In this two-part paper, we presents a simulation-driven graph attention reinforcement learning (SGA-RL) method to perform robust active power corrective control, as well as a multi-agent approach to address challenges brought in by increasing renewable penetrations. This is joint work of my team and the team from Wuhan University, resulted from participation of power system AI competition NeurIPS L2RPN 2020 (both tracks).
Active Power Correction Strategies Based on Deep Reinforcement Learning——Part I: A Simulation-driven Solution for Robustness Active Power Correction Strategies Based on Deep Reinforcement Learning——Part II: A Distributed Solution for Adaptability
