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A Joint Planning Method for Substations and Lines in Distribution Systems Based on the Parallel Bird Swarm Algorithm

Author

Listed:
  • Kuihua Wu

    (Economic & Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, China)

  • Kun Li

    (Economic & Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, China)

  • Rong Liang

    (Economic & Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, China)

  • Runze Ma

    (School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Yuxuan Zhao

    (School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Jian Wang

    (State Grid Shandong Electric Power Research Institute, Jinan 250002, Shandong, China)

  • Lujie Qi

    (Economic & Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, China)

  • Shengyuan Liu

    (School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Chang Han

    (School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Li Yang

    (School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Minxiang Huang

    (School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

Reasonable distribution network planning schemes can not only improve the power quality and power supply capacity of the power system, but also increase the economic benefits and welfare of the whole society. In this work, a bi-level optimization model is proposed for the joint planning of substations and lines in looped urban distribution systems. The upper-level model aims to address the substation locating and sizing problem, whereas the lower-level model the network planning problem. Both the substations directly supplying power to a load and the contralateral substations that act as backup power source to the load are considered in the bi-level model. In order to solve the bi-level planning model which is mathematically mixed integer programing and with plenty of continuous and discrete variables, the bird swarm algorithm is improved and applied based on the idea of parallel computing of big data theory. Simulations on actual planning problems are employed to verify the effectiveness of the proposed bi-level distribution network planning model and the parallel bird swarm algorithm.

Suggested Citation

  • Kuihua Wu & Kun Li & Rong Liang & Runze Ma & Yuxuan Zhao & Jian Wang & Lujie Qi & Shengyuan Liu & Chang Han & Li Yang & Minxiang Huang, 2018. "A Joint Planning Method for Substations and Lines in Distribution Systems Based on the Parallel Bird Swarm Algorithm," Energies, MDPI, vol. 11(10), pages 1-14, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2669-:d:174088
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    References listed on IDEAS

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    1. Yi Yu & Xishan Wen & Jian Zhao & Zhao Xu & Jiayong Li, 2018. "Co-Planning of Demand Response and Distributed Generators in an Active Distribution Network," Energies, MDPI, vol. 11(2), pages 1-18, February.
    2. Alessia Cagnano & Enrico De Tuglie & Marco Bronzini, 2018. "Multiarea Voltage Controller for Active Distribution Networks," Energies, MDPI, vol. 11(3), pages 1-20, March.
    3. Jafar Jallad & Saad Mekhilef & Hazlie Mokhlis & Javed Laghari & Ola Badran, 2018. "Application of Hybrid Meta-Heuristic Techniques for Optimal Load Shedding Planning and Operation in an Islanded Distribution Network Integrated with Distributed Generation," Energies, MDPI, vol. 11(5), pages 1-25, May.
    4. Mingxiao Zhu & Jiacai Li & Dingge Chang & Guanjun Zhang & Jiming Chen, 2018. "Optimization of Antenna Array Deployment for Partial Discharge Localization in Substations by Hybrid Particle Swarm Optimization and Genetic Algorithm Method," Energies, MDPI, vol. 11(7), pages 1-18, July.
    5. Rui Li & Wei Wang & Zhe Chen & Jiuchun Jiang & Weige Zhang, 2017. "A Review of Optimal Planning Active Distribution System: Models, Methods, and Future Researches," Energies, MDPI, vol. 10(11), pages 1-27, October.
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    Cited by:

    1. Su-qi Zhang & Kuo-Ping Lin, 2020. "Short-Term Traffic Flow Forecasting Based on Data-Driven Model," Mathematics, MDPI, vol. 8(2), pages 1-17, January.

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