Hierarchical Combination of Deep Reinforcement Learning and Quadratic Programming for Distribution System Restoration

This paper proposes a model for hierarchical combination of deep reinforcement learning (DRL) with quadratic programming for distribution system restoration after major outages. In the proposed model, optimal power dispatch of a collection of distributed energy resources, called integrated hybrid resources (IHRs), is determined by a DRL-trained controller, while a grid-level quadratic programming problem checks grid constraints and performs critical restoration operation. DRL is implemented using Soft Actor-Critic (SAC) algorithm, which is shown to outperform the common Deep Deterministic Policy Gradient in continuous action spaces. The numerical studies, performed on the 123-bus test distribution system, demonstrates that the hierarchical combination of DRL and quadratic programming not only speeds up the local operation of multiple IHRs, but also ensures that the network constraints are satisfied during the restoration operation.

Automated summary

This paper proposes a hierarchical combination model of deep reinforcement learning (DRL) and quadratic programming for distribution system restoration after major outages. DRL-trained controller determines optimal power dispatch of distributed energy resources, while grid-level quadratic programming checks grid constraints and performs critical restoration operation. Numerical studies show that this combined approach improves the speed of local operation and ensures network constraints satisfaction during restoration.