Digitalisierung und Elektromobilität

Eine umfassende Analyse der künftigen Elektrifizierungsszenarien im Verkehrssektor ist entscheidend, um Auswirkungen auf das Stromnetz rechtzeitig zu erkennen und zu bewältigen. Dieser Bericht prognostiziert die Anzahl der elektrischen Lastkraftwagen und Zugmaschinen, die bis 2050 in Hamburg im Einsatz sein werden, sowie den resultierenden Energieverbrauch in den einzelnen Stadtteilen bis 2050. Drei Szenarien werden erarbeitet, um unterschiedliche Entwicklungen und Annahmen zur Elektrifizierung und Energienutzung im Güterverkehr abzubilden. Außerdem wird eine Lineare Programmierung (LP) zur Optimierung der Verteilung in der Stadt genutzt, um die effizienteste und nachhaltigste Verteilung der elektrischen Lasten in den verschiedenen Stadtteilen zu ermitteln. Die Ergebnisse können als Grundlage für Strategien zur Integration der elektrischen Lastkraftwagen und Zugmaschinen in das Stromnetz in Hamburg dienen.

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 electrification of public transportation fleets worldwide can pose a challenge to multiple stakeholders, such as the fleet operator or the operator of the local electrical grid. One of the important prerequisites for the successful integration of these fleets into the existing system is the knowledge of the energy consumption of the buses during their trips. The energy consumption varies depending on multiple factors such as the vehicle or route-related parameters, operational, and environmental parameters. This paper gives an overview of the latest research regarding these influential factors. Another essential prerequisite for the implementation of intelligent management systems for electric bus fleets is the forecasting of energy consumption. Researchers take different approaches to tackle this issue. A review of the latest research considering empirical approaches, physical models, regression, and machine learning is also provided in this paper. The findings of this paper provide a quick overview of different aspects of the energy consumption of electric buses and can therefore support other researchers or decision-makers in their work.

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.

Owing to the immense climate changes recently, the city of Hamburg has decided to allow the purchase of only emission-free buses for public transportation. Meanwhile, Hamburg focuses on the implementation of electric buses. For this purpose, the two public transportation companies in Hamburg which are the Hamburger Hochbahn AG (HOCHBAHN), and the Verkehrsbetriebe Hamburg-Holstein GmbH (VHH) decided to build new charging infrastructure for electric bus depots. In addition, they started by electrifying their existing stations. This study proposes an optimal method for electrifying bus depots by modularizing the subsystems in electrical power systems. An approach that allows the study of different configurations of power system components. Analyzing these configurations results in the conclusion of the most technically feasible configuration, achieving the lowest cost. Furthermore, the model objectives include reducing the required area, which is a challenging criterion for bus depots in many cities. Mixed-Integer Quadratic Programming (MIQP) is used to generate this combination based on predefined constraints that must satisfy all implemented constraints of the system.

One of the crucial steps for a successful integration of electric bus fleets into the existing electric power systems is the active and intelligent usage of their flexibility. This is important not only for reducing the eventual negative effects on the power grid but also for reducing energy and infrastructure costs. The first step in the optimal usage of flexibility is its quantification, which al-lows the maximum provision of flexibility without any negative effects for the fleet operation. This paper explores the available flexibility of large‐scale electric bus fleets with a concept of centralized and unidirectional depot charging. An assessment of available positive and negative flexibility was conducted based on the data from two real bus depots in the city of Hamburg, Germany. The analysis shows the biggest flexibility potential was in the period from 16:00 h to 24:00 h, and the smallest one was in the periods from 08:00 h to 16:00 h, as well as from 02:00 h to 08:00 h. The paper also gives an overview of the possible markets for flexibility commercialization in Germany, which can provide an additional economic benefit for the fleet operators. A further analysis of the impact of parameters such as the timeline (working day or weekend), charging concept, ambient temperature, and electrical preconditioning provides an additional understanding of available flexibility.