Accelerating Investment Returns in Island Community Microgrids: An AI-Driven, Carbon-Aware Demand Response Framework with Techno-Economic and Environmental Analysis

For small island developing states (SIDS), the high upfront cost of battery storage hinders investment in renewable energy microgrids. This study proposes that AI-driven, carbon-aware demand-side management can improve project economics by aligning flexible loads (e.g., water pumping) with renewable generation. We introduce a simulation framework for a community-owned microgrid, utilizing a transfer-learned AI model to forecast carbon intensity and a deep reinforcement learning agent to optimize load scheduling. Our techno-economic analysis for a Pacific Island community shows that this AI-optimized approach significantly reduces diesel consumption and battery use. Compared to conventional operation, it lowers the Levelized Cost of Energy (LCOE) and shortens the investment payback period, while quantifying CO₂ reductions. This demonstrates AI's role as a financial catalyst for sustainable, inclusive energy access in data-scarce island settings.