Stadler, Andreas

The large-scale deployment of grid-friendly controllable charging infrastructure or electric vehicle supply equipment for e.g. congestion management does pose the necessity for dedicated testing equipment. This work presents the design requirements and test device concepts for such a test device. The proposed test device combines test capabilities for both Mode 3/AC and Mode 4/DC charging infrastructure. The norm-conforming communication protocols are implemented and available for monitoring, while also testing under real load conditions is available while being independent of a mains connection. Further, the test device was validated using extensive testing with different EVSE. The applicability for in-field usage is shown.

The continuous efforts to enhance transparency and digitalization of the low-voltage grid will impact grid operation and the load distribution of its components. The impact of an increasing share of flexible and controllable devices in the residential sector in conjunction with an ongoing introduction of modern high power devices has not been devoted the attention this topic deserves in the literature. The major share in literature deals with a maximum extend in flexible and controllable devices. This paper expands the agility factor-based method to a wider set of Demand Response programs and analyses the ramp-up of modern newly introduced devices in the residential sector in a reference grid, based on grid data of the city of Hamburg, Germany. Due to this method the loading development can be evaluated and critical grid sections can be selected for further analysis. Additionally, the dynamic grid connection point power limitation based on the agility factor method is introduced as Demand Response program and a possible countermeasure against energy paybacks following variable energy pricing schemes.

In recent years, electric vehicles (EVs) are considered to be a promising way to reduce greenhouse gas emissions from the transportation sector. However, the increasing penetration of EVs into the distribution network (DN) raises serious concerns about the network’s safe and reliable operation. The uncontrolled EV charging with random behavior will lead to volatile load peaks on the distribution transformer. In order to obtain more transformer loading capacity available for integration of further EVs, distributed energy resources (DERs) and related devices, such as heat pumps, the transformer loading must be limited to a certain range. For this reason, an intelligent charging management based on model-free Reinforcement Learning (RL) is proposed in this work. The RL management is able to control the charging power of all EVs connected to the network without previous knowledge about the arriving- and leaving time. The needed information for the RL-agent to perceive the current state of the system is formed with cumulated values such as the total energy requirement and the total charging power demand of all EVs. In this paper, the RL algorithm is trained on real-world energy consumption data for a month and on a reference network, created with selected characteristics of a substation network area in the northeast of Hamburg. Comparing with uncontrolled charging, the simulation results show that the RL-based charging management avoids 99 % of threshold violations regarding transformer loading and results in 1% of EV energy requirement is not satisfied. Through sensitivity analysis regarding the state space representation in the employed RL process, the necessity of providing the state of charge (SOC) or the energy requirements of EV users are proven to improve the charging control performance.