Avdevicius, Edvard

The importance of electrifying buses in public transportation is increasing massively during the last few years. This owes to the health detrimental emissions of diesel buses and their effect on the climate changes. Correspondingly, the two transportation companies in Hamburg, the Hamburger Hochbahn AG and Verkehrsbetriebe Hamburg-Holstein GmbH (VHH), decided to electrify their bus depots. This ambitious goal is combined with many challenges concerning the design and operation of the charging infrastructure at the minimum costs. Among others also load management, grid impact, power quality. The aim of implementing the presented model is to search for possible usage of flexibility of electric bus depots in the energy market. This is realized by considering the bus depot as an aggregator of positive or negative flexibility. The offering of this flexibility is based on the predefined atypical grid usage in Germany. This enables electricity customers with an annual energy consumption of more than 100,000 kWh to save in grid fees for their load regulation in coordination with grid operators. Nevertheless, the operation of the bus depot has the highest priority in this study to guarantee the ability of buses to travel their routes. This paper analyses three different scenarios for atypical grid usage: the role of load management, the role of a second-life stationary battery and the combination of both cases. As a result, the required supplying periods and capacities of the stationary battery are calculated. Finally, a combined scenario between the supply from the stationary battery and the supply from the grid is presented.

Electricity price forecasting is important for managing supply and demand, planning investments in energy projects, ensuring energy security and efficient use of resources. This paper presents a hybrid neural network of two types of neural networks: the convolutional neural network (CNN) and the recurrent neural network (RNN) for energy market data analysis to forecast electricity prices. CNN is used to extract features from the raw data by applying a convolution operation on the temporal axis with different filters. At the same time, bidirectional gated recurrent units (BiGRU) of RNN are used for subsequent analysis of the temporal dependence of the extracted features, allowing the historical data to be considered. BiGRU is particularly useful for applications where the current input depends on past and future contexts. Thus, the ID-CNN-BiGRU method allows effective time series analysis and prediction of future values and can be widely used in the tasks of forecasting electricity prices, stocks, traffic, and other time series. The results indicate that the presented model is promising for use in a highly dynamic energy market.

In diesem Beitrag wird der Einsatz hybrider neuronaler Netze für Zeitreihenprognosen in verschiedenen Bereichen wie Energie, Verkehr, Finanzen und Umweltüberwachung untersucht. Es werden die grundlegenden Bausteine hybrider neuronaler Netze und die Verwendung struktureller Lösungen wie der Bidirektionalität vorgestellt. Außerdem werden die Genauigkeit, Anwendbarkeit und Nutzbarkeit von vier Hybridmodellen bewertet, die Faltungsschichten und rekurrente Einheitenblöcke zur Vorhersage zukünftiger Werte von Zeitreihendaten verwenden. Das Papier zeigt die Funktionalität des Modells, um automatisch zeitliche Muster aus historischen Daten zu extrahieren und zeitliche Vorhersagen zu treffen. Darüber hinaus werden die Ergebnisse von Open-Loop-Simulationen von Szenarien unterschiedlicher Komplexität vorgestellt sowie Schlussfolgerungen und Perspektiven für die weitere Forschung beurteilt. Dieses Paper dient als Übersicht für Forscher und Praktiker, die an der Verwendung neuronaler Netze für Zeitreihenprognosen interessiert sind.

Die Elektromobilität hat im öffentlichen Personennahverkehr (ÖPNV) einen bedeutenden Wandel eingeleitet. Verkehrsunternehmen wie die HOCHBAHN stellen ihre Bus-Flotten zunehmend auf emissionsfreie Antriebe um. Dies verändert nicht nur die Art und Weise, wie wir uns fortbewegen, sondern auch, wie wir Energie nutzen. Hieraus ergeben sich neue Herausforderungen und Chancen für unser Stromnetz. Die Kopplung der Sektoren Mobilität und Stromnetz spielt dabei eine entscheidende Rolle. Das Laden von Elektrobussen benötigt große Energiemengen. Diese stellen zum einen eine Belastung des Netzes dar, zum anderen können sie innerhalb betrieblicher Grenzen flexibel abgerufen werden. Die Flexibilisierung der Ladevorgänge durch ein gesteuertes Lade- und Lastmanagement ermöglicht es, Energie effizienter zu nutzen und die Umweltauswirkungen zu reduzieren. Hierbei spielt auch der Strommarkt eine bedeutende Rolle. Im Rahmen des Projektes „Koordinierungsfunktion des Verteilnetzes und Lastmanagement für den elektrifizierten Personenverkehr“ (KoLa) wird erarbeitet, wie dieses Potential der Kopplung ausgeschöpft werden kann. Dazu wird zum einen eine Optimierung des bestehenden Last- und Lademanagements der HOCHBAHN durchgeführt, indem dieses um weitere Faktoren wie den Netzzustand und eine kostengünstige Energiebeschaffung erweitert wird. Darüber hinaus wird auf dem Gelände eines Betriebshofes ein Batteriespeicher aufgebaut, um Lastspitzen zu reduzieren und die Flexibilität zu erhöhen. Zur präventiven Vermeidung von Engpässen wird eine Koordinierungsfunktion (KOF) des Verteilnetzes entwickelt. Diese prüft, ob am Vortag geplante Energiebezüge sich mit den Kapazitäten des Stromnetzes decken. Die Kombination dieser beiden neuen Systeme kann zukünftig einen wichtigen Beitrag zu nachhaltiger Mobilität und einem resilienten Energiesystem leisten.

The transition towards sustainable energy systems has led to an increasing integration of renewable energy sources and the electrification of transportation. As the adoption of electric vehicles (EVs) continues to grow, leveraging their capabilities to enhance the resilience of electrical grids becomes an intriguing possibility. EVs are capable of providing emergency power supply in a variety of situations. When traditional power sources are unavailable or unreliable, EVs can be used as backup power sources to provide electricity to homes, businesses, hospitals, and other critical infrastructure. One of the primary benefits of using EVs for emergency power supply is their ability to store large amounts of energy in their batteries. In addition to their energy storage capabilities, EVs can also be used as mobile power sources. Overall, the use of EVs for emergency power supply has the potential to improve preparation, response, and to provide a more reliable source of electricity during power outages and other emergencies. This study presents an optimization methodology of calculating possible support of electric bus depots in emergencies using Mixed-Integer Linear Programming (MILP). It targets simulating possible utilization of mobile energy storage in the improvement of power system resilience through Vehicle-to-Storage (V2S) implementation.

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.

Designing the infrastructure of bus depots involves numerous factors and considerations, but it is often subject to uncertainties that can affect the efficiency, cost, and overall performance of the depots. This study analyzes various sources of possible uncertainties encountered during the design phase of bus depots and highlights their potential impact. Generally, uncertainties in bus depot infrastructure design can arise from several aspects, including technological advancements and regulatory changes. Also, financial constraints and evolving operational requirements play an important role. The adoption of emerging technologies, such as electric buses, introduces uncertainties regarding the charging infrastructure, energy storage capacity, and compatibility with existing depot layouts. This study considers operational uncertainties, such as changes in the loading of transformers or the occurrence of blackouts, which consequently pose challenges to depot design. This is realized by employing many sensitivity case studies to evaluate various operation and design options under different uncertainty scenarios. The analysis in this study can be used to calculate the loading of transformers at bus depots in advance. Additionally, it is possible to estimate the required stationary battery in the bus depot for supplying the buses during different blackout times.

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.

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.