IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v83y2015icp384-397.html
   My bibliography  Save this article

Extended modulating functions for simulation of wind velocities with weak and strong nonstationarity

Author

Listed:
  • Li, Jinhua
  • Li, Chunxiang
  • He, Liang
  • Shen, Jianhong

Abstract

Following the theory of evolutionary power spectral density (EPSD) for nonstationary processes, it is anticipated that the nonstationary wind velocities can be generated through modulating the stationary wind velocities by resorting to desirable modulating functions. Naturally, the key to the simulation of the nonstationary wind velocities lies in seeking out desirable modulating functions. The purpose of this study thus is how to obtain the modulating functions suitable for modulating stationary wind velocities. This work begins with systematically deducing the modulating function according to Kaimal power spectrum. The modulating functions corresponding respectively to other power spectra are subsequently presented. Derived modulating functions herein are collectively referred to as the extended modulating functions (EMFs). Employing the recent research by Li et al. (2009) that integration of both the spline interpolation algorithm (SIA) and spectral representation (SR) method, then forming the SIA-SR method, is able to remarkably reduce the increasing number of Cholesky decomposition of the time-varying spectral density matrix, the simulation of the nonstationary wind velocities has been carried out with an EMF. It can be inferred in terms of simulation results that the EMFs can fully capture the nonstationarity of wind velocities, including both the weak and strong nonstationary wind velocities.

Suggested Citation

  • Li, Jinhua & Li, Chunxiang & He, Liang & Shen, Jianhong, 2015. "Extended modulating functions for simulation of wind velocities with weak and strong nonstationarity," Renewable Energy, Elsevier, vol. 83(C), pages 384-397.
  • Handle: RePEc:eee:renene:v:83:y:2015:i:c:p:384-397
    DOI: 10.1016/j.renene.2015.04.044
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148115003249
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2015.04.044?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. Aksoy, Hafzullah & Fuat Toprak, Z & Aytek, Ali & Erdem Ünal, N, 2004. "Stochastic generation of hourly mean wind speed data," Renewable Energy, Elsevier, vol. 29(14), pages 2111-2131.
    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. Salami, Akim Adekunle & Ajavon, Ayite Senah Akoda & Kodjo, Mawugno Koffi & Bedja, Koffi-Sa, 2013. "Contribution to improving the modeling of wind and evaluation of the wind potential of the site of Lome: Problems of taking into account the frequency of calm winds," Renewable Energy, Elsevier, vol. 50(C), pages 449-455.
    2. Kulwinder Parmar & Rashmi Bhardwaj, 2015. "River Water Prediction Modeling Using Neural Networks, Fuzzy and Wavelet Coupled Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(1), pages 17-33, January.
    3. 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.
    4. 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.
    5. Xiaodong Li & Xiang Song & Djamila Ouelhadj, 2023. "A Cost Optimisation Model for Maintenance Planning in Offshore Wind Farms with Wind Speed Dependent Failure Rates," Mathematics, MDPI, vol. 11(13), pages 1-21, June.
    6. Osmani, Atif & Zhang, Jun, 2014. "Optimal grid design and logistic planning for wind and biomass based renewable electricity supply chains under uncertainties," Energy, Elsevier, vol. 70(C), pages 514-528.
    7. Higinio Sánchez-Sáinz & Carlos-Andrés García-Vázquez & Francisco Llorens Iborra & Luis M. Fernández-Ramírez, 2019. "Methodology for the Optimal Design of a Hybrid Charging Station of Electric and Fuel Cell Vehicles Supplied by Renewable Energies and an Energy Storage System," Sustainability, MDPI, vol. 11(20), pages 1-20, October.
    8. Srikanth Bashetty & Joaquin I. Guillamon & Shanmukha S. Mutnuri & Selahattin Ozcelik, 2020. "Design of a Robust Adaptive Controller for the Pitch and Torque Control of Wind Turbines," Energies, MDPI, vol. 13(5), pages 1-22, March.
    9. Lujano-Rojas, Juan M. & Dufo-López, Rodolfo & Bernal-Agustín, José L., 2013. "Probabilistic modelling and analysis of stand-alone hybrid power systems," Energy, Elsevier, vol. 63(C), pages 19-27.
    10. 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).
    11. 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.
    12. Cabello, M. & Orza, J.A.G., 2010. "Wind speed analysis in the province of Alicante, Spain. Potential for small-scale wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 3185-3191, December.
    13. Andreas Wagner, 2014. "Residual Demand Modeling and Application to Electricity Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    14. I. Bremer & R. Henrion & A. Möller, 2015. "Probabilistic constraints via SQP solver: application to a renewable energy management problem," Computational Management Science, Springer, vol. 12(3), pages 435-459, July.
    15. Srikanth Bashetty & Selahattin Ozcelik, 2021. "Review on Dynamics of Offshore Floating Wind Turbine Platforms," Energies, MDPI, vol. 14(19), pages 1-30, September.
    16. Yuan, Shengxi & Kocaman, Ayse Selin & Modi, Vijay, 2017. "Benefits of forecasting and energy storage in isolated grids with large wind penetration – The case of Sao Vicente," Renewable Energy, Elsevier, vol. 105(C), pages 167-174.
    17. Ouammi, Ahmed & Ghigliotti, Valeria & Robba, Michela & Mimet, Abdelaziz & Sacile, Roberto, 2012. "A decision support system for the optimal exploitation of wind energy on regional scale," Renewable Energy, Elsevier, vol. 37(1), pages 299-309.
    18. Zubi, Ghassan, 2011. "Technology mix alternatives with high shares of wind power and photovoltaics—case study for Spain," Energy Policy, Elsevier, vol. 39(12), pages 8070-8077.
    19. 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.
    20. Carapellucci, Roberto & Giordano, Lorena, 2013. "A methodology for the synthetic generation of hourly wind speed time series based on some known aggregate input data," Applied Energy, Elsevier, vol. 101(C), pages 541-550.

    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:83:y:2015:i:c:p:384-397. 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.