Self-Learning Smart Grid Framework for Decentralized Energy Distribution

The rapidly increasing participation of pro solar users and adoption of renewable energy has shifted traditional power systems to complex, decentralized networks. The conventional approach of managing the grid as a single entity fails to meet the dynamic variability, demandresponse, and security needs of modern smart grids. This paper proposes the Self-Learning Smart Grid framework which decentralizes energy distribution through the incorporation of machine learning optimization, multi-agent reinforcement learning, and blockchain P2P trading. Each framework node can make autonomous decisions and perform selfoptimizing load distribution while securely executing inter-node decentralized transactions without a centralized controller. A simulationbased case study reveals a self-learning smart grid model demonstrated better grid resilience, load forecasting, and renewable energy utilization than traditional approaches. These results indicate self-learning smart grids will underlie future energy systems, operationally and securely, within a decentralized framework.