Research Topic "Applications of Advanced Control and Artificial Intelligence in Smart Grids"

Qiuye Sun, Jianfeng Xiao, Yonghao Gui, Dazhong Ma, Lei Xi

Publikation: Bidrag til tidsskriftLederpeer review

Abstract

The increasing pressure from energy and environment protection has made the research for advanced energy infrastructures urgent, such as microgrids, intelligent transportation networks, and so on. To this end, smart grids (SGs), focusing on the deep integration of advanced multiple renewable sources and information technologies, is regarded as the popular form of resilient energy utilization for future development. Based on intelligent perception and cyber communication, SG realizes the deep integration and real-time interaction of calculation, communication, control, and artificial intelligence. Furthermore, the SG will generate and utilize abundant data to achieve optimal energy distribution. Many challenges require further research and development on the modeling, planning, control, and optimization of the distributed information and energy systems, which may include interdependent infrastructures such as electricity, transportation, communication networks.

This Research Topic will bring together researchers from diverse fields and specializations, such as communications engineering, computer science, electrical and electronics engineering, transportation engineering, mathematics, and specialists in the areas related to SG technologies. Topics of interest include, but are not limited to the following:
• Standard modeling of smart grids with numerous distributed generators;
• Applications of advanced control and modern AI techniques in the optimization of SGs;
• AI