AI-Driven Vehicle-to-Grid (V2G) in Smart Grids: A Theoretical Study Article Swipe
With the rapid development of renewable energy such as solar and wind power, the matching of supply and demand in the power system faces greater challenges. Electric vehicles (EVs) provide distributed energy storage services such as peak and valley regulation and frequency regulation to the power grid through a two-way vehicle-to-grid (V2G) system, which improves the flexibility and stability of the power grid. This paper proposes a unified framework based on artificial intelligence (AI) that integrates load forecasting, battery health-aware reinforcement learning scheduling, dynamic pricing, and multi-agent collaborative control, aiming to achieve efficient V2G operation in large-scale smart grids. By reviewing relevant literature, this paper analyzes the potential of the framework in improving grid stability, reducing operating costs, promoting the use of renewable energy, and extending battery life, and explores key challenges such as battery degradation, network security, system interoperability, and regulatory complexity. The study points out that the current model is mainly based on theory and simulation, lacking the support of large-scale empirical data. In the future, it is necessary to combine actual operation data and pilot projects to improve battery aging modeling and user behavior differentiation analysis to promote the practical application and optimization of the framework.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.54254/2755-2721/2026.ka26232
- https://www.ewadirect.com/proceedings/ace/article/view/26232/pdf
- OA Status
- hybrid
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413760902
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413760902Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.54254/2755-2721/2026.ka26232Digital Object Identifier
- Title
-
AI-Driven Vehicle-to-Grid (V2G) in Smart Grids: A Theoretical StudyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-26Full publication date if available
- Authors
-
Q. ShenList of authors in order
- Landing page
-
https://doi.org/10.54254/2755-2721/2026.ka26232Publisher landing page
- PDF URL
-
https://www.ewadirect.com/proceedings/ace/article/view/26232/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://www.ewadirect.com/proceedings/ace/article/view/26232/pdfDirect OA link when available
- Concepts
-
Vehicle-to-grid, Smart grid, Computer science, Grid, Electrical engineering, Engineering, Electric vehicle, Mathematics, Physics, Geometry, Quantum mechanics, Power (physics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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