Distribution Service Restoration: A Distributionally Robust Reinforcement Learning Approach

In this paper, a multi-agent distributionally robust reinforcement learning approach is proposed to solve the crew dispatch problem for service restoration in the distribution network. The proposed approach leverages a novel representation of the environment formulated as a Markov decision process. The model of the environment incorporates the energy management problem as a linear programming problem, which yields the volume of the restored load to determine each agents reward. The optimal repair and restoration policy is determined using a distributionally robust multi-agent soft actor-critic (DR-MASAC) algorithm. The developed framework is applied to the service restoration in the modified IEEE 37-bus and the IEEE 123-bus test systems. The performance of the DR-MASAC algorithm is compared with the multi-agent soft actor-critic and multi-agent deep deterministic policy gradient algorithms to demonstrate its superiority in increasing the mean value and decreasing the standard deviation of the cumulative reward and average episode length during the training phase. The higher mean reward reflects a larger weighted restored load, while the lower standard deviation ensures consistent restoration policy performance under varying conditions. Shorter episodes indicate fewer restoration actions to restore the load. The performance of the proposed multi-agent algorithm is further evaluated during testing using the cost and the volume of unserved load as systemwide measures.