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Weight Calculation Alternative Methods in Prime’s Algorithm Dedicated for Power System Restoration Strategies

Author

Listed:
  • Artur Łukaszewski

    (Electrical Power Engineering Institute, Warsaw University of Technology, Koszykowa Street 75, 00-662 Warsaw, Poland)

  • Łukasz Nogal

    (Electrical Power Engineering Institute, Warsaw University of Technology, Koszykowa Street 75, 00-662 Warsaw, Poland)

  • Sylwester Robak

    (Electrical Power Engineering Institute, Warsaw University of Technology, Koszykowa Street 75, 00-662 Warsaw, Poland)

Abstract

In self-healing grid systems, high utility in the use of greedy algorithms is observed. One of the most popular solutions is based on Prim’s algorithm. In the computation, the power grid is represented as a weighted graph. This paper presents a few possibilities of calculation of the numerical weight of a branch of the graph. The proposition of a modified edge weight calculation based on active power belongs to this group. The other solutions are novel subalgorithms bounded by real power, reactive power, and normalized factor. This factor is a mathematical combination of active and reactive power multiplied by influence coefficients. Requirements necessary for a power system are applied in the considered algorithms. Each of these proposed algorithms includes the power source limits, voltage level at busbars, and power system transmission features, such as transmission lines rated currents and power losses. All mentioned methods were compiled into separate algorithms, which can be used to compute graph model parameters. A simulation model based on Prim’s algorithm was prepared to compare the suitability of presented concepts. All weights of the subalgorithms were compared to each other. That is why different power system restoration strategies may require various methods of calculating weights of the graph’s branches.

Suggested Citation

  • Artur Łukaszewski & Łukasz Nogal & Sylwester Robak, 2020. "Weight Calculation Alternative Methods in Prime’s Algorithm Dedicated for Power System Restoration Strategies," Energies, MDPI, vol. 13(22), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6063-:d:447872
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    References listed on IDEAS

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    1. Lingling Bin & Haiyang Pan & Li He & Jijian Lian, 2019. "An Importance Analysis–Based Weight Evaluation Framework for Identifying Key Components of Multi-Configuration Off-Grid Wind Power Generation Systems under Stochastic Data Inputs," Energies, MDPI, vol. 12(22), pages 1-22, November.
    2. Jerzy Andruszkiewicz & Józef Lorenc & Agnieszka Weychan, 2019. "Demand Price Elasticity of Residential Electricity Consumers with Zonal Tariff Settlement Based on Their Load Profiles," Energies, MDPI, vol. 12(22), pages 1-22, November.
    3. Tian Shi & Fei Mei & Jixiang Lu & Jinjun Lu & Yi Pan & Cheng Zhou & Jianzhang Wu & Jianyong Zheng, 2019. "Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting," Energies, MDPI, vol. 12(22), pages 1-17, November.
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    Cited by:

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    2. 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.

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