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Piece-Wise Linear (PWL) Probabilistic Analysis of Power Grid with High Penetration PV Integration

Author

Listed:
  • Giambattista Gruosso

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci, 32-20133 Milano, Italy)

  • Luca Daniel

    (Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA)

  • Paolo Maffezzoni

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci, 32-20133 Milano, Italy)

Abstract

This paper aims at presenting a novel effective approach to probabilistic analysis of distribution power grid with high penetration of PV sources. The novel method adopts a Gaussian Mixture Model for reproducing the uncertainty of correlated PV sources along with a piece-wise-linear approximation of the voltage-power relationship established by load flow problem. The method allows the handling of scenarios with a large number of uncertain PV sources in an efficient yet accurate way. A distinctive feature of the proposed probabilistic analysis is that of directly providing, in closed-form, the joint probability distribution of the set of observable variables of interest. From such a comprehensive statistical representation, remarkable information about grid uncertainty can be deduced. This includes the probability of violating the safe operation conditions as a function of PV penetration.

Suggested Citation

  • Giambattista Gruosso & Luca Daniel & Paolo Maffezzoni, 2022. "Piece-Wise Linear (PWL) Probabilistic Analysis of Power Grid with High Penetration PV Integration," Energies, MDPI, vol. 15(13), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4752-:d:850949
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    References listed on IDEAS

    as
    1. Zain Anwer Memon & Riccardo Trinchero & Yanzhao Xie & Flavio G. Canavero & Igor S. Stievano, 2020. "An Iterative Scheme for the Power-Flow Analysis of Distribution Networks based on Decoupled Circuit Equivalents in the Phasor Domain," Energies, MDPI, vol. 13(2), pages 1-16, January.
    2. Li, Fen & Lin, Yilun & Guo, Jianping & Wang, Yue & Mao, Ling & Cui, Yang & Bai, Yongqing, 2020. "Novel models to estimate hourly diffuse radiation fraction for global radiation based on weather type classification," Renewable Energy, Elsevier, vol. 157(C), pages 1222-1232.
    3. Harshavardhan Palahalli & Paolo Maffezzoni & Giambattista Gruosso, 2021. "Gaussian Copula Methodology to Model Photovoltaic Generation Uncertainty Correlation in Power Distribution Networks," Energies, MDPI, vol. 14(9), pages 1-16, April.
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