Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.2
Journals
Energies
Special Issues
Smart EV Charging
energies-logo
Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Smart EV Charging

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (8 December 2021) | Viewed by 12311

Special Issue Editor

Dr. Jan Rosenow

E-Mail Website
Guest Editor
1. RAP, Rue de la Science 23, B – 1040, Brussels, Belgium 2. Environmental Change Institute, University of Oxford, Oxford OX1 2JD, UK
Interests: electrification; flexibility; energy efficiency; policy; energy; energy saving; energy conservation; energy efficiency in building; energy economics; public policy; energy policy

Special Issue Information

Dear Colleagues,

The number of EVs has increased exponentially since the early 2010s, and they bring with them tremendous opportunities for both the power sector and mobility because they form the nexus between two revolutions—decarbonising electricity and electrifying transport.

The key lies in leveraging the potential for complementarity between the needs of electricity decarbonisation and the needs of transport electrification. Electricity grids are looking for ways to make productive use of large volumes of low-cost, zero-carbon energy that will be available at times when it may not have been needed to meet traditional demands for electricity. One such use is the shifting of flexible loads from times when variable production or grid capacity is scarce. At the same time, EVs represent large, inherently flexible loads, the growth of which would benefit greatly from opportunities to lower operating costs to offset higher upfront costs, and from opportunities to minimise the need to build and recover the costs of new grid infrastructure.

This Special Issue invites papers discussing the opportunities and challenges associated with smart charging. We therefore invite papers on innovative technical developments, reviews, case studies, analytical, as well as assessment papers from different disciplines, which are relevant to smart charging.

Dr. Jan Rosenow
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart charging
  • electric vehicles
  • dynamic tariffs
  • load shifting

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

28 pages, 5349 KiB  
Article
Impact of Dynamic Electricity Tariff and Home PV System Incentives on Electric Vehicle Charging Behavior: Study on Potential Grid Implications and Economic Effects for Households
by Michael von Bonin, Elias Dörre, Hadi Al-Khzouz, Martin Braun and Xian Zhou
Energies 2022, 15(3), 1079; https://0-doi-org.brum.beds.ac.uk/10.3390/en15031079 - 01 Feb 2022
Cited by 16 | Viewed by 3922
Abstract
The rapid increase of electric vehicles (EVs) would lead to a rise in load demand on power grids but create different potential benefits as well. Those benefits comprise EVs serving as a mobile energy storage system to participate in adjusting the load on [...] Read more.
The rapid increase of electric vehicles (EVs) would lead to a rise in load demand on power grids but create different potential benefits as well. Those benefits comprise EVs serving as a mobile energy storage system to participate in adjusting the load on the power grids and helping manage renewable energy resources. This paper evaluates the effect of dynamic electricity prices and home photovoltaic (PV) system incentives on users’ EVs charging behavior and potential impacts on grid load and household economy. This has been done by establishing and assessing three different optimized charging configurations and comparing them to an uncontrolled charging strategy. In this study, the charging incentives are applied to a representative sample of 100 households with EVs and PV systems in a metropolitan area. The results show that an optimized charging strategy based on the dynamic electricity tariff can reduce charging costs by 18.5%, while a PV-based optimized strategy can reduce the costs by 33.7%. Moreover, the PV-integrated optimization strategies significantly increase the utilization of PV energy by almost 46% on average, compared to uncontrolled charging. In addition, the simulations of this research have depicted the capability of using home PV systems’ incentives to smoothen the charging profiles and hence significantly reduce the maximum grid load. However, the electricity price optimization strategy increases the aggregated charging peaks, which can only be slightly reduced by peak shaving. Therefore, an identical price signal for all households might be critical. Further analyses have shown that direct charging occurs simultaneously with household electricity assigned to a specific low-voltage grid while PV and price incentive charging configurations shift the charging peaks away from household load peaks. Full article
(This article belongs to the Special Issue Smart EV Charging)
►▼ Show Figures

Figure 1

24 pages, 1374 KiB  
Article
Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning
by Christopher Hecht, Jan Figgener and Dirk Uwe Sauer
Energies 2021, 14(23), 7834; https://0-doi-org.brum.beds.ac.uk/10.3390/en14237834 - 23 Nov 2021
Cited by 14 | Viewed by 5390
Abstract
Electric vehicles may reduce greenhouse gas emissions from individual mobility. Due to the long charging times, accurate planning is necessary, for which the availability of charging infrastructure must be known. In this paper, we show how the occupation status of charging infrastructure can [...] Read more.
Electric vehicles may reduce greenhouse gas emissions from individual mobility. Due to the long charging times, accurate planning is necessary, for which the availability of charging infrastructure must be known. In this paper, we show how the occupation status of charging infrastructure can be predicted for the next day using machine learning models— Gradient Boosting Classifier and Random Forest Classifier. Since both are ensemble models, binary training data (occupied vs. available) can be used to provide a certainty measure for predictions. The prediction may be used to adapt prices in a high-load scenario, predict grid stress, or forecast available power for smart or bidirectional charging. The models were chosen based on an evaluation of 13 different, typically used machine learning models. We show that it is necessary to know past charging station usage in order to predict future usage. Other features such as traffic density or weather have a limited effect. We show that a Gradient Boosting Classifier achieves 94.8% accuracy and a Matthews correlation coefficient of 0.838, making ensemble models a suitable tool. We further demonstrate how a model trained on binary data can perform non-binary predictions to give predictions in the categories “low likelihood” to “high likelihood”. Full article
(This article belongs to the Special Issue Smart EV Charging)
►▼ Show Figures

Graphical abstract

19 pages, 3282 KiB  
Article
Heuristic Optimization of Overloading Due to Electric Vehicles in a Low Voltage Grid
by Sajjad Haider and Peter Schegner
Energies 2020, 13(22), 6069; https://0-doi-org.brum.beds.ac.uk/10.3390/en13226069 - 19 Nov 2020
Cited by 4 | Viewed by 1648
Abstract
It is important to understand the effect of increasing electric vehicles (EV) penetrations on the existing electricity transmission infrastructure and to find ways to mitigate it. While, the easiest solution is to opt for equipment upgrades, the potential for reducing overloading, in terms [...] Read more.
It is important to understand the effect of increasing electric vehicles (EV) penetrations on the existing electricity transmission infrastructure and to find ways to mitigate it. While, the easiest solution is to opt for equipment upgrades, the potential for reducing overloading, in terms of voltage drops, and line loading by way of optimization of the locations at which EVs can charge, is significant. To investigate this, a heuristic optimization approach is proposed to optimize EV charging locations within one feeder, while minimizing nodal voltage drops, cable loading and overall cable losses. The optimization approach is compared to typical unoptimized results of a monte-carlo analysis. The results show a reduction in peak line loading in a typical benchmark 0.4 kV by up to 10%. Further results show an increase in voltage available at different nodes by up to 7 V in the worst case and 1.5 V on average. Optimization for a reduction in transmission losses shows insignificant savings for subsequent simulation. These optimization methods may allow for the introduction of spatial pricing across multiple nodes within a low voltage network, to allow for an electricity price for EVs independent of temporal pricing models already in place, to reflect the individual impact of EVs charging at different nodes across the network. Full article
(This article belongs to the Special Issue Smart EV Charging)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop