IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i19p6998-d1255595.html
   My bibliography  Save this article

Optimal Integration of Distribution Network Reconfiguration and Conductor Selection in Power Distribution Systems via MILP

Author

Listed:
  • Luis A. Gallego Pareja

    (Department of Electrical Engineering, State University of Londrina (UEL), Londrina 86057-970, PR, Brazil)

  • Jesús M. López-Lezama

    (Research Group in Efficient Energy Management (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia, Calle 67 No. 53-108, Medellín 050010, Colombia)

  • Oscar Gómez Carmona

    (Facultad de Tecnología, Universidad Tecnológica de Pereira, Cr 27 No 10-02, Pereira 660003, Colombia)

Abstract

Power distribution systems (PDS) comprise essential electrical components and infrastructure that facilitate the delivery of electrical energy from a power transmission system to end users. Typically, the topology of distribution systems is radial, so that power goes from the substations to end users through main lines or feeders. However, the expansion of new feeders to accommodate new users and ever-growing energy demand have led to higher energy losses and deterioration of the voltage profile. To address these challenges, several solutions have been proposed, including the selection of optimal conductors, allocation of voltage regulators, utilization of capacitor banks, implementation of distributed generation, and optimal reconfiguration. Although reconfiguring the network is the most cost-effective approach, this solution might not be sufficient to completely minimize technical losses and improve system performance. This paper presents a novel approach that combines optimal distribution network reconfiguration (ODNR) with optimal conductor selection (OCS) to minimize power losses and enhance the voltage profiles of PDS. The key contribution lies in the integration of the ODNR and OCS into a single MILP problem, ensuring the attainment of globally optimal solutions. The proposed model was tested with benchmark 33-, 69-, and 85-bus test systems. The results allowed us to conclude that the combined effect of ODNR and OCS presents better results than when any of these approaches are applied either separately or sequentially.

