Comparative Evaluation of RL-TD3 PID Control against Hybrid and Conventional Optimizers in Renewable -Based Islanded Microgrids
This paper demonstrates that a Reinforcement Learning-based Twin Delayed Deep Deterministic Policy Gradient (RL-TD3) optimized PID controller outperforms conventional and hybrid optimization methods, such as Coati Optimization and Grey Wolf-Cuckoo Search, in enhancing load frequency control stability and disturbance rejection within renewable-based islanded microgrids.