IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v96y2012icp21-32.html
   My bibliography  Save this article

Assessment of the power reduction of wind farms under extreme wind condition by a high resolution simulation model

Author

Listed:
  • Lin, Jin
  • Sun, Yuan-zhang
  • Cheng, Lin
  • Gao, Wen-zhong

Abstract

Comparing with conventional power plants, wind power generations are highly dependent on the natural environment. Especially under extreme wind condition, individual wind turbine might automatically disconnect from the grid once the wind speed is near or over the cut-out speed. This results in the output power reduction of a wind farm, which raises the security concern due to the imbalance between generation and demand, particularly for power systems with high wind power penetration. Different from the continuous wind power fluctuation under normal weather condition, the power output of individual wind turbine drops suddenly under extreme weather. Thus, the power reduction of individual wind turbine requires to be simulated by a higher resolution tool in order to illustrate the sudden power drop under an extreme condition. Also, the wind turbines are dispersed geographically within a wind farm and a region, which has a large impact on the magnitude of power reduction of a wind farm and the region under extreme weather. Therefore under an extreme weather condition, the simulation of wind power reduction requires a model with higher resolution as well as considering “geography dispersion”, which cannot be satisfied properly by most of current existing models. This paper hence proposes to use a model in frequency domain to assess the power reduction of wind farms under extremely high wind speed condition. Though this model is originally designed for application under normal wind condition, the originality of this paper is the study of the applicability of this model under an extreme wind condition, because this model provides a second-by-second simulation resolution and uses the coherence matrix in frequency domain to describe the coherences of power reduction among wind turbines. Also, for a regional wind farm cluster, an additional advantage of this model is to provide a reasonable estimate of wind power reduction under extreme wind condition without using extensive history data of the whole region. This model is verified by both qualitative and quantitative analysis and then some statistics-based tools are further developed to assess the reserve requirement due to wind power reduction under extreme wind condition. A case study of Zhangjiakou (ZJK) power region shows the effectiveness of the proposed assessment methodology in an extended wind power system region, and then this model is demonstrated to be valuable for both power system planning and operation with high wind penetration under extreme wind condition.

