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Deep Reinforcement Learning-Based Operation of Transmission Battery Storage with Dynamic Thermal Line Rating

Author

Listed:
  • Vadim Avkhimenia

    (Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Matheus Gemignani

    (Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Tim Weis

    (Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Petr Musilek

    (Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
    Applied Cybernetics, University of Hradec Králové, 500 03 Hradec Králové, Czech Republic)

Abstract

It is well known that dynamic thermal line rating has the potential to use power transmission infrastructure more effectively by allowing higher currents when lines are cooler; however, it is not commonly implemented. Some of the barriers to implementation can be mitigated using modern battery energy storage systems. This paper proposes a combination of dynamic thermal line rating and battery use through the application of deep reinforcement learning. In particular, several algorithms based on deep deterministic policy gradient and soft actor critic are examined, in both single- and multi-agent settings. The selected algorithms are used to control battery energy storage systems in a 6-bus test grid. The effects of load and transmissible power forecasting on the convergence of those algorithms are also examined. The soft actor critic algorithm performs best, followed by deep deterministic policy gradient, and their multi-agent versions in the same order. One-step forecasting of the load and ampacity does not provide any significant benefit for predicting battery action.

Suggested Citation

  • Vadim Avkhimenia & Matheus Gemignani & Tim Weis & Petr Musilek, 2022. "Deep Reinforcement Learning-Based Operation of Transmission Battery Storage with Dynamic Thermal Line Rating," Energies, MDPI, vol. 15(23), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9032-:d:988076
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    References listed on IDEAS

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    1. Holger C. Hesse & Rodrigo Martins & Petr Musilek & Maik Naumann & Cong Nam Truong & Andreas Jossen, 2017. "Economic Optimization of Component Sizing for Residential Battery Storage Systems," Energies, MDPI, vol. 10(7), pages 1-19, June.
    2. Akhtar Hussain & Van-Hai Bui & Hak-Man Kim, 2017. "Impact Analysis of Demand Response Intensity and Energy Storage Size on Operation of Networked Microgrids," Energies, MDPI, vol. 10(7), pages 1-19, June.
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