IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i9p3063-d799601.html
   My bibliography  Save this article

A Mixed-Integer Linear Programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation

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, Medellin 050010, Colombia)

  • Oscar Gómez Carmona

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

Abstract

Distributed generation (DG) aims to generate part of the required electrical energy on a small scale closer to the places of consumption. Integration of DG into an existing electric distribution network (EDN) has technical, economic, and environmental benefits. DG placement is typically determined by investors and local conditions such as the availability of energy resources, space, and licenses, among other factors. When the location of DG is not a decision of the distribution network operator (DNO), the simultaneous integration of distribution network reconfiguration (DNR) and DG placement can maximize the benefits of DG and mitigate eventual negative impacts. DNR consists of altering the EDN topology to improve its performance while maintaining the radiality of the network. DNR and optimal placement of DG (OPDG) are challenging optimization problems since they involve integer and continuous variables subject to nonlinear constraints and a nonlinear objective function. Due to their nonlinear and nonconvex nature, most approaches to solve these problems resort to metaheuristic techniques. The main drawbacks of such methodologies lie in the fact that they are not guaranteed to reach an optimal solution, and most of them require the fine-tuning of several parameters. This paper recasts the nonlinear DNR and OPGD problems into linear equivalents to obtain a mixed-integer linear programming (MILP) model that guarantees global optimal solutions. Several tests were carried out on benchmark EDNs evidencing the applicability and effectiveness of the proposed approach. It was found that when no DG units are considered, the proposed model can find the same results reported in the specialized literature but in less computational time; furthermore, the inclusion of DG units along with DNR usually allows the model to find better solutions than those previously reported in the specialized literature.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3063-:d:799601
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/9/3063/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/9/3063/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ben Hamida, Imen & Salah, Saoussen Brini & Msahli, Faouzi & Mimouni, Mohamed Faouzi, 2018. "Optimal network reconfiguration and renewable DG integration considering time sequence variation in load and DGs," Renewable Energy, Elsevier, vol. 121(C), pages 66-80.
    2. Cassio Gerez & Eduardo Coelho Marques Costa & Alfeu J. Sguarezi Filho, 2022. "Distribution Network Reconfiguration Considering Voltage and Current Unbalance Indexes and Variable Demand Solved through a Selective Bio-Inspired Metaheuristic," Energies, MDPI, vol. 15(5), pages 1-27, February.
    3. Mirna Fouad Abd El-salam & Eman Beshr & Magdy B. Eteiba, 2018. "A New Hybrid Technique for Minimizing Power Losses in a Distribution System by Optimal Sizing and Siting of Distributed Generators with Network Reconfiguration," Energies, MDPI, vol. 11(12), pages 1-26, November.
    4. 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.
    5. 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.
    6. Sedighizadeh, Mostafa & Esmaili, Masoud & Esmaeili, Mobin, 2014. "Application of the hybrid Big Bang-Big Crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution systems," Energy, Elsevier, vol. 76(C), pages 920-930.
    7. Arun Onlam & Daranpob Yodphet & Rongrit Chatthaworn & Chayada Surawanitkun & Apirat Siritaratiwat & Pirat Khunkitti, 2019. "Power Loss Minimization and Voltage Stability Improvement in Electrical Distribution System via Network Reconfiguration and Distributed Generation Placement Using Novel Adaptive Shuffled Frogs Leaping," Energies, MDPI, vol. 12(3), pages 1-12, February.
    8. Andrés Felipe Pérez Posada & Juan G. Villegas & Jesús M. López-Lezama, 2017. "A Scatter Search Heuristic for the Optimal Location, Sizing and Contract Pricing of Distributed Generation in Electric Distribution Systems," Energies, MDPI, vol. 10(10), pages 1-16, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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. 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.
    3. 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.
    4. Guillermo Alonso & Ricardo F. Alonso & Antonio Carlos Zambroni Zambroni De Souza & Walmir Freitas, 2022. "Enhanced Artificial Immune Systems and Fuzzy Logic for Active Distribution Systems Reconfiguration," Energies, MDPI, vol. 15(24), pages 1-18, December.
    5. 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.
    6. 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.
    7. 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.