Suggested Citation

  • Lin, Jin & Sun, Yuan-zhang & Cheng, Lin & Gao, Wen-zhong, 2012. "Assessment of the power reduction of wind farms under extreme wind condition by a high resolution simulation model," Applied Energy, Elsevier, vol. 96(C), pages 21-32.
  • Handle: RePEc:eee:appene:v:96:y:2012:i:c:p:21-32
    DOI: 10.1016/j.apenergy.2011.10.028
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261911006829
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2011.10.028?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. Fadare, D.A., 2010. "The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria," Applied Energy, Elsevier, vol. 87(3), pages 934-942, March.
    2. Tuohy, Aidan & Meibom, Peter & Denny, Eleanor & O'Malley, Mark, 2009. "Unit commitment for systems with significant wind penetration," MPRA Paper 34849, University Library of Munich, Germany.
    3. Chang, Tian Pau, 2011. "Estimation of wind energy potential using different probability density functions," Applied Energy, Elsevier, vol. 88(5), pages 1848-1856, May.
    4. Gökçek, Murat & Genç, Mustafa Serdar, 2009. "Evaluation of electricity generation and energy cost of wind energy conversion systems (WECSs) in Central Turkey," Applied Energy, Elsevier, vol. 86(12), pages 2731-2739, December.
    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. Jin, Yuqing & Ju, Ping & Rehtanz, Christian & Wu, Feng & Pan, Xueping, 2018. "Equivalent modeling of wind energy conversion considering overall effect of pitch angle controllers in wind farm," Applied Energy, Elsevier, vol. 222(C), pages 485-496.
    2. Wang, Jianzhou & Qin, Shanshan & Jin, Shiqiang & Wu, Jie, 2015. "Estimation methods review and analysis of offshore extreme wind speeds and wind energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 26-42.
    3. Roy, Sanjoy, 2014. "Performance prediction of active pitch-regulated wind turbine with short duration variations in source wind," Applied Energy, Elsevier, vol. 114(C), pages 700-708.
    4. Yip, Chak Man Andrew & Gunturu, Udaya Bhaskar & Stenchikov, Georgiy L., 2016. "Wind resource characterization in the Arabian Peninsula," Applied Energy, Elsevier, vol. 164(C), pages 826-836.
    5. Wang, Yibo & Shao, Xinyao & Liu, Chuang & Cai, Guowei & Kou, Lei & Wu, Zhiqiang, 2019. "Analysis of wind farm output characteristics based on descriptive statistical analysis and envelope domain," Energy, Elsevier, vol. 170(C), pages 580-591.
    6. Ricardo Bessa & Carlos Moreira & Bernardo Silva & Manuel Matos, 2014. "Handling renewable energy variability and uncertainty in power systems operation," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 3(2), pages 156-178, March.
    7. Akdemir, Kerem Ziya & Kern, Jordan D. & Lamontagne, Jonathan, 2022. "Assessing risks for New England's wholesale electricity market from wind power losses during extreme winter storms," Energy, Elsevier, vol. 251(C).
    8. Judy P. Che-Castaldo & Rémi Cousin & Stefani Daryanto & Grace Deng & Mei-Ling E. Feng & Rajesh K. Gupta & Dezhi Hong & Ryan M. McGranaghan & Olukunle O. Owolabi & Tianyi Qu & Wei Ren & Toryn L. J. Sch, 2021. "Critical Risk Indicators (CRIs) for the electric power grid: a survey and discussion of interconnected effects," Environment Systems and Decisions, Springer, vol. 41(4), pages 594-615, December.
    9. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
    10. Fu, Xiaopeng & Wang, Chengshan & Li, Peng & Wang, Liwei, 2019. "Exponential integration algorithm for large-scale wind farm simulation with Krylov subspace acceleration," Applied Energy, Elsevier, vol. 254(C).
    11. Zendehboudi, Sohrab & Rezaei, Nima & Lohi, Ali, 2018. "Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review," Applied Energy, Elsevier, vol. 228(C), pages 2539-2566.
    12. Cadini, Francesco & Agliardi, Gian Luca & Zio, Enrico, 2017. "A modeling and simulation framework for the reliability/availability assessment of a power transmission grid subject to cascading failures under extreme weather conditions," Applied Energy, Elsevier, vol. 185(P1), pages 267-279.
    13. Zhu, Xiaojie & Guo, Ruipeng & Chen, Bin & Zhang, Jing & Hayat, Tasawar & Alsaedi, Ahmed, 2015. "Embodiment of virtual water of power generation in the electric power system in China," Applied Energy, Elsevier, vol. 151(C), pages 345-354.
    14. Yuan-Zhang Sun & Jin Lin & Yong-Hua Song & Jian Xu & Xiao-Ming Li & Jian-Xun Dong, 2012. "An Industrial System Powered by Wind and Coal for Aluminum Production: A Case Study of Technical Demonstration and Economic Feasibility," Energies, MDPI, vol. 5(11), pages 1-26, November.
    15. Jose R. Vargas-Jaramillo & Jhon A. Montanez-Barrera & Michael R. von Spakovsky & Lamine Mili & Sergio Cano-Andrade, 2019. "Effects of Producer and Transmission Reliability on the Sustainability Assessment of Power System Networks," Energies, MDPI, vol. 12(3), pages 1-21, February.

