The city of Hamburg has decided to electrify its bus fleets. The two public transportation companies in this city expect to operate up to 1500 buses by 2030. In order to accomplish this ambitious goal, both companies need to build an appropriate charging infrastructure. They have both decided to implement the centralized depot charging concept. Buses can therefore charge only at the depot and do not have the possibility for opportunity charging at intermediate stations. The load profile of such a bus depot is highly dependent on the charging schedule of buses. Without an intelligent scheduling system, the buses charge on demand as soon as they arrive to the depot. This can lead to an unevenly distributed load profile with high load peaks, which is problematic for the local grid as well as for the equipment dimensioning at the depot. Charging scheduling on large-scale bus depots is a relatively new and poorly researched topic. This paper addresses the issue and proposes two algorithms for charging scheduling on large-scale bus depots with the goal to minimize the peak load. The schedules created with the proposed algorithms were both tested and validated in the Bus Depot Simulator, a cosimulation platform used for bus depot simulations.

Route scheduling is crucial for uninterrupted operation of modern bus fleets consisting of electric buses. This paper proposes an exact route scheduling optimization model for centralized bus depots based on mixed integer linear programming. In order to adjust to the current situation at many electric bus depots, the model considers a heterogeneous fleet consisting of multiple types of electric buses with different battery capacities. With additional charging scheduling the model can minimize the number of buses charging simultaneously which directly leads to load peak reduction. This allows considering further parameters, such as for example the grid capacity limit. The model can be used to minimize the necessary number of buses, to define the optimum composition of the fleet as well as to minimize the total cost of the fleet. The results show a clear cost advantage of operating a heterogeneous fleet as well as the benefits of combined route- and charging scheduling. Timetables from five real depots from the city of Hamburg in Germany were used as examples in this paper. Analysis of the proposed model using real data can provide a valuable input to other transportation companies preparing for the electrification of their fleet.

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.

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 allows 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.