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Efficient Modeling of Distributed Energy Resources’ Impact on Electric Grid Technical Losses: A Dynamic Regression Approach

Author

Listed:
  • Alain Aoun

    (Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski (UQAR), Rimouski, QC G5L 3A1, Canada)

  • Mehdi Adda

    (Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski (UQAR), Rimouski, QC G5L 3A1, Canada)

  • Adrian Ilinca

    (Ecole de Technologie Supérieure (ETS), Montréal, QC H3C 1K3, Canada)

  • Mazen Ghandour

    (Faculty of Engineering, Lebanese University, Beirut 1003, Lebanon)

  • Hussein Ibrahim

    (Ecole de Technologie Supérieure (ETS), Montréal, QC H3C 1K3, Canada
    Centre National Intégré du Manufacturier Intelligent (CNIMI), Université du Québec à Trois-Rivières (UQTR), Drummondville, QC J2C 0R5, Canada)

  • Saba Salloum

    (Faculty of Engineering, Lebanese University, Beirut 1003, Lebanon)

Abstract

Technical losses in electrical grids are inherent inefficiencies induced by the transmission and distribution of electricity, resulting in energy losses that can reach up to 40% of the generated energy. These losses pose significant challenges to grid operators regarding energy sustainability, reliability, and economic viability. Distributed Energy Resources (DERs) offer promising solutions to lower technical losses by decentralizing energy generation and consumption, reducing the need for long-distance transmission and optimizing grid operation. Hence, estimating the impact of DERs on grid technical losses becomes paramount for grid operators and planners. In response, this article proposes the application of regression modeling and nonlinear curve fitting algorithms to provide a more nuanced understanding and better characterize the intricate interplay between DER deployment and technical losses. Through a comprehensive case study based on more than 1080 computer simulations, we demonstrate the effectiveness of our proposed dynamic polynomial varying coefficient regression model in estimating the impact of DERs on technical losses within electrical grids. The proposed model offers a simple and effective methodology that allows grid operators to gain insights into the nonlinear dynamics of DER integration and make quicker and more informed decisions regarding grid management strategies, infrastructure investments, and policy interventions. Also, this research contributes to advancing the field of grid optimization by offering a simple equation that enhances our ability and haste to assess and mitigate technical losses in the context of an evolving energy landscape characterized by increasing DER adoption.

Suggested Citation

  • Alain Aoun & Mehdi Adda & Adrian Ilinca & Mazen Ghandour & Hussein Ibrahim & Saba Salloum, 2024. "Efficient Modeling of Distributed Energy Resources’ Impact on Electric Grid Technical Losses: A Dynamic Regression Approach," Energies, MDPI, vol. 17(9), pages 1-28, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2053-:d:1383305
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    References listed on IDEAS

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    1. Mengting Yao & Yun Zhu & Junjie Li & Hua Wei & Penghui He, 2019. "Research on Predicting Line Loss Rate in Low Voltage Distribution Network Based on Gradient Boosting Decision Tree," Energies, MDPI, vol. 12(13), pages 1-14, June.
    2. Kalambe, Shilpa & Agnihotri, Ganga, 2014. "Loss minimization techniques used in distribution network: bibliographical survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 184-200.
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