A Personalized Federated Deep RL Framework for Carbon-Aware and Battery-Safe Smart Microgrid Intelligence in Edge-IoT Environments

Abstract

The distribution of renewable energy resources and Edge-IoT infrastructures have brought new challenges in intelligent smart microgrid management, such as dynamic-energy-demand changes, carbon-heavy energy-scheduling, communication overhead, and battery degradation. The deployment of renewable energy resources, distributed battery storage systems, and Edge-IoT infrastructures has created a number of challenges in the management of smart microgrids, such as dynamic changes in energy demand, carbon-heavy energy-scheduling, communication overhead, and battery degradation. To tackle these challenges, this paper introduces a personalized Federated Deep Reinforcement Learning (GridMind-FDRL) framework for carbon-aware and battery-safe decentralized smart microgrid optimization. The proposed framework combines federated learning, deep reinforcement learning, edge intelligence, carbon-aware energy scheduling and battery-aware adaptive optimization with a centralized framework for energy management. Unlike traditional centralized optimization methods, GridMind-FDRL allows for collaborative learning among distributed microgrid nodes while maintaining privacy and enabling low latency real-time optimization in dynamic Edge-IoT environments. The framework was tested with different operating conditions of intermittent renewables, varying load levels, and battery stress conditions. The results of the experiment showed that the energy efficiency could be 93.86%, carbon reduction 24.36%, battery health preservation 90.42%, communication efficiency 88.08%, and decision latency reduction 34.21%. The acquired results support the scalability, sustainability, and smart energy optimization ability of the suggested framework for the next-generation decentralized smart energy ecosystems.