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Energy Management Simulation with Multi-Agent Reinforcement Learning: An Approach to Achieve Reliability and Resilience

Author

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  • Kapil Deshpande

    (Profactor GmbH, Robotics and Automation Systems Department, 4407 Steyr, Austria
    Current address: Profactor GmbH, Im Stadtgut D1 Steyr-Gleink, Upper Austria, 4407 Steyr, Austria.)

  • Philipp Möhl

    (Profactor GmbH, Robotics and Automation Systems Department, 4407 Steyr, Austria)

  • Alexander Hämmerle

    (Profactor GmbH, Robotics and Automation Systems Department, 4407 Steyr, Austria)

  • Georg Weichhart

    (Profactor GmbH, Robotics and Automation Systems Department, 4407 Steyr, Austria)

  • Helmut Zörrer

    (Profactor GmbH, Robotics and Automation Systems Department, 4407 Steyr, Austria)

  • Andreas Pichler

    (Profactor GmbH, Robotics and Automation Systems Department, 4407 Steyr, Austria)

Abstract

The share of energy produced by small-scale renewable energy sources, including photovoltaic panels and wind turbines, will significantly increase in the near future. These systems will be integrated in microgrids to strengthen the independence of energy consumers. This work deals with energy management in microgrids, taking into account the volatile nature of renewable energy sources. In the developed approach, Multi-Agent Reinforcement Learning is applied, where agents represent microgrid components. The individual agents are trained to make good decisions with respect to adapting to the energy load in the grid. Training of agents leverages the historic energy profile data for energy consumption and renewable energy production. The implemented energy management simulation shows good performance and balances the energy flows. The quantitative performance evaluation includes comparisons with the exact solutions from a linear program. The computational results demonstrate good generalisation capabilities of the trained agents and the impact of these capabilities on the reliability and resilience of energy management in microgrids.

Suggested Citation

  • Kapil Deshpande & Philipp Möhl & Alexander Hämmerle & Georg Weichhart & Helmut Zörrer & Andreas Pichler, 2022. "Energy Management Simulation with Multi-Agent Reinforcement Learning: An Approach to Achieve Reliability and Resilience," Energies, MDPI, vol. 15(19), pages 1-35, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7381-:d:936094
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    References listed on IDEAS

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    Cited by:

    1. Sana Qaiyum & Martin Margala & Pravin R. Kshirsagar & Prasun Chakrabarti & Kashif Irshad, 2023. "Energy Performance Analysis of Photovoltaic Integrated with Microgrid Data Analysis Using Deep Learning Feature Selection and Classification Techniques," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    2. Mudhafar Al-Saadi & Maher Al-Greer & Michael Short, 2023. "Reinforcement Learning-Based Intelligent Control Strategies for Optimal Power Management in Advanced Power Distribution Systems: A Survey," Energies, MDPI, vol. 16(4), pages 1-38, February.

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