Special Issue "Artificial Intelligence in Renewable Energy and Storage Systems"

Submission Deadline: 28 October 2022
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Guest Editors
Prof. Kai Wang, Qingdao University, China
Dr. Xiufeng Liu, Technical University of Denmark, Denmark
Prof. Licheng Wang, Zhejiang University of Technology, China
Dr. Yang Zhang, Strategic Research Institute, State Power Investment Corporation, China

Summary

Energy demand worldwide grows every year. Thus, there is a great interest in reducing energy consumption (both domestic and industrial) and in optimizing energy supply systems. The amount of data available from industrial systems or domestic buildings can be used to prevent faults or to optimize production in energy systems. An additional important goal is to use these data to optimize maintenance and control strategies with the goal of reducing energy consumption in industrial applications or in domestic buildings.

 

The increasing penetration of stochastic and uncertain inverter-based distributed energy resources (DERs), such as wind and solar photovoltaic (PV), has a considerable influence on power system dynamics, causing reliability and resilience concerns. This requires innovations in power system modelling, operation, and control to deal with these emerging challenges. In addition, coordinated control among different devices typically relies on communication systems. Communication-control coupled systems bring both opportunities and challenges to the future development of DER-rich power systems.

 

Artificial intelligence systems can make use of the available data to address the challenges discussed above. Accordingly, this Special Issue will focus on the artificial intelligence in renewable energy and storage systems (e.g., wind, solar, supercapacitor and fuel cells). We invite papers on innovative technical developments, case studies, and theoretical papers from different disciplines, which are relevant to renewable energy and storage systems. Original research and review articles are both welcome.

 

Potential topics include but are not limited to the following:

 

●    Energy storage technologies and systems

●    Plug-in hybrid electric vehicle (PHEV) systems, Compressed natural gas (CNG) vehicles, clean Energy

●    Power electronic converters and drives

●    Demand monitoring and energy efficient systems

●    Modelling of communication-control coupled systems

●    Frequency regulation in low inertia systems with high wind penetration

●    Grid modelling, simulation, and data management

●    Energy efficiency, conservation, and savings

●    Big data for industrial and energy systems

●    Grid protection, reliability, energy / power quality, and maintenance

●    Smart metering, measurement, instrumentation, and control

●    Renewable energy, wind, solar, fuel cells, and distributed generation within microgrids

●    Computational intelligence and optimization

●    Life cycle assessment, pricing, policies, and energy planning

●    Artificial Intelligence for industrial process optimization

●    Optimization of industrial applications and energy systems

●    Artificial intelligence for renewable energies


Keywords
distributed energy resources, renewable energy, electric vehicle, storage systems, data driven, energy management