    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. Nasreddine Belbachir & Mohamed Zellagui & Samir Settoul & Claude Ziad El-Bayeh & Ragab A. El-Sehiemy, 2023. "Multi Dimension-Based Optimal Allocation of Uncertain Renewable Distributed Generation Outputs with Seasonal Source-Load Power Uncertainties in Electrical Distribution Network Using Marine Predator Al," Energies, MDPI, vol. 16(4), pages 1-24, February.
    3. 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.
    4. 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).
    5. Ola Badran & Jafar Jallad, 2023. "Multi-Objective Decision Approach for Optimal Real-Time Switching Sequence of Network Reconfiguration Realizing Maximum Load Capacity," Energies, MDPI, vol. 16(19), pages 1-32, September.
    6. Ayşe Aybike Şeker & Tuba Gözel & Mehmet Hakan Hocaoğlu, 2021. "BIBC Matrix Modification for Network Topology Changes: Reconfiguration Problem Implementation," Energies, MDPI, vol. 14(10), pages 1-16, May.
    7. Azad-Farsani, Ehsan & Sardou, Iman Goroohi & Abedini, Saeed, 2021. "Distribution Network Reconfiguration based on LMP at DG connected busses using game theory and self-adaptive FWA," Energy, Elsevier, vol. 215(PB).
    8. Chandrasekaran Venkatesan & Raju Kannadasan & Mohammed H. Alsharif & Mun-Kyeom Kim & Jamel Nebhen, 2021. "A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems," Sustainability, MDPI, vol. 13(6), pages 1-34, March.
    9. Manuel S. Alvarez-Alvarado & Johnny Rengifo & Rommel M. Gallegos-Núñez & José G. Rivera-Mora & Holguer H. Noriega & Washington Velasquez & Daniel L. Donaldson & Carlos D. Rodríguez-Gallegos, 2022. "Particle Swarm Optimization for Optimal Frequency Response with High Penetration of Photovoltaic and Wind Generation," Energies, MDPI, vol. 15(22), pages 1-12, November.
    10. Pavlos S. Georgilakis, 2020. "Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Researc," Energies, MDPI, vol. 13(1), pages 1-37, January.
    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. Fatma Yaprakdal & Mustafa Baysal & Amjad Anvari-Moghaddam, 2019. "Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources," Energies, MDPI, vol. 12(10), pages 1-17, May.
    13. Papadimitrakis, M. & Giamarelos, N. & Stogiannos, M. & Zois, E.N. & Livanos, N.A.-I. & Alexandridis, A., 2021. "Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    14. Abdulaziz Alanazi & Mohana Alanazi, 2022. "Artificial Electric Field Algorithm-Pattern Search for Many-Criteria Networks Reconfiguration Considering Power Quality and Energy Not Supplied," Energies, MDPI, vol. 15(14), pages 1-27, July.
    15. Das, Bikash & Mukherjee, V. & Das, Debapriya, 2019. "Optimum DG placement for known power injection from utility/substation by a novel zero bus load flow approach," Energy, Elsevier, vol. 175(C), pages 228-249.
    16. 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.
    17. Li, Peng & Ji, Jie & Ji, Haoran & Song, Guanyu & Wang, Chengshan & Wu, Jianzhong, 2020. "Self-healing oriented supply restoration method based on the coordination of multiple SOPs in active distribution networks," Energy, Elsevier, vol. 195(C).
    18. Habib Ur Rehman & Arif Hussain & Waseem Haider & Sayyed Ahmad Ali & Syed Ali Abbas Kazmi & Muhammad Huzaifa, 2023. "Optimal Planning of Solar Photovoltaic (PV) and Wind-Based DGs for Achieving Techno-Economic Objectives across Various Load Models," Energies, MDPI, vol. 16(5), pages 1-38, March.
    19. Panyawoot Boonluk & Sirote Khunkitti & Pradit Fuangfoo & Apirat Siritaratiwat, 2021. "Optimal Siting and Sizing of Battery Energy Storage: Case Study Seventh Feeder at Nakhon Phanom Substation in Thailand," Energies, MDPI, vol. 14(5), pages 1-20, March.
    20. Xiao, Hao & Pei, Wei & Deng, Wei & Ma, Tengfei & Zhang, Shizhong & Kong, Li, 2021. "Enhancing risk control ability of distribution network for improved renewable energy integration through flexible DC interconnection," Applied Energy, Elsevier, vol. 284(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:15:y:2022:i:9:p:3063-:d:799601. 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.