Microgrids, small and localized energy systems, hold promise as a solution to the challenges of centralized energy systems. These microgrids can operate independently from the larger grid, providing participants with resilience and control. Figure 1 shows how these systems integrate renewable energy sources and storage to efficiently manage local energy needs.

AI improves energy reliability by integrating data about energy consumption, market prices, and weather forecasts, necessary when using wind and solar power, which rely on weather conditions. Advanced forecasting predicts renewable energy availability, while AI-driven analytics determine when to generate, store, or sell electricity. This increases efficiency and stabilizes the grid by balancing supply and demand.

When powered by AI, microgrids can also contribute to energy equity. In many rural parts of the US, flat-rate billing models are still common, often leading to unfair pricing. AI-enabled microgrids provide an alternative by allowing communities to pay only for the energy they use. By analyzing consumption patterns, AI can ensure optimized distribution that promotes equitable pricing and access. These systems also improve resilience during crises, enabling communities to manage energy distribution more effectively and reduce reliance on centralized utilities. AI allows microgrids to predict energy demands, identify system vulnerabilities, and recover quickly during outages.