Multiagent reinforcement learning framework for optimal grid integration of distributed renewable electricity sources with energy storage systems
Azher M. AbedAir Conditioning and Refrigeration Techniques Engineering Department, College of Engineering and Technologies, Al-Mustaqbal University , Babylon 51001 ,Sanjarbek MadaminovDepartment of Transport Systems, Urgench State University , Kh. Alimdjan str. 14, Urgench 220100 ,Alisher AbduvokhidovDepartment of Mathematics, National Research University TIIAME , Kori Niyoziy 39, Tashkent 100000 ,Egambergan KhudoynazarovDepartment of Exact sciences, Mamun University , Bolkhovuz Street 2, Khiva 220900 ,Wubshet IbrahimDepartment of Mathematics, Ambo University , Ambo ,
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Abstract
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.
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