IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v265y2023ics036054422203170x.html
   My bibliography  Save this article

Introducing a new wind speed complementarity model

Author

Listed:
  • Jung, Christopher
  • Schindler, Dirk

Abstract

Minimizing the residual load is essential in countries with a high share of variable renewable energies (VRE). Optimally, new wind turbine sites provide high resource availability and the potential for smoothing the temporal course of the residual load. Current high spatial resolution wind atlases are limited to the ability to find high capacity factor (CF) sites but do not provide CF during periods of high residual load. Thus, we derive a new high-spatiotemporal resolution wind speed model for adjusting wind turbine site selection to residual load. The model (WiCoMo) is developed at six-hourly temporal resolution and 25 m × 25 spatial resolution up to 200 m height from 1991 to 2018 in the study area of Germany. Residual load-weighted and adjusted CF in 2015–2018 is mapped using the new model and considering the national residual load. Besides, complementarity between CF and VRE is evaluated. According to the standard CF, there is a northwest-southeast CF gradient. Relying on the residual load adjusted CF site suitability in southern Germany increases compared to the rest of the country. The introduced model may be a valuable tool for adapting wind resource assessment in Germany on the increasing role of VRE in the electricity mix.

Suggested Citation

  • Jung, Christopher & Schindler, Dirk, 2023. "Introducing a new wind speed complementarity model," Energy, Elsevier, vol. 265(C).
    • Handle: RePEc:eee:energy:v:265:y:2023:i:c:s036054422203170x
    DOI: 10.1016/j.energy.2022.126284
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422203170X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.126284?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jakub Jurasz & Jerzy Mikulik & Paweł B. Dąbek & Mohammed Guezgouz & Bartosz Kaźmierczak, 2021. "Complementarity and ‘Resource Droughts’ of Solar and Wind Energy in Poland: An ERA5-Based Analysis," Energies, MDPI, vol. 14(4), pages 1-24, February.
    2. Veronesi, F. & Grassi, S. & Raubal, M., 2016. "Statistical learning approach for wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 836-850.
    3. Garrido-Perez, Jose M. & Ordóñez, Carlos & Barriopedro, David & García-Herrera, Ricardo & Paredes, Daniel, 2020. "Impact of weather regimes on wind power variability in western Europe," Applied Energy, Elsevier, vol. 264(C).
    4. Christian M. Grams & Remo Beerli & Stefan Pfenninger & Iain Staffell & Heini Wernli, 2017. "Balancing Europe’s wind-power output through spatial deployment informed by weather regimes," Nature Climate Change, Nature, vol. 7(8), pages 557-562, August.
    5. Carvalho, D. & Rocha, A. & Santos, C. Silva & Pereira, R., 2013. "Wind resource modelling in complex terrain using different mesoscale–microscale coupling techniques," Applied Energy, Elsevier, vol. 108(C), pages 493-504.
    6. Giovanni Gualtieri, 2021. "Reliability of ERA5 Reanalysis Data for Wind Resource Assessment: A Comparison against Tall Towers," Energies, MDPI, vol. 14(14), pages 1-21, July.
    7. Dayal, Kunal K. & Cater, John E. & Kingan, Michael J. & Bellon, Gilles D. & Sharma, Rajnish N., 2021. "Wind resource assessment and energy potential of selected locations in Fiji," Renewable Energy, Elsevier, vol. 172(C), pages 219-237.
