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Statistical analysis of electricity supply deficits from renewable energy sources across an Alpine transect

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  • François, B.
  • Puspitarini, H.D.
  • Volpi, E.
  • Borga, M.

Abstract

While most of renewable integration studies rely on indicators that are integrated in time, little is known about the statistical properties of the electricity supply deficit periods. This study investigates the frequency and intensity of the electricity supply deficit using the Complete-Time-Analysis (CTA), an approach that allows assessing recurrence intervals of values over thresholds. Conversely to typical extreme analysis, CTA makes uses of the entire time series rather than only using values above the chosen thresholds. As such, it is suitable for short length time series and does not require the independence of the events above thresholds. Intensity-Duration-Frequency (IDF) curves are derived for the considered renewable sources and the role of the complementarity among sources and the use of electricity storage is investigated to reduce the occurrence of electricity supply deficit. The approach is illustrated for a region in North-Eastern Italy where solar photovoltaic (PV) and small run-of the river (RoR) hydropower are the main sources of electricity. Our results highlight that solar PV and small electricity storage capacities are effective to reduce significantly the occurrence of deficits, and RoR hydropower and large electricity storage is more effective to prevent long periods of deficit from happening.

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  • François, B. & Puspitarini, H.D. & Volpi, E. & Borga, M., 2022. "Statistical analysis of electricity supply deficits from renewable energy sources across an Alpine transect," Renewable Energy, Elsevier, vol. 201(P1), pages 1200-1212.
    • Handle: RePEc:eee:renene:v:201:y:2022:i:p1:p:1200-1212
    DOI: 10.1016/j.renene.2022.10.125
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    References listed on IDEAS

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    Cited by:

    1. Martin Kittel & Wolf-Peter Schill, 2024. "Measuring the Dunkelflaute: How (not) to analyze variable renewable energy shortage," Papers 2402.06758, arXiv.org, revised Aug 2024.

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