Safe Deep Reinforcement Learning for Real-time AC Optimal Power Flow: A Near-optimal Solution

The real-time AC optimal power flow (OPF) problem is a key issue in making fast and accurate decisions to ensure the safety and economy of power systems. With the rapid development of renewable energies, the fluctuation has grown more vibrant, thus a novel approach called safe deep reinforcement learning is proposed in this paper. Herein, the real-time ACOPF problem is modeled as a constrained Markov decision process, and primal-dual optimization (PDO) based proximal policy optimization (PPO) is used to learn the optimal generator outputs in the primal domain and security constraints in the dual domain, which avoids manually selecting a tradeoff between penalties for constraint violations and rewards for the…