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Optimal Reactive Power Dispatch in Electric Transmission Systems Using the Multi-Agent Model with Volt-VAR Control

Author

Listed:
  • Alex Chamba

    (Department of Electrical Engineering, Universidad Politécnica Salesiana, Quito EC170702, Ecuador
    These authors contributed equally to this work.)

  • Carlos Barrera-Singaña

    (Department of Electrical Engineering, Universidad Politécnica Salesiana, Quito EC170702, Ecuador
    These authors contributed equally to this work.)

  • Hugo Arcos

    (Escuela Politécnica Nacional, Quito EC170525, Ecuador
    These authors contributed equally to this work.)

Abstract

The optimal dispatch of reactive power is a fundamental task in the operational planning of electrical power systems. This task aims to minimize active power losses and improve voltage levels within the electrical power system. This paper presents the application of the particle swarm optimization methodology to achieve optimal reactive power dispatch. The methodology’s performance is demonstrated by its high processing speed and the results obtained through a comprehensive global search for reactive power dispatch. Additionally, experimental results confirm the algorithm’s effectiveness in optimizing the objective function across different case studies, highlighting its ability to achieve optimal reactive power dispatch. This study represents a significant advancement in the field of power system optimization and provides a useful tool for managing and controlling these systems.

Suggested Citation

  • Alex Chamba & Carlos Barrera-Singaña & Hugo Arcos, 2023. "Optimal Reactive Power Dispatch in Electric Transmission Systems Using the Multi-Agent Model with Volt-VAR Control," Energies, MDPI, vol. 16(13), pages 1-25, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5004-:d:1181522
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    References listed on IDEAS

    as
    1. Anand, Himanshu & Narang, Nitin & Dhillon, J.S., 2019. "Multi-objective combined heat and power unit commitment using particle swarm optimization," Energy, Elsevier, vol. 172(C), pages 794-807.
    2. Gao, Yuanqi & Yu, Nanpeng, 2022. "Model-augmented safe reinforcement learning for Volt-VAR control in power distribution networks," Applied Energy, Elsevier, vol. 313(C).
    3. Izzah Afandi & Ashish P. Agalgaonkar & Sarath Perera, 2022. "Integrated Volt/Var Control Method for Voltage Regulation and Voltage Unbalance Reduction in Active Distribution Networks," Energies, MDPI, vol. 15(6), pages 1-21, March.
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    Cited by:

    1. Lefeng Cheng & Xin Wei & Manling Li & Can Tan & Meng Yin & Teng Shen & Tao Zou, 2024. "Integrating Evolutionary Game-Theoretical Methods and Deep Reinforcement Learning for Adaptive Strategy Optimization in User-Side Electricity Markets: A Comprehensive Review," Mathematics, MDPI, vol. 12(20), pages 1-56, October.
    2. Ricardo Villacrés & Diego Carrión, 2023. "Optimizing Real and Reactive Power Dispatch Using a Multi-Objective Approach Combining the ϵ -Constraint Method and Fuzzy Satisfaction," Energies, MDPI, vol. 16(24), pages 1-17, December.

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