AI-Powered Microgrids: Optimizing the Balance
New research across 11 operational microgrids reveals the conditions under which artificial intelligence delivers both cost savings and carbon reductions — and when it doesn’t. Drawing on two years of operational data from 11 microgrids across Europe, North America, and Australia, the study distinguishes between what the physical infrastructure achieves on its own and what algorithmic intelligence adds. Key finding — design matters more than code: before any AI enters the picture, a simple microgrid operating under basic self-consumption rules already delivers substantial environmental value across all 16 impact categories analyzed (climate change, resource depletion, water use, toxicity, etc.). Local photovoltaic generation displaces carbon-intensive grid imports, battery storage smooths demand, and reduced transmission losses compound the effect. Environmental performance begins with infrastructure design, not digital sophistication. The role of AI: AI amplifies the logic of the systems it serves — for better or worse. It can refine performance but cannot manufacture benefits that the system’s architecture does not already enable. The research challenges common assumptions about digital optimization and reveals a more nuanced picture of when AI genuinely serves both economic and environmental objectives in distributed energy systems.