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Dual-Layer Q-Learning Strategy for Energy Management of Battery Storage in Grid-Connected Microgrids

Author

Listed:
  • Khawaja Haider Ali

    (Faculty of Environment, Science and Economy, University of Exeter, Penryn Campus, Cornwall TR10 9FE, UK
    Department of Electrical Engineering, Sukkur IBA University, Sukkur 65200, Pakistan)

  • Mohammad Abusara

    (Faculty of Environment, Science and Economy, University of Exeter, Penryn Campus, Cornwall TR10 9FE, UK)

  • Asif Ali Tahir

    (Faculty of Environment, Science and Economy, University of Exeter, Penryn Campus, Cornwall TR10 9FE, UK)

  • Saptarshi Das

    (Faculty of Environment, Science and Economy, University of Exeter, Penryn Campus, Cornwall TR10 9FE, UK)

Abstract

Real-time energy management of battery storage in grid-connected microgrids can be very challenging due to the intermittent nature of renewable energy sources (RES), load variations, and variable grid tariffs. Two reinforcement learning (RL)–based energy management systems have been previously used, namely, offline and online methods. In offline RL, the agent learns the optimum policy using forecasted generation and load data. Once the convergence is achieved, battery commands are dispatched in real time. The performance of this strategy highly depends on the accuracy of the forecasted data. An agent in online RL learns the best policy by interacting with the system in real time using real data. Online RL deals better with the forecasted error but can take a longer time to converge. This paper proposes a novel dual layer Q -learning strategy to address this challenge. The first (upper) layer is conducted offline to produce directive commands for the battery system for a 24 h horizon. It uses forecasted data for generation and load. The second (lower) Q -learning-based layer refines these battery commands every 15 min by considering the changes happening in the RES and load demand in real time. This decreases the overall operating cost of the microgrid as compared with online RL by reducing the convergence time. The superiority of the proposed strategy (dual-layer RL) has been verified by simulation results after comparing it with individual offline and online RL algorithms.

Suggested Citation

  • Khawaja Haider Ali & Mohammad Abusara & Asif Ali Tahir & Saptarshi Das, 2023. "Dual-Layer Q-Learning Strategy for Energy Management of Battery Storage in Grid-Connected Microgrids," Energies, MDPI, vol. 16(3), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1334-:d:1048005
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    References listed on IDEAS

    as
    1. Oussama Ouramdane & Elhoussin Elbouchikhi & Yassine Amirat & Ehsan Sedgh Gooya, 2021. "Optimal Sizing and Energy Management of Microgrids with Vehicle-to-Grid Technology: A Critical Review and Future Trends," Energies, MDPI, vol. 14(14), pages 1-45, July.
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