Deep Reinforcement Learning For Online Distribution Power System Cybersecurity Protection

The sophistication and regularity of power system cybersecurity attacks has been growing in the last decade, leading researchers to investigate new innovative, cyber-resilient tools to help grid operators defend their networks and power systems. One promising approach is to apply recent advances in deep reinforcement learning (DRL) to aid grid operators in making real-time changes to the power system equipment to counteract malicious actions. While multiple transmission studies have been conducted in the past, in this work we investigate the possibility of defending distribution power systems using a DRL agent who has control of a collection of utility-owned distributed energy resources (DER). A game board using a modified version of the IEEE 13-bus model was simulated using OpenDSS to train the DRL agent and compare its performance to a random agent, a greedy agent, and human players. Both the DRL agent and the greedy approach performed well, suggesting a greedy approach can be appropriate for computationally tractable system configurations and a DRL agent is a viable path forward for systems of increased complexity. This work paves the way to create multi-player distribution system control games which could be designed to defend the power grid under a sophisticated cyber-attack.