IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i9p2053-d1383305.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/9/2053/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/9/2053/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    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. Rezaee Jordehi, Ahmad, 2016. "Allocation of distributed generation units in electric power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 893-905.
    2. Sultana, U. & Khairuddin, Azhar B. & Aman, M.M. & Mokhtar, A.S. & Zareen, N., 2016. "A review of optimum DG placement based on minimization of power losses and voltage stability enhancement of distribution system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 363-378.
    3. Michał Jasiński & Tomasz Sikorski & Zbigniew Leonowicz & Klaudiusz Borkowski & Elżbieta Jasińska, 2020. "The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation," Energies, MDPI, vol. 13(9), pages 1-19, May.
    4. Firas M. F. Flaih & Xiangning Lin & Mohammed Kdair Abd & Samir M. Dawoud & Zhengtian Li & Owolabi Sunday Adio, 2017. "A New Method for Distribution Network Reconfiguration Analysis under Different Load Demands," Energies, MDPI, vol. 10(4), pages 1-19, April.
    5. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Akif Zia Khan & Dong Ryeol Shin, 2017. "Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective," Energies, MDPI, vol. 10(4), pages 1-47, April.
    6. Ma, Chenjie & Menke, Jan-Hendrik & Dasenbrock, Johannes & Braun, Martin & Haslbeck, Matthias & Schmid, Karl-Heinz, 2019. "Evaluation of energy losses in low voltage distribution grids with high penetration of distributed generation," Applied Energy, Elsevier, vol. 256(C).
    7. Bin Li & Yuxiang Tan & Qingqing Guo & Weihuan Wang, 2023. "Application of Comprehensive Evaluation of Line Loss Lean Management Based on Big-Data-Driven Paradigm," Sustainability, MDPI, vol. 15(15), pages 1-21, August.
    8. Tomin, Nikita & Shakirov, Vladislav & Kozlov, Aleksander & Sidorov, Denis & Kurbatsky, Victor & Rehtanz, Christian & Lora, Electo E.S., 2022. "Design and optimal energy management of community microgrids with flexible renewable energy sources," Renewable Energy, Elsevier, vol. 183(C), pages 903-921.
    9. S. Angalaeswari & P. Sanjeevikumar & K. Jamuna & Zbigniew Leonowicz, 2020. "Hybrid PIPSO-SQP Algorithm for Real Power Loss Minimization in Radial Distribution Systems with Optimal Placement of Distributed Generation," Sustainability, MDPI, vol. 12(14), pages 1-21, July.
    10. Mohamed Tolba & Hegazy Rezk & Ahmed A. Zaki Diab & Mujahed Al-Dhaifallah, 2018. "A Novel Robust Methodology Based Salp Swarm Algorithm for Allocation and Capacity of Renewable Distributed Generators on Distribution Grids," Energies, MDPI, vol. 11(10), pages 1-34, September.
    11. Syed Ali Abbas Kazmi & Abdul Kashif Janjua & Dong Ryeol Shin, 2018. "Enhanced Voltage Stability Assessment Index Based Planning Approach for Mesh Distribution Systems," Energies, MDPI, vol. 11(5), pages 1-36, May.
    12. Syed Ali Abbas Kazmi & Usama Ameer Khan & Hafiz Waleed Ahmad & Sajid Ali & Dong Ryeol Shin, 2020. "A Techno-Economic Centric Integrated Decision-Making Planning Approach for Optimal Assets Placement in Meshed Distribution Network Across the Load Growth," Energies, MDPI, vol. 13(6), pages 1-71, March.
    13. Mahmoud M. Sayed & Mohamed Y. Mahdy & Shady H. E. Abdel Aleem & Hosam K. M. Youssef & Tarek A. Boghdady, 2022. "Simultaneous Distribution Network Reconfiguration and Optimal Allocation of Renewable-Based Distributed Generators and Shunt Capacitors under Uncertain Conditions," Energies, MDPI, vol. 15(6), pages 1-27, March.
    14. Hongmei Li & Hantao Cui & Chunjie Li, 2019. "Distribution Network Power Loss Analysis Considering Uncertainties in Distributed Generations," Sustainability, MDPI, vol. 11(5), pages 1-17, March.
    15. Jordehi, A. Rezaee, 2015. "Optimisation of electric distribution systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1088-1100.
    16. Jain, Sanjay & Kalambe, Shilpa & Agnihotri, Ganga & Mishra, Anuprita, 2017. "Distributed generation deployment: State-of-the-art of distribution system planning in sustainable era," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 363-385.
    17. Joisa Dutra, Flavio M. Menezes, and Xuemei Zheng, 2016. "Price Regulation and the Incentives to Pursue Energy Efficiency by Minimizing Network Losses," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    18. Mukhopadhyay, Bineeta & Das, Debapriya, 2020. "Multi-objective dynamic and static reconfiguration with optimized allocation of PV-DG and battery energy storage system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    19. Prashant & Anwar Shahzad Siddiqui & Md Sarwar & Ahmed Althobaiti & Sherif S. M. Ghoneim, 2022. "Optimal Location and Sizing of Distributed Generators in Power System Network with Power Quality Enhancement Using Fuzzy Logic Controlled D-STATCOM," Sustainability, MDPI, vol. 14(6), pages 1-31, March.
    20. Sultana, U. & Khairuddin, Azhar B. & Mokhtar, A.S. & Zareen, N. & Sultana, Beenish, 2016. "Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system," Energy, Elsevier, vol. 111(C), pages 525-536.

    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:17:y:2024:i:9:p:2053-:d:1383305. 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.