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Investigation of Var Compensation Schemes in Unbalanced Distribution Systems

Author

Listed:
  • Yinuo Huang
  • Licheng Wang
  • Kai Wang

Abstract

Distributed rooftop photovoltaic (PV) generators prospered distributed generation (DG) in recent years. Certain randomness of rooftop PV connection may lead to significant PV power imbalance across three phases, especially in low-voltage distribution systems. Due to interphase line coupling, traditional Var compensation methods which typically have competent voltage regulation performance may become less effective in such PV imbalance scenarios. In this paper, the limitation of traditional Var compensation methods in voltage regulation with unbalanced PV power integration is demonstrated and comprehensively analyzed. After describing the voltage regulation challenge, based on the voltage sensitivity analysis, it is revealed that PV power unbalanced level together with equivalent mutual impedance among phase conductors has a significant impact on the effectiveness of traditional Var compensation methods on voltage regulation. On this basis, to improve the performance of voltage regulation methods, some suggestions are proposed for both current system operation and future distribution system planning. Numerical studies demonstrate the effectiveness of the proposed suggestions. Future rooftop PV integration in LV systems can benefit from this research.

Suggested Citation

  • Yinuo Huang & Licheng Wang & Kai Wang, 2019. "Investigation of Var Compensation Schemes in Unbalanced Distribution Systems," Complexity, Hindawi, vol. 2019, pages 1-13, October.
    • Handle: RePEc:hin:complx:7824743
    DOI: 10.1155/2019/7824743
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    References listed on IDEAS

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    1. Chuanliang Xiao & Bo Zhao & Ming Ding & Zhihao Li & Xiaohui Ge, 2017. "Zonal Voltage Control Combined Day-Ahead Scheduling and Real-Time Control for Distribution Networks with High Proportion of PVs," Energies, MDPI, vol. 10(10), pages 1-23, September.
    2. Cheng Guo & Delin Wang, 2019. "Frequency Regulation and Coordinated Control for Complex Wind Power Systems," Complexity, Hindawi, vol. 2019, pages 1-12, May.
    3. Yang-Wu Shen & Jin-Rong Yuan & Fei-Fan Shen & Jia-Zhu Xu & Chen-Kun Li & Ding Wang, 2019. "Finite Control Set Model Predictive Control for Complex Energy System with Large-Scale Wind Power," Complexity, Hindawi, vol. 2019, pages 1-13, July.
    4. Li, Yang & Feng, Bo & Li, Guoqing & Qi, Junjian & Zhao, Dongbo & Mu, Yunfei, 2018. "Optimal distributed generation planning in active distribution networks considering integration of energy storage," Applied Energy, Elsevier, vol. 210(C), pages 1073-1081.
    5. João Soares & Fernando Lezama & Tiago Pinto & Hugo Morais, 2018. "Complex Optimization and Simulation in Power Systems," Complexity, Hindawi, vol. 2018, pages 1-3, October.
    6. João Soares & Tiago Pinto & Fernando Lezama & Hugo Morais, 2018. "Survey on Complex Optimization and Simulation for the New Power Systems Paradigm," Complexity, Hindawi, vol. 2018, pages 1-32, August.
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