IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i12p12402-13910d60140.html
   My bibliography  Save this article

A Two-stage Optimal Network Reconfiguration Approach for Minimizing Energy Loss of Distribution Networks Using Particle Swarm Optimization Algorithm

Author

Listed:
  • Wei-Tzer Huang

    (Department of Industrial Education and Technology, National Changhua University of Education, No. 2, Shida Road, Changhua 500, Taiwan)

  • Tsai-Hsiang Chen

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, No.43, Section 4, Keelung Road, Da’an District, Taipei City 106, Taiwan)

  • Hong-Ting Chen

    (Department of Industrial Education and Technology, National Changhua University of Education, No. 2, Shida Road, Changhua 500, Taiwan)

  • Jhih-Siang Yang

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, No.43, Section 4, Keelung Road, Da’an District, Taipei City 106, Taiwan)

  • Kuo-Lung Lian

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, No.43, Section 4, Keelung Road, Da’an District, Taipei City 106, Taiwan)

  • Yung-Ruei Chang

    (The Institute of Nuclear Energy Research, 1000 Wenhua Road, Jiaan Village, Longtan District, Taoyuan City 325, Taiwan)

  • Yih-Der Lee

    (The Institute of Nuclear Energy Research, 1000 Wenhua Road, Jiaan Village, Longtan District, Taoyuan City 325, Taiwan)

  • Yuan-Hsiang Ho

    (The Institute of Nuclear Energy Research, 1000 Wenhua Road, Jiaan Village, Longtan District, Taoyuan City 325, Taiwan)

Abstract

This study aimed to minimize energy losses in traditional distribution networks and microgrids through a network reconfiguration and phase balancing approach. To address this problem, an algorithm composed of a multi-objective function and operation constraints is proposed. Network connection matrices based on graph theory and the backward/forward sweep method are used to analyze power flow. A minimizing energy loss approach is developed for network reconfiguration and phase balancing, and the particle swarm optimization (PSO) algorithm is adopted to solve this optimal combination problem. The proposed approach is tested on the IEEE 37-bus test system and the first outdoor microgrid test bed established by the Institute of Nuclear Energy Research (INER) in Taiwan. Simulation results demonstrate that the proposed two-stage approach can be applied in network reconfiguration to minimize energy loss.

