Integration of High-Efficiency DC Generators with AI-Driven Maximum Power Point Tracking in Hybrid Solar-Wind Microgrids

Abstract

The combination of renewable power with microgrids also requires intelligent control systems that get the most power being utilized and regulate the power flow in an even manner. We have developed a new paradigm that comprises high-performance DC generators coupled with artificial intelligence-based maximum power point tracking (AI-MPPT) for hybrid solar-wind microgrids. Our system possesses an embedded 36-kW solar photovoltaic panel array, a 10-kW wind turbine system, and 50 kWh battery storage with dynamic grid interaction ability. We developed a deep Q-network (DQN) energy management system to harvest maximum real-time power from renewable sources. It controls charge and discharge cycles of batteries and grid interaction according to time-of-use electricity rates. The AI-MPPT algorithm performs better than traditional algorithms in tracking optimal operating points under changing environmental parameters. It learns and dynamically adapts. Simulation efficiency for 24-hour operation cycles proves the AI-based system has a daily operating cost reduction of 67% compared to traditional rule-based operating practices. The system proves energy autonomy with successful peak load shaving at 57% and grid autonomy at 57.3%. Battery life was enhanced through intelligent scheduling of dynamic electricity prices. The model provides an expandable platform for residential and commercial microgrids. It shows real economic benefit while promoting the integration of renewable power and grid stability. Our approach provides meaningful enhancement in economic efficiency and system reliability for distributed energy consumption