IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i2p361-d1568104.html
   My bibliography  Save this article

Linear Quadratic Regulator-Based Coordinated Voltage and Power Control for Flexible Distribution Networks

Author

Listed:
  • Zhipeng Jing

    (Economic and Technological Research Institute, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China)

  • Lipo Gao

    (Economic and Technological Research Institute, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China)

  • Chengao Wu

    (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China)

  • Dong Liang

    (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China)

Abstract

Multi-port soft open points (SOPs) are effective devices for alleviating issues such as voltage violation and transformer overloading in distribution networks caused by the high penetration of distributed energy resources. This paper proposes a coordinated voltage and power control method for flexible distribution networks based on a linear quadratic regulator (LQR). First, the principle of coordinated voltage and power control is analyzed based on SOPs’ control strategies and a linear power flow model. Then, a discrete-time state-space model is constructed for flexible distribution networks with multi-port SOPs, using the voltage magnitude deviations at the AC side of all PQ-controlled voltage source converters (VSCs) and the loading rate deviations of the transformers corresponding to all PQ-controlled VSCs as state variables. An LQR-based optimal control model is then established, aiming to simultaneously minimize deviations of voltage magnitudes and transformer loading rates from their reference values, which correspond to the V dc -controlled VSC. The optimal state feedback law is obtained by solving the discrete-time algebraic Riccati equation. The proposed method has been evaluated on two typical flexible distribution networks, and the simulation results demonstrate the effectiveness of the proposed control method in improving voltage profiles and alleviating transformer overloading conditions using local measurements and very limited communication. In specific situations, the imbalance of voltages and transformer loading rates among the interconnected feeders can be reduced by 40%.

Suggested Citation

  • Zhipeng Jing & Lipo Gao & Chengao Wu & Dong Liang, 2025. "Linear Quadratic Regulator-Based Coordinated Voltage and Power Control for Flexible Distribution Networks," Energies, MDPI, vol. 18(2), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:361-:d:1568104
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/2/361/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/2/361/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Zhengfa & da Silva, Filipe Faria & Guo, Yifei & Bak, Claus Leth & Chen, Zhe, 2021. "Double-layer stochastic model predictive voltage control in active distribution networks with high penetration of renewables," Applied Energy, Elsevier, vol. 302(C).
    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. Si, Zhiyuan & Yang, Ming & Yu, Yixiao & Ding, Tingting, 2021. "Photovoltaic power forecast based on satellite images considering effects of solar position," Applied Energy, Elsevier, vol. 302(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Ziqi & Li, Peng & Ji, Haoran & Zhao, Jinli & Xi, Wei & Wu, Jianzhong & Wang, Chengshan, 2024. "Combined central-local voltage control of inverter-based DG in active distribution networks11The short version of the paper was presented at CUE2023. This paper is a substantial extension of the short," Applied Energy, Elsevier, vol. 372(C).
    2. Yu, Peipei & Zhang, Hongcai & Hu, Zechun & Song, Yonghua, 2025. "Voltage control of distribution grid with district cooling systems based on scenario-classified reinforcement learning," Applied Energy, Elsevier, vol. 377(PB).
    3. Hai, Tao & Hussein Kadir, Dler & Ghanbari, Afshin, 2023. "Modeling the emission characteristics of the hydrogen-enriched natural gas engines by multi-output least-squares support vector regression: Comprehensive statistical and operating analyses," Energy, Elsevier, vol. 276(C).
    4. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    5. 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).
    6. Zhao, He & Huang, Xiaoqiao & Xiao, Zenan & Shi, Haoyuan & Li, Chengli & Tai, Yonghang, 2024. "Week-ahead hourly solar irradiation forecasting method based on ICEEMDAN and TimesNet networks," Renewable Energy, Elsevier, vol. 220(C).
    7. Zhao, Yincheng & Zhang, Guozhou & Hu, Weihao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2023. "Meta-learning based voltage control strategy for emergency faults of active distribution networks," Applied Energy, Elsevier, vol. 349(C).
    8. Kabir, Farzana & Yu, Nanpeng & Gao, Yuanqi & Wang, Wenyu, 2023. "Deep reinforcement learning-based two-timescale Volt-VAR control with degradation-aware smart inverters in power distribution systems," Applied Energy, Elsevier, vol. 335(C).
    9. Paletta, Quentin & Arbod, Guillaume & Lasenby, Joan, 2023. "Omnivision forecasting: Combining satellite and sky images for improved deterministic and probabilistic intra-hour solar energy predictions," Applied Energy, Elsevier, vol. 336(C).
    10. 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).
    11. Dong Hua & Fei Peng & Suisheng Liu & Qinglin Lin & Jiahui Fan & Qian Li, 2025. "Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement Learning," Energies, MDPI, vol. 18(2), pages 1-12, January.
    12. Yantao Wang & Yinhan Zhang & Xuesong Qi & Meiqi Wang & Xinyue Wang, 2023. "A Bi-Level Optimization Model for Virtual Power Plant Membership Selection Considering Load Time Series," Sustainability, MDPI, vol. 15(3), pages 1-15, January.
    13. Ye Wang & Zhaiaibai Ma & Mostafa M. Salah & Ahmed Shaker, 2023. "An Evolutionarily Based Type-2 Fuzzy-PID for Multi-Machine Power System Stabilization," Mathematics, MDPI, vol. 11(11), pages 1-18, May.
    14. Nayak, Dhyaan Sandeep & Misra, Shamik, 2024. "An operational scheduling framework for Electric Vehicle Battery Swapping Station under demand uncertainty," Energy, Elsevier, vol. 290(C).
    15. Guo, Guodong & Zhang, Mengfan & Gong, Yanfeng & Xu, Qianwen, 2023. "Safe multi-agent deep reinforcement learning for real-time decentralized control of inverter based renewable energy resources considering communication delay," Applied Energy, Elsevier, vol. 349(C).
    16. Su, Qingyu & Chen, Cong & Huang, Xin & Li, Jian, 2022. "Interval TrendRank method for grid node importance assessment considering new energy," Applied Energy, Elsevier, vol. 324(C).
    17. Changqian Wu & Yulin Zhang & Na Li & Amin Rezaeipanah, 2024. "An intelligent fuzzy-based routing algorithm for video conferencing service provisioning in software defined networking," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 87(4), pages 887-898, December.
    18. Liu, Jingxuan & Zang, Haixiang & Cheng, Lilin & Ding, Tao & Wei, Zhinong & Sun, Guoqiang, 2023. "A Transformer-based multimodal-learning framework using sky images for ultra-short-term solar irradiance forecasting," Applied Energy, Elsevier, vol. 342(C).
    19. Yuan, Yi & Ding, Tao & Chang, Xinyue & Jia, Wenhao & Xue, Yixun, 2024. "A distributed multi-objective optimization method for scheduling of integrated electricity and hydrogen systems," Applied Energy, Elsevier, vol. 355(C).
    20. Zhi, Yuan & Yang, Xudong, 2023. "Scenario-based multi-objective optimization strategy for rural PV-battery systems," Applied Energy, Elsevier, vol. 345(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:361-:d:1568104. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.