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Voltage and Reactive Power Optimization Using a Simplified Linear Equations at Distribution Networks with DG

Author

Listed:
  • Seok-Il Go

    (Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Korea)

  • Sang-Yun Yun

    (Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Korea)

  • Seon-Ju Ahn

    (Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Korea)

  • Joon-Ho Choi

    (Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Korea)

Abstract

In this paper, the VVO (Volt/Var optimization) is proposed using simplified linear equations. For fast computation, the characteristics of voltage control devices in a distribution system are expressed as a simplified linear equation. The voltage control devices are classified according to the characteristics of voltage control and represented as the simplified linear equation. The estimated voltage of distribution networks is represented by the sum of the simplified linear equations for the voltage control devices using the superposition principle. The voltage variation by the reactive power of distributed generations (DGs) can be expressed as the matrix of reactance. The voltage variation of tap changing devices can be linearized into the control area factor. The voltage variation by capacitor banks can also be expressed as the matrix of reactance. The voltage equations expressed as simplified linear equations are formulated by quadratic programming (QP). The variables of voltage control devices are defined, and the objective function is formulated as the QP form. The constraints are set using operating voltage range of distribution networks and the control ranges of each voltage control device. In order to derive the optimal solution, mixed-integer quadratic programming (MIQP), which is a type of mixed-integer nonlinear programming (MINLP), is used. The optimal results and proposed method results are compared by using MATLAB simulation and are confirmed to be close to the optimal solution.

Suggested Citation

  • Seok-Il Go & Sang-Yun Yun & Seon-Ju Ahn & Joon-Ho Choi, 2020. "Voltage and Reactive Power Optimization Using a Simplified Linear Equations at Distribution Networks with DG," Energies, MDPI, vol. 13(13), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3334-:d:378221
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    References listed on IDEAS

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    1. Junwei Cao & Wanlu Zhang & Zeqing Xiao & Haochen Hua, 2019. "Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach," Energies, MDPI, vol. 12(8), pages 1-17, April.
    2. Junyong Wu & Chen Shi & Meiyang Shao & Ran An & Xiaowen Zhu & Xing Huang & Rong Cai, 2019. "Reactive Power Optimization of a Distribution System Based on Scene Matching and Deep Belief Network," Energies, MDPI, vol. 12(17), pages 1-24, August.
    3. Fengli Jiang & Yichi Zhang & Yu Zhang & Xiaomeng Liu & Chunling Chen, 2019. "An Adaptive Particle Swarm Optimization Algorithm Based on Guiding Strategy and Its Application in Reactive Power Optimization," Energies, MDPI, vol. 12(9), pages 1-14, May.
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    Cited by:

    1. Yusheng Sun & Yaqian Zhao & Zhifeng Dou & Yanyan Li & Leilei Guo, 2020. "Model Predictive Virtual Synchronous Control of Permanent Magnet Synchronous Generator-Based Wind Power System," Energies, MDPI, vol. 13(19), pages 1-14, September.
    2. Pairach Kitworawut & Nipon Ketjoy & Tawat Suriwong & Malinee Kaewpanha, 2023. "Best Practice in Battery Energy Storage for Photovoltaic Systems in Low Voltage Distribution Network: A Case Study of Thailand Provincial Electricity Authority Network," Energies, MDPI, vol. 16(5), pages 1-23, March.
    3. Chan-Hyeok Oh & Joon-Ho Choi & Sang-Yun Yun & Seon-Ju Ahn, 2021. "Short-Term Cooperative Operational Scheme of Distribution System with High Hosting Capacity of Renewable-Energy-Based Distributed Generations," Energies, MDPI, vol. 14(19), pages 1-25, October.

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