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The assessment of the contribution of short-term wind power predictions to the efficiency of stand-alone hybrid systems

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  • Tascikaraoglu, A.
  • Uzunoglu, M.
  • Vural, B.

Abstract

Distribution of electricity to the rural areas, particularly in regions which have rough topography causes high costs and losses. Hybrid systems can provide electricity at a relatively economic price at these regions. This paper designs and tests a stand-alone hybrid system combining variable speed wind turbine (WT), fuel cell (FC) and battery. The main objective is to optimize the hydrogen utilization while guaranteeing the load balance implicitly as well as to achieve proper FC operation. To this end, a wind power prediction based controller is proposed in order to take action according to foreseen amount of power deficit or excess in the system. For the purpose of investigating the effects of predictions on the system efficiency, a case study is carried out on a coastal area with a high wind potential in Izmir, Turkey. The results obtained provide insights about the advantages of utilizing wind power predictions in a hybrid system.

Suggested Citation

  • Tascikaraoglu, A. & Uzunoglu, M. & Vural, B., 2012. "The assessment of the contribution of short-term wind power predictions to the efficiency of stand-alone hybrid systems," Applied Energy, Elsevier, vol. 94(C), pages 156-165.
    • Handle: RePEc:eee:appene:v:94:y:2012:i:c:p:156-165
    DOI: 10.1016/j.apenergy.2012.01.017
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    Cited by:

    1. Besseris, George J., 2014. "Using qualimetric engineering and extremal analysis to optimize a proton exchange membrane fuel cell stack," Applied Energy, Elsevier, vol. 128(C), pages 15-26.
    2. Tascikaraoglu, Akin & Sanandaji, Borhan M. & Poolla, Kameshwar & Varaiya, Pravin, 2016. "Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform," Applied Energy, Elsevier, vol. 165(C), pages 735-747.
    3. Wang, Jianzhou & Hu, Jianming, 2015. "A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vec," Energy, Elsevier, vol. 93(P1), pages 41-56.
    4. Poncela, Marta & Poncela, Pilar & Perán, José Ramón, 2013. "Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting," Applied Energy, Elsevier, vol. 108(C), pages 349-362.
    5. Chen, Hung-Cheng, 2013. "Optimum capacity determination of stand-alone hybrid generation system considering cost and reliability," Applied Energy, Elsevier, vol. 103(C), pages 155-164.
    6. Tascikaraoglu, A. & Erdinc, O. & Uzunoglu, M. & Karakas, A., 2014. "An adaptive load dispatching and forecasting strategy for a virtual power plant including renewable energy conversion units," Applied Energy, Elsevier, vol. 119(C), pages 445-453.
    7. Díaz-González, Francisco & Sumper, Andreas & Gomis-Bellmunt, Oriol & Bianchi, Fernando D., 2013. "Energy management of flywheel-based energy storage device for wind power smoothing," Applied Energy, Elsevier, vol. 110(C), pages 207-219.
    8. Gupta, R.A. & Kumar, Rajesh & Bansal, Ajay Kumar, 2015. "BBO-based small autonomous hybrid power system optimization incorporating wind speed and solar radiation forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1366-1375.
    9. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
    10. Shahid, Farah & Zameer, Aneela & Mehmood, Ammara & Raja, Muhammad Asif Zahoor, 2020. "A novel wavenets long short term memory paradigm for wind power prediction," Applied Energy, Elsevier, vol. 269(C).
    11. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.

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