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Impacts of intermittent sources on the quality of power supply: The key role of reliability indicators

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  • Drouineau, Mathilde
  • Maïzi, Nadia
  • Mazauric, Vincent

Abstract

The reliability of power supply, defined as the ability to recover a steady-state condition after a sudden disturbance, is crucial for operating power systems. It is usually ensured by controlling voltage and frequency deviations and involves events occurring from a few milliseconds to a few hours. However, reliability requirements are largely ignored when dealing with long-term issues. To reconcile such contrasting timescales, it seems logical to rely on energy considerations based on thermodynamics. Two reliability indicators, assessing the magnetic and kinetic energy reserves of a power system, are derived from this approach. They enable to quantify the reliability of a given production mix and make it possible to choose between increasing shares of intermittent sources and maintaining an expected level of reliability. Since the indicators tackle reliability issues without focusing on a specific timescale, they are effective for both discussing the long-term evolution of reliability and improving the real-time management of a power system.

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  • Drouineau, Mathilde & Maïzi, Nadia & Mazauric, Vincent, 2014. "Impacts of intermittent sources on the quality of power supply: The key role of reliability indicators," Applied Energy, Elsevier, vol. 116(C), pages 333-343.
    • Handle: RePEc:eee:appene:v:116:y:2014:i:c:p:333-343
    DOI: 10.1016/j.apenergy.2013.11.069
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    17. Colmenar-Santos, Antonio & Linares-Mena, Ana-Rosa & Borge-Diez, David & Quinto-Alemany, Carlos-Domingo, 2017. "Impact assessment of electric vehicles on islands grids: A case study for Tenerife (Spain)," Energy, Elsevier, vol. 120(C), pages 385-396.
    18. Drouineau, Mathilde & Assoumou, Edi & Mazauric, Vincent & Maïzi, Nadia, 2015. "Increasing shares of intermittent sources in Reunion Island: Impacts on the future reliability of power supply," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 120-128.
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    21. Wang, Shouxiang & Chen, Haiwen, 2019. "A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network," Applied Energy, Elsevier, vol. 235(C), pages 1126-1140.

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