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Optimal Planning Method of On-load Capacity Regulating Distribution Transformers in Urban Distribution Networks after Electric Energy Replacement Considering Uncertainties

Author

Listed:
  • Yu Su

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology; Chongqing University, Chongqing 400044, China)

  • Niancheng Zhou

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology; Chongqing University, Chongqing 400044, China)

  • Qianggang Wang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology; Chongqing University, Chongqing 400044, China)

  • Chao Lei

    (State Grid Sichuan Electric Power Company Tianfu Power Supply Company, Chengdu 610000, China)

  • Jian Fang

    (China Southern Power Grid Guangzhou Power Supply Co., Ltd., Guangzhou 510000, China)

Abstract

Electric energy replacement is the umbrella term for the use of electric energy to replace oil (e.g., electric automobiles), coal (e.g., electric heating), and gas (e.g., electric cooking appliances), which increases the electrical load peak, causing greater valley/peak differences. On-load capacity regulating distribution transformers have been used to deal with loads with great valley/peak differences, so reasonably replacing conventional distribution transformers with on-load capacity regulating distribution transformers can effectively cope with load changes after electric energy replacement and reduce the no-load losses of distribution transformers. Before planning for on-load capacity regulating distribution transformers, the nodal effective load considering uncertainties within the life cycle after electric energy replacement was obtained by a Monte Carlo method. Then, according to the loss relation between on-load capacity regulating distribution transformers and conventional distribution transformers, three characteristic indexes of annual continuous apparent power curve and replacement criteria for on-load capacity regulating distribution transformers were put forward in this paper, and a set of distribution transformer replaceable points was obtained. Next, based on cost benefit analysis, a planning model of on-load capacity regulating distribution transformers which consists of investment profitability index within the life cycle, investment cost recouping index and capacity regulating cost index was put forward. The branch and bound method was used to solve the planning model within replaceable point set to obtain upgrading and reconstruction scheme of distribution transformers under a certain investment. Finally, planning analysis of on-load capacity regulating distribution transformers was carried out for electric energy replacement points in one urban distribution network under three scenes: certain load, uncertain load and nodal effective load considering uncertainties. Results showed that the planning method of on-load capacity regulating distribution transformers proposed in this paper was very feasible and is of great guiding significance to distribution transformer planning after electric energy replacement and the popularization of on-load capacity regulating distribution transformers.

Suggested Citation

  • Yu Su & Niancheng Zhou & Qianggang Wang & Chao Lei & Jian Fang, 2018. "Optimal Planning Method of On-load Capacity Regulating Distribution Transformers in Urban Distribution Networks after Electric Energy Replacement Considering Uncertainties," Energies, MDPI, vol. 11(6), pages 1-25, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1457-:d:150763
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    References listed on IDEAS

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    1. Yohan Shim & Marte Fodstad & Steven Gabriel & Asgeir Tomasgard, 2013. "A branch-and-bound method for discretely-constrained mathematical programs with equilibrium constraints," Annals of Operations Research, Springer, vol. 210(1), pages 5-31, November.
    2. Chiodo, Elio & Lauria, Davide & Mottola, Fabio & Pisani, Cosimo, 2016. "Lifetime characterization via lognormal distribution of transformers in smart grids: Design optimization," Applied Energy, Elsevier, vol. 177(C), pages 127-135.
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    Cited by:

    1. Sung-Min Cho & Jin-Su Kim & Jae-Chul Kim, 2019. "Optimal Operation Parameter Estimation of Energy Storage for Frequency Regulation," Energies, MDPI, vol. 12(9), pages 1-21, May.

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