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Advanced Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies
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Advanced 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: 30 August 2026 | Viewed by 1601

Special Issue Editor

Dr. Mahmoud Ghofrani

E-Mail Website
Guest Editor
School of Science, Technology, Engineering and Mathematics (STEM), University of Washington, Bothell, WA, USA
Interests: power systems operation and planning; renewable energy systems; smart grids; electric vehicles; electricity market
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue entitled “Advanced Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies” to be published in Energies. The rapid growth in the integration of Electric Vehicles (EVs) and the increasing prevalence of renewable energy sources present new opportunities and challenges in power grid management. As the demand for sustainable energy solutions continues to grow, the interplay between electric vehicles and the grid has become a critical area of research.

This Special Issue invites original and innovative research papers and review articles that address advancements in G2V and V2G technologies, their applications, and future directions. Topics of interest for this special issue include, but are not limited to, the following:

  • Advanced grid-to-vehicle (G2V) communication and control techniques;
  • Vehicle-to-grid (V2G) system architectures, algorithms, and methodologies;
  • Smart charging and discharging strategies for EVs;
  • V2G and G2V integration with renewable energy sources (e.g., solar, wind);
  • Grid stability and balancing using V2G and G2V systems;
  • Economic and business models for G2V and V2G systems;
  • Impacts of V2G/G2V on grid operations and energy management;
  • Cybersecurity and privacy concerns in V2G/G2V systems;
  • Multi-agent systems and machine learning for V2G/G2V optimization;
  • Policy and regulatory frameworks for V2G and G2V technologies;
  • Data analytics for V2G and G2V integration;
  • Case studies and real-world implementations of V2G and G2V.

We believe that by sharing knowledge and insights, we can enhance the understanding of the challenges and opportunities associated with the integration of advanced Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies into power grids. Your contribution to this Special Issue will be crucial in advancing the field and shaping the future of smart grid systems and energy management.

Dr. Mahmoud Ghofrani
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 250 words) can be sent to the Editorial Office for assessment.

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
  • grid-to-vehicle
  • smart charging and discharging
  • renewable energy sources
  • cybersecurity
  • privacy
  • machine learning
  • multi-agent systems
  • data analytics

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

37 pages, 4109 KB  
Article
Bi-Level Collaborative Optimization of Dynamic Wireless Charging Systems Considering Traffic Flow Distribution
by Jiacheng Qi, Wei Zhang and Dong Han
Energies 2026, 19(6), 1396; https://doi.org/10.3390/en19061396 - 10 Mar 2026
Viewed by 335
Abstract
To address the challenges of facility–demand mismatch, aggravated congestion, and imbalanced benefit distribution caused by the interdependence between dynamic wireless charging systems (DWCS) and transportation networks, this study proposes an optimization scheme that coordinates DWCS planning, travel flow guidance for electric vehicle (EV) [...] Read more.
To address the challenges of facility–demand mismatch, aggravated congestion, and imbalanced benefit distribution caused by the interdependence between dynamic wireless charging systems (DWCS) and transportation networks, this study proposes an optimization scheme that coordinates DWCS planning, travel flow guidance for electric vehicle (EV) owners, and transportation network operations. We develop a bi-level dynamic collaborative optimization model. The upper-level model aims to maximize the annual net profit of DWCS operators and determines DWCS planning by optimizing the traffic flow distribution. The lower-level model, based on the user equilibrium principle, guides EV route choices via a traffic flow guidance mechanism to mitigate peak-hour congestion and minimize vehicle owners’ travel costs. We validate the model using a test network comprising 9 nodes and 13 links. Results indicate that, compared with a full-coverage planning scenario, the proposed bi-level optimization scheme significantly reduces operational losses by accounting for owners’ optimal travel flow distribution. Introducing a traffic flow guidance mechanism further improves traffic flow distribution, enhances operator revenue, and effectively reduces owners’ travel time costs. Sensitivity analysis reveals that increased battery capacity decreases construction and maintenance costs, thereby improving annual net profit, while lower energy consumption reduces charging demand and weakens dependence on charging infrastructure. These factors are interrelated; specifically, lower energy consumption implies reduced battery capacity requirements for the same driving range. Additionally, the effectiveness of the traffic flow guidance mechanism becomes more pronounced as traffic flow increases. Overall, the proposed framework integrates DWCS planning and traffic flow guidance to achieve a win–win outcome for both operators and owners. These findings demonstrate the practicality and economic feasibility of interactive optimization between DWCS and transportation networks. Full article
(This article belongs to the Special Issue Advanced Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies)
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33 pages, 6670 KB  
Article
Two-Stage Energy Dispatch for Microgrids Based on CVaR-Dynamic Cooperative Game Theory Considering EV Dispatch Potential and Travel Risks
by Jianjun Ma, Wei Dong, Baiqiang Shen and Jingchen Zhang
Energies 2025, 18(23), 6105; https://doi.org/10.3390/en18236105 - 21 Nov 2025
Cited by 2 | Viewed by 748
Abstract
With the rapid development of microgrids (MGs) and electric vehicles (EVs), leveraging the flexibility of EVs in MG optimization scheduling has attracted significant attention. However, existing research does not consider the impact of EV scheduling potential on MG uncertainty or the avoidance of [...] Read more.
With the rapid development of microgrids (MGs) and electric vehicles (EVs), leveraging the flexibility of EVs in MG optimization scheduling has attracted significant attention. However, existing research does not consider the impact of EV scheduling potential on MG uncertainty or the avoidance of conflicts in EV users’ mobility needs and their charging/discharging activities. Therefore, this paper proposes a two-stage microgrid energy scheduling model integrated with the conditional value-at-risk (CVaR) and dynamic cooperative game theory. In addition, the aforementioned issues are specifically addressed by considering both EV scheduling potential and travel risk. The day-ahead model minimizes the MG’s operational costs, where a CVaR-based uncertainty model for MG net load is established to quantify risks from both renewable energy generation and load. The EV dispatchable potential is calculated using Minkowski summation theory. In the real-time stage, the adjustment of participating EVs and optimal incentive compensation costs are determined through the proposed EV travel risk model and dynamic cooperative game, aiming to minimizing the MG’s real-time adjustment costs. The simulation results validate the effectiveness of the proposed method, which can help to reduce the operational costs of MGs by 4%, reduce real-time adjustment costs by about 85%, and decrease load variability by 3%. For the main grid, the proposed method can avoid the “peak-on-peak” phenomenon. For EV users, travel demands can be fully satisfied, charging costs can be reduced for 34% of users, and 2.4% of users gain profits. Full article
(This article belongs to the Special Issue Advanced Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Technologies)
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