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Affiliation

Transport and Planning Department, Faculty of Civil Engineering and Geosciences (CiTG), Delft University of Technology (TU Delft), Delft, The Netherlands

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2 Papers

Understanding Car Usage Patterns for V2G Integration: Insights from Dutch Travel Diaries

Topics: Driver Behavior Analysis; Smart Grid and V2G

Authors: Simon Leu ¹ , Gonçalo Correia ² , Hans van Lint ² and Axel Leonhardt ¹

Affiliations: ¹ Institute of Transport and Spatial Planning, University of the Bundeswehr Munich, Werner-Heisenberg-Weg 39, 85577 Neubiberg, Germany , ² Department of Transport and Planning, TU Delft, Stevinweg 1, 2628 CN Delft, The Netherlands

Digital Object Identifier: 10.5220/0013412500003941

Proceedings of VEHITS 2025, ISBN: 978-989-758-745-0

Quick Abstract

Integrating renewable energy sources, such as solar and wind, challenges grid stability due to their intermittent nature. Vehicle-to-grid (V2G) technology provides a promising solution by utilizing electric vehicles (EVs) as decentralized energy storage systems, enabling the storage of surplus energy during low demand and its release during peak demand. The effectiveness of V2G depends critically on car usage patterns. Data from the Netherlands Mobility Panel (MPN) of 2022, comprising travel diaries from 2,505 households, was analyzed to explore this. A methodology was developed to create car usage profiles based on parking durations and locations, distinguishing weekday and weekend patterns. The analysis shows that vehicles are predominantly parked at home, with weekday profiles reflecting work-related parking and weekend profiles highlighting increased leisure activity. Households with shared cars showed higher driving activity and shorter parking durations than households with a 1:1 car-to-license ratio or surplus vehicles. Six distinct car usage clusters were identified for weekdays and four for weekends.

A Conceptual Framework Supporting Pattern Design Selection for Scientific Workflow Applications in Cloud Computing

Topics: Cloud Workflow Management Systems; Cloud Management Platforms; Cloud Application Architectures

Authors: Ehab Nabiel Alkhanak ¹ , Saif Ur Rehman Khan ² , Alexander Verbraeck ³ and Hans van Lint ¹

Affiliations: ¹ Transport and Planning Department, Faculty of Civil Engineering and Geosciences (CiTG), Delft University of Technology (TU Delft), Delft, The Netherlands , ² Department of Computer Science, COMSATS University Islmabad (CUI), Islamabad, Pakistan , ³ Department Multi-actor Systems, Faculty of Technology, Policy and Management (TPM), Delft University of Technology (TU Delft), Delft, The Netherlands

Digital Object Identifier: 10.5220/0008916102290236

Proceedings of CLOSER 2020, ISBN: 978-989-758-424-4

Quick Abstract

Scientific Workflow Applications (SWFA) play a vital role for both service consumers and service providers in designing and implementing large and complex scientific processes. Previously, researchers used parallel and distributed computing technologies, such as utility and grid computing to execute the SWFAs, these technologies provide limited utilization for the shared resources. In contrast, the scalability and flexibility challenges are better handled by using cloud-computing technologies for SWFA. Since cloud computing offers a technology that can significantly utilize the amounts of storage space and computing resources necessary for processing large-size and complex SWFAs. The workflow pattern design has provided the facility of re-using previously developed workflow solutions that enable the developers to adopt them for the considered SWFA. Inspired by this, the researchers have adopted several patterns of design to better design the SWFA. Effective pattern design that can consider challenges that may not become visible only in the implementation stage of a SWFA. However, the selection of the most effective pattern design in accordance with an execution method, data size, and problem complexity of a SWFA remains a challenging task. Motivated by this, we have proposed a conceptual framework that facilitates in recommending a suitable pattern design based on the quality requirements and capabilities are given and advertised by cloud consumers and providers, respectively. Finally, guidelines to assist in a smooth migrating of SWFA from other computation paradigms to cloud computing.