IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v213y2020ics0360544220319496.html
   My bibliography  Save this article

The Ornstein-Uhlenbeck process for estimating wind power under a memoryless transformation

Author

Listed:
  • Arenas-López, J. Pablo
  • Badaoui, Mohamed

Abstract

The main purpose is to build a wind speed model to assess its impact on the power output estimate of a wind turbine. This modelling is based on stochastic differential equations and memoryless transformations, considering a stable numerical scheme capable to optimize the computational complexity. According to statistical characteristics of a wind speed dataset recorded at a given location in Mexico, the model is parameterized for six probability density functions (PDF), showing exponential decay of the autocorrelation function. The suitability of the model for wind speed modelling and power output estimate is evaluated through a comparative analysis between the real dataset and the simulations; moreover, the quality of the findings have been assessed employing statistical measures criteria. Finally, and contrary to what has been reported, that is, the Weibull PDF is the best one to characterize wind speed in many regions of the world including Mexico, it turns out that among the six PDFs considered in this work, the probability density functions Beta and Log-Pearson 3 represent the best fit to the real wind speed data as well as providing an estimate of less than 1% of the semiannual produced energy by a wind turbine.

Suggested Citation

  • Arenas-López, J. Pablo & Badaoui, Mohamed, 2020. "The Ornstein-Uhlenbeck process for estimating wind power under a memoryless transformation," Energy, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:energy:v:213:y:2020:i:c:s0360544220319496
    DOI: 10.1016/j.energy.2020.118842
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220319496
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.118842?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. Jaramillo, O.A & Saldaña, R & Miranda, U, 2004. "Wind power potential of Baja California Sur, México," Renewable Energy, Elsevier, vol. 29(13), pages 2087-2100.
    2. Zárate-Miñano, Rafael & Anghel, Marian & Milano, Federico, 2013. "Continuous wind speed models based on stochastic differential equations," Applied Energy, Elsevier, vol. 104(C), pages 42-49.
    3. Yao, Jinran & Gan, Siqing, 2018. "Stability of the drift-implicit and double-implicit Milstein schemes for nonlinear SDEs," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 294-301.
    4. Loukatou, Angeliki & Howell, Sydney & Johnson, Paul & Duck, Peter, 2018. "Stochastic wind speed modelling for estimation of expected wind power output," Applied Energy, Elsevier, vol. 228(C), pages 1328-1340.
    5. Hernández-Escobedo, Q. & Saldaña-Flores, R. & Rodríguez-García, E.R. & Manzano-Agugliaro, F., 2014. "Wind energy resource in Northern Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 890-914.
    6. Pedro, Hugo T.C. & Coimbra, Carlos F.M. & David, Mathieu & Lauret, Philippe, 2018. "Assessment of machine learning techniques for deterministic and probabilistic intra-hour solar forecasts," Renewable Energy, Elsevier, vol. 123(C), pages 191-203.
    7. Mohamad M. Alayat & Youssef Kassem & Hüseyin Çamur, 2018. "Assessment of Wind Energy Potential as a Power Generation Source: A Case Study of Eight Selected Locations in Northern Cyprus," Energies, MDPI, vol. 11(10), pages 1-22, October.
    8. Wang, Jianzhou & Hu, Jianming & Ma, Kailiang, 2016. "Wind speed probability distribution estimation and wind energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 881-899.
    9. Thapar, Vinay & Agnihotri, Gayatri & Sethi, Vinod Krishna, 2011. "Critical analysis of methods for mathematical modelling of wind turbines," Renewable Energy, Elsevier, vol. 36(11), pages 3166-3177.
    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. Vanem, Erik, 2023. "Analysing multivariate extreme conditions using environmental contours and accounting for serial dependence," Renewable Energy, Elsevier, vol. 202(C), pages 470-482.
    2. Sergey Obukhov & Emad M. Ahmed & Denis Y. Davydov & Talal Alharbi & Ahmed Ibrahim & Ziad M. Ali, 2021. "Modeling Wind Speed Based on Fractional Ornstein-Uhlenbeck Process," Energies, MDPI, vol. 14(17), pages 1-15, September.