    8. Jung, Christopher & Schindler, Dirk, 2018. "On the inter-annual variability of wind energy generation – A case study from Germany," Applied Energy, Elsevier, vol. 230(C), pages 845-854.
    9. Steven Chu & Arun Majumdar, 2012. "Opportunities and challenges for a sustainable energy future," Nature, Nature, vol. 488(7411), pages 294-303, August.
    10. Daaou Nedjari, H. & Haddouche, S. Kheder & Balehouane, A. & Guerri, O., 2018. "Optimal windy sites in Algeria: Potential and perspectives," Energy, Elsevier, vol. 147(C), pages 1240-1255.
    11. Crippa, Paola & Alifa, Mariana & Bolster, Diogo & Genton, Marc G. & Castruccio, Stefano, 2021. "A temporal model for vertical extrapolation of wind speed and wind energy assessment," Applied Energy, Elsevier, vol. 301(C).
    12. Asquith, William H., 2007. "L-moments and TL-moments of the generalized lambda distribution," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4484-4496, May.
    13. Jung, Christopher & Schindler, Dirk, 2019. "The role of air density in wind energy assessment – A case study from Germany," Energy, Elsevier, vol. 171(C), pages 385-392.
    14. Jung, Christopher & Schindler, Dirk, 2019. "Wind speed distribution selection – A review of recent development and progress," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    15. Monforti, F. & Gaetani, M. & Vignati, E., 2016. "How synchronous is wind energy production among European countries?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1622-1638.
    16. Gualtieri, Giovanni, 2019. "A comprehensive review on wind resource extrapolation models applied in wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 102(C), pages 215-233.
    17. Murthy, K.S.R. & Rahi, O.P., 2017. "A comprehensive review of wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1320-1342.
    18. Miao, Haozeyu & Dong, Danhong & Huang, Gang & Hu, Kaiming & Tian, Qun & Gong, Yuanfa, 2020. "Evaluation of Northern Hemisphere surface wind speed and wind power density in multiple reanalysis datasets," Energy, Elsevier, vol. 200(C).
    19. Raynaud, D. & Hingray, B. & François, B. & Creutin, J.D., 2018. "Energy droughts from variable renewable energy sources in European climates," Renewable Energy, Elsevier, vol. 125(C), pages 578-589.
    20. Katikas, Loukas & Dimitriadis, Panayiotis & Koutsoyiannis, Demetris & Kontos, Themistoklis & Kyriakidis, Phaedon, 2021. "A stochastic simulation scheme for the long-term persistence, heavy-tailed and double periodic behavior of observational and reanalysis wind time-series," Applied Energy, Elsevier, vol. 295(C).
    21. Radünz, William Corrêa & Mattuella, Jussara M. Leite & Petry, Adriane Prisco, 2020. "Wind resource mapping and energy estimation in complex terrain: A framework based on field observations and computational fluid dynamics," Renewable Energy, Elsevier, vol. 152(C), pages 494-515.
    22. Jung, Christopher & Schindler, Dirk, 2022. "On the influence of wind speed model resolution on the global technical wind energy potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Houndekindo, Freddy & Ouarda, Taha B.M.J., 2024. "Prediction of hourly wind speed time series at unsampled locations using machine learning," Energy, Elsevier, vol. 299(C).

