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Optimal Power Flow (OPF) is a very traditional research area within the powersystems field that seeks for the optimal operation point of electric powerplants, and which needs to be solved every few minutes in real-world scenarios.However, due to the nonconvexities that arise in power generation systems,there is not yet a fast, robust solution technique for the full AlternatingCurrent Optimal Power Flow (ACOPF). In the last decades, power grids haveevolved into a typical dynamic, non-linear and large-scale control system,known as the power system, so searching for better and faster ACOPF solutionsis becoming crucial. Appearance of Graph Neural Networks (GNN) has allowed thenatural use of Machine Learning (ML) algorithms on graph data, such as powernetworks. On the other hand, Deep Reinforcement Learning (DRL) is known for itspowerful capability to solve complex decision-making problems. Althoughsolutions that use these two methods separately are beginning to appear in theliterature, none has yet combined the advantages of both. We propose a novelarchitecture based on the Proximal Policy Optimization algorithm with GraphNeural Networks to solve the Optimal Power Flow. The objective is to design anarchitecture that learns how to solve the optimization problem and that is atthe same time able to generalize to unseen scenarios. We compare our solutionwith the DCOPF in terms of cost after having trained our DRL agent on IEEE 30bus system and then computing the OPF on that base network with topologychanges

Proximal Policy Optimization with Graph Neural Networks for Optimal Power Flow