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Smart V2G for the Smart Grid
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Smart V2G for the Smart Grid

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 6265

Special Issue Editor

Dr. Rob Shipman

E-Mail Website
Guest Editor
Faculty of Engineering, University of Nottingham, Nottingham, UK
Interests: energy informatics; vehicle-to-grid; smart grid; artificial intelligence; machine learning; complex systems

Special Issue Information

Dear Colleagues,

The accelerating uptake of electric vehicles introduces profound challenges for the management of electricity networks. However, it also introduces significant opportunities. The vehicle batteries have the potential to act as a large-scale distributed energy store that can be used to help manage the impact of electric vehicles on the grid and the intermittency of renewable energy for example. While such Vehicle-to-Grid (V2G) technology has been envisaged for decades it has now reached a level of maturity to support the development of the first commercial services.

In this special issue we invite high quality submissions that advance the state-of-the-art in such technology and the utilization of V2G in the smart grid. Given the status of commercial services, papers are encouraged that consider issues impacting the commercial viability of V2G such as battery degradation, round-trip efficiencies and AC V2G technology. As such services develop, technologies and systems will be required that are able to predict and exploit the availability of vehicle batteries to act as energy sinks and sources that are inherently mobile, distributed and uncertain. Papers are particularly welcome therefore that utilize technologies such as machine learning to help predict user behavior, vehicle movements and energy availability in uncertain environments. Papers are also welcome that consider how V2G may operate within a microgrid that may also include local renewable generation, stationary batteries and Vehicle-to-Building (V2B) or Vehicle-to-Everything (V2X) capabilities.

Dr. Rob Shipman
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

  • Vehicle-to-grid
  • vehicle-to-building
  • vehicle-to-everything
  • smart grid
  • microgrids
  • machine learning
  • artificial intelligence

Published Papers (3 papers)

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Research

23 pages, 7832 KiB  
Article
Proposal of Priority Schemes for Controlling Electric Vehicle Charging and Discharging in a Workplace Power System with High Penetration of Photovoltaic Systems
by Helindu Cumaratunga, Masaki Imanaka, Muneaki Kurimoto, Shigeyuki Sugimoto and Takeyoshi Kato
Energies 2021, 14(22), 7483; https://doi.org/10.3390/en14227483 - 09 Nov 2021
Cited by 2 | Viewed by 1339
Abstract
Using Electric Vehicles (EV) as Flexible Resources (FR) to increase surplus Photovoltaic (PV) power utilisation is a well-researched topic. Our previous study showed that EVs are viable as supplementary FRs in large capacity PV power systems, where EVs are likely to gather (i.e., [...] Read more.
Using Electric Vehicles (EV) as Flexible Resources (FR) to increase surplus Photovoltaic (PV) power utilisation is a well-researched topic. Our previous study showed that EVs are viable as supplementary FRs in large capacity PV power systems, where EVs are likely to gather (i.e., workplaces). However, that study assumed all EVs to have identical arrival and departure times (availability), and battery capacities. As these characteristics may vary between EVs and affect their performance as FRs, this study expands the modelling of EVs to consider a variety of availabilities and battery capacities. To effectively utilise a variety of EVs as FRs, an Optimisation Electric-load Dispatching model is used to formulate priority schemes for charging and discharging the EVs based on their potential to contribute to the power system. The priority schemes are evaluated by simulating the annual operation of the power system both with and without the priority schemes, and comparing results. The power system is simulated using a Unit-Scheduling and Time-series Electric-load Dispatching model. The priority schemes reduced annual CO2 emissions by nearly 1%, compared to the case without the priority schemes. The performances of different EVs as FRs when the priority schemes are used and not used are also analysed. Full article
(This article belongs to the Special Issue Smart V2G for the Smart Grid)
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16 pages, 4679 KiB  
Article
Online Machine Learning of Available Capacity for Vehicle-to-Grid Services during the Coronavirus Pandemic
by Rob Shipman, Rebecca Roberts, Julie Waldron, Chris Rimmer, Lucelia Rodrigues and Mark Gillott
Energies 2021, 14(21), 7176; https://doi.org/10.3390/en14217176 - 01 Nov 2021
Cited by 5 | Viewed by 2122
Abstract
Vehicle-to-grid services make use of the aggregated capacity available from a fleet of vehicles to participate in energy markets, help integrate renewable energy in the grid and balance energy use. In this paper, the critical components of such a service are described in [...] Read more.
Vehicle-to-grid services make use of the aggregated capacity available from a fleet of vehicles to participate in energy markets, help integrate renewable energy in the grid and balance energy use. In this paper, the critical components of such a service are described in the context of a commercial service that is currently under development. Key among these components is the prediction of available capacity at a future time. In this paper, we extend a previous work that used a deep learning recurrent neural network for this task to include online machine learning, which enables the network to continually refine its predictions based on observed behaviour. The coronavirus pandemic that was declared in 2020 resulted in closures of the university and substantial changes to the behaviour of the university fleet. In this work, the impact of this change in vehicles usage was used to test the predictions of a network initially trained using vehicle trip data from 2019 with and without online machine learning. It is shown that prediction error is significantly reduced using online machine learning, and it is concluded that a similar capability will be of critical importance for a commercial service such as the one described in this paper. Full article
(This article belongs to the Special Issue Smart V2G for the Smart Grid)
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30 pages, 9420 KiB  
Article
Contribution of Driving Efficiency to Vehicle-to-Building
by David Borge-Diez, Pedro Miguel Ortega-Cabezas, Antonio Colmenar-Santos and Jorge Juan Blanes-Peiró
Energies 2021, 14(12), 3483; https://doi.org/10.3390/en14123483 - 11 Jun 2021
Cited by 1 | Viewed by 2048
Abstract
Energy consumption in the transport sector and buildings are of great concern. This research aims to quantify how eco-routing, eco-driving and eco-charging can increase the amount of energy available for vehicle-to-building. To do this, the working population was broken into social groups (freelancers, [...] Read more.
Energy consumption in the transport sector and buildings are of great concern. This research aims to quantify how eco-routing, eco-driving and eco-charging can increase the amount of energy available for vehicle-to-building. To do this, the working population was broken into social groups (freelancers, local workers and commuters) who reside in two cities with different climate zones (Alcalá de Henares-Spain and Jaén-Spain) since the way of using electric vehicles is different. An algorithm based on the Here® application program interface and neural networks was implemented to acquire data of the stochastic usage of EVs of each social group. Finally, an increase in the amount of energy available for vehicle-to-building was assessed thanks to the algorithm. The results per day were as follows. Owing to the algorithm proposed a reduction ranging from 0.6 kWh to 2.2 kWh was obtained depending on social groups. The proposed algorithm facilitated an increase in energy available for vehicle-to-building ranging from 13.2 kWh to 33.6 kWh depending on social groups. The results show that current charging policies are not compatible with all social groups and do not consider the renewable energy contribution to the total electricity demand. Full article
(This article belongs to the Special Issue Smart V2G for the Smart Grid)
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