IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i5p1063-d1344684.html
   My bibliography  Save this article

Power Production, Inter- and Intra-Array Wake Losses from the U.S. East Coast Offshore Wind Energy Lease Areas

Author

Listed:
  • Sara C. Pryor

    (Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853, USA)

  • Rebecca J. Barthelmie

    (Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA)

Abstract

There is an urgent need to develop accurate predictions of power production, wake losses and array–array interactions from multi-GW offshore wind farms in order to enable developments that maximize power benefits, minimize levelized cost of energy and reduce investment uncertainty. New, climatologically representative simulations with the Weather Research and Forecasting (WRF) model are presented and analyzed to address these research needs with a specific focus on offshore wind energy lease areas along the U.S. east coast. These, uniquely detailed, simulations are designed to quantify important sources of wake-loss projection uncertainty. They sample across different wind turbine deployment scenarios and thus span the range of plausible installed capacity densities (ICDs) and also include two wind farm parameterizations (WFPs; Fitch and explicit wake parameterization (EWP)) and consider the precise WRF model release used. System-wide mean capacity factors for ICDs of 3.5 to 6.0 MWkm −2 range from 39 to 45% based on output from Fitch and 50 to 55% from EWP. Wake losses are 27–37% (Fitch) and 11–19% (EWP). The discrepancy in CF and wake losses from the two WFPs derives from two linked effects. First, EWP generates a weaker ‘deep array effect’ within the largest wind farm cluster (area of 3675 km 2 ), though both parameterizations indicate substantial within-array wake losses. If 15 MW wind turbines are deployed at an ICD of 6 MWkm −2 the most heavily waked wind turbines generate an average of only 32–35% of the power of those that experience the freestream (undisturbed) flow. Nevertheless, there is no evidence for saturation of the resource. The wind power density (electrical power generation per unit of surface area) increases with ICD and lies between 2 and 3 Wm −2 . Second, EWP also systematically generates smaller whole wind farm wakes. Sampling across all offshore wind energy lease areas and the range of ICD considered, the whole wind farm wake extent for a velocity deficit of 5% is 1.18 to 1.38 times larger in simulations with Fitch. Over three-quarters of the variability in normalized wake extents is attributable to variations in freestream wind speeds, turbulent kinetic energy and boundary layer depth. These dependencies on meteorological parameters allow for the development of computationally efficient emulators of wake extents from Fitch and EWP.

