Resilient dynamic microgrid formation by deep reinforcement learning integrating physics-informed neural networks

Abstract

Dynamic microgrid formation can enhance topological flexibility within the distribution system, particularly during extreme events, thereby facilitating a more efficient restoration process. However, existing research has overlooked the impact of cold load pickup on system restoration efforts. A sudden load spike can lead to the overloading of generators and transformers, which can result in the failure of the system restoration process. This study leverages the topological flexibility through dynamic microgrid formation of the system to mitigate the impact of cold load pickup, thereby enhancing the efficiency of sequential load restoration. To alleviate the computational complexity arising from intricate operational constraints and the uncertainties inherent in cold load pickup conditions, this paper proposes a novel model-free framework. Unlike existing deep reinforcement learning models, we incorporate physical constraint information into the model by means of physics-informed neural networks, where the solution of an optimization problem is regarded as knowledge, enabling the agent to learn operational constraints more efficiently and stably. The proposed approach is compatible with and can be integrated into any deep reinforcement learning algorithm that utilizes the advantage actor–critic framework with neural networks. This research employs the deep deterministic policy gradient algorithm as a representative example for investigation. The effectiveness and generalization performance of the proposed method are validated on a modified IEEE 123-node test feeder, while its scalability is assessed using the IEEE 8500-node test feeder system.