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A robust structural electric system model with significant share of intermittent renewables under auto-correlated residual demand

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Listed:
  • Pierre Cayet
  • Arash Farnoosh

Abstract

In this paper, we propose a robust structural investment and dispatch model of electric systems including commitment and storage constraints under auto-correlated residual demand. We associate it to a novel approach to robust optimization focusing on uncertainty parameter trajectories. Using Principal Component Analysis, we approximate conditional order statistics for the differential distribution of components of residual demand using parametric polynomial regression. This flexible method allows us to derive a set of extreme trajectories maximizing the level and variability of residual demand. Finally, we apply our dynamic robust model to the electric system of the French region Auvergne Rhône-Alpes and discuss the implications in terms of investment decisions and cost performance.

Suggested Citation

  • Pierre Cayet & Arash Farnoosh, 2022. "A robust structural electric system model with significant share of intermittent renewables under auto-correlated residual demand," EconomiX Working Papers 2022-6, University of Paris Nanterre, EconomiX.
    • Handle: RePEc:drm:wpaper:2022-6
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    References listed on IDEAS

    as
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    2. Cany, C. & Mansilla, C. & Mathonnière, G. & da Costa, P., 2018. "Nuclear contribution to the penetration of variable renewable energy sources in a French decarbonised power mix," Energy, Elsevier, vol. 150(C), pages 544-555.
    3. Hou, Qingchun & Zhang, Ning & Du, Ershun & Miao, Miao & Peng, Fei & Kang, Chongqing, 2019. "Probabilistic duck curve in high PV penetration power system: Concept, modeling, and empirical analysis in China," Applied Energy, Elsevier, vol. 242(C), pages 205-215.
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    More about this item

    Keywords

    Optimal electricity mix; Robust optimization; Dynamic uncertainty;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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