Plenz, Maik

The process of electrification of the public transportation sector is resulting in a growing number of electric buses on the streets. Modelling and simulating the electric bus fleets can not only identify possible issues in time but can also provide valuable inputs for the optimal integration of these buses into existing operational plans and management systems. One of the important requirements for accurate modelling is knowledge of the energy consumption of the buses. This paper uses a data-driven approach to analyze the factors impacting energy consumption. The considered factors are: average daily temperature, trip length, total trip time, state of charge at the beginning of the trip, and average vehicle speed during the trip. Additionally, the impact of different buses and routes is analyzed by considering their ID numbers. The data from 96 different electric buses were collected in the city of Hamburg for 13 months. The analysis of individual parameters provides an insight into the actual operation of electric bus fleets. Additionally, using correlation analysis, it is possible to understand the relationship among all mentioned parameters. The analysis of the energy consumption of electric buses provided in this paper offers valuable inputs for future studies and the successful electrification of further bus fleets.

The emergence of new energy optimisation and control technologies with the concept of power system flexibility is a promising way to achieve the desired optimum, secure management within the smart grid and green energy transition. In this context, demand response is available through flexible demand management, taking into account various technical and time constraints. Accordingly, the aim of this paper is to address existing constraints in the field of electric mobility, in particular the operation of the charging infrastructure of bus depots, in order to actively and effectively participate in demand response events by forecasting day-ahead charging costs and load profiles of public transport infrastructure. In line with the development of a methodology for forecasting more accurately, this paper develops a prediction model based on machine learning (ML). A charging schedule is then produced based on Mixed-Integer Linear Programming (MILP) with various objective function scenarios, taking into consideration the electricity price forecast and load distribution. As a result, calculating new provisional load profiles involves assessing the flexibility potential of the bus fleet and preparing solutions in advance based on the electricity market situation.

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.

Many bus operators worldwide have started with the electrification of their fleets. Analysis of the total cost of ownership is an often-used tool in this process allowing the bus operators to compare different technologies, find the cost optimum composition of their fleet and make strategic decisions. This paper provides a unique combination of analyzing the total cost of ownership for two electric bus depots depending on the impact of route and charging scheduling. Two different approaches to both route and charging scheduling were analyzed enabling a quantification of their effect on the total costs. As the analysis shows, the optimized scheduling can have a significant effect on the costs, emphasizing the importance of intelligent management systems for the future electric bus depots. Additionally, this paper provides a sensitivity analysis investigating the effects of diesel and electricity prices, CO2 tax and prices for electric buses on the total cost of ownership and on the break-even point compared to the conventional fleets. The analysis was conducted using real timetables from two existing bus depots in the City of Hamburg in Germany.

The high importance of Demand-Side-Management for the stability of future smart grids is consensus among a wide spectrum of energy market participants and within the research community. While it is accepted that Demand-Side-Management will yield positive contributions, it remains challenging to identify, access, and communicate available flexibilities to the flexibility managers in order to determine impacts and choose temporarily required demand-side flexibility to ensure system stability. In this paper we introduce a methodology to determine and communicate local flexibility potential of end-users to flexibility managers for short-term access. The presented approach achieves a reliable calculation of flexibility, a standardized low bandwidth data aggregation, and communication. With the integration into an existing system architecture the general applicability is outlined with a one end-user use case. The approach yields a transparent short-term flexibility potential within the flexibility manager system.

In this work, a frequency-dependent transformer model is derived with the help of a measurement device that determines the frequency response of the grid impedance at an arbitrary point of connection to the power grid. In this case, the measurement device was connected to a 20 kV medium voltage grid of a wind farm. The disconnection and reconnection of the wind turbine’s transformers revealed the impact the open circuit input impedance of the transformers has on the positive sequence component of the grid impedance. First deriving a positive sequence grid model from the measurement without the transformers, later analyzing the impact the transformers have on the grid impedance, allowed the calculation of the positive sequence component of a frequency-dependent open circuit transformer model, which results in the same impact on the overall grid impedance as observed in the measurements.

With the implementation of a high secure communication infrastructure in Germany above the low voltage grid the integration of a wide spectrum of energy applications is conceivable, but not easily testable. The lack of a fundamental model, depicting the properties of the Smart Meter Gateway (SMGW) as the central communication device leads to integration studies with possible variations in the basic assumptions and their comparability. The aim of this paper is the development of a modeling approach that can accurately capture the SMGw properties and the challenges of the utilities and companies under various conditions. The developed methodology can be applied to several grid-compliant or market-oriented engineering questions and their potential for new business models through, e.g. switch-off flexibility communication, utilizing the SMGw as additional signal and data exchange layer of the future grid.

Optimized grid integration of proton exchange membrane fuel cells in various possible applications requires a suitable protection system. For this reason, this paper examines the transient behavior of a fuel cell stack after an external electrical shortening. In order to show the influence of operating parameters on the short-circuit behavior, various experiments with changed anode and cathode humidity, cell temperature and anode and cathode stoichiometry are carried out. With this, manufacturers can estimate the short-circuit magnitude of their stacks and recommend a suitable plant protection system. It could be shown that the peak short-circuit current depends on the operating point as well as the operating conditions. For the steady-state short-circuit current, the gas stoichiometry has an impact on the deliverable current. For all other operating conditions the steady-state short-circuit current is approximately twice the recommended maximum operating current. Furthermore, a method to estimate the effective fuel cell stack capacity out of the transient short-circuit current is presented.