IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i13p4752-d850949.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/13/4752/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/13/4752/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Starke, Allan R. & Lemos, Leonardo F.L. & Barni, Cristian M. & Machado, Rubinei D. & Cardemil, José M. & Boland, John & Colle, Sergio, 2021. "Assessing one-minute diffuse fraction models based on worldwide climate features," Renewable Energy, Elsevier, vol. 177(C), pages 700-714.
    2. Hassan, Muhammed A. & Abubakr, Mohamed & Khalil, Adel, 2021. "A profile-free non-parametric approach towards generation of synthetic hourly global solar irradiation data from daily totals," Renewable Energy, Elsevier, vol. 167(C), pages 613-628.
    3. Yin, Kaili & Zhang, Xiaojing & Xie, Jingchao & Hao, Ziyang & Xiao, Guofeng & Liu, Jiaping, 2023. "Modeling hourly solar diffuse fraction on a horizontal surface based on sky conditions clustering," Energy, Elsevier, vol. 272(C).
    4. Yazdani, Hamed & Yaghoubi, Mahmood, 2021. "Techno-economic study of photovoltaic systems performance in Shiraz, Iran," Renewable Energy, Elsevier, vol. 172(C), pages 251-262.
    5. Igor Simone Stievano & Riccardo Trinchero, 2023. "Advanced Techniques for the Modeling and Simulation of Energy Networks," Energies, MDPI, vol. 16(5), pages 1-3, February.
    6. Hassan, Muhammed A. & Akoush, Bassem M. & Abubakr, Mohamed & Campana, Pietro Elia & Khalil, Adel, 2021. "High-resolution estimates of diffuse fraction based on dynamic definitions of sky conditions," Renewable Energy, Elsevier, vol. 169(C), pages 641-659.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4752-:d:850949. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.