Deep Reinforcement Learning-Based Adaptive Switching for Risk-Cost-Optimized Renewable Smart Grids

Abstract

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.