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

Azher M Abed, Sanjarbek Madaminov, Alisher Abduvokhidov, Egambergan Khudoynazarov and Wubshet Ibrahim

International Journal of Low-Carbon Technologies, 2026, vol. 21, 1-21

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.

Keywords: distributed renewable electricity; energy storage systems; graph neural networks; multiagent reinforcement learning; smart grid optimization (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:21:y:2026:i::p:1-21.

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