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Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning

Author

Listed:
  • Nikita Tomin

    (Melentiev Energy Systems Institute SB RAS, Elecric Power Systems Department, 664033 Irkutsk, Russia)

  • Nikolai Voropai

    (Melentiev Energy Systems Institute SB RAS, Elecric Power Systems Department, 664033 Irkutsk, Russia)

  • Victor Kurbatsky

    (Melentiev Energy Systems Institute SB RAS, Elecric Power Systems Department, 664033 Irkutsk, Russia)

  • Christian Rehtanz

    (Institute of Energy Systems, Energy Efficiency and Energy Economics (ie3), TU Dortmund University, 44227 Dortmund, Germany)

Abstract

The increase in the use of converter-interfaced generators (CIGs) in today’s electrical grids will require these generators both to supply power and participate in voltage control and provision of grid stability. At the same time, new possibilities of secondary QU droop control in power grids with a large proportion of CIGs (PV panels, wind generators, micro-turbines, fuel cells, and others) open new ways for DSO to increase energy flexibility and maximize hosting capacity. This study extends the existing secondary QU droop control models to enhance the efficiency of CIG integration into electrical networks. The paper presents an approach to decentralized control of secondary voltage through converters based on a multi-agent reinforcement learning (MARL) algorithm. A procedure is also proposed for analyzing hosting capacity and voltage flexibility in a power grid in terms of secondary voltage control. The effectiveness of the proposed static MARL control is demonstrated by the example of a modified IEEE 34-bus test feeder containing CIGs. Experiments have shown that the decentralized approach at issue is effective in stabilizing nodal voltage and preventing overcurrent in lines under various heavy load conditions often caused by active power injections from CIGs themselves and power exchange processes within the TSO/DSO market interaction.

Suggested Citation

  • Nikita Tomin & Nikolai Voropai & Victor Kurbatsky & Christian Rehtanz, 2021. "Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning," Energies, MDPI, vol. 14(24), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8270-:d:697867
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    Citations

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    Cited by:

    1. Tianhao Wang & Shiqian Ma & Zhuo Tang & Tianchun Xiang & Chaoxu Mu & Yao Jin, 2023. "A Multi-Agent Reinforcement Learning Method for Cooperative Secondary Voltage Control of Microgrids," Energies, MDPI, vol. 16(15), pages 1-18, July.
    2. Yu Fujimoto & Akihisa Kaneko & Yutaka Iino & Hideo Ishii & Yasuhiro Hayashi, 2023. "Challenges in Smartizing Operational Management of Functionally-Smart Inverters for Distributed Energy Resources: A Review on Machine Learning Aspects," Energies, MDPI, vol. 16(3), pages 1-26, January.
    3. Md Tariqul Islam & M. J. Hossain, 2023. "Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-33, February.
    4. Jude Suchithra & Duane Robinson & Amin Rajabi, 2023. "Hosting Capacity Assessment Strategies and Reinforcement Learning Methods for Coordinated Voltage Control in Electricity Distribution Networks: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.

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