scholarly journals Using rooftop photovoltaic generation to cover individual electric vehicle demand—A detailed case study

2022 ◽  
Vol 157 ◽  
pp. 111969
Author(s):  
H. Martin ◽  
R. Buffat ◽  
D. Bucher ◽  
J. Hamper ◽  
M. Raubal
Keyword(s):  
Electric Vehicle ◽  
Photovoltaic Generation ◽  
Detailed Case ◽  
Vehicle Demand

scholarly journals Vehicle Energy Consumption in Python (VencoPy): Presenting and Demonstrating an Open-Source Tool to Calculate Electric Vehicle Charging Flexibility

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4349
Author(s):  
Niklas Wulff ◽  
Fabia Miorelli ◽  
Hans Christian Gils ◽  
Patrick Jochem
Keyword(s):  
Open Source ◽  
Electric Vehicle ◽  
Full Range ◽  
Mobility Patterns ◽  
Electric Vehicle Charging ◽  
Survey Results ◽  
Vehicle Demand ◽  
Vehicle Fleet ◽  
Electric Loads

As electric vehicle fleets grow, rising electric loads necessitate energy systems models to incorporate their respective demand and potential flexibility. Recently, a small number of tools for electric vehicle demand and flexibility modeling have been released under open source licenses. These usually sample discrete trips based on aggregate mobility statistics. However, the full range of variables of travel surveys cannot be accessed in this way and sub-national mobility patterns cannot be modeled. Therefore, a tool is proposed to estimate future electric vehicle fleet charging flexibility while being able to directly access detailed survey results. The framework is applied in a case study involving two recent German national travel surveys (from the years 2008 and 2017) to exemplify the implications of different mobility patterns of motorized individual vehicles on load shifting potential of electric vehicle fleets. The results show that different mobility patterns, have a significant impact on the resulting load flexibilites. Most obviously, an increased daily mileage results in higher electricty demand. A reduced number of trips per day, on the other hand, leads to correspondingly higher grid connectivity of the vehicle fleet. VencoPy is an open source, well-documented and maintained tool, capable of assessing electric vehicle fleet scenarios based on national travel surveys. To scrutinize the tool, a validation of the simulated charging by empirically observed electric vehicle fleet charging is advised.

scholarly journals Flexibility of Electric Vehicle Demand: Analysis of Measured Charging Data and Simulation for the Future

2019 ◽  
Vol 10 (1) ◽  
pp. 14 ◽  
Author(s):  
Marte K. Gerritsma ◽  
Tarek A. AlSkaif ◽  
Henk A. Fidder ◽  
Wilfried G.J.H.M. van Sark
Keyword(s):  
Electric Vehicle ◽  
Congestion Management ◽  
Charging Station ◽  
Smart Charging ◽  
Vehicle Demand ◽  
High Flexibility ◽  
Charging Stations ◽  
High Scenario ◽  
Evening Peak

This paper proposes a method for analyzing and simulating the time-dependent flexibility of electric vehicle (EV) demand. This flexibility is influenced by charging power, which depends on the charging stations, the EV characteristics, and several environmental factors. Detailed charging station data from a Dutch case study have been analysed and used as input for a simulation. In the simulation, the interdependencies between plug-in time, connection duration, and required energy are respected. The data analysis of measured data reveals that 59% of the aggregated EV demand can be delayed for more than 8 h, and 16% for even more than 24 h. The evening peak shows high flexibility, confirming the feasibility of congestion management using smart charging within flexibility constraints. The results from the simulation show that the average daily EV demand increases by a factor 21 between the ‘Present-day’ and the ‘High’ scenario, while the maximum EV demand peak increases only by a factor 6, as a result of the limited simultaneity of the transactions. Further, simulations using the average charging power of individual measured transactions yield more accurate results than simulations using a fixed value for charging power. The proposed method for simulating future EV flexibility provides a basis for testing different smart charging algorithms.

Introduction to Part I

2018 ◽  
Author(s):  
Andrew Berg ◽  
Rafael Portillo
Keyword(s):  
Monetary Policy ◽  
Structural Models ◽  
Stylized Facts ◽  
Policy Regimes ◽  
Detailed Case ◽  
Inaccurate Data ◽  
Cross Country

Developing an understanding of monetary policy in LICs must start with the evidence. This chapter briefly reviews the challenges facing the empirical researcher in SSA, including scarce and inaccurate data, short policy regimes that make powerful inference difficult, and the lack of structural models to help interpret the data. It provides an overview of Chapters 4–6, which take three very different approaches to looking at these data: a broad search for cross-country stylized facts (Chapter 4), a detailed case study of a major monetary policy event (Chapter 5), and an examination of whether vector auto-regressions (VARs)—the workhorse empirical tool in this area—are likely to yield useful results in the SSA context (Chapter 6).

scholarly journals Optimized Strategic Planning of Future Norwegian Low-Voltage Networks with a Genetic Algorithm Applying Empirical Electric Vehicle Charging Data

Electricity ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 91-109
Author(s):  
Julian Wruk ◽  
Kevin Cibis ◽  
Matthias Resch ◽  
Hanne Sæle ◽  
Markus Zdrallek
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Low Voltage ◽  
Network Planning ◽  
Voltage Regulation ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
The Impact

This article outlines methods to facilitate the assessment of the impact of electric vehicle charging on distribution networks at planning stage and applies them to a case study. As network planning is becoming a more complex task, an approach to automated network planning that yields the optimal reinforcement strategy is outlined. Different reinforcement measures are weighted against each other in terms of technical feasibility and costs by applying a genetic algorithm. Traditional reinforcements as well as novel solutions including voltage regulation are considered. To account for electric vehicle charging, a method to determine the uptake in equivalent load is presented. For this, measured data of households and statistical data of electric vehicles are combined in a stochastic analysis to determine the simultaneity factors of household load including electric vehicle charging. The developed methods are applied to an exemplary case study with Norwegian low-voltage networks. Different penetration rates of electric vehicles on a development path until 2040 are considered.

scholarly journals A clarified typology of core-periphery structure in networks

2021 ◽  
Vol 7 (12) ◽  
pp. eabc9800
Author(s):  
Ryan J. Gallagher ◽  
Jean-Gabriel Young ◽  
Brooke Foucault Welles
Keyword(s):  
Dense Core ◽  
Block Model ◽  
Hub And Spoke ◽  
Block Modeling ◽  
Domain Specific ◽  
The Core ◽  
Detailed Case ◽  
Rich Diversity ◽  
Modeling Techniques

Core-periphery structure, the arrangement of a network into a dense core and sparse periphery, is a versatile descriptor of various social, biological, and technological networks. In practice, different core-periphery algorithms are often applied interchangeably despite the fact that they can yield inconsistent descriptions of core-periphery structure. For example, two of the most widely used algorithms, the k-cores decomposition and the classic two-block model of Borgatti and Everett, extract fundamentally different structures: The latter partitions a network into a binary hub-and-spoke layout, while the former divides it into a layered hierarchy. We introduce a core-periphery typology to clarify these differences, along with Bayesian stochastic block modeling techniques to classify networks in accordance with this typology. Empirically, we find a rich diversity of core-periphery structure among networks. Through a detailed case study, we demonstrate the importance of acknowledging this diversity and situating networks within the core-periphery typology when conducting domain-specific analyses.

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