Energy Swarm: Agentic AI for Decentralized Smart Grid Load Balancing

ABSTRACT

The integration of decentralized renewable energy sources and the massive power demands of AI datacenters have created unprecedented volatility in modern power grids. We introduce 'Energy Swarm', a multi-agent reinforcement learning architecture where autonomous agents act on behalf of individual households, batteries, and substations. These agents negotiate energy prices and coordinate load shedding in real-time micro-auctions. By treating grid management as an agentic economy rather than a centralized optimization problem, our simulations demonstrate a 34% reduction in peak load stress and total prevention of rolling blackouts during extreme weather events.