  Distribution network reconfiguration (DNR) has proved to be an economical and
effective way to improve the reliability of distribution systems. As optimal
network configuration depends on system operating states (e.g., loads at each
node), existing analytical and population-based approaches need to repeat the
entire analysis and computation to find the optimal network configuration with
a change in system operating states. Contrary to this, if properly trained,
deep reinforcement learning (DRL)-based DNR can determine optimal or
near-optimal configuration quickly even with changes in system states. In this
paper, a Deep Q Learning-based framework is proposed for the optimal DNR to
improve reliability of the system. An optimization problem is formulated with
an objective function that minimizes the average curtailed power. Constraints
of the optimization problem are radial topology constraint and all nodes
traversing constraint. The distribution network is modeled as a graph and the
optimal network configuration is determined by searching for an optimal
spanning tree. The optimal spanning tree is the spanning tree with the minimum
value of the average curtailed power. The effectiveness of the proposed
framework is demonstrated through several case studies on 33-node and 69-node
distribution test systems.
