Guest Editors
Prof. Bo Yang
Email: yangbo_ac@outlook.com
Affiliation: Faculty of Electric Power Engineering, Kunming University of Science and Technology, No. 727, Jingming South Road, Chenggong District, Kunming, 650500, China
Homepage:
Research Interests: optimization and control, smart grid, renewable energy, artificial intelligence
Dr. Jianfeng Wen
Email: J.Wen7@liverpool.ac.uk
Affiliation: Department of Electrical Engineering and Electronics, University of Liverpool, Brownlow Hill, Liverpool, L69 3BX, United Kingdom
Homepage:
Research Interests: vehicle-to-grid (V2G) interaction, artificial intelligence, collaborative optimization, electric vehicles, grid scheduling
Dr. Ning Yang
Email: ning.yang@strath.ac.uk
Affiliation: Department of Electronic & Electrical Engineering, University of Strathclyde, Royal College Building, 204 George St, Glasgow, G1 1XW, United Kingdom
Homepage:
Research Interests: vehicle-to-grid (V2G) interaction, artificial intelligence, collaborative optimization, electric vehicles, grid scheduling
Dr. Kaiping Qu
Email: kpqu@ie.cuhk.edu.hk
Affiliation: Department of Information Engineering, The Chinese University of Hong Kong, Room 834, 8th Floor, Ho Sin Hang Engineering Building, Hong Kong, 999077, China
Homepage:
Research Interests: vehicle-to-grid (V2G) interaction, artificial intelligence, collaborative optimization, electric vehicles, grid scheduling
Dr. Zoey Zhou
Email: zoey.zhou@aut.ac.nz
Affiliation: School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, 31 Symonds St, Auckland, 1010, New Zealand
Homepage:
Research Interests: vehicle-to-grid (V2G) interaction, artificial intelligence, collaborative optimization, electric vehicles, grid scheduling
Summary
The global pursuit of "dual carbon" goals and the rapid advancement of renewable energy technologies have propelled the widespread adoption of new energy vehicles (NEVs), particularly electric vehicles (EVs). Concurrently, breakthroughs in artificial intelligence (AI) have unlocked unprecedented opportunities for optimizing the synergy among EVs, charging station, and power grids. Vehicle-to-grid (V2G) interaction, a cornerstone technology enabling bidirectional energy exchange between EVs and grids, is emerging as a critical enabler for grid resilience, renewable energy integration, and economic efficiency.
EV charging station is a critical component of both the power distribution network and the transportation network. For the power grid, the planning and operation of charging station must consider the safety, reliability, and economic challenges posed by large-scale EV charging loads. At the same time, the construction and operation of charging piles are constrained by grid capacity and operational reliability. For the transportation network, EV travel behavior and the siting and capacity planning of charging piles significantly alter the spatiotemporal distribution of EV traffic flows, which in turn affects the planning and operation of charging station. However, the rapid growth in the number of EVs and their large-scale operation pose significant challenges to the rational siting and capacity planning of charging piles. Moreover, the integration of large-scale EVs and charging station has a profound impact on grid stability, creating substantial pressure on grid planning and scheduling. In this context, leveraging AI technology to conduct in-depth research on EV-charging station-grid interactions, implement rational siting and capacity planning for charging piles, and ensure the safe and stable operation of grids with large-scale EV integration is of great significance for advancing NEV technology and enhancing grid security, stability, and economic efficiency.
This special section aims to explore and study the application of AI in the collaborative optimization, scheduling, and planning of EVs, charging station, and power grids, and to discuss the challenges, opportunities, and development trends in this field. We invite researchers and experts from all over the world to submit high-quality original research papers and commentary articles on potential future topics.
Potential topics aim at covering themes including, but not limited to:
1. AI-based EV charging load forecasting techniques;
2. AI-driven planning and scheduling for the deep coupling of transportation networks and power grids;
3. AI-enabled siting and capacity planning for charging station;
4. AI-based integrated scheduling of charging station and EV traffic networks;
5. Safe and stable operation of power grids with large-scale EV and charging station integration;
6. Planning and scheduling of integrated energy systems incorporating EVs;
7. Vehicle-to-grid (V2G) interaction technologies and their optimization using AI;
8. AI-based fault diagnosis and predictive maintenance for EV-charging station-grid systems;
9. Data-driven approaches for enhancing V2G efficiency and grid resilience.
Keywords
vehicle-to-grid (V2G) interaction, artificial intelligence, collaborative optimization, electric vehicles, grid scheduling
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