    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. Deep, Sneh & Sarkar, Arnab & Ghawat, Mayur & Rajak, Manoj Kumar, 2020. "Estimation of the wind energy potential for coastal locations in India using the Weibull model," Renewable Energy, Elsevier, vol. 161(C), pages 319-339.
    2. Celik, Ali N. & Kolhe, Mohan, 2013. "Generalized feed-forward based method for wind energy prediction," Applied Energy, Elsevier, vol. 101(C), pages 582-588.
    3. Carapellucci, Roberto & Giordano, Lorena, 2013. "The effect of diurnal profile and seasonal wind regime on sizing grid-connected and off-grid wind power plants," Applied Energy, Elsevier, vol. 107(C), pages 364-376.
    4. Ohunakin, S. Olayinka & Ojolo, S. Joshua & Ogunsina, S. Babatunde & Dinrifo, R. Rufus, 2012. "Analysis of cost estimation and wind energy evaluation using wind energy conversion systems (WECS) for electricity generation in six selected high altitude locations in Nigeria," Energy Policy, Elsevier, vol. 48(C), pages 594-600.
    5. He, Junyi & Chan, P.W. & Li, Qiusheng & Lee, C.W., 2020. "Spatiotemporal analysis of offshore wind field characteristics and energy potential in Hong Kong," Energy, Elsevier, vol. 201(C).
    6. Dong, Yao & Wang, Jianzhou & Jiang, He & Shi, Xiaomeng, 2013. "Intelligent optimized wind resource assessment and wind turbines selection in Huitengxile of Inner Mongolia, China," Applied Energy, Elsevier, vol. 109(C), pages 239-253.
    7. Jung, Sungmoon & Arda Vanli, O. & Kwon, Soon-Duck, 2013. "Wind energy potential assessment considering the uncertainties due to limited data," Applied Energy, Elsevier, vol. 102(C), pages 1492-1503.
    8. Després, Jacques & Hadjsaid, Nouredine & Criqui, Patrick & Noirot, Isabelle, 2015. "Modelling the impacts of variable renewable sources on the power sector: Reconsidering the typology of energy modelling tools," Energy, Elsevier, vol. 80(C), pages 486-495.
    9. Tim Felling & Björn Felten & Paul Osinski & Christoph Weber, 2023. "Assessing Improved Price Zones in Europe: Flow-Based Market Coupling in Central Western Europe in Focus," The Energy Journal, , vol. 44(6), pages 71-112, November.
    10. Vithayasrichareon, Peerapat & MacGill, Iain F., 2013. "Assessing the value of wind generation in future carbon constrained electricity industries," Energy Policy, Elsevier, vol. 53(C), pages 400-412.
    11. Osório, G.J. & Lujano-Rojas, J.M. & Matias, J.C.O. & Catalão, J.P.S., 2015. "A probabilistic approach to solve the economic dispatch problem with intermittent renewable energy sources," Energy, Elsevier, vol. 82(C), pages 949-959.
    12. Ayodele, T.R. & Ogunjuyigbe, A.S.O. & Odigie, O. & Munda, J.L., 2018. "A multi-criteria GIS based model for wind farm site selection using interval type-2 fuzzy analytic hierarchy process: The case study of Nigeria," Applied Energy, Elsevier, vol. 228(C), pages 1853-1869.
    13. Fazelpour, Farivar & Markarian, Elin & Soltani, Nima, 2017. "Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran," Renewable Energy, Elsevier, vol. 109(C), pages 646-667.
    14. Bingke Yan & Bo Wang & Lin Zhu & Hesen Liu & Yilu Liu & Xingpei Ji & Dichen Liu, 2015. "A Novel, Stable, and Economic Power Sharing Scheme for an Autonomous Microgrid in the Energy Internet," Energies, MDPI, vol. 8(11), pages 1-24, November.
    15. Ayman Al-Quraan & Bashar Al-Mhairat, 2022. "Intelligent Optimized Wind Turbine Cost Analysis for Different Wind Sites in Jordan," Sustainability, MDPI, vol. 14(5), pages 1-24, March.
    16. Katrin Trepper & Michael Bucksteeg & Christoph Weber, 2013. "An integrated approach to model redispatch and to assess potential benefits from market splitting in Germany," EWL Working Papers 1319, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Apr 2014.
    17. Gokturk Poyrazoglu & HyungSeon Oh, 2019. "Co-optimization of Transmission Maintenance Scheduling and Production Cost Minimization," Energies, MDPI, vol. 12(15), pages 1-18, July.
    18. Ugwoke, B. & Gershon, O. & Becchio, C. & Corgnati, S.P. & Leone, P., 2020. "A review of Nigerian energy access studies: The story told so far," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    19. Nyamdash, Batsaikhan & Denny, Eleanor, 2013. "The impact of electricity storage on wholesale electricity prices," Energy Policy, Elsevier, vol. 58(C), pages 6-16.
    20. Yin, Yue & Liu, Tianqi & He, Chuan, 2019. "Day-ahead stochastic coordinated scheduling for thermal-hydro-wind-photovoltaic systems," Energy, Elsevier, vol. 187(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:appene:v:96:y:2012:i:c:p:21-32. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.