V2G Potential Estimation and Optimal Discharge Scheduling for MMC-based Charging Stations

IEEE Workshop on the Electronic Grid (eGRID), pp. 1–6

Abstract

This paper investigates the vehicle-to-grid (V2G) potential of the large scale electric vehicle (EV) charging infrastructures where the grid-side interface is based on a Modular Multilevel Converter (MMC). In this topology, each phase of the grid-side interface consists of two arms, each of which accommodates a number of bidirectional EV chargers. Heterogeneous connection and disconnection of EVs with different State-of-Charge (SOC) may lead to unbalanced power flow among the MMC arms. Although the studied MMC topology can tolerate limited unbalances in the loading, extreme unbalances must be prevented to limit the internal current amplitudes and avoid increased losses. Due to such unbalance limitations, the feasible V2G potential of the overall system can be smaller than the summation of individual discharge potentials of the participating EV batteries. The algorithm presented in this paper estimates the feasible V2G potential of the MMC-based system over a time window by the help of a mathematical optimization model. This model maximizes the aggregated discharge potential of the overall system by scheduling the discharge profiles of individual EV batteries while respecting the unbalance constraints of the MMC topology. Furthermore, this model is able to schedule the discharging activities in such a way that the priorities of the EV users in this regard are considered while the entire feasible V2G potential of the overall system is utilized.

Authors 7

1. Erdem Gümrükcü Aachen Institute for Automation of Complex Power Systems

   RWTH Aachen University

   Affiliation as printed

   Institute for Automation of Complex Power Systems, E.ON ERC - RWTH Aachen University, Aachen, Germany

2. Harshvardhan Samsukha Aachen Institute for Automation of Complex Power Systems

   RWTH Aachen University

   Affiliation as printed

   Institute for Automation of Complex Power Systems, E.ON ERC - RWTH Aachen University, Aachen, Germany

3. Ferdinanda Ponci Aachen Institute for Automation of Complex Power Systems

   RWTH Aachen University

   Affiliation as printed

   Institute for Automation of Complex Power Systems, E.ON ERC - RWTH Aachen University, Aachen, Germany

4. Antonello Monti Aachen Institute for Automation of Complex Power Systems

   RWTH Aachen University

   Affiliation as printed

   Institute for Automation of Complex Power Systems, E.ON ERC - RWTH Aachen University, Aachen, Germany

5. Giuseppe Guidi

   SINTEF Energy Research

   Affiliation as printed

   SINTEF Energy Research, Trondheim, Norway

6. Salvatore D’Arco

   SINTEF Energy Research

   Affiliation as printed

   SINTEF Energy Research, Trondheim, Norway

7. Jon Are Suul

   Norwegian University of Science and Technology

   Affiliation as printed

   Department of Eng. Cybernetics, NTNU, Trondheim, Norway