A Deep Reinforcement Learning Framework for Smart Grid Energy Distribution

Modern power grids face growing complexity due to the rise of distributed energy resources (DERs), electric vehicles, and fluctuating renewable sources. Traditional energy management methods often fall short in handling this dynamic environment. This paper introduces a deep reinforcement learning (DRL)–based framework for smart grid energy distribution. It uses multiple intelligent agents operating at different grid levels to make real-time decisions based on supply, demand, pricing, and grid conditions. The agents are trained within a simulated environment and tested across varied scenarios. The framework demonstrates improved energy efficiency, peak load reduction, and system resilience. Overall, this study highlights how DRL can effectively manage energy in a decentralized, adaptive grid environment