Optimal Energy Management of a Fast Charging Service Station with Physics-Informed Neural Networks

In this paper, we investigate the use of Physics-Informed Neural Networks (PINNs) for the solution of an optimal control problem related with the optimal control of a stationary electric storage system (ESS) installed in a service station for plug-in electric vehicles (PEVs). The ESS is used to balance the PEVs charging power, in order to mitigate the impact on the grid, and keep low the power flow at the point of connection with the grid. The proposed PINN is trained in order to learn the optimality conditions of the optimal control problem so that, after training, it can provide the solution with no significant computation effort. This one represents a promising alternative to the analytical computation of the optimal control (which is possible only in very simple settings), and to the solution of the optimal control problem with numerical methods, which requires significant time in the more complex and realistic settings. Numerical simulations are presented to evaluate the effectiveness of the trained PINN in solving the optimal control problem.