IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v35y2010i11p2602-2609.html
   My bibliography  Save this article

Simulation of aggregate wind farm short-term production variations

Author

Listed:
  • Goić, R.
  • Krstulović, J.
  • Jakus, D.

Abstract

The variability of wind power production poses the greatest challenge in the integration of large-scale wind power in power systems. Furthermore, larger-scale penetration implies a wider geographical spreading of installed wind power, resulting in reduced variability and the smoothing effect of total power generation. Therefore, analysis of the impact of wind power variations on power system operation requires adequate modeling of aggregate power output from geographically dispersed wind farms. This paper analyzes different aspects of Markov chain Monte Carlo simulation methods for the synthetic generation of dependent wind power time series. However, testing indicates that these approaches do not adequately model the stochastic dependence between wind power time series in conjunction with individual persistence which is necessary to obtain realistic distributions of aggregate power output and total power variations. Consequently, a novel approach based on a modified second-order Markov chain Monte Carlo simulation is proposed. Simulation results show that this method obtains synthetic time series of aggregate wind power which very closely fit the original data, with respect to both the cumulative density function of total output power and the probability density function of power variations.

Suggested Citation

  • Goić, R. & Krstulović, J. & Jakus, D., 2010. "Simulation of aggregate wind farm short-term production variations," Renewable Energy, Elsevier, vol. 35(11), pages 2602-2609.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:11:p:2602-2609
    DOI: 10.1016/j.renene.2010.04.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148110001576
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2010.04.005?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. Ettoumi, F.Youcef & Sauvageot, H & Adane, A.-E.-H, 2003. "Statistical bivariate modelling of wind using first-order Markov chain and Weibull distribution," Renewable Energy, Elsevier, vol. 28(11), pages 1787-1802.
    2. Shamshad, A. & Bawadi, M.A. & Wan Hussin, W.M.A. & Majid, T.A. & Sanusi, S.A.M., 2005. "First and second order Markov chain models for synthetic generation of wind speed time series," Energy, Elsevier, vol. 30(5), pages 693-708.
    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. Ingeborg Graabak & Magnus Korpås, 2016. "Variability Characteristics of European Wind and Solar Power Resources—A Review," Energies, MDPI, vol. 9(6), pages 1-31, June.
    2. Ikegami, Takashi & Urabe, Chiyori T. & Saitou, Tetsuo & Ogimoto, Kazuhiko, 2018. "Numerical definitions of wind power output fluctuations for power system operations," Renewable Energy, Elsevier, vol. 115(C), pages 6-15.
    3. Jussi Ekström & Matti Koivisto & Ilkka Mellin & Robert John Millar & Matti Lehtonen, 2018. "A Statistical Modeling Methodology for Long-Term Wind Generation and Power Ramp Simulations in New Generation Locations," Energies, MDPI, vol. 11(9), pages 1-18, September.
    4. Punda, Luka & Capuder, Tomislav & Pandžić, Hrvoje & Delimar, Marko, 2017. "Integration of renewable energy sources in southeast Europe: A review of incentive mechanisms and feasibility of investments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 77-88.
    5. Nuño Martinez, Edgar & Cutululis, Nicolaos & Sørensen, Poul, 2018. "High dimensional dependence in power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 197-213.
    6. Nycander, Elis & Morales-España, Germán & Söder, Lennart, 2022. "Power-based modelling of renewable variability in dispatch models with clustered time periods," Renewable Energy, Elsevier, vol. 186(C), pages 944-956.
    7. Olauson, Jon & Bergkvist, Mikael, 2015. "Modelling the Swedish wind power production using MERRA reanalysis data," Renewable Energy, Elsevier, vol. 76(C), pages 717-725.
    8. Widén, Joakim & Carpman, Nicole & Castellucci, Valeria & Lingfors, David & Olauson, Jon & Remouit, Flore & Bergkvist, Mikael & Grabbe, Mårten & Waters, Rafael, 2015. "Variability assessment and forecasting of renewables: A review for solar, wind, wave and tidal resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 356-375.
    9. Ekström, Jussi & Koivisto, Matti & Mellin, Ilkka & Millar, John & Saarijärvi, Eero & Haarla, Liisa, 2015. "Assessment of large scale wind power generation with new generation locations without measurement data," Renewable Energy, Elsevier, vol. 83(C), pages 362-374.
    10. Carapellucci, Roberto & Giordano, Lorena, 2013. "A new approach for synthetically generating wind speeds: A comparison with the Markov chains method," Energy, Elsevier, vol. 49(C), pages 298-305.

