This paper presents a real time control strategy for energy storage systems integration in electric vehicles fast charging applications combined with generation from intermittent renewable energy sources. A two steps approach taking advantage of the model predictive control methodology is designed on purpose to optimally allocate the reference charging power while managing the priority among the plugged vehicles and then control the storage for efficiently sustaining the charging process. Two different use cases are considered: in the former the charging area is disconnected from the grid, so that the objective is to minimize the deviation of electric vehicles charging power from the nominal value; in the latter the focus is on the point of connection to the grid and the need of mitigating the related power flow. In both cases the fundamental requirement for feasible control system operation is to guarantee stability of the storage's state of charge over the time. Simulation results are provided and discussed in detail, showing the effectiveness of the proposed approach

This paper presents a robust protection scheme to protect the power transmission network against a class of feedback-based attacks referred in the literature as "Dynamic Load Altering Attacks" (D-LAAs). The proposed scheme envisages the usage of Energy Storage Systems (ESSs) to avoid the destabilising effects that a malicious state feedback has on the power network generators. The methodologies utilised are based on results from polytopic uncertain systems, invariance theory and Lyapunov arguments. Numerical simulations on a test scenario validate the proposed approach.

As a result of the increasing charging rate implemented by car manufacturers in the new generation of plug-in electric vehicles (PEVs), charging point operators are continuously adjusting the charging infrastructure accordingly. In order to maximize the charging operator's return of investment and minimize the impact on the electricity grid, a key aspect is finding technical solutions which allow to downsize the nominal power flow at the point of connection between the charging station/charging area and the electricity grid, as the operating expenses are significantly affected by this parameter. In this regard, this study discusses the optimal control of an energy storage system (ESS) and PEVs fast charging for reducing the impact on the grid of the charging load in a charging area. A trade-off is achieved between the objectives of keeping limited the charging power withdrawal from the grid and the one of keeping limited the fluctuation of the state of charge of the ESS around a given reference, while keeping the charging power near to the nominal one. We present a deterministic solution, under the realistic assumption that the charging operator knows a piece-wise constant estimate of the aggregated charging power demand over the control period. Numeric simulations are provided to validate the proposed approach.

This work deals with the problem of enabling plug-in electric vehicles (PEV) to the provisioning of ancillary services to the grid, for frequency regulation purposes. The paper presents and discusses a reference scenario for such a use case, describing the systems and actors involved. A system architecture for enabling the use case is proposed, detailing the relevant system components, technologies, and control algorithms. The paper also discusses the issue of the coexistence of the PEV-based frequency regulation services with PEV smart charging. Finally, it is discussed the crucial role that 5G technologies could play for making actually feasible the implementation of the proposed scheme for PEV-based frequency regulation service provisioning.

This paper presents a decentralized Model Predictive Control (MPC) for Plug-in Electric Vehicles (PEVs) charging, in presence of both network and drivers' requirements. The open loop optimal control problem at the basis of MPC is modeled as a consesus with regularization optimization problem and solved by means of the decentralized Alternating Direction Method of Multipliers (ADMM). Simulations performed on a realistic test case show the potential of the proposed control approach and allow to provide a preliminary evaluation of the compatibility between the required computational effort and the application in real time charging control system.