A Reinforcement Learning Approach to Solve Service Restoration and Load Management Simultaneously for Distribution Networks

Energy and economy are increasing the relationship over the years, where the energy becomes a significant resource to keep a country developing, and it supports its economy. Then, more reliable the energy should become, especially the distribution network, to keep the entire process running. In this level of energy distribution, where residential consumers and medium and small industries are supplied, the number of interconnections of the network is enormous. However, for economic and environmental aspects, these complex systems, which are operating close to their capacity, needs to increase the automation, appearing the concept of smart grids and the Advanced Distribution Management System (ADMS) and its methods to control. Inside of the ADMS, there are a lot of essential techniques. Among them, there are two techniques which are the most relevant for this paper: the self-healing and load management. In an ADMS system, these two techniques are treated separately, but the best solution occurs when they are computed together. In this paper, it is proposed an approach that can address both problems at the same time or individually, i.e., in place to have a sequential method to solve step-by-step the issues in the networks. The proposed algorithm, through reinforcement learning technique, can handle both problems together. The proposed approach is tested in a real urban distribution network with some created scenarios to compare the results with outages and overloads. Some comparisons with other methods are carried out.