Resilient Operation of Distribution Grids Using Deep Reinforcement Learning

This article utilizes deep reinforcement learning to develop an intelligent resilience controller (IRC) that devises fast real-time operation decisions to strategically dispatch distributed generation and energy storage units for restoring power to customers after sudden outages. The proposed IRC learns the failure development pattern of uncertain high-impact events and is able to explore a large action space in the partially observable state space of distribution grids under widespread outages. A spatiotemporal hurricane impact analysis model is presented as an example of uncertain high-impact events, and its parameters are used in training the IRC model and preparing it for similar events. In the proposed model, the distribution grid operation under uncertainty is modeled as a Markov decision process (MDP), and actions taken by the operator are rewarded based on operation costs. Since the number of distributed energy resources can be significant, the scalability issue of the method is addressed by reformulating the problem as a sequential MDP. The proposed model is implemented on a test distribution grid undergoing a hurricane, and its performance is compared with common operation strategies, indicating the superiority of the proposed model in terms of reduced operation cost and close to zero running time. Further analysis shows the adaptability of the proposed model to hurricanes of various intensities.

Automated summary

This article introduces an intelligent resilience controller (IRC) developed using deep reinforcement learning, which generates real-time operation decisions to dispatch distributed generation and energy storage units for power restoration after sudden outages. The proposed model successfully learns the failure development pattern of uncertain high-impact events and performs well in a simulated hurricane scenario with reduced operation costs and minimal running time.