Deep Learning‐Enabled Digital Twins for Prosumers: A Holistic Energy Management Framework for Smart Grids Using Deep Reinforcement Learning and Big Data Analytics

The increasing integration of renewables and electric vehicles into the grid introduces complexities for decentralized prosumers, necessitating advanced energy management systems. A new energy management framework is presented in this article that combines deep learning-enabled digital twins with reinforcement learning (RL) and big data analytics to optimize the energy flow among prosumers. An IEEE 30-bus system simulated energy transactions for variable renewable generation and battery energy storage system (BESS) to represent the power grid. The RL algorithm efficiently coordinates BESS’s charging and discharging cycles to ensure optimal energy utilization while maintaining power grid stability. The proposed framework forecasts supply and demand, enabling proactive energy transactions that enhance grid stability, reduce costs, and demonstrate scalability and real-time adaptability. Comparative analysis shows the proposed framework outperforms traditional methods by (a) maximizing utilization of renewable energy, (b) minimizing peak-hour grid reliance, (c) maintaining grid stability (grid stability index more than 0.905) with more than 60% RES penetration, (d) achieving near-perfect economic efficiency (cost saving ratio equal to 0.9968), and (e) preserving battery health via optimal cycling.