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