    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. Tang, Jie & Brouste, Alexandre & Tsui, Kwok Leung, 2015. "Some improvements of wind speed Markov chain modeling," Renewable Energy, Elsevier, vol. 81(C), pages 52-56.
    2. 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.
    3. D׳Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2015. "Reliability measures for indexed semi-Markov chains applied to wind energy production," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 170-177.
    4. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    5. D’Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2013. "First and second order semi-Markov chains for wind speed modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1194-1201.
    6. Evans, S.P. & Clausen, P.D., 2015. "Modelling of turbulent wind flow using the embedded Markov chain method," Renewable Energy, Elsevier, vol. 81(C), pages 671-678.
    7. Nuño Martinez, Edgar & Cutululis, Nicolaos & Sørensen, Poul, 2018. "High dimensional dependence in power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 197-213.
    8. Kerkhove, L.-P. & Vanhoucke, M., 2017. "Optimised scheduling for weather sensitive offshore construction projects," Omega, Elsevier, vol. 66(PA), pages 58-78.
    9. Feijóo, Andrés & Villanueva, Daniel, 2016. "Assessing wind speed simulation methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 473-483.
    10. Chiacchio, Ferdinando & D’Urso, Diego & Famoso, Fabio & Brusca, Sebastian & Aizpurua, Jose Ignacio & Catterson, Victoria M., 2018. "On the use of dynamic reliability for an accurate modelling of renewable power plants," Energy, Elsevier, vol. 151(C), pages 605-621.
    11. Hong, Ying-Yi & Chang, Huei-Lin & Chiu, Ching-Sheng, 2010. "Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs," Energy, Elsevier, vol. 35(9), pages 3870-3876.
    12. Joselin Herbert, G.M. & Iniyan, S. & Sreevalsan, E. & Rajapandian, S., 2007. "A review of wind energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(6), pages 1117-1145, August.
    13. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
    14. Meschede, Henning & Dunkelberg, Heiko & Stöhr, Fabian & Peesel, Ron-Hendrik & Hesselbach, Jens, 2017. "Assessment of probabilistic distributed factors influencing renewable energy supply for hotels using Monte-Carlo methods," Energy, Elsevier, vol. 128(C), pages 86-100.
    15. Zhang, Jin-hua & Liu, Yong-qian & Tian, De & Yan, Jie, 2015. "Optimal power dispatch in wind farm based on reduced blade damage and generator losses," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 64-77.
    16. Ma, Jinrui & Fouladirad, Mitra & Grall, Antoine, 2018. "Flexible wind speed generation model: Markov chain with an embedded diffusion process," Energy, Elsevier, vol. 164(C), pages 316-328.
    17. Rehman, Shafiqur, 2005. "Prospects of wind farm development in Saudi Arabia," Renewable Energy, Elsevier, vol. 30(3), pages 447-463.
    18. Aasim, & Singh, S.N. & Mohapatra, Abheejeet, 2019. "Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting," Renewable Energy, Elsevier, vol. 136(C), pages 758-768.
    19. Jae Ho Kim & Warren B. Powell, 2011. "Optimal Energy Commitments with Storage and Intermittent Supply," Operations Research, INFORMS, vol. 59(6), pages 1347-1360, December.
    20. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.

    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:35:y:2010:i:11:p:2602-2609. 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.