Optimal Management for Megawatt Level Electric Vehicle Charging Stations With a Grid Interface Based on Modular Multilevel Converter

Abstract

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-tophase 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.

Performance of Modular Multilevel Converter in Electric Vehicles Charging Station

“…Hamed Nademi [5] developed a two layer control framework in modular multi-level converter which is used to reduce the dc ripples and decrease the harmonic current in the grid. Erdem Gümrükcü [9] proposed an optimal management method for horizontal and vertical load unbalancing with MMC arms. In this power flow management algorithm is implemented which helps to control the circulating current and regulate the module voltages to provide sufficient energy with pure desired DC.…”

Decentralized Energy Management Concept for Urban Charging Hubs With Multiple V2G Aggregators

“…Therefore, the allocation step minimizes the inter-cluster unbalances to avoid peak power violation. The authors implement the same approach also in [22] for charging stations with grid side interface based on modular multilevel converters (MMC). This approach reduces the unbalances between the MMC arms where charging units arc clustered.…”

“…This approach reduces the unbalances between the MMC arms where charging units arc clustered. Nevertheless, since the strategy proposed in [21], [22] does not exploit V2G transfer, it is not a generally appli cable solution for the UCHs with aggregators providing grid services. Furthermore, balancing between clusters, as pursued in [21] and [22], is not necessarily desired in an environment with multiple competitive aggregators.…”

“…Nevertheless, since the strategy proposed in [21], [22] does not exploit V2G transfer, it is not a generally appli cable solution for the UCHs with aggregators providing grid services. Furthermore, balancing between clusters, as pursued in [21] and [22], is not necessarily desired in an environment with multiple competitive aggregators. When an aggregator offers cheaper charging, the allocation of the incoming EV to this aggregator's cluster would be more reasonable.…”

“…The inequality (22) indicates that the aggregate net power of the cluster must be within a certain operating range. Pc and Pc define the soft limits of this range.…”