This paper proposes a strategy to manage an electric vehicle charging station (EVCSs) with a grid-side interface based on a Modular Multilevel Converter (MMC). The MMC topology is studied due to its potential for reducing the footprint and the use of active material in the internal distribution system by allowing for transformer-less connection to the medium voltage distribution grid. However, heterogeneous charging demands and arrival-departure profiles of the electric vehicles (EVs) could lead to significant loading unbalances among the MMC arms and among the modules of a single arm. Nevertheless, the current in the grid interface must be kept balanced and sinusoidal. Furthermore, the voltages of the modules of an arm must be balanced. This work combines a load management (LM) algorithm with a power flow management (PFM) algorithm to achieve the required characteristics of grid current and module voltages under the heterogeneity of the charging demand in MMC-based EVCSs. The PFM algorithm controls the circulating currents to compensate the phase-to-phase, arm-to-arm and intra-arm unbalances of the given loading. To minimize the additional losses resulting from active balancing by the PFM, the LM optimizes the charging schedules and allocations of incoming EVs into charging units in order to minimize phase-to-phase and arm-to-arm unbalances in the system. The performance of the proposed optimization-based LM is compared with a rule-based benchmark LM by simulating the daily operation of an example shopping mall parking with MMC-based grid interface. In scenarios with pronounced unbalance limitations, the optimization-based LM increases the supplied energy significantly. Real-time (RT) simulations demonstrate a balanced and sinusoidal grid current profile and balanced module voltages in MMC arms over the daily scenarios. These results indicate that the proposed strategy combining LM and PFM is applicable for real-world deployments.

Electric Vehicles (EVs) are gaining acceptance due to the advantages they offer in the reduction of nitrogen oxide and carbon dioxide emissions. The need for emission reduction and the potential of EVs for these reductions is reflected in the current sustainable mobility policies of the EU as well as the German government. Increasing the penetration of EVs in the grid requires an expansion of EV charging infrastructure, which in turn requires either grid reinforcement or solutions for more efficient use of existing infrastructure to avoid or postpone grid reinforcement. Distribution transformers face increased loading due to EV charging and need to be protected from overloading during peak load periods to ensure continuity of service. Therefore, transformers are one of the components that are upgraded or replaced as a part of grid reinforcement. In this paper, we propose the connection of a Solid-State Transformers (SST) between two buses operating at the same-voltage level as an alternative to replacement or upgrading of conventional transformer as well as to prevent their overloading. We analyse how the proposed topology can be useful to reduce the impact of EV integration on the overloading of distribution transformers and node voltage violations in the distribution grid.

Electric vehicle (EV) charging infrastructure hardware–software solutions and communication protocols have inherent vulnerabilities against cyberattacks. Due to the wide range of back doors and infiltration possibilities, there is an important need for solutions that can maintain critical service continuity during incidents. This study proposes a dynamic capacity sharing method for effective use of the constrained grid capacity between neighboring charging clusters in distribution grids when the communication link between the clusters’ operators and the grid operator is disrupted due to hardware faults or cyberattacks. The performance of the developed solution is thoroughly investigated in a Denial-of-Service cyberattack scenario that may take place at different times of the day in realistic scenarios involving residential demand and stochastic EV charging behavior. The analyses validated the effectiveness of the proposed method in improving the deteriorated service level per charging cluster and better utilization of an overall constrained capacity.