    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. Arenas-López, J. Pablo & Badaoui, Mohamed, 2020. "Stochastic modelling of wind speeds based on turbulence intensity," Renewable Energy, Elsevier, vol. 155(C), pages 10-22.
    2. Allouhi, A. & Zamzoum, O. & Islam, M.R. & Saidur, R. & Kousksou, T. & Jamil, A. & Derouich, A., 2017. "Evaluation of wind energy potential in Morocco's coastal regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 311-324.
    3. 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).
    4. Loukatou, Angeliki & Howell, Sydney & Johnson, Paul & Duck, Peter, 2018. "Stochastic wind speed modelling for estimation of expected wind power output," Applied Energy, Elsevier, vol. 228(C), pages 1328-1340.
    5. Loukatou, Angeliki & Johnson, Paul & Howell, Sydney & Duck, Peter, 2021. "Optimal valuation of wind energy projects co-located with battery storage," Applied Energy, Elsevier, vol. 283(C).
    6. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    7. Eryilmaz, Serkan & Devrim, Yilser, 2019. "Theoretical derivation of wind plant power distribution with the consideration of wind turbine reliability," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 192-197.
    8. Wekesa, David Wafula & Wang, Cong & Wei, Yingjie & Danao, Louis Angelo M., 2017. "Analytical and numerical investigation of unsteady wind for enhanced energy capture in a fluctuating free-stream," Energy, Elsevier, vol. 121(C), pages 854-864.
    9. Santos, Lúcia & Soares, Isabel & Mendes, Carla & Ferreira, Paula, 2014. "Real Options versus Traditional Methods to assess Renewable Energy Projects," Renewable Energy, Elsevier, vol. 68(C), pages 588-594.
    10. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
    11. 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.
    12. Xiao, Qing & Zhou, Shaowu, 2018. "Probabilistic power flow computation considering correlated wind speeds," Applied Energy, Elsevier, vol. 231(C), pages 677-685.
    13. Hernández-Escobedo, Q. & Rodríguez-García, E. & Saldaña-Flores, R. & Fernández-García, A. & Manzano-Agugliaro, F., 2015. "Solar energy resource assessment in Mexican states along the Gulf of Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 216-238.
    14. Liu, Yufen & Cao, Wanrong & Li, Yuelin, 2022. "Split-step balanced θ-method for SDEs with non-globally Lipschitz continuous coefficients," Applied Mathematics and Computation, Elsevier, vol. 413(C).
    15. Ilkiliç, Cumali & Aydin, Hüseyin, 2015. "Wind power potential and usage in the coastal regions of Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 78-86.
    16. Hernández-Escobedo, Q. & Fernández-García, A. & Manzano-Agugliaro, F., 2017. "Solar resource assessment for rural electrification and industrial development in the Yucatan Peninsula (Mexico)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 1550-1561.
    17. Mehrabian, M.J. & Khoshgoftar Manesh, M.H., 2023. "4E, risk, diagnosis, and availability evaluation for optimal design of a novel biomass-solar-wind driven polygeneration system," Renewable Energy, Elsevier, vol. 219(P2).
    18. Katinas, Vladislovas & Gecevicius, Giedrius & Marciukaitis, Mantas, 2018. "An investigation of wind power density distribution at location with low and high wind speeds using statistical model," Applied Energy, Elsevier, vol. 218(C), pages 442-451.
    19. Montoya, Francisco G. & García-Cruz, Amós & Montoya, Maria G. & Manzano-Agugliaro, Francisco, 2016. "Power quality techniques research worldwide: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 846-856.
    20. Han, Qinkai & Chu, Fulei, 2021. "Directional wind energy assessment of China based on nonparametric copula models," Renewable Energy, Elsevier, vol. 164(C), pages 1334-1349.

    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:213:y:2020:i:c:s0360544220319496. 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.