Cédric Bodet

Optimization of charging infrastructure usage under varying traffic and capacity conditions

In the future energy landscape, high attention will be paid to the intelligent management of the balance between generation and demand. The shift of transportation towards electrification is a crucial part of the future efforts, but also a great challenge regarding the alignment with renewable energy supply. In this paper, we investigate the EV charging capacity management, with a focus on sustainable electric vehicle (EV) charging capacity for long-distance traffic on highways. The proposed method is based upon the concept of an Intelligent Dynamic Charging Assignment which takes various parameters of traffic information, user information and charging facility information into account in order to optimize charging facility usage by increasing its utilization and maximizing the energy usage, enable higher EV throughput in given traffic conditions and comply with user preferences and EV car characteristics. The optimization results have been validated in a simulation environment with different parameter variations. With the dynamic assignment, an increase of 30% of the utilization of the infrastructure with equal charging station deployment at each location can be reached. This is also reflected in an increase of the throughput of the EVs which is limited by waiting times. The given studies show a 30% higher throughput efficiency through the proposed dynamic assignment method. A reshuffling of the charging infrastructure is also considered. While the energy utilization itself increases to a small extend, the improvement on user experience regarding waiting times has a greater impact towards user satisfaction.

Cooperative balancing between energy communities using traffic information and charging assignments

Integrating high penetration of fluctuating renewable energy sources causes many challenges for the future smart grids. The integration of intelligence from e.g. transportation provides opportunities for new services for local balancing. In this paper we introduce a method of cooperative balancing between adjacent grid segments for local power balancing which uses the mobility of electric vehicles with short-term charging needs in the load profile adaptation for grid segments through the exchange of EV charging demands between grid segments. Hereby travel tolerances like speed, direction, and charging needs are exploited. The method will be assessed through simulations in a two-grid-segment setup and a fleet of electric vehicles. Practically, the method serves a dynamic charging station assignment respecting immediate user preferences. Results show, that the method contributes to the prevention of critical load levels to impact the grid balancing by linking with the traffic flow as input (prediction) as well as controllable variable (via charging assignments).

An optimal local charging strategy for competing electric vehicles on quick charging facilities

Electrification of Transportation is seen as a sustainable way for future mobility. Electric vehicles (EV) play therefore a substantial role, and are strongly supported for their market appearance while at the same time, they pose substantial challenges for the electric grid infrastructure. In contrast to the current gasoline fuel stations, electric charging stations do depend on the conditions of the power grid they are connected to. Based on the generation profile, these conditions vary regarding the provided power level. We present an approach for dynamic on-site reservation (locally on a single charging station) for instant charging needs increasing the efficiency and exploits the local quick charging capacities for the charging station owner and the conditions of the power grid. Results show an optimized usage of the available power resources of the station, as well as an increase in the number of EVs that can be served.

Hierarchical control of combined power control resources mitigating local power grid fluctuations

Power grid fluctuations are an increasing concern with the high penetration of intermittent generation sources. This paper describes a hierarchical control scheme defined by the dynamics of de-coupled power units over possibly different business domains into an integrated control scheme providing fluctuation mitigation. Specifically, a method built on the combination of charging control of a variable EV fleet and a small-scale energy storage system in a prioritized control hierarchy is investigated with its impacts on both domains. The control hierarchy uses the EV charging dynamics as prime resource for the mitigation of local generation fluctuations and accommodates the residual lack with local electric storage. The definition of a controllable power range for the charging stations allows for the EV charging infrastructure to serve as a power service. The underlying two-layer control algorithms is evaluated through simulations of the dynamics of a residential grid segment combining different grid resources. The results show significantly enhanced performance for effectively reducing the energy surplus-needs through the prime control layer (here: EV charging), but also the need for careful design of secondary power capacities (here: storage).