What question did this study set out to answer?

The research aims to develop a multiagent reinforcement learning framework for managing distributed renewable energy and storage effectively.

February 14, 2026Open Access

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

Puntos clave

The research aims to develop a multiagent reinforcement learning framework for managing distributed renewable energy and storage effectively.
Utilized a 24-month dataset from Saudi Eastern Province
Developed a topology-aware multiagent reinforcement learning framework
Compared performance against traditional economic dispatch and model predictive control (MPC) methods
Reduced curtailment of renewable energy by up to 69.1% compared to traditional methods
Cut total annual operating costs by 27.9%
Maintained frequency within ±0.1 Hz during 97.3% of periods
Achieved emissions reduction of 0.85 to 1.46 Mt CO2-equivalent annually

Resumen

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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Cite This Study

Abed et al. (Mon,) studied this question.

synapsesocial.com/papers/699011032ccff479cfe5755e https://doi.org/https://doi.org/10.1093/ijlct/ctaf142

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