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Vehicle to Grid—Energy Conversion and Conservation

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 14 June 2024 | Viewed by 963

Special Issue Editors


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Guest Editor
Department of Electrical Engineering, The University of Lahore, 1-km Defence Road, Lahore 54000, Pakistan
Interests: power system optimization; control systems; power electronics; smart grid; V2G
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Net Zero Industry Innovation Centre, Campus Masterplan, Teesside University, Middlesbrough TS1 3BA, UK
Interests: intelligent energy systems; application of advanced methods in energy management; power electronics in power system applications; smart energy systems and renewable energy optimization; demand response and energy flexibility; electric vehicle (EV) charging infrastructure; micro-grid stability and analysis; power electronics device development

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Guest Editor
Department of Electrical and Computer Engineering, New York University, New York, NY 10012, USA
Interests: DERs and EV integration with power systems; grid resilience enhancement; power system planning and operation

Special Issue Information

Dear Colleagues,

The Guest Editors are inviting submissions to a Special Issue of Sustainability in the subject area of “Vehicle to Grid—Energy Conversion and Conservation”.

Electric and plug-in hybrid vehicles are becoming increasingly popular as a result of high oil prices and public interest in protecting the environment. This inspires the researchers to develop the so-called V2G concept, an intellectual framework for connecting electric vehicles to the grid. One wants enhancements to the electric grid, including improved reliability and more stable frequency, but would rather not build any new power plants or transmission lines.

Battery-powered vehicles are viewed as distributed energy resources in V2G technology, capable of serving as both supply and demand resources. This is accomplished using a charger that becomes an energy conversion device that ties an HV battery into the grid. Bidirectional DC-DC converters and bidirectional AC-DC converters are involved in energy conversion from grid to vehicle or vehicle to grid, causing a number of issues. The heart of V2G/Vehicle-Grid-Integration (VGI) technology is this intelligent, bidirectional flow of energy. V2G technology also involves the concept of an on-board/off-board V2G integrator.

This Special Issue focuses on emerging trends, potential barriers, possible research directions, and challenges affecting V2G technology involving on-board/off-board V2G integrators, multi-objective optimal scheduling strategy, bidirectional DC-DC and  AC-DC converters and their control, V2G economic prospects, V2G penetration on a distribution system, frequency regulation control, energy storage sizing, V2G communication, modeling of V2G components, integration of renewables such as wind/solar using V2G as flexible storage, power factors, power loss, and reactive power issues, smart battery management scheme, cost–benefit analysis, etc.

The submission of multimedia with each article, if possible, is highly appreciated as it significantly enhances the visibility, downloads, and citations of articles.

Prof. Dr. Ghulam Abbas
Dr. Irfan Ahmad Khan
Dr. Mousa Marzband
Dr. Muhammad Faisal Nadeem Khan
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. Sustainability 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 2400 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

  • V2G integration
  • V2G-based PEV charging and discharging
  • Frequency regulation control
  • on-board/off-board V2G integrators
  • bidirectional DC-DC and AC-DC converters
  • control of bidirectional converters
  • communication network structures for V2G systems
  • reactive power compensation
  • aggregator of PEVs
  • smart battery management scheme
  • cost–benefit analysis

Published Papers (1 paper)

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Research

18 pages, 1668 KiB  
Article
Deep-Reinforcement-Learning-Based Vehicle-to-Grid Operation Strategies for Managing Solar Power Generation Forecast Errors
by Moon-Jong Jang and Eunsung Oh
Sustainability 2024, 16(9), 3851; https://doi.org/10.3390/su16093851 - 3 May 2024
Viewed by 599
Abstract
This study proposes a deep-reinforcement-learning (DRL)-based vehicle-to-grid (V2G) operation strategy that focuses on the dynamic integration of charging station (CS) status to refine solar power generation (SPG) forecasts. To address the variability in solar energy and CS status, this study proposes a novel [...] Read more.
This study proposes a deep-reinforcement-learning (DRL)-based vehicle-to-grid (V2G) operation strategy that focuses on the dynamic integration of charging station (CS) status to refine solar power generation (SPG) forecasts. To address the variability in solar energy and CS status, this study proposes a novel approach by formulating the V2G operation as a Markov decision process and leveraging DRL to adaptively manage SPG forecast errors. Utilizing real-world data from the Korea Southern Power Corporation, the effectiveness of this strategy in enhancing SPG forecasts is proven using the PyTorch framework. The results demonstrate a significant reduction in the mean squared error by 40% to 56% compared to scenarios without V2G. Our investigation into the effects of blocking probability thresholds and discount factors revealed insights into the optimal V2G system performance, suggesting a balance between immediate operational needs and long-term strategic objectives. The findings highlight the possibility of using DRL-based strategies to achieve more reliable and efficient renewable energy integration in power grids, marking a significant step forward in smart grid optimization. Full article
(This article belongs to the Special Issue Vehicle to Grid—Energy Conversion and Conservation)
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