Eskander, Mina
Loading...
21 results
Now showing 1 - 10 of 21
- PublicationMetadata onlyRole of stationary energy storage systems in large-scale bus depots in the case of atypical grid usage(VDE Verlag, 2024-06-13)
; ; ; ;Soliman, RamyThe 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. - PublicationMetadata onlyEnergy market predictions with hybrid neural network 1D-CNN-BiGRUElectricity 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.
- PublicationOpen AccessÜberblick über hybride neuronale Netze mit CNN- und RNN-Schichten für Zeitreihenprognosen(Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg, Fakultät für Elektrotechnik, Professur für Elektrische Energiesysteme, 2023-12)
; ; ; ; 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. - PublicationOpen AccessKoLa – Koordinierungsfunktion des Verteilnetzes und Lastmanagement für den elektrifizierten Personenverkehr(Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg, Fakultät für Elektrotechnik, Professur für Elektrische Energiesysteme, 2023-12)
;Clausen, Sören ;Soliman, Ramy ;Dammasch, Arne ;Schüssler, Gina ;Rottenberger, Amelie ;Nußbaum, Finn ;Steen, Anna-Lena ;Becker, Christian; ; ; 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. - PublicationMetadata onlyGeneric methodology for electrical grid resilience using V2S of large-scale electric bus depotsThe 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.
- PublicationMetadata onlyInvestigation of parameters impacting the energy consumption of electric buses(IEEE, 2023-11-21)
; ;Soliman, Ramy; ; ; 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. - PublicationMetadata onlyAssessment of bus depot infrastructure under various uncertainties to maximize system reliabilityDesigning 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.
- PublicationMetadata onlyEnergy consumption of battery-electric buses: review of influential parameters and modelling approachesThe 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.
- PublicationMetadata onlyBus charging management based on AI prediction and MILP optimizationThe 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.
- PublicationMetadata onlyOptimal Design of Modular Electrical Infrastructure for Large-Scale Electric Bus DepotsOwing 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.
- «
- 1 (current)
- 2
- 3
- »