    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, J.Y. & Chan, P.W. & Li, Q.S. & Lee, C.W., 2022. "Characterizing coastal wind energy resources based on sodar and microwave radiometer observations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    2. Yang, Zihao & Dong, Sheng, 2024. "A novel framework for wind energy assessment at multi-time scale based on non-stationary wind speed models: A case study in China," Renewable Energy, Elsevier, vol. 226(C).
    3. Christopher Jung & Dirk Schindler, 2023. "Reasons for the Recent Onshore Wind Capacity Factor Increase," Energies, MDPI, vol. 16(14), pages 1-17, July.
    4. He, J.Y. & Li, Q.S. & Chan, P.W. & Zhao, X.D., 2023. "Assessment of future wind resources under climate change using a multi-model and multi-method ensemble approach," Applied Energy, Elsevier, vol. 329(C).
    5. Jung, Christopher & Schindler, Dirk, 2021. "A global wind farm potential index to increase energy yields and accessibility," Energy, Elsevier, vol. 231(C).
    6. Lattawan Niyomtham & Charoenporn Lertsathittanakorn & Jompob Waewsak & Yves Gagnon, 2022. "Mesoscale/Microscale and CFD Modeling for Wind Resource Assessment: Application to the Andaman Coast of Southern Thailand," Energies, MDPI, vol. 15(9), pages 1-19, April.
    7. Yang, Xinrong & Jiang, Xin & Liang, Shijing & Qin, Yingzuo & Ye, Fan & Ye, Bin & Xu, Jiayu & He, Xinyue & Wu, Jie & Dong, Tianyun & Cai, Xitian & Xu, Rongrong & Zeng, Zhenzhong, 2024. "Spatiotemporal variation of power law exponent on the use of wind energy," Applied Energy, Elsevier, vol. 356(C).
    8. Bracken, Cameron & Voisin, Nathalie & Burleyson, Casey D. & Campbell, Allison M. & Hou, Z. Jason & Broman, Daniel, 2024. "Standardized benchmark of historical compound wind and solar energy droughts across the Continental United States," Renewable Energy, Elsevier, vol. 220(C).
    9. Radünz, William Corrêa & Mattuella, Jussara M. Leite & Petry, Adriane Prisco, 2020. "Wind resource mapping and energy estimation in complex terrain: A framework based on field observations and computational fluid dynamics," Renewable Energy, Elsevier, vol. 152(C), pages 494-515.
    10. Alain Ulazia & Ander Nafarrate & Gabriel Ibarra-Berastegi & Jon Sáenz & Sheila Carreno-Madinabeitia, 2019. "The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential," Energies, MDPI, vol. 12(13), pages 1-18, July.
    11. He, J.Y. & Chan, P.W. & Li, Q.S. & Huang, Tao & Yim, Steve Hung Lam, 2024. "Assessment of urban wind energy resource in Hong Kong based on multi-instrument observations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    12. Gangopadhyay, A. & Seshadri, A.K. & Sparks, N.J. & Toumi, R., 2022. "The role of wind-solar hybrid plants in mitigating renewable energy-droughts," Renewable Energy, Elsevier, vol. 194(C), pages 926-937.
    13. Otero, Noelia & Martius, Olivia & Allen, Sam & Bloomfield, Hannah & Schaefli, Bettina, 2022. "A copula-based assessment of renewable energy droughts across Europe," Renewable Energy, Elsevier, vol. 201(P1), pages 667-677.
    14. Siyavash Filom & Soheil Radfar & Roozbeh Panahi & Erfan Amini & Mehdi Neshat, 2021. "Exploring Wind Energy Potential as a Driver of Sustainable Development in the Southern Coasts of Iran: The Importance of Wind Speed Statistical Distribution Model," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    15. Guedes, Kevin S. & de Andrade, Carla F. & Rocha, Paulo A.C. & Mangueira, Rivanilso dos S. & de Moura, Elineudo P., 2020. "Performance analysis of metaheuristic optimization algorithms in estimating the parameters of several wind speed distributions," Applied Energy, Elsevier, vol. 268(C).
    16. Pérez Albornoz, C. & Escalante Soberanis, M.A. & Ramírez Rivera, V. & Rivero, M., 2022. "Review of atmospheric stability estimations for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    17. Jung, Christopher & Schindler, Dirk, 2022. "On the influence of wind speed model resolution on the global technical wind energy potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    18. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2020. "Spatial and temporal correlation analysis of wind power between different provinces in China," Energy, Elsevier, vol. 191(C).
    19. Yang, Xiaolei & Milliren, Christopher & Kistner, Matt & Hogg, Christopher & Marr, Jeff & Shen, Lian & Sotiropoulos, Fotis, 2021. "High-fidelity simulations and field measurements for characterizing wind fields in a utility-scale wind farm," Applied Energy, Elsevier, vol. 281(C).
    20. Juan, Y.-H. & Wen, C.-Y. & Chen, W.-Y. & Yang, A.-S., 2021. "Numerical assessments of wind power potential and installation arrangements in realistic highly urbanized areas," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(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:eee:energy:v:265:y:2023:i:c:s036054422203170x. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.