The increasing penetration of weather-driven renewable energy sources in smart grids introduces operational instability, harmonic distortion, and elevated switching costs due to the limitations of rule-based and deterministic control strategies. This study proposes a deep reinforcement learning-based adaptive switching framework to enhance renewable utilization while minimizing operational risk and economic cost. A simulation-derived dataset incorporating renewable generation, load demand, total harmonic distortion, voltage deviation, frequency variation, and risk–cost indices was generated from a risk–cost optimized smart grid model and implemented in Google Colab. The switching problem was formulated as a Markov decision process with a state space composed of power quality and economic variables, and a discrete action space representing operational modes. A Deep Q-Network agent was trained over 24-hour episodes to learn optimal switching policies. Comparative evaluation against conventional, rule-based, and analytical risk–cost optimization strategies demonstrated up to 14% reduction in total harmonic distortion, 22% reduction in switching frequency, 17% reduction in operational cost, and 19% improvement in risk mitigation, while increasing renewable penetration by 11%. The proposed framework provides a scalable and intelligent solution for industrial smart grid applications.
M. Meyyappan, P. Avirajamanjula, P. Marimuthu et al.· ITEGAM- Journal of Engineeri...· 0 citations
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.
Jaichandran R, P. Marimuthu, K.Nethra Devi et al.· 2026 6th International Confe...· 0 citations