IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i3p653-d1580752.html
   My bibliography  Save this article

Multi-Timescale Voltage Control Method Using Limited Measurable Information with Explainable Deep Reinforcement Learning

Author

Listed:
  • Fumiya Matsushima

    (Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan)

  • Mutsumi Aoki

    (Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan)

  • Yuta Nakamura

    (Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan)

  • Suresh Chand Verma

    (Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan)

  • Katsuhisa Ueda

    (Department of Electric Power Research and Development Center, Chubu Electric Power Co., Inc., Nagoya 459-8522, Japan)

  • Yusuke Imanishi

    (Department of Electric Power Research and Development Center, Chubu Electric Power Co., Inc., Nagoya 459-8522, Japan)

Abstract

The integration of photovoltaic (PV) power generation systems has significantly increased the complexity of voltage distribution in power grids, making it challenging for conventional Load Ratio Control Transformers (LRTs) to manage voltage fluctuations caused by weather-dependent PV output variations. Power Conditioning Systems (PCSs) interconnected with PV installations are increasingly considered for voltage control to address these challenges. This study proposes a Machine Learning (ML)-based control method for sub-transmission grids, integrating long-term LRT tap-changing with short-term reactive power control of PCSs. The approach estimates the voltage at each grid node using a Deep Neural Network (DNN) that processes measurable substation data. Based on these estimated voltages, the method determines optimal LRT tap positions and PCS reactive power outputs using Deep Reinforcement Learning (DRL). This enables real-time voltage monitoring and control using only substation measurements, even in grids without extensive sensor installations, ensuring all node voltages remain within specified limits. To improve the model’s transparency, Shapley Additive Explanation (SHAP), an Explainable AI (XAI) technique, is applied to the DRL model. SHAP enhances interpretability and confirms the effectiveness of the proposed method. Numerical simulations further validate its performance, demonstrating its potential for effective voltage management in modern power grids.

Suggested Citation

  • Fumiya Matsushima & Mutsumi Aoki & Yuta Nakamura & Suresh Chand Verma & Katsuhisa Ueda & Yusuke Imanishi, 2025. "Multi-Timescale Voltage Control Method Using Limited Measurable Information with Explainable Deep Reinforcement Learning," Energies, MDPI, vol. 18(3), pages 1-28, January.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:653-:d:1580752
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/3/653/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/18/3/653/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Omar Alrumayh & Khairy Sayed & Abdulaziz Almutairi, 2023. "LVRT and Reactive Power/Voltage Support of Utility-Scale PV Power Plants during Disturbance Conditions," Energies, MDPI, vol. 16(7), pages 1-20, April.
    2. Daisuke Iioka & Takahiro Fujii & Toshio Tanaka & Tsuyoshi Harimoto & Junpei Motoyama, 2020. "Voltage Reduction in Medium Voltage Distribution Systems Using Constant Power Factor Control of PV PCS," Energies, MDPI, vol. 13(20), pages 1-17, October.
    3. Xiaozhi Gao & Jiaqi Zhang & Huiqin Sun & Yongchun Liang & Leiyuan Wei & Caihong Yan & Yicong Xie, 2024. "A Review of Voltage Control Studies on Low Voltage Distribution Networks Containing High Penetration Distributed Photovoltaics," Energies, MDPI, vol. 17(13), pages 1-24, June.
    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. Juan A. Tejero-Gómez & Ángel A. Bayod-Rújula, 2024. "Analysis of Grid-Scale Photovoltaic Plants Incorporating Battery Storage with Daily Constant Setpoints," Energies, MDPI, vol. 17(23), pages 1-23, December.
    2. Daisuke Iioka & Kenichi Kusano & Takahiro Matsuura & Hiromu Hamada & Teru Miyazaki, 2022. "Appropriate Volt–Var Curve Settings for PV Inverters Based on Distribution Network Characteristics Using Match Rate of Operating Point," Energies, MDPI, vol. 15(4), pages 1-19, February.
    3. Kwang-Hoon Yoon & Joong-Woo Shin & Tea-Yang Nam & Jae-Chul Kim & Won-Sik Moon, 2022. "Operation Method of On-Load Tap Changer on Main Transformer Considering Reverse Power Flow in Distribution System Connected with High Penetration on Photovoltaic System," Energies, MDPI, vol. 15(17), pages 1-17, September.
    4. Amer S. Alsalman & Talal Alharbi & Ahmed A. Mahfouz, 2023. "Enhancing the Stability of an Isolated Electric Grid by the Utilization of Energy Storage Systems: A Case Study on the Rafha Grid," Sustainability, MDPI, vol. 15(17), pages 1-24, September.
    5. Joao Soares & Bruno Canizes & Zita Vale, 2021. "Rethinking the Distribution Power Network Planning and Operation for a Sustainable Smart Grid and Smooth Interaction with Electrified Transportation," Energies, MDPI, vol. 14(23), pages 1-4, November.
    6. Seyedmohammad Hasheminasab & Mohamad Alzayed & Hicham Chaoui, 2024. "A Review of Control Techniques for Inverter-Based Distributed Energy Resources Applications," Energies, MDPI, vol. 17(12), pages 1-39, June.

    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:3:p:653-:d:1580752. 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.