Jahic, Amra

Die fortlaufende Elektrifizierung der Busflotten führt zu neuen Herausforderungen, sowohl für den Flottenbetreiber als auch für das bestehende Energieversorgungssystem. Ein detailliertes Verständnis der Lastprofile der entsprechenden Busbetriebshöfe kann eventuelle Risiken mildern und zusätzlich eine Reihe von unterschiedlichen markt- und netzorientierten Use Cases ermöglichen. Die synthetischen Lastprofile bieten sich hierbei als eine gute Grundlage für die relevanten Design-, Betrieb- oder Optimierungsanalysen. Dieses Paper definiert synthetische Lastprofile der elektrischen Busbetriebshöfe, basierend auf der Analyse der 17 bestehenden Busbetriebshöfe in der Metropolregion Hamburg. Mithilfe einer Clustering-Analyse konnten drei unterschiedliche synthetische Lastprofile definiert werden, abhängig von den Charakteristiken der Busbetriebshöfe und den dazugehörigen Umläufen.

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.

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.

The high importance of demand-side management for the stability of future smart grids came into focus years ago and is today undisputed among a wide spectrum of energy market participants, and within the research community. The increasing development of communication infrastructure, in tandem with the rising transparency of power grids, supports the efforts for deploying demand-side management applications. While it is then accepted that demand-side management will yield positive contributions, it remains challenging to identify, communicate, and access available flexibility to the flexibility managers. The knowledge about the system potential is essential to determine impacts of control and adjustment signals, and employ temporarily required demand-side flexibility to ensure power grid stability. The aim of this article is to introduce a methodology to determine and communicate local flexibility potential of end-user energy systems to flexibility managers for short-term access. The presented approach achieves a reliable calculation of flexibility, a standardized data aggregation, and a secure communication. With integration into an existing system architecture, the general applicability is outlined with a use case scenario for one end-user energy system. The approach yields a transparent short-term flexibility potential within the flexibility operator system.

To capture reliable information about the existing flexibility potential of electric vehicles forecasts of charging processes are required in order to allow of utilization and management of available flexibility. Measurements of vehicles from different manufacturers show a heterogeneous behaviour of charging characteristics and charging characteristic switching points when connected to 11 kW charging stations. This paper analyzes the relationship between vehicle-specific easy to identify parameters and the charging characteristics. The proposed generic model is able to capture different charging characteristics with only a small set of parameters, to generate vehicle-specific forecast profiles.

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.