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Energies
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Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies
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Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies

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

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 35485

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Sekyung Han

E-Mail Website
Guest Editor
Department of Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
Interests: power system analysis and modeling; distributed power control; vehicle-to-Grid; energy management system (EMS) design
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Moses Amoasi Acquah

E-Mail Website
Guest Editor
Department of Electrical Energy Engineering, Keimyung University, Daegu 42403, Korea
Interests: smart grid; energy management system; vehicle-to-grid (V2G); big data; machine learning; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, there has been a global outcry for the decarbonization of energy systems to provide a secure and sustainable energy supply while cutting down on greenhouse gas emissions (GHGs) in an effort to avoid global warming.

With the explosion in use of battery energy storage, electric vehicles (EV) are considered an efficient eco-friendly means of transportation. They also play a role through their interaction with electricity grids, delivering power as well as controlling the charging rate for a faster charging time.

EVs are able to meet this role due to grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operation providing bidirectional power flow to tackle the twin challenges of faster charging and providing ancillary services to the grid.

This Special Issue entitled “Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies” invites articles on current state-of-the-art technologies and solutions in G2V and V2G, including but not limited to the operation and control of gridable vehicles, energy storage and management systems, charging infrastructure and chargers, EV demand and load forecasting, V2G interfaces and applications, V2G and energy reliability and security, environmental impacts and economic benefits, as well as demonstration projects and case studies in the aforementioned areas.

Articles that deal with the latest hot topics in V2G are of particular interest, such as V2G and demand-side response control technique, smart charging infrastructure and grid planning, advanced power electronics for V2G systems, adaptation of V2G systems in the smart grid, adaptation of smart cities for a large number of EVs, integration, and the optimization of V2G systems, utilities and transportation assets for advanced V2G systems, wireless power transfer systems for advanced V2G systems, fault detection, maintenance and diagnostics in V2G processes, communications protocols for V2G systems, energy management system (EMS) in V2G systems, IoT for V2G systems, distributed energy and storage systems for V2G, transportation networks and V2G, energy management for V2G, smart charging/discharging stations for efficient V2G, environmental and socio-economic benefits and challenges of V2G systems, and building integrated V2G systems (BIV2G).

Prof. Dr. Sekyung Han
Dr. Acquah Amoasi Moses
Guest Editors

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

  • electric vehicles
  • smart transportation
  • smart grids
  • integration of EVs
  • energy arbitrage
  • charging/discharging infrastructure
  • sustainable energy
  • ev storage systems
  • DC/DC converters
  • energy management systems

Published Papers (5 papers)

