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

Author

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  • 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
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    References listed on IDEAS

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    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.
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    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.
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    Citations

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    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Ying-Yi Hong, 2016. "Electric Power Systems Research," Energies, MDPI, vol. 9(10), pages 1-4, October.
    7. 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.

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