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Distributed voltage control for active distribution networks based on distribution phasor measurement units

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  • Wang, Xiaoxue
  • Wang, Chengshan
  • Xu, Tao
  • Meng, He
  • Li, Peng
  • Yu, Li

Abstract

This paper explores a distributed coordinated control algorithm for active distribution networks (ADNs) based on measurements of distribution phasor measurement units (DPMUs) that are only installed at a portion of network nodes. First of all, a network topology simplification method is proposed to deal with the lack of impedance and power information in ADNs. Specifically, sub-networks with unknown impedance and power information are transformed to simplified networks with known equivalent parameters that are derived from the measurements of DPMUs at the sub-network boundaries. After that, a fully distributed control strategy utilizing a multiple agent system (MAS) is employed to control voltage during voltage excursions in ADNs, and to optimize network losses if no voltage excursions occur. MAS achieves global voltage control through distributed decision-making according to the voltage-power sensitivities that are calculated by each agent based on local and neighbourhood measurements. MAS balances reactive power locally to reduce network active power losses. The convergence of the proposed distributed voltage control method is proved. Finally, the feasibility and effectiveness of the proposed method has been demonstrated on a modified IEEE 33-bus system.

Suggested Citation

  • Wang, Xiaoxue & Wang, Chengshan & Xu, Tao & Meng, He & Li, Peng & Yu, Li, 2018. "Distributed voltage control for active distribution networks based on distribution phasor measurement units," Applied Energy, Elsevier, vol. 229(C), pages 804-813.
    • Handle: RePEc:eee:appene:v:229:y:2018:i:c:p:804-813
    DOI: 10.1016/j.apenergy.2018.08.042
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    References listed on IDEAS

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    3. Wang, Xiaoxue & Wang, Chengshan & Xu, Tao & Guo, Lingxu & Li, Peng & Yu, Li & Meng, He, 2018. "Optimal voltage regulation for distribution networks with multi-microgrids," Applied Energy, Elsevier, vol. 210(C), pages 1027-1036.
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    2. Tsao, Yu-Chung & Beyene, Tsehaye Dedimas & Thanh, Vo-Van & Gebeyehu, Sisay Geremew & Kuo, Tsai-Chi, 2022. "Power distribution network design considering the distributed generations and differential and dynamic pricing," Energy, Elsevier, vol. 241(C).
    3. Zhao, Zhida & Yu, Hao & Li, Peng & Li, Peng & Kong, Xiangyu & Wu, Jianzhong & Wang, Chengshan, 2019. "Optimal placement of PMUs and communication links for distributed state estimation in distribution networks," Applied Energy, Elsevier, vol. 256(C).
    4. Zhu, Xingxu & Hou, Xiangchen & Li, Junhui & Yan, Gangui & Li, Cuiping & Wang, Dongbo, 2023. "Distributed online prediction optimization algorithm for distributed energy resources considering the multi-periods optimal operation," Applied Energy, Elsevier, vol. 348(C).
    5. Su, Hongzhi & Wang, Chengshan & Li, Peng & Liu, Zhelin & Yu, Li & Wu, Jianzhong, 2019. "Optimal placement of phasor measurement unit in distribution networks considering the changes in topology," Applied Energy, Elsevier, vol. 250(C), pages 313-322.
    6. Kang, Wenfa & Chen, Minyou & Guan, Yajuan & Wei, Baoze & Vasquez Q., Juan C. & Guerrero, Josep M., 2022. "Event-triggered distributed voltage regulation by heterogeneous BESS in low-voltage distribution networks," Applied Energy, Elsevier, vol. 312(C).
    7. Ghadikolaee, Ebad Talebi & Kazemi, Ahad & Shayanfar, Heydar Ali, 2020. "Novel multi-objective phasor measurement unit placement for improved parallel state estimation in distribution network," Applied Energy, Elsevier, vol. 279(C).
    8. Huo, Yuchong & Bouffard, François & Joós, Géza, 2021. "Decision tree-based optimization for flexibility management for sustainable energy microgrids," Applied Energy, Elsevier, vol. 290(C).
    9. Almasalma, Hamada & Claeys, Sander & Deconinck, Geert, 2019. "Peer-to-peer-based integrated grid voltage support function for smart photovoltaic inverters," Applied Energy, Elsevier, vol. 239(C), pages 1037-1048.
    10. Huo, Yuchong & Bouffard, François & Joós, Géza, 2022. "Integrating learning and explicit model predictive control for unit commitment in microgrids," Applied Energy, Elsevier, vol. 306(PA).
    11. Zhang, Bin & Hu, Weihao & Ghias, Amer M.Y.M. & Xu, Xiao & Chen, Zhe, 2022. "Multi-agent deep reinforcement learning-based coordination control for grid-aware multi-buildings," Applied Energy, Elsevier, vol. 328(C).
    12. Tang, Chong & Liu, Mingbo & Dai, Yue & Wang, Zhijun & Xie, Min, 2019. "Decentralized saddle-point dynamics solution for optimal power flow of distribution systems with multi-microgrids," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    13. Su, Hongzhi & Wang, Chengshan & Li, Peng & Li, Peng & Liu, Zhelin & Wu, Jianzhong, 2019. "Novel voltage-to-power sensitivity estimation for phasor measurement unit-unobservable distribution networks based on network equivalent," Applied Energy, Elsevier, vol. 250(C), pages 302-312.
    14. Ferreira, Willian M. & Meneghini, Ivan R. & Brandao, Danilo I. & Guimarães, Frederico G., 2020. "Preference cone based multi-objective evolutionary algorithm applied to optimal management of distributed energy resources in microgrids," Applied Energy, Elsevier, vol. 274(C).

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