IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v93y2016icp68-76.html
   My bibliography  Save this article

Fuzzy copula model for wind speed correlation and its application in wind curtailment evaluation

Author

Listed:
  • Sun, Can
  • Bie, Zhaohong
  • Xie, Min
  • Jiang, Jiangfeng

Abstract

Wind parks always produce diverse percentages of their nominal power at the same time, leading to a concern about correlation between wind speeds. The assessments of wind speed correlation have been particularly focused on probabilistic modeling of aleatory uncertainty. However, poor historical data, imprecise parameter estimation and incomplete knowledge of wind speeds lead to another type of uncertainty, possibilistic uncertainty, which requires an explicit analysis. Therefore, a fuzzy copula model is firstly proposed to express the possibilistic uncertainty of wind speed correlation. The advantage of the proposed model is that the copula parameters can be interval numbers, triangular or trapezoidal fuzzy numbers based on the wind speed data and subjective judgment of decision makers. For estimating copula parameters, a complete decision rule and interval estimation method is developed based on cumulative probability and probability distributions of correlated wind speeds. The effectiveness of the proposed model is validated by the application in wind curtailment evaluation while a method is developed to evaluate and quantify wind curtailment in a hybrid power system involving different types of generation. The results demonstrate that the proposed model and method are capable of describing the possibilistic uncertainty and evaluating its effect on wind curtailment. Compared with previous research, the proposed model develops a new universal parameter estimation method and selection rule to provide more interval results, by calculating the membership function of copula parameters and wind curtailment. System planners and operators can apply the fuzzy results to various topics like reserve capacity evaluation or real-time dispatch depending on their level of risk tolerance.

Suggested Citation

  • Sun, Can & Bie, Zhaohong & Xie, Min & Jiang, Jiangfeng, 2016. "Fuzzy copula model for wind speed correlation and its application in wind curtailment evaluation," Renewable Energy, Elsevier, vol. 93(C), pages 68-76.
  • Handle: RePEc:eee:renene:v:93:y:2016:i:c:p:68-76
    DOI: 10.1016/j.renene.2016.02.049
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148116301501
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2016.02.049?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. Gregor Weiß, 2011. "Copula parameter estimation by maximum-likelihood and minimum-distance estimators: a simulation study," Computational Statistics, Springer, vol. 26(1), pages 31-54, March.
    2. Li, Yanfu & Zio, Enrico, 2012. "Uncertainty analysis of the adequacy assessment model of a distributed generation system," Renewable Energy, Elsevier, vol. 41(C), pages 235-244.
    3. Shafiullah, G.M. & M.T. Oo, Amanullah & Shawkat Ali, A.B.M. & Wolfs, Peter, 2013. "Potential challenges of integrating large-scale wind energy into the power grid–A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 306-321.
    4. Feijóo, Andrés & Villanueva, Daniel & Pazos, José Luis & Sobolewski, Robert, 2011. "Simulation of correlated wind speeds: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 2826-2832, August.
    5. Michael S. Smith & Mohamad A. Khaled, 2012. "Estimation of Copula Models With Discrete Margins via Bayesian Data Augmentation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 290-303, March.
    6. Mc Garrigle, E.V. & Deane, J.P. & Leahy, P.G., 2013. "How much wind energy will be curtailed on the 2020 Irish power system?," Renewable Energy, Elsevier, vol. 55(C), pages 544-553.
    7. Hart, Elaine K. & Jacobson, Mark Z., 2011. "A Monte Carlo approach to generator portfolio planning and carbon emissions assessments of systems with large penetrations of variable renewables," Renewable Energy, Elsevier, vol. 36(8), pages 2278-2286.
    8. Hering, Amanda S. & Genton, Marc G., 2010. "Powering Up With Space-Time Wind Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 92-104.
    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. Yu, L. & Li, Y.P. & Huang, G.H. & Fan, Y.R. & Nie, S., 2018. "A copula-based flexible-stochastic programming method for planning regional energy system under multiple uncertainties: A case study of the urban agglomeration of Beijing and Tianjin," Applied Energy, Elsevier, vol. 210(C), pages 60-74.
    2. Zhang, Tianyuan & Tan, Qian & Wang, Shuping & Zhang, Tong & Hu, Kejia & Zhang, Shan, 2022. "Assessment and management of composite risk in irrigated agriculture under water-food-energy nexus and uncertainty," Agricultural Water Management, Elsevier, vol. 262(C).
    3. Yin, Yue & Liu, Tianqi & He, Chuan, 2019. "Day-ahead stochastic coordinated scheduling for thermal-hydro-wind-photovoltaic systems," Energy, Elsevier, vol. 187(C).
    4. Dong, Xinghui & Li, Jia & Gao, Di & Zheng, Kai, 2020. "Wind speed modeling for cascade clusters of wind turbines part 1: The cascade clusters of wind turbines," Energy, Elsevier, vol. 205(C).
    5. Sreekumar, Sreenu & Yamujala, Sumanth & Sharma, Kailash Chand & Bhakar, Rohit & Simon, Sishaj P. & Rana, Ankur Singh, 2022. "Flexible Ramp Products: A solution to enhance power system flexibility," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    6. Han, Qinkai & Hao, Zhuolin & Hu, Tao & Chu, Fulei, 2018. "Non-parametric models for joint probabilistic distributions of wind speed and direction data," Renewable Energy, Elsevier, vol. 126(C), pages 1032-1042.
    7. Huang, Yu & Zhang, Bingzhe & Pang, Huizhen & Wang, Biao & Lee, Kwang Y. & Xie, Jiale & Jin, Yupeng, 2022. "Spatio-temporal wind speed prediction based on Clayton Copula function with deep learning fusion," Renewable Energy, Elsevier, vol. 192(C), pages 526-536.
    8. Xiaojun Shen & Chongcheng Zhou & Xuejiao Fu, 2018. "Study of Time and Meteorological Characteristics of Wind Speed Correlation in Flat Terrains Based on Operation Data," Energies, MDPI, vol. 11(1), pages 1-16, January.
    9. Jun Liu & Xudong Hao & Peifen Cheng & Wanliang Fang & Shuanbao Niu, 2016. "A Parallel Probabilistic Load Flow Method Considering Nodal Correlations," Energies, MDPI, vol. 9(12), pages 1-16, December.