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Research

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20 pages, 8425 KiB  
Article
An Ensemble Stochastic Forecasting Framework for Variable Distributed Demand Loads
by Kofi Afrifa Agyeman, Gyeonggak Kim, Hoonyeon Jo, Seunghyeon Park and Sekyung Han
Energies 2020, 13(10), 2658; https://doi.org/10.3390/en13102658 - 25 May 2020
Cited by 10 | Viewed by 2509
Abstract
Accurate forecasting of demand load is momentous for the efficient economic dispatch of generating units with enormous economic and reliability implications. However, with the high integration levels of grid-tie generations, the precariousness in demand load forecasts is unreliable. This paper proposes a data-driven [...] Read more.
Accurate forecasting of demand load is momentous for the efficient economic dispatch of generating units with enormous economic and reliability implications. However, with the high integration levels of grid-tie generations, the precariousness in demand load forecasts is unreliable. This paper proposes a data-driven stochastic ensemble model framework for short-term and long-term demand load forecasts. Our proposed framework reduces uncertainties in the load forecast by fusing homogenous models that capture the dynamics in load state characteristics and exploit model diversities for accurate prediction. The ensemble model caters for factors such as meteorological and exogenous variables that affect load prediction accuracy with adaptable, scalable algorithms that consider weather conditions, load features, and state characteristics of the load. We defined a heuristic trained combiner model and an error correction model to estimate the contributions and compensate for forecast errors of each prediction model, respectively. Acquired data from the Korean Electric Power Company (KEPCO), and building data from the Korea Research Institute, together with testbed datasets, were used to evaluate the developed framework. The results obtained prove the efficacy of the proposed model for demand load forecasting. Full article
(This article belongs to the Special Issue Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies)
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15 pages, 3282 KiB  
Article
Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid
by Rob Shipman, Julie Waldron, Sophie Naylor, James Pinchin, Lucelia Rodrigues and Mark Gillott
Energies 2020, 13(8), 1933; https://doi.org/10.3390/en13081933 - 14 Apr 2020
Cited by 8 | Viewed by 4392
Abstract
Vehicle-to-grid services draw power or curtail demand from electric vehicles when they are connected to a compatible charging station. In this paper, we investigated automated machine learning for predicting when vehicles are likely to make such a connection. Using historical data collected from [...] Read more.
Vehicle-to-grid services draw power or curtail demand from electric vehicles when they are connected to a compatible charging station. In this paper, we investigated automated machine learning for predicting when vehicles are likely to make such a connection. Using historical data collected from a vehicle tracking service, we assessed the technique’s ability to learn and predict when a fleet of 48 vehicles was parked close to charging stations and compared this with two moving average techniques. We found the ability of all three approaches to predict when individual vehicles could potentially connect to charging stations to be comparable, resulting in the same set of 30 vehicles identified as good candidates to participate in a vehicle-to-grid service. We concluded that this was due to the relatively small feature set and that machine learning techniques were likely to outperform averaging techniques for more complex feature sets. We also explored the ability of the approaches to predict total vehicle availability and found that automated machine learning achieved the best performance with an accuracy of 91.4%. Such technology would be of value to vehicle-to-grid aggregation services. Full article
(This article belongs to the Special Issue Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies)
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14 pages, 3333 KiB  
Article
An MPC Scheme with Enhanced Active Voltage Vector Region for V2G Inverter
by Lie Xia, Lianghui Xu, Qingbin Yang, Feng Yu and Shuangqing Zhang
Energies 2020, 13(6), 1312; https://doi.org/10.3390/en13061312 - 12 Mar 2020
Cited by 1 | Viewed by 2303
Abstract
In this paper, a model predictive control (MPC) scheme with an enhanced active voltage vector region (AV2R) was developed and implemented to achieve better steady-state performance and lower total harmonic distortion (THD) of the output current for a vehicle-to-grid (V2G) inverter. [...] Read more.
In this paper, a model predictive control (MPC) scheme with an enhanced active voltage vector region (AV2R) was developed and implemented to achieve better steady-state performance and lower total harmonic distortion (THD) of the output current for a vehicle-to-grid (V2G) inverter. Firstly, the existing MPC methods conducted with single vector and two vectors during one sampling period were analyzed and the corresponding AV2Rs were elaborated. Secondly, the proposed strategy was investigated, aiming at expanding the AV2R and improving the steady-state performance accordingly. A formal mathematical methodology was studied in terms of duty ratio calculation. Lastly, the proposed method was carried out through experimentation. For comparison, the experimental results of the three mentioned methods were provided as well, proving the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies)
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22 pages, 901 KiB  
Article
Electric Vehicles Energy Management with V2G/G2V Multifactor Optimization of Smart Grids
by Gabriel Antonio Salvatti, Emerson Giovani Carati, Rafael Cardoso, Jean Patric da Costa and Carlos Marcelo de Oliveira Stein
Energies 2020, 13(5), 1191; https://doi.org/10.3390/en13051191 - 05 Mar 2020
Cited by 43 | Viewed by 6204
Abstract
Energy Storage Systems (ESS) and Distributed Generation (DG) are topics in a large number of recent research works. Moreover, given the increasing adoption of EVs, high capacity EV batteries can be used as ESS, as most vehicles remain idle for long periods during [...] Read more.
Energy Storage Systems (ESS) and Distributed Generation (DG) are topics in a large number of recent research works. Moreover, given the increasing adoption of EVs, high capacity EV batteries can be used as ESS, as most vehicles remain idle for long periods during work or home parking. However, the high EV penetration introduces some issues related to the charging power requirements, thereby increasing the peak demand for microgrids where EV chargers are installed. In addition, photovoltaic distributed generation is becoming another issue to deal with in EV charging microgrids. Therefore, this new scenario requires an Energy Management System (EMS) able to deal with charging demand, as well as with generation intermittency. This paper presents an EMS strategy for Microgrids that contain an EV parking lot (EVM), Photovoltaic (PV) arrays, and dynamic loads connected to the grid considering a Point of Common Coupling (PCC). The EVM-EMS utilizes the projections of future PV generation and future demand to accomplish a dynamic programming technique that optimizes the EVs’ charging (G2V) or discharging (V2G) profiles. This algorithm attends to user preferences while reducing the demand grid dependences and improves the microgrid efficiency. Full article
(This article belongs to the Special Issue Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies)
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Review

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27 pages, 9477 KiB  
Review
A Review on Communication Standards and Charging Topologies of V2G and V2H Operation Strategies
by Seyfettin Vadi, Ramazan Bayindir, Alperen Mustafa Colak and Eklas Hossain
Energies 2019, 12(19), 3748; https://doi.org/10.3390/en12193748 - 30 Sep 2019
Cited by 50 | Viewed by 7808
Abstract
Electric vehicles are the latest form of technology developed to create an environmentally friendly transportation sector and act as an additional energy source to minimize the demand on the grid. This comprehensive research review presents the vehicle-to-grid (V2G) and the vehicle-to-home (V2H) technologies, [...] Read more.
Electric vehicles are the latest form of technology developed to create an environmentally friendly transportation sector and act as an additional energy source to minimize the demand on the grid. This comprehensive research review presents the vehicle-to-grid (V2G) and the vehicle-to-home (V2H) technologies, along with their structures, components, power electronic topologies, communication standards, socket structure, and charging methods. In addition, the charging topologies in V2G and V2H are given in detail. This study is planned as a useful guide for future studies that can be achieved in that it compares the results obtained and analyzes the studies in the literature, finding the advantages and disadvantages of charging topologies in V2G and V2H. Full article
(This article belongs to the Special Issue Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies)
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