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Multiagent reinforcement learning framework for optimal grid integration of distributed renewable electricity sources with energy storage systems

Author

Listed:
  • Azher M Abed
  • Sanjarbek Madaminov
  • Alisher Abduvokhidov
  • Egambergan Khudoynazarov
  • Wubshet Ibrahim

Abstract

This study develops a topology-aware multiagent reinforcement learning framework that coordinates distributed renewables and storage for transmission-level control. Using a 24-month Saudi Eastern Province dataset, the framework reduces curtailment by up to 69.1% versus traditional economic dispatch and 10.3% versus MPC, cuts total annual operating costs by 27.9%, maintains frequency within ±0.1 Hz during 97.3% of periods, and adapts with 234 ms median latency. Emissions decrease by 0.85 to 1.46 Mt CO2-equivalent annually. Results demonstrate scalable, sub-second control that improves stability and economics while enabling higher renewable integration.

Suggested Citation

  • Azher M Abed & Sanjarbek Madaminov & Alisher Abduvokhidov & Egambergan Khudoynazarov & Wubshet Ibrahim, 2026. "Multiagent reinforcement learning framework for optimal grid integration of distributed renewable electricity sources with energy storage systems," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 21, pages 1-21.
    • Handle: RePEc:oup:ijlctc:v:21:y:2026:i::p:1-21.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctaf142
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