Call for Papers
SGGE 2025 is the premier forum for the presentation of new advances and research results in the fields of theoretical, experimental, and applied Smart Grid and Green Energy. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. Topics of interest for submission include, but are not limited to:
Energy management systems (with application to building and home automation)
Communication and control in energy systems
Intelligent monitoring and outage management
Smart sensors and advanced metering infrastructure
Wide-area protection
Microgrids
Renewable energy
Energy storage and distributed energy resources
Plug-in vehicles and low-carbon transportation alternatives
Smart Grid Architectures
National/Global Energy Interconnection
Smart Grid Strategy, Planning, and Standards
Smart Grid Architectures and Models
Smart Grid Security and Reliability Management
Smart Grid Demand Response and Market Operations
Integration of Distributed Resources
Large-Scale Energy Storage
Energy Transfer and Interactive Technology
Smart Grid Cyber & Physical Security
Smart Grid Networking and Communications
Large Power Grid Safety Control
Communication-Based Control and Protection
Smart Grid Technologies and Equipment
Smart Grid Implementation and Field trials
Smart Grid Load and Energy Management
Smart Grid Testing and Assessing Technologies
Smart Sensors and Advanced Metering Infrastructure
Urban PV Applications and Microgrids
Buildings, Communities, Districts and Cities
Clean and Renewable Energy
Photovoltaic Systems and Solar Energy Engineering
Wind Energy Generation
Renewable Energy Grid
Renewable Energy Utilizations
Electric Vehicles and Vehicle-to-Grid (V2G)
Energy-Efficient Technologies
New Energy Materials and Devices
Power and Energy Engineering
Electricity Market and Power System Economics
Power Generation, Transmission and Distribution
Energy Storage and Distributed Energy Resources
Intelligent Monitoring and Outage Management
Fault Monitoring and Predictive Maintenance