IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i11p2744-d1408501.html

Network Reconfiguration for Loss Reduction Using Tabu Search and a Voltage Drop

Author

Listed:
  • Dionicio Zocimo Ñaupari Huatuco

    (Faculty of Electrical Engineering, National University of Engineering, Lima 15333, Peru)

  • Luiz Otávio Pinheiro Filho

    (Center of Alternative and Renewable Energy, Federal University of Paraíba, João Pessoa 58051-900, PB, Brazil)

  • Franklin Jesus Simeon Pucuhuayla

    (Faculty of Electrical Engineering, National University of Engineering, Lima 15333, Peru)

  • Yuri Percy Molina Rodriguez

    (Center of Alternative and Renewable Energy, Federal University of Paraíba, João Pessoa 58051-900, PB, Brazil)

Abstract

This paper introduces a new algorithm designed to address the challenge of distribution network reconfiguration, employing the tabu search metaheuristic in conjunction with the voltage drop concept. Distinguishing itself from existing methods, our proposed approach not only utilizes voltage drop for obtaining the initial solution but also introduces a novel technique for generating a candidate solution neighborhood. This method leverages both randomness and voltage drop, ensuring a smooth and steady descent during algorithm execution. The primary goal of our algorithm is to minimize active power losses within distribution networks. To validate its effectiveness, the proposed method underwent testing on three commonly referenced distribution systems: the 33-Bus, 69-Bus, and 94-Bus systems, widely acknowledged in the literature. A pivotal aspect of our work involves the synergy of the tabu search algorithm with a combination of both random and deterministic methods for generating neighbors. This strategic amalgamation plays a crucial role, enabling rapid execution while consistently yielding high-quality solutions. Additionally, the adoption of the electric distance method for generating the initial solution adds significant value, offering a commendable solution with minimal computational effort. Comparative assessments against other algorithms documented in the literature underscore the superior efficiency of our proposed algorithm.

Suggested Citation

  • Dionicio Zocimo Ñaupari Huatuco & Luiz Otávio Pinheiro Filho & Franklin Jesus Simeon Pucuhuayla & Yuri Percy Molina Rodriguez, 2024. "Network Reconfiguration for Loss Reduction Using Tabu Search and a Voltage Drop," Energies, MDPI, vol. 17(11), pages 1-18, June.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2744-:d:1408501
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/11/2744/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/11/2744/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fred Glover, 1990. "Tabu Search: A Tutorial," Interfaces, INFORMS, vol. 20(4), pages 74-94, August.
    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. Chinyere Ngwu & Ying Liu & Rui Wu, 2026. "Reinforcement learning in dynamic job shop scheduling: a comprehensive review of AI-driven approaches in modern manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 37(3), pages 1093-1108, March.
    2. H. A. J. Crauwels & C. N. Potts & L. N. Van Wassenhove, 1998. "Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 10(3), pages 341-350, August.
    3. E A Silver, 2004. "An overview of heuristic solution methods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(9), pages 936-956, September.
    4. Cazzaro, Davide & Fischetti, Martina & Fischetti, Matteo, 2020. "Heuristic algorithms for the Wind Farm Cable Routing problem," Applied Energy, Elsevier, vol. 278(C).
    5. Xi Chen & Enlu Zhou, 2015. "Population model-based optimization," Journal of Global Optimization, Springer, vol. 63(1), pages 125-148, September.
    6. David R. Morrison & Jason J. Sauppe & Wenda Zhang & Sheldon H. Jacobson & Edward C. Sewell, 2017. "Cyclic best first search: Using contours to guide branch‐and‐bound algorithms," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(1), pages 64-82, February.
    7. Vaithyanathan, Shivakumar & Burke, Laura I. & Magent, Michael A., 1996. "Massively parallel analog tabu search using neural networks applied to simple plant location problems," European Journal of Operational Research, Elsevier, vol. 93(2), pages 317-330, September.
    8. Kolahan, F. & Liang, M., 1998. "An adaptive TS approach to JIT sequencing with variable processing times and sequence-dependent setups," European Journal of Operational Research, Elsevier, vol. 109(1), pages 142-159, August.
    9. Ko, Young Dae, 2019. "The airfare pricing and seat allocation problem in full-service carriers and subsidiary low-cost carriers," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 92-102.
    10. Piñeyro, Pedro & Viera, Omar, 2010. "The economic lot-sizing problem with remanufacturing and one-way substitution," International Journal of Production Economics, Elsevier, vol. 124(2), pages 482-488, April.
    11. Tarasewich, Peter & McMullen, Patrick R., 2001. "A pruning heuristic for use with multisource product design," European Journal of Operational Research, Elsevier, vol. 128(1), pages 58-73, January.
    12. Tsai, Ying-Wei & D. Gemmill, Douglas, 1998. "Using tabu search to schedule activities of stochastic resource-constrained projects," European Journal of Operational Research, Elsevier, vol. 111(1), pages 129-141, November.
    13. Daniels, Richard L. & Rummel, Jeffrey L. & Schantz, Robert, 1998. "A model for warehouse order picking," European Journal of Operational Research, Elsevier, vol. 105(1), pages 1-17, February.
    14. Coslovich, Luca & Pesenti, Raffaele & Ukovich, Walter, 2006. "A two-phase insertion technique of unexpected customers for a dynamic dial-a-ride problem," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1605-1615, December.
    15. Rolland, Erik & Schilling, David A. & Current, John R., 1997. "An efficient tabu search procedure for the p-Median Problem," European Journal of Operational Research, Elsevier, vol. 96(2), pages 329-342, January.
    16. Fernando Rubio & Ismael Rodríguez, 2019. "Water-Based Metaheuristics: How Water Dynamics Can Help Us to Solve NP-Hard Problems," Complexity, Hindawi, vol. 2019, pages 1-13, April.
    17. Martina Fischetti & Michele Monaci, 2016. "Proximity search heuristics for wind farm optimal layout," Journal of Heuristics, Springer, vol. 22(4), pages 459-474, August.
    18. Rex K. Kincaid & Keith E. Laba & Sharon L. Padula, 1997. "Quelling Cabin Noise in Turboprop Aircraft via Active Control," Journal of Combinatorial Optimization, Springer, vol. 1(3), pages 229-250, October.
    19. Graeme J. Doole & David J. Pannell, 2008. "Optimisation of a Large, Constrained Simulation Model using Compressed Annealing," Journal of Agricultural Economics, Wiley Blackwell, vol. 59(1), pages 188-206, February.
    20. Mayer, D. G. & Belward, J. A. & Widell, H. & Burrage, K., 1999. "Survival of the fittest--genetic algorithms versus evolution strategies in the optimization of systems models," Agricultural Systems, Elsevier, vol. 60(2), pages 113-122, May.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:11:p:2744-:d:1408501. 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 The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (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.