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Synthetic wind speed scenarios including diurnal effects: Implications for wind power dimensioning

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  • Suomalainen, K.
  • Silva, C.A.
  • Ferrão, P.
  • Connors, S.

Abstract

This paper presents the application of a new approach for taking into consideration the variability of the wind resource at different temporal scales (hourly, daily, seasonal, annual) in generating scenarios for energy systems modelling. It is argued that Markov models and auto-regressive models, generally used for synthetic wind speed data generation, do not contain sufficient low-frequency information related to seasonal and diurnal patterns. Under high wind penetration scenarios, the daily pattern of the wind becomes increasingly important to energy system modelling and design. Statistical analysis of various wind locations in the Azores, Portugal, indicates that there are strong seasonal differences in magnitude and shape within a given day that will affect energy system design and performance. The proposed methodology evaluates the frequency of different wind day types, such as afternoon winds or morning winds, along with the magnitude of wind for locations with different quality wind resources. Application of the new methodology indicates that the inclusion of diurnal wind characteristics for the analysis of future energy systems provides better design information, especially as it pertains to generation investment requirements to meet island specific renewable penetration targets, and intra-day surpluses or shortages of wind generation in small energy networks.

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  • Suomalainen, K. & Silva, C.A. & Ferrão, P. & Connors, S., 2012. "Synthetic wind speed scenarios including diurnal effects: Implications for wind power dimensioning," Energy, Elsevier, vol. 37(1), pages 41-50.
    • Handle: RePEc:eee:energy:v:37:y:2012:i:1:p:41-50
    DOI: 10.1016/j.energy.2011.08.001
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    References listed on IDEAS

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    1. 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.
    2. Nfaoui, H. & Essiarab, H. & Sayigh, A.A.M., 2004. "A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco," Renewable Energy, Elsevier, vol. 29(8), pages 1407-1418.
    3. Chang, Tsang-Jung & Tu, Yi-Long, 2007. "Evaluation of monthly capacity factor of WECS using chronological and probabilistic wind speed data: A case study of Taiwan," Renewable Energy, Elsevier, vol. 32(12), pages 1999-2010.
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    Cited by:

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    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. 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.
    4. 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).
    5. 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.
    6. Laslett, Dean & Creagh, Chris & Jennings, Philip, 2016. "A simple hourly wind power simulation for the South-West region of Western Australia using MERRA data," Renewable Energy, Elsevier, vol. 96(PA), pages 1003-1014.
    7. Scholz, Teresa & Lopes, Vitor V. & Estanqueiro, Ana, 2014. "A cyclic time-dependent Markov process to model daily patterns in wind turbine power production," Energy, Elsevier, vol. 67(C), pages 557-568.
    8. Suomalainen, K. & Silva, C. & Ferrão, P. & Connors, S., 2013. "Wind power design in isolated energy systems: Impacts of daily wind patterns," Applied Energy, Elsevier, vol. 101(C), pages 533-540.
    9. Purvins, Arturs & Papaioannou, Ioulia T. & Oleinikova, Irina & Tzimas, Evangelos, 2012. "Effects of variable renewable power on a country-scale electricity system: High penetration of hydro power plants and wind farms in electricity generation," Energy, Elsevier, vol. 43(1), pages 225-236.

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