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A Network Reconfiguration Method Considering Data Uncertainties in Smart Distribution Networks

Author

Listed:
  • Ke-yan Liu

    (Power Distribution Department, China Electric Power Research Institute, Beijing 100192, China)

  • Wanxing Sheng

    (Power Distribution Department, China Electric Power Research Institute, Beijing 100192, China)

  • Yongmei Liu

    (Power Distribution Department, China Electric Power Research Institute, Beijing 100192, China)

  • Xiaoli Meng

    (Power Distribution Department, China Electric Power Research Institute, Beijing 100192, China)

Abstract

This work presents a method for distribution network reconfiguration with the simultaneous consideration of distributed generation (DG) allocation. The uncertainties of load fluctuation before the network reconfiguration are also considered. Three optimal objectives, including minimal line loss cost, minimum Expected Energy Not Supplied, and minimum switch operation cost, are investigated. The multi-objective optimization problem is further transformed into a single-objective optimization problem by utilizing weighting factors. The proposed network reconfiguration method includes two periods. The first period is to create a feasible topology network by using binary particle swarm optimization (BPSO). Then the DG allocation problem is solved by utilizing sensitivity analysis and a Harmony Search algorithm (HSA). In the meanwhile, interval analysis is applied to deal with the uncertainties of load and devices parameters. Test cases are studied using the standard IEEE 33-bus and PG&E 69-bus systems. Different scenarios and comparisons are analyzed in the experiments. The results show the applicability of the proposed method. The performance analysis of the proposed method is also investigated. The computational results indicate that the proposed network reconfiguration algorithm is feasible.

Suggested Citation

  • Ke-yan Liu & Wanxing Sheng & Yongmei Liu & Xiaoli Meng, 2017. "A Network Reconfiguration Method Considering Data Uncertainties in Smart Distribution Networks," Energies, MDPI, vol. 10(5), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:618-:d:97386
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    Citations

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    Cited by:

    1. Prashant & Anwar Shahzad Siddiqui & Md Sarwar & Ahmed Althobaiti & Sherif S. M. Ghoneim, 2022. "Optimal Location and Sizing of Distributed Generators in Power System Network with Power Quality Enhancement Using Fuzzy Logic Controlled D-STATCOM," Sustainability, MDPI, vol. 14(6), pages 1-31, March.
    2. Mirna Fouad Abd El-salam & Eman Beshr & Magdy B. Eteiba, 2018. "A New Hybrid Technique for Minimizing Power Losses in a Distribution System by Optimal Sizing and Siting of Distributed Generators with Network Reconfiguration," Energies, MDPI, vol. 11(12), pages 1-26, November.
    3. Mahesh Kumar & Perumal Nallagownden & Irraivan Elamvazuthi, 2017. "Optimal Placement and Sizing of Renewable Distributed Generations and Capacitor Banks into Radial Distribution Systems," Energies, MDPI, vol. 10(6), pages 1-25, June.
    4. Mohd Ikhwan Muhammad Ridzuan & Sasa Z. Djokic, 2019. "Energy Regulator Supply Restoration Time," Energies, MDPI, vol. 12(6), pages 1-16, March.
    5. Klyapovskiy, Sergey & You, Shi & Michiorri, Andrea & Kariniotakis, George & Bindner, Henrik W., 2019. "Incorporating flexibility options into distribution grid reinforcement planning: A techno-economic framework approach," Applied Energy, Elsevier, vol. 254(C).
    6. 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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