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.

In the context of Mobile Augmented Reality, satisfying the challenging users’ requirements about Quality of Service and Quality of Experience is not an easy task due to the limited computing capabilities of mobile devices, and the rapid, free movement of users within the environment. To deal with these issues, graphical computations are typically offloaded from mobile devices to edge servers. While traditional offloading strategies rely on static optimization or heuristics, this work proposes a multi–input data–driven dynamic control of uplink power and image compression rate, introducing a Policy Broadcasting Deep Reinforcement Learning approach, based on the Deep Deterministic Policy Gradient algorithm. The proposed solution is aimed at matching the challenging Quality of Service constraints, in terms of maximum round–trip latency and minimum resolution accuracy, while minimizing the energy consumption. Simulations show the effectiveness and scalability of the proposed approach for real–time applications.

This paper proposes a control strategy for an Energy Storage System (ESS) installed on a secondary substation of an electricity distribution line in order to mitigate power variations with respect to the day-ahead planning caused by renewable energy sources on the distribution line.In particular, the aim of the controller is to keep the power profile of at primary substations close to a reference profile foreseen on a day-ahead basis while guaranteeing the stable operation of its ESS, in terms of their state-of-charge dynamics. The inclusion of the ESS contribution to the network operation is attained by the integration of properly defined power flow constraints in a Model Predictive Control Framework. The proposed approach has been validated through numerical simulations, representative of real operative scenarios.