Suggested Citation

  • Sara C. Pryor & Rebecca J. Barthelmie, 2024. "Power Production, Inter- and Intra-Array Wake Losses from the U.S. East Coast Offshore Wind Energy Lease Areas," Energies, MDPI, vol. 17(5), pages 1-30, February.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1063-:d:1344684
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/5/1063/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/5/1063/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pryor, Sara C. & Barthelmie, Rebecca J., 2024. "Wind shadows impact planning of large offshore wind farms," Applied Energy, Elsevier, vol. 359(C).
    2. Malte Jansen & Philipp Beiter & Iegor Riepin & Felix Muesgens & Victor Juarez Guajardo-Fajardo & Iain Staffell & Bernard Bulder & Lena Kitzing, 2022. "Policy choices and outcomes for offshore wind auctions globally," Papers 2202.12548, arXiv.org, revised Apr 2022.
    3. Ioannou, Anastasia & Angus, Andrew & Brennan, Feargal, 2020. "Stochastic financial appraisal of offshore wind farms," Renewable Energy, Elsevier, vol. 145(C), pages 1176-1191.
    4. Ignacio Losada Carreño & Michael T. Craig & Michael Rossol & Moetasim Ashfaq & Fulden Batibeniz & Sue Ellen Haupt & Caroline Draxl & Bri-Mathias Hodge & Carlo Brancucci, 2020. "Potential impacts of climate change on wind and solar electricity generation in Texas," Climatic Change, Springer, vol. 163(2), pages 745-766, November.
    5. Michael F. Howland & Jesús Bas Quesada & Juan José Pena Martínez & Felipe Palou Larrañaga & Neeraj Yadav & Jasvipul S. Chawla & Varun Sivaram & John O. Dabiri, 2022. "Collective wind farm operation based on a predictive model increases utility-scale energy production," Nature Energy, Nature, vol. 7(9), pages 818-827, September.
    6. Caputo, Antonio C. & Federici, Alessandro & Pelagagge, Pacifico M. & Salini, Paolo, 2023. "Offshore wind power system economic evaluation framework under aleatory and epistemic uncertainty," Applied Energy, Elsevier, vol. 350(C).
    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. He, Ruiyang & Yang, Hongxing & Lu, Lin & Gao, Xiaoxia, 2024. "Site-specific wake steering strategy for combined power enhancement and fatigue mitigation within wind farms," Renewable Energy, Elsevier, vol. 225(C).
    2. Simshauser, Paul, 2024. "On static vs. dynamic line ratings in renewable energy zones," Energy Economics, Elsevier, vol. 129(C).
    3. Hongyun Zhang & Michael G. Pollitt, 2023. "Comparison of policy instruments in the development process of offshore wind power in North Sea countries," Working Papers EPRG2323, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    4. Jaime Liew & Kirby S. Heck & Michael F. Howland, 2024. "Unified momentum model for rotor aerodynamics across operating regimes," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    5. Pryor, Sara C. & Barthelmie, Rebecca J., 2024. "Wind shadows impact planning of large offshore wind farms," Applied Energy, Elsevier, vol. 359(C).
    6. Shin, Heesoo & Rüttgers, Mario & Lee, Sangseung, 2023. "Effects of spatiotemporal correlations in wind data on neural network-based wind predictions," Energy, Elsevier, vol. 279(C).
    7. 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).
    8. Rivera-Arreba, Irene & Li, Zhaobin & Yang, Xiaolei & Bachynski-Polić, Erin E., 2024. "Comparison of the dynamic wake meandering model against large eddy simulation for horizontal and vertical steering of wind turbine wakes," Renewable Energy, Elsevier, vol. 221(C).
    9. Batz Liñeiro, Taimyra & Müsgens, Felix, 2023. "Evaluating the German onshore wind auction programme: An analysis based on individual bids," Energy Policy, Elsevier, vol. 172(C).
    10. Jansen, Malte & Gross, Rob & Staffell, Iain, 2024. "Quantitative evidence for modelling electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    11. Mingyu Li & Dongxiao Niu & Zhengsen Ji & Xiwen Cui & Lijie Sun, 2021. "Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net," Sustainability, MDPI, vol. 13(21), pages 1-19, November.
    12. Huanqiang, Zhang & Xiaoxia, Gao & Hongkun, Lu & Qiansheng, Zhao & Xiaoxun, Zhu & Yu, Wang & Fei, Zhao, 2024. "Investigation of a new 3D wake model of offshore floating wind turbines subjected to the coupling effects of wind and wave," Applied Energy, Elsevier, vol. 365(C).
    13. Guanying Chen & Zhenming Ji, 2024. "A Review of Solar and Wind Energy Resource Projection Based on the Earth System Model," Sustainability, MDPI, vol. 16(8), pages 1-19, April.
    14. Esmail Mahmoodi & Mohammad Khezri & Arash Ebrahimi & Uwe Ritschel & Leonardo P. Chamorro & Ali Khanjari, 2023. "A Simple Model for Wake-Induced Aerodynamic Interaction of Wind Turbines," Energies, MDPI, vol. 16(15), pages 1-13, July.
    15. Pawar, Suraj & Sharma, Ashesh & Vijayakumar, Ganesh & Bay, Chrstopher J. & Yellapantula, Shashank & San, Omer, 2022. "Towards multi-fidelity deep learning of wind turbine wakes," Renewable Energy, Elsevier, vol. 200(C), pages 867-879.
    16. Xing Su & Xudong Wang & Wanli Xu & Liqian Yuan & Chunhua Xiong & Jinmao Chen, 2024. "Offshore Wind Power: Progress of the Edge Tool, Which Can Promote Sustainable Energy Development," Sustainability, MDPI, vol. 16(17), pages 1-22, September.
    17. Schlecht, Ingmar & Maurer, Christoph & Hirth, Lion, 2024. "Financial contracts for differences: The problems with conventional CfDs in electricity markets and how forward contracts can help solve them," Energy Policy, Elsevier, vol. 186(C).
    18. Wu, Yunna & Liu, Fangtong & Wu, Junhao & He, Jiaming & Xu, Minjia & Zhou, Jianli, 2022. "Barrier identification and analysis framework to the development of offshore wind-to-hydrogen projects," Energy, Elsevier, vol. 239(PB).
    19. Hastings-Simon, Sara & Leach, Andrew & Shaffer, Blake & Weis, Tim, 2022. "Alberta's Renewable Electricity Program: Design, results, and lessons learned," Energy Policy, Elsevier, vol. 171(C).
    20. Ehrhart, Karl-Martin & Ott, Marion & Seifert, Stefan & Wang, Runxi, 2024. "Combinatorial auctions for renewable energy — potentials and challenges," Energy Policy, Elsevier, vol. 186(C).

    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:17:y:2024:i:5:p:1063-:d:1344684. 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.