Energy, Elsevier, vol. 179(C), pages 553-564.
    4. Böhme, Gustavo S. & Fadigas, Eliane A. & Soares, Dorel & Gimenes, André L.V. & Macedo, Bruno C., 2020. "Wind speed variability and portfolio effect – A case study in the Brazilian market," Energy, Elsevier, vol. 207(C).
    5. Lobato, E. & Doenges, K. & Egido, I. & Sigrist, L., 2020. "Limits to wind aggregation: Empirical assessment in the Spanish electricity system," Renewable Energy, Elsevier, vol. 147(P1), pages 1321-1330.
    6. Grothe, Oliver & Müsgens, Felix, 2013. "The influence of spatial effects on wind power revenues under direct marketing rules," Energy Policy, Elsevier, vol. 58(C), pages 237-247.
    7. Álvarez-García, Francisco J. & Fresno-Schmolk, Gonzalo & OrtizBevia, María J. & Cabos, William & RuizdeElvira, Antonio, 2020. "Reduction of aggregate wind power variability using Empirical Orthogonal Teleconnections: An application in the Iberian Peninsula," Renewable Energy, Elsevier, vol. 159(C), pages 151-161.
    8. Xinkai Li & Ke Yang & Hao Hu & Xiaodong Wang & Shun Kang, 2019. "Effect of Tailing-Edge Thickness on Aerodynamic Noise for Wind Turbine Airfoil," Energies, MDPI, vol. 12(2), pages 1-25, January.
    9. Shahriari, M. & Cervone, G. & Clemente-Harding, L. & Delle Monache, L., 2020. "Using the analog ensemble method as a proxy measurement for wind power predictability," Renewable Energy, Elsevier, vol. 146(C), pages 789-801.
    10. Kocaman, Ayse Selin & Ozyoruk, Emin & Taneja, Shantanu & Modi, Vijay, 2020. "A stochastic framework to evaluate the impact of agricultural load flexibility on the sizing of renewable energy systems," Renewable Energy, Elsevier, vol. 152(C), pages 1067-1078.
    11. Yuan, Qiheng & Zhou, Keliang & Yao, Jing, 2020. "A new measure of wind power variability with implications for the optimal sizing of standalone wind power systems," Renewable Energy, Elsevier, vol. 150(C), pages 538-549.
    12. Williams, Eric & Hittinger, Eric & Carvalho, Rexon & Williams, Ryan, 2017. "Wind power costs expected to decrease due to technological progress," Energy Policy, Elsevier, vol. 106(C), pages 427-435.
    13. Han, Chanok & Vinel, Alexander, 2022. "Reducing forecasting error by optimally pooling wind energy generation sources through portfolio optimization," Energy, Elsevier, vol. 239(PB).
    14. Gomes, I.L.R. & Melicio, R. & Mendes, V.M.F. & Pousinho, H.M.I., 2019. "Decision making for sustainable aggregation of clean energy in day-ahead market: Uncertainty and risk," Renewable Energy, Elsevier, vol. 133(C), pages 692-702.
    15. Grothe, Oliver & Schnieders, Julius, 2011. "Spatial dependence in wind and optimal wind power allocation: A copula-based analysis," Energy Policy, Elsevier, vol. 39(9), pages 4742-4754, September.
    16. Blumsack, Seth & Fernandez, Alisha, 2012. "Ready or not, here comes the smart grid!," Energy, Elsevier, vol. 37(1), pages 61-68.
    17. Alexis Tantet & Marc Stéfanon & Philippe Drobinski & Jordi Badosa & Silvia Concettini & Anna Cretì & Claudia D’Ambrosio & Dimitri Thomopulos & Peter Tankov, 2019. "e 4 clim 1.0: The Energy for a Climate Integrated Model: Description and Application to Italy," Energies, MDPI, vol. 12(22), pages 1-37, November.
    18. Grothe, Oliver & Müsgens, Felix, 2012. "The influence of spatial effects on wind power revenues under direct marketing rules," EWI Working Papers 2012-7, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    19. Rahmani, Mohsen & Jaramillo, Paulina & Hug, Gabriela, 2016. "Implications of environmental regulation and coal plant retirements in systems with large scale penetration of wind power," Energy Policy, Elsevier, vol. 95(C), pages 196-210.
    20. Jägemann, Cosima, 2014. "An illustrative note on the system price effect of wind and solar power - The German case," EWI Working Papers 2014-10, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1486-:d:224239. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.