Artificial Intelligence Framework for Simulated Multiagent Reinforcement Learning in Hybrid Microgrid Control

• Multi-agent RL achieves 2.01–3.94% solar nRMSE and 95.8–98.5% voltage compliance • Distributed control cuts LCOE by ∼12% and saves ∼$0.9–1.1B over 10 years • Morocco, Denmark, UAE reach 120–292% net renewable penetration in simulation • Framework maintains 45–46 ms response times over 26,280 simulated hours • Simulation results provide proof-of-concept for hardware-in-the-loop validation Autonomous control of hybrid renewable energy microgrids with intermittent solar and wind generation presents fundamental challenges for centralized optimization approaches that struggle with scalability, single point failures, and real time coordination under uncertainty. This research establishes an artificial intelligence framework intelligence framework integrating Long Short-Term Memory recurrent neural networks for wind forecasting, Multi Agent Deep Deterministic Policy Gradient actor critic deep neural networks with hidden layer architecture for battery storage arbitrage, and autoregressive integrated moving average models for solar prediction within distributed multi agent reinforcement learning coordination. Validation encompassed 26,280 simulated operational hours across Morocco achieving 140.6% solar penetration, Denmark reaching 291.7% renewable integration, and UAE maintaining 120.0% penetration. Neural network forecasting achieved 3.2% normalized root mean square error for solar and 1.5% for wind, representing 28.3% and 31.8% improvements over single method baselines. Statistical validation revealed solar forecasting exhibited medium regional effect size (η²=0.0245, F=329.45, p