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Presentation of a stochastic model estimating the wind energy contribution in remote island electrical networks

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  • Kaldellis, J.K.
  • Kapsali, M.
  • Tiligadas, D.

Abstract

The electrification in remote islands whose electricity distribution network is not connected to the mainland’s grid is mostly based on Autonomous Power Stations (APSs) that are usually characterized by a considerably high electricity production cost, while at the same time the contribution of Renewable Energy Sources (RES) in these regions accounts for less than 10% of the total electricity generation. This actually results from the fact that despite the excellent wind potential of most of these islands, the wind energy contribution is significantly restricted from limits imposed to protect the remote electrical grids from possible instability problems, due to the stochastic wind speed behavior and the variable electricity consumption. On the basis of probability distribution of the load demand of a representative Greek island and the corresponding data related to the available wind potential, the present study estimates the maximum – acceptable by the local grid – wind energy contribution. For that reason, an integrated computational algorithm has been developed from first principles, based on a stochastic analysis. According to the results obtained, it becomes evident that with the current wind turbine technology, wind energy cannot play a key role in coping with the electrification problems encountered in many Greek island regions, excluding however the case of introducing bulk energy storage systems that may provide considerable recovery of the remarkable wind energy rejections expected.

Suggested Citation

  • Kaldellis, J.K. & Kapsali, M. & Tiligadas, D., 2012. "Presentation of a stochastic model estimating the wind energy contribution in remote island electrical networks," Applied Energy, Elsevier, vol. 97(C), pages 68-76.
  • Handle: RePEc:eee:appene:v:97:y:2012:i:c:p:68-76
    DOI: 10.1016/j.apenergy.2011.11.055
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    2. Manchester, Sebastian C. & Swan, Lukas G. & Groulx, Dominic, 2015. "Regenerative air energy storage for remote wind–diesel micro-grid communities," Applied Energy, Elsevier, vol. 137(C), pages 490-500.
    3. Psarros, Georgios N. & Nanou, Sotirios I. & Papaefthymiou, Stefanos V. & Papathanassiou, Stavros A., 2018. "Generation scheduling in non-interconnected islands with high RES penetration," Renewable Energy, Elsevier, vol. 115(C), pages 338-352.
    4. John K. Kaldellis, 2021. "Supporting the Clean Electrification for Remote Islands: The Case of the Greek Tilos Island," Energies, MDPI, vol. 14(5), pages 1-22, March.
    5. 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.
    6. Foteinis, S. & Tsoutsos, T., 2017. "Strategies to improve sustainability and offset the initial high capital expenditure of wave energy converters (WECs)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 775-785.
    7. Alessandro Corsini & Luca Cedola & Francesca Lucchetta & Eileen Tortora, 2019. "Gen-Set Control in Stand-Alone/RES Integrated Power Systems," Energies, MDPI, vol. 12(17), pages 1-17, August.
    8. Lyons, P.F. & Wade, N.S. & Jiang, T. & Taylor, P.C. & Hashiesh, F. & Michel, M. & Miller, D., 2015. "Design and analysis of electrical energy storage demonstration projects on UK distribution networks," Applied Energy, Elsevier, vol. 137(C), pages 677-691.
    9. Kapsali, M. & Anagnostopoulos, J.S., 2017. "Investigating the role of local pumped-hydro energy storage in interconnected island grids with high wind power generation," Renewable Energy, Elsevier, vol. 114(PB), pages 614-628.
    10. Kapsali, M. & Anagnostopoulos, J.S. & Kaldellis, J.K., 2012. "Wind powered pumped-hydro storage systems for remote islands: A complete sensitivity analysis based on economic perspectives," Applied Energy, Elsevier, vol. 99(C), pages 430-444.

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