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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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    11. Polleux, Louis & Guerassimoff, Gilles & Marmorat, Jean-Paul & Sandoval-Moreno, John & Schuhler, Thierry, 2022. "An overview of the challenges of solar power integration in isolated industrial microgrids with reliability constraints," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
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    17. 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.
    18. Seck, Gondia Sokhna & Krakowski, Vincent & Assoumou, Edi & Maïzi, Nadia & Mazauric, Vincent, 2020. "Embedding power system’s reliability within a long-term Energy System Optimization Model: Linking high renewable energy integration and future grid stability for France by 2050," Applied Energy, Elsevier, vol. 257(C).
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    20. 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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