Modeling and Operation Optimization of Energy Vehicles and Smart Grids
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".
Deadline for manuscript submissions: 20 March 2025 | Viewed by 56
Special Issue Editors
Interests: machine learning; smart cities; renewable energy; smart mobility; e-mobility
Special Issues, Collections and Topics in MDPI journals
Interests: electric vehicles; optimization algorithm; smart mobility; transportation sector; e-mobility; infrastructure
Special Issues, Collections and Topics in MDPI journals
Interests: electric vehicle; vehicle-to-grid; renewable energy; solar power; wind power; electricity storage; grid; microgrid; energy systems; artificial neural networks; multi-objective optimization of energy systems; energy reliability; phase change materials; greenhouses; microclimate; green roofs
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of energy vehicles, particularly electric vehicles (EVs), with smart grids is a critical area of research in the context of sustainable energy systems. As the demand for cleaner energy solutions grows, the interaction between EVs and smart grids becomes pivotal to enhancing energy efficiency, reducing carbon footprints, and improving overall grid reliability. This Special Issue delves into advanced methodologies for modeling and optimizing the operation of energy vehicles within smart-grid environments, with particular attention to the transformative role of artificial intelligence (AI) and machine learning (ML) techniques.
The topics of interest include but are not limited to the following:
- Modeling of Energy Vehicles and Smart Grids: The accurate modeling and integration of EVs into smart grids is crucial for efficient energy distribution and grid stability. It involves dynamic models that capture EV charging/discharging behaviors, load forecasting, and energy consumption patterns. Key challenges include balancing EV energy demands with grid capacity and minimizing charging costs without disrupting grid operations.
- Operation Optimization of Energy Vehicles: EV operation within a grid can be optimized using techniques like demand response, vehicle-to-grid (V2G) interaction, and smart charging. These methods aim to improve charging schedules, reduce peak demand, and use EVs as distributed energy resources to support the grid during high-demand periods.
- Application of AI and ML in Smart-Grid Optimization: AI and ML are advancing smart-grid management through improved demand forecasting, real-time monitoring, and predictive maintenance. AI optimizes the charging infrastructure, while ML models predict charging behavior, energy demand, and grid loads. Reinforcement learning enhances decision making, enabling adaptive energy management systems.
- Energy Management and V2G Systems: V2G technology allows EVs to store and feed energy back into the grid during high-demand periods. The AI-driven optimization of V2G systems predicts the best times for energy exchange, minimizes the costs for EV owners, and improves grid stability.
Dr. Seyed Mahdi Miraftabzadeh
Dr. Michela Longo
Dr. Nicoletta Matera
Guest Editors
Manuscript Submission Information
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Keywords
- smart grid optimization
- electric vehicles
- vehicle-to-grid
- artificial intelligence
- machine learning
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