Suggested Citation

  • Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2023. "Optimal Integration of Distribution Network Reconfiguration and Conductor Selection in Power Distribution Systems via MILP," Energies, MDPI, vol. 16(19), pages 1-25, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6998-:d:1255595
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/19/6998/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/19/6998/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhenghui Zhao & Joseph Mutale, 2019. "Optimal Conductor Size Selection in Distribution Networks with High Penetration of Distributed Generation Using Adaptive Genetic Algorithm," Energies, MDPI, vol. 12(11), pages 1-20, May.
    2. Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2022. "A Mixed-Integer Linear Programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation," Energies, MDPI, vol. 15(9), pages 1-26, April.
    3. Alex Guamán & Alex Valenzuela, 2021. "Distribution Network Reconfiguration Applied to Multiple Faulty Branches Based on Spanning Tree and Genetic Algorithms," Energies, MDPI, vol. 14(20), pages 1-16, October.
    4. Minsheng Yang & Jianqi Li & Jianying Li & Xiaofang Yuan & Jiazhu Xu, 2021. "Reconfiguration Strategy for DC Distribution Network Fault Recovery Based on Hybrid Particle Swarm Optimization," Energies, MDPI, vol. 14(21), pages 1-15, November.
    5. Dhivya Swaminathan & Arul Rajagopalan & Oscar Danilo Montoya & Savitha Arul & Luis Fernando Grisales-Noreña, 2023. "Distribution Network Reconfiguration Based on Hybrid Golden Flower Algorithm for Smart Cities Evolution," Energies, MDPI, vol. 16(5), pages 1-24, March.
    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. Matheus Diniz Gonçalves-Leite & Edgar Manuel Carreño-Franco & Jesús M. López-Lezama, 2023. "Impact of Distributed Generation on the Effectiveness of Electric Distribution System Reconfiguration," Energies, MDPI, vol. 16(17), pages 1-20, August.
    2. Min Zhu & Saber Arabi Nowdeh & Aspassia Daskalopulu, 2023. "An Improved Human-Inspired Algorithm for Distribution Network Stochastic Reconfiguration Using a Multi-Objective Intelligent Framework and Unscented Transformation," Mathematics, MDPI, vol. 11(17), pages 1-23, August.
    3. Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2022. "A Mixed-Integer Linear Programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation," Energies, MDPI, vol. 15(9), pages 1-26, April.
    4. Gubbala Venkata Naga Lakshmi & Askani Jaya Laxmi & Venkataramana Veeramsetty & Surender Reddy Salkuti, 2022. "Optimal Placement of Distributed Generation Based on Power Quality Improvement Using Self-Adaptive Lévy Flight Jaya Algorithm," Clean Technol., MDPI, vol. 4(4), pages 1-13, November.
    5. Lewis Waswa & Munyaradzi Justice Chihota & Bernard Bekker, 2021. "A Probabilistic Conductor Size Selection Framework for Active Distribution Networks," Energies, MDPI, vol. 14(19), pages 1-19, October.
    6. Xin Yan & Qian Zhang, 2023. "Research on Combination of Distributed Generation Placement and Dynamic Distribution Network Reconfiguration Based on MIBWOA," Sustainability, MDPI, vol. 15(12), pages 1-34, June.
    7. Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2023. "A MILP Model for Optimal Conductor Selection and Capacitor Banks Placement in Primary Distribution Systems," Energies, MDPI, vol. 16(11), pages 1-21, May.
    8. Wallisson C. Nogueira & Lina P. Garcés Negrete & Jesús M. López-Lezama, 2023. "Optimal Allocation and Sizing of Distributed Generation Using Interval Power Flow," Sustainability, MDPI, vol. 15(6), pages 1-24, March.
    9. Julián David Pradilla-Rozo & Julián Alejandro Vega-Forero & Oscar Danilo Montoya, 2023. "Application of the Gradient-Based Metaheuristic Optimizerto Solve the Optimal Conductor Selection Problemin Three-Phase Asymmetric Distribution Networks," Energies, MDPI, vol. 16(2), pages 1-29, January.
    10. Wei-Chen Lin & Chao-Hsien Hsiao & Wei-Tzer Huang & Kai-Chao Yao & Yih-Der Lee & Jheng-Lun Jian & Yuan Hsieh, 2024. "Network Reconfiguration Framework for CO 2 Emission Reduction and Line Loss Minimization in Distribution Networks Using Swarm Optimization Algorithms," Sustainability, MDPI, vol. 16(4), pages 1-19, February.
    11. Juan Li & Qing An & Hong Lei & Qian Deng & Gai-Ge Wang, 2022. "Survey of Lévy Flight-Based Metaheuristics for Optimization," Mathematics, MDPI, vol. 10(15), pages 1-27, August.
    12. Santiago Bustamante-Mesa & Jorge W. Gonzalez-Sanchez & Sergio D. Saldarriaga-Zuluaga & Jesús M. López-Lezama & Nicolás Muñoz-Galeano, 2024. "Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection," Energies, MDPI, vol. 17(2), pages 1-20, January.
    13. Tianxiang Ma & Ziqi Hu & Yan Xu & Haoran Dong, 2022. "Fault Location Based on Comprehensive Grey Correlation Degree Analysis for Flexible DC Distribution Network," Energies, MDPI, vol. 15(20), pages 1-16, October.
    14. Elham Mahdavi & Seifollah Asadpour & Leonardo H. Macedo & Rubén Romero, 2023. "Reconfiguration of Distribution Networks with Simultaneous Allocation of Distributed Generation Using the Whale Optimization Algorithm," Energies, MDPI, vol. 16(12), pages 1-19, June.
    15. Abdullah Shaheen & Ragab El-Sehiemy & Salah Kamel & Ali Selim, 2022. "Optimal Operational Reliability and Reconfiguration of Electrical Distribution Network Based on Jellyfish Search Algorithm," Energies, MDPI, vol. 15(19), pages 1-14, September.
    16. Yang Wang & Yifan Wang & Zhenghui Zhao & Zhiquan Zhou & Zhihao Hou, 2023. "Multi-Timescale Optimal Operation Strategy for Renewable Energy Power Systems Based on Inertia Evaluation," Energies, MDPI, vol. 16(8), pages 1-15, April.
    17. Moradi-Sarvestani, Sajjad & Jooshaki, Mohammad & Fotuhi-Firuzabad, Mahmud & Lehtonen, Matti, 2023. "Incorporating direct load control demand response into active distribution system planning," Applied Energy, Elsevier, vol. 339(C).
    18. Mohammed Alqahtani & Ponnusamy Marimuthu & Veerasamy Moorthy & B. Pangedaiah & Ch. Rami Reddy & M. Kiran Kumar & Muhammad Khalid, 2023. "Investigation and Minimization of Power Loss in Radial Distribution Network Using Gray Wolf Optimization," Energies, MDPI, vol. 16(12), pages 1-15, June.
    19. Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2023. "Optimal Feeder Reconfiguration and Placement of Voltage Regulators in Electrical Distribution Networks Using a Linear Mathematical Model," Sustainability, MDPI, vol. 15(1), pages 1-20, January.
    20. Yi, Ji Hyun & Cherkaoui, Rachid & Paolone, Mario & Shchetinin, Dmitry & Knezovic, Katarina, 2022. "Expansion planning of active distribution networks achieving their dispatchability via energy storage systems," Applied Energy, Elsevier, vol. 326(C).

    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:16:y:2023:i:19:p:6998-:d:1255595. 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.