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Alternative Current Injection Newton and Fast Decoupled Power Flow

Author

Listed:
  • Cristina Coutinho de Oliveira

    (Federal Institute of Amapá (IFAP), Pedra Branca do Amapari Center, Macapá 68945-000, Brazil)

  • Alfredo Bonini Neto

    (School of Sciences and Engineering, São Paulo State University (Unesp), Tupã 17602-496, Brazil)

  • Dilson Amancio Alves

    (School of Engineering, São Paulo State University (Unesp), Ilha Solteira 15385-000, Brazil)

  • Carlos Roberto Minussi

    (School of Engineering, São Paulo State University (Unesp), Ilha Solteira 15385-000, Brazil)

  • Carlos Alberto Castro

    (Technology Center, Pontifical Catholic University of Campinas (PUC), Campinas 13087-571, Brazil)

Abstract

This article presents an alternative Newton-Raphson power flow method version. This method has been developed based on current injection equations formulated in polar coordinates. Likewise, the fast decoupled power flow, elaborated using current injection (BX version), is presented. These methods are tested considering electrical power systems composed of 57-, 118-, and 300-bus, as well as a realistic system of 787-bus. For the robustness analysis, simulations were performed considering different loading conditions and R/X ratios of the transmission line. Based on the simulations that were realized, there is evidence that the performance of the proposed current injection methods is similar to the power injection methods.

Suggested Citation

  • Cristina Coutinho de Oliveira & Alfredo Bonini Neto & Dilson Amancio Alves & Carlos Roberto Minussi & Carlos Alberto Castro, 2023. "Alternative Current Injection Newton and Fast Decoupled Power Flow," Energies, MDPI, vol. 16(6), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2548-:d:1091099
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    References listed on IDEAS

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    1. Alfredo Bonini Neto & Dilson Amancio Alves & Carlos Roberto Minussi, 2022. "Artificial Neural Networks: Multilayer Perceptron and Radial Basis to Obtain Post-Contingency Loading Margin in Electrical Power Systems," Energies, MDPI, vol. 15(21), pages 1-14, October.
    2. Yan Huang & Yuntao Ju & Zeping Zhu, 2019. "An Asymptotic Numerical Continuation Power Flow to Cope with Non-Smooth Issue," Energies, MDPI, vol. 12(18), pages 1-17, September.
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    Cited by:

    1. Chao Tang & Yufeng Zhang & Fan Wu & Zhuo Tang, 2024. "An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems," Energies, MDPI, vol. 17(10), pages 1-16, May.
    2. Guilherme Barbosa Lima & Alfredo Bonini Neto & Dilson Amancio Alves & Carlos Roberto Minussi & Estélio da Silva Amorim & Luiz Carlos Pereira da Silva, 2023. "Technique for Reactive Loss Reduction and Loading Margin Enhancement Using the Curves of Losses versus Voltage Magnitude," Energies, MDPI, vol. 16(16), pages 1-21, August.
    3. Sebastian Bottler & Christian Weindl, 2023. "State-Space Load Flow Calculation of an Energy System with Sector-Coupling Technologies," Energies, MDPI, vol. 16(12), pages 1-22, June.

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