    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. Sansavini, G. & Piccinelli, R. & Golea, L.R. & Zio, E., 2014. "A stochastic framework for uncertainty analysis in electric power transmission systems with wind generation," Renewable Energy, Elsevier, vol. 64(C), pages 71-81.
    2. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    3. Devlin, Joseph & Li, Kang & Higgins, Paraic & Foley, Aoife, 2017. "Gas generation and wind power: A review of unlikely allies in the United Kingdom and Ireland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 757-768.
    4. Pablo González-Inostroza & Claudia Rahmann & Ricardo Álvarez & Jannik Haas & Wolfgang Nowak & Christian Rehtanz, 2021. "The Role of Fast Frequency Response of Energy Storage Systems and Renewables for Ensuring Frequency Stability in Future Low-Inertia Power Systems," Sustainability, MDPI, vol. 13(10), pages 1-16, May.
    5. Popović, Željko N. & KovaÄ ki, Neven V. & Popović, Dragan S., 2020. "Resilient distribution network planning under the severe windstorms using a risk-based approach," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    6. Rao, A. Gangoli & van den Oudenalder, F.S.C. & Klein, S.A., 2019. "Natural gas displacement by wind curtailment utilization in combined-cycle power plants," Energy, Elsevier, vol. 168(C), pages 477-491.
    7. Azcarate, I. & Gutierrez, J.J. & Lazkano, A. & Saiz, P. & Redondo, K. & Leturiondo, L.A., 2016. "Towards limiting the sensitivity of energy-efficient lighting to voltage fluctuations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1384-1395.
    8. Azam, Kazim & Pitt, Michael, 2014. "Bayesian Inference for a Semi-Parametric Copula-based Markov Chain," The Warwick Economics Research Paper Series (TWERPS) 1051, University of Warwick, Department of Economics.
    9. Jeffrey Racine, 2015. "Mixed data kernel copulas," Empirical Economics, Springer, vol. 48(1), pages 37-59, February.
    10. Trinh Thi, Huong & Simioni, Michel & Thomas-Agnan, Christine, 2018. "Decomposition of changes in the consumption of macronutrients in Vietnam between 2004 and 2014," Economics & Human Biology, Elsevier, vol. 31(C), pages 259-275.
    11. Kumar, Yogesh & Ringenberg, Jordan & Depuru, Soma Shekara & Devabhaktuni, Vijay K. & Lee, Jin Woo & Nikolaidis, Efstratios & Andersen, Brett & Afjeh, Abdollah, 2016. "Wind energy: Trends and enabling technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 209-224.
    12. Antti Alahäivälä & Juha Kiviluoma & Jyrki Leino & Matti Lehtonen, 2017. "System-Level Value of a Gas Engine Power Plant in Electricity and Reserve Production," Energies, MDPI, vol. 10(7), pages 1-13, July.
    13. Hui Li & Gengyin Li & Yaowu Wu & Zhidong Wang & Jiaming Wang, 2016. "Operation Modeling of Power Systems Integrated with Large-Scale New Energy Power Sources," Energies, MDPI, vol. 9(10), pages 1-17, October.
    14. Amanda Hering, 2014. "Comments on: Space-time wind speed forecasting for improved power system dispatch," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 34-44, March.
    15. Zichen Ma & Shannon W. Davis & Yen‐Yi Ho, 2023. "Flexible copula model for integrating correlated multi‐omics data from single‐cell experiments," Biometrics, The International Biometric Society, vol. 79(2), pages 1559-1572, June.
    16. Frew, Bethany A. & Becker, Sarah & Dvorak, Michael J. & Andresen, Gorm B. & Jacobson, Mark Z., 2016. "Flexibility mechanisms and pathways to a highly renewable US electricity future," Energy, Elsevier, vol. 101(C), pages 65-78.
    17. Daniel Ambach & Robert Garthoff, 2016. "Vorhersagen der Windgeschwindigkeit und Windenergie in Deutschland," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(1), pages 15-36, February.
    18. Sayegh, Hasan & Leconte, Antoine & Fraisse, Gilles & Wurtz, Etienne & Rouchier, Simon, 2022. "Computational time reduction using detailed building models with Typical Short Sequences," Energy, Elsevier, vol. 244(PB).
    19. Amanda S. Hering & Karen Kazor & William Kleiber, 2015. "A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales," Resources, MDPI, vol. 4(1), pages 1-23, February.
    20. Taylor, James W., 2017. "Probabilistic forecasting of wind power ramp events using autoregressive logit models," European Journal of Operational Research, Elsevier, vol. 259(2), pages 703-712.

    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:renene:v:93:y:2016:i:c:p:68-76. 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/renewable-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.