Suggested Citation

  • Wei-Tzer Huang & Tsai-Hsiang Chen & Hong-Ting Chen & Jhih-Siang Yang & Kuo-Lung Lian & Yung-Ruei Chang & Yih-Der Lee & Yuan-Hsiang Ho, 2015. "A Two-stage Optimal Network Reconfiguration Approach for Minimizing Energy Loss of Distribution Networks Using Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 8(12), pages 1-17, December.
  • Handle: RePEc:gam:jeners:v:8:y:2015:i:12:p:12402-13910:d:60140
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/12/12402/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/8/12/12402/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ying-Yi Hong & Yuan-Ming Lai & Yung-Ruei Chang & Yih-Der Lee & Pang-Wei Liu, 2015. "Optimizing Capacities of Distributed Generation and Energy Storage in a Small Autonomous Power System Considering Uncertainty in Renewables," Energies, MDPI, vol. 8(4), pages 1-20, March.
    2. Wei-Tzer Huang & Kai-Chao Yao & Chun-Ching Wu, 2014. "Using the Direct Search Method for Optimal Dispatch of Distributed Generation in a Medium-Voltage Microgrid," Energies, MDPI, vol. 7(12), pages 1-19, December.
    3. Hak-Man Kim & Tetsuo Kinoshita & Myong-Chul Shin, 2010. "A Multiagent System for Autonomous Operation of Islanded Microgrids Based on a Power Market Environment," Energies, MDPI, vol. 3(12), pages 1-19, December.
    4. Hak-Man Kim & Yujin Lim & Tetsuo Kinoshita, 2012. "An Intelligent Multiagent System for Autonomous Microgrid Operation," Energies, MDPI, vol. 5(9), pages 1-16, September.
    5. Cheol-Hee Yoo & Il-Yop Chung & Hak-Ju Lee & Sung-Soo Hong, 2013. "Intelligent Control of Battery Energy Storage for Multi-Agent Based Microgrid Energy Management," Energies, MDPI, vol. 6(10), pages 1-24, 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. Yongming Zhang & Zhe Yan & Feng Yuan & Jiawei Yao & Bao Ding, 2018. "A Novel Reconstruction Approach to Elevator Energy Conservation Based on a DC Micro-Grid in High-Rise Buildings," Energies, MDPI, vol. 12(1), pages 1-17, December.
    2. Muhammad Yousif & Qian Ai & Yang Gao & Waqas Ahmad Wattoo & Ziqing Jiang & Ran Hao, 2018. "Application of Particle Swarm Optimization to a Scheduling Strategy for Microgrids Coupled with Natural Gas Networks," Energies, MDPI, vol. 11(12), pages 1-16, December.
    3. Filipe F. C. Silva & Pedro M. S. Carvalho & Luís A. F. M. Ferreira, 2021. "Improving PV Resilience by Dynamic Reconfiguration in Distribution Grids: Problem Complexity and Computation Requirements," Energies, MDPI, vol. 14(4), pages 1-15, February.
    4. Yih-Der Lee & Jheng-Lun Jiang & Yuan-Hsiang Ho & Wei-Chen Lin & Hsin-Ching Chih & Wei-Tzer Huang, 2020. "Neutral Current Reduction in Three-Phase Four-Wire Distribution Feeders by Optimal Phase Arrangement Based on a Full-Scale Net Load Model Derived from the FTU Data," Energies, MDPI, vol. 13(7), pages 1-20, April.
    5. Rade Čađenović & Damir Jakus & Petar Sarajčev & Josip Vasilj, 2018. "Optimal Distribution Network Reconfiguration through Integration of Cycle-Break and Genetic Algorithms," Energies, MDPI, vol. 11(5), pages 1-19, May.
    6. Gayo-Abeleira, Miguel & Santos, Carlos & Javier Rodríguez Sánchez, Francisco & Martín, Pedro & Antonio Jiménez, José & Santiso, Enrique, 2022. "Aperiodic two-layer energy management system for community microgrids based on blockchain strategy," Applied Energy, Elsevier, vol. 324(C).
    7. Ying-Yi Hong, 2016. "Electric Power Systems Research," Energies, MDPI, vol. 9(10), pages 1-4, 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. Xi Wu & Ping Jiang & Jing Lu, 2014. "Multiagent-Based Distributed Load Shedding for Islanded Microgrids," Energies, MDPI, vol. 7(9), pages 1-13, September.
    2. Nah-Oak Song & Ji-Hye Lee & Hak-Man Kim & Yong Hoon Im & Jae Yong Lee, 2015. "Optimal Energy Management of Multi-Microgrids with Sequentially Coordinated Operations," Energies, MDPI, vol. 8(8), pages 1-20, August.
