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The effect of diurnal profile and seasonal wind regime on sizing grid-connected and off-grid wind power plants

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  • Carapellucci, Roberto
  • Giordano, Lorena

Abstract

In a high wind penetration scenario in electricity production, the availability of models for synthetically generating hourly wind speed data becomes increasingly important for both sizing and modeling integrated renewable systems. A methodology for generating hourly wind speed time series is presented in this paper, adopting a physical-statistical approach based on some known aggregate input data. The proposed approach, developed in a previous study by the same authors, has been here improved using a new diurnal wind speed profile function. The improved methodology is first described and validated through experimental wind speed data, comparing the results with those obtained using the basic model. Then, hourly wind speeds generated with the improved methodology are used as input data for the optimal design of grid-connected and off-grid wind power plants. For each configuration, the influence of the diurnal wind speed profile and wind regime on system sizing and its economic parameters has been evaluated. Results have shown that the diurnal variation of wind speed does not affect the size of wind turbine, but strongly influences the storage capacity of off-grid wind power plants.

Suggested Citation

  • Carapellucci, Roberto & Giordano, Lorena, 2013. "The effect of diurnal profile and seasonal wind regime on sizing grid-connected and off-grid wind power plants," Applied Energy, Elsevier, vol. 107(C), pages 364-376.
    • Handle: RePEc:eee:appene:v:107:y:2013:i:c:p:364-376
    DOI: 10.1016/j.apenergy.2013.02.044
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    Cited by:

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    2. Sharifzadeh, Mahdi & Lubiano-Walochik, Helena & Shah, Nilay, 2017. "Integrated renewable electricity generation considering uncertainties: The UK roadmap to 50% power generation from wind and solar energies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 385-398.
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    4. Ziel, Florian & Croonenbroeck, Carsten & Ambach, Daniel, 2016. "Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity," Applied Energy, Elsevier, vol. 177(C), pages 285-297.
    5. 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.
    6. Guarino, Francesco & Cassarà, Pietro & Longo, Sonia & Cellura, Maurizio & Ferro, Erina, 2015. "Load match optimisation of a residential building case study: A cross-entropy based electricity storage sizing algorithm," Applied Energy, Elsevier, vol. 154(C), pages 380-391.
    7. Mahela, Om Prakash & Shaik, Abdul Gafoor, 2016. "Comprehensive overview of grid interfaced wind energy generation systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 260-281.
    8. 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.
    9. Ambach, Daniel & Schmid, Wolfgang, 2017. "A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting," Energy, Elsevier, vol. 135(C), pages 833-850.

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