Smart Grid Energy Management with Reinforcement Learning

Abstract

We develop a multi-agent reinforcement learning framework for real-time energy management in smart grids with high penetration of renewable energy sources. The framework coordinates distributed energy resources, battery storage, and demand response programs to minimize operational costs while maintaining grid stability. Simulation results on a IEEE 33-bus test system validate the approach.

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