    3. Nah-Oak Song & Ji-Hye Lee & Hak-Man Kim, 2016. "Optimal Electric and Heat Energy Management of Multi-Microgrids with Sequentially-Coordinated Operations," Energies, MDPI, vol. 9(6), pages 1-18, June.
    4. Ming-Tse Kuo & Shiue-Der Lu, 2013. "Design and Implementation of Real-Time Intelligent Control and Structure Based on Multi-Agent Systems in Microgrids," Energies, MDPI, vol. 6(11), pages 1-15, November.
    5. Liuming Jing & Dae-Hee Son & Sang-Hee Kang & Soon-Ryul Nam, 2016. "A Novel Protection Method for Single Line-to-Ground Faults in Ungrounded Low-Inertia Microgrids," Energies, MDPI, vol. 9(6), pages 1-16, June.
    6. Tine L. Vandoorn & Jan Van de Vyver & Louis Gevaert & Lieven Degroote & Lieven Vandevelde, 2015. "Congestion Control Algorithm in Distribution Feeders: Integration in a Distribution Management System," Energies, MDPI, vol. 8(6), pages 1-20, June.
    7. Thai-Thanh Nguyen & Hyeong-Jun Yoo & Hak-Man Kim, 2015. "A Flywheel Energy Storage System Based on a Doubly Fed Induction Machine and Battery for Microgrid Control," Energies, MDPI, vol. 8(6), pages 1-16, June.
    8. Van-Hai Bui & Akhtar Hussain & Hak-Man Kim, 2017. "Optimal Operation of Microgrids Considering Auto-Configuration Function Using Multiagent System," Energies, MDPI, vol. 10(10), pages 1-16, September.
    9. Luis Hernandez & Carlos Baladrón & Javier M. Aguiar & Belén Carro & Antonio J. Sanchez-Esguevillas & Jaime Lloret, 2013. "Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks," Energies, MDPI, vol. 6(3), pages 1-24, March.
    10. Ning Zhang & Wei Gu & Haojun Yu & Wei Liu, 2013. "Application of Coordinated SOFC and SMES Robust Control for Stabilizing Tie-Line Power," Energies, MDPI, vol. 6(4), pages 1-16, April.
    11. Wang, Linyuan & Zhao, Lin & Mao, Guozhu & Zuo, Jian & Du, Huibin, 2017. "Way to accomplish low carbon development transformation: A bibliometric analysis during 1995–2014," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 57-69.
    12. Ali Hadi Abdulwahid & Shaorong Wang, 2016. "A Novel Approach for Microgrid Protection Based upon Combined ANFIS and Hilbert Space-Based Power Setting," Energies, MDPI, vol. 9(12), pages 1-25, December.
    13. Qingwu Gong & Jiazhi Lei & Jun Ye, 2016. "Optimal Siting and Sizing of Distributed Generators in Distribution Systems Considering Cost of Operation Risk," Energies, MDPI, vol. 9(1), pages 1-18, January.
    14. Vu, Ba Hau & Chung, Il-Yop, 2022. "Optimal generation scheduling and operating reserve management for PV generation using RNN-based forecasting models for stand-alone microgrids," Renewable Energy, Elsevier, vol. 195(C), pages 1137-1154.
    15. Kaiye Gao & Tianshi Wang & Chenjing Han & Jinhao Xie & Ye Ma & Rui Peng, 2021. "A Review of Optimization of Microgrid Operation," Energies, MDPI, vol. 14(10), pages 1-39, May.
    16. Mingqi Wang & Xinqiao Zheng, 2017. "Sensitivity Analysis of Time Length of Photovoltaic Output Power to Capacity Configuration of Energy Storage Systems," Energies, MDPI, vol. 10(10), pages 1-15, October.
    17. Tsuguhiro Takuno & Yutaro Kitamori & Ryo Takahashi & Takashi Hikihara, 2011. "AC Power Routing System in Home Based on Demand and Supply Utilizing Distributed Power Sources," Energies, MDPI, vol. 4(5), pages 1-10, April.
    18. Maher Selim & Ryan Zhou & Wenying Feng & Peter Quinsey, 2021. "Estimating Energy Forecasting Uncertainty for Reliable AI Autonomous Smart Grid Design," Energies, MDPI, vol. 14(1), pages 1-15, January.
    19. Il-Seok Choi & Akhtar Hussain & Van-Hai Bui & Hak-Man Kim, 2018. "A Multi-Agent System-Based Approach for Optimal Operation of Building Microgrids with Rooftop Greenhouse," Energies, MDPI, vol. 11(7), pages 1-24, July.
    20. Akhtar Hussain & Van-Hai Bui & Hak-Man Kim, 2017. "Impact Analysis of Demand Response Intensity and Energy Storage Size on Operation of Networked Microgrids," Energies, MDPI, vol. 10(7), pages 1-19, June.

    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:8:y:2015:i:12:p:12402-13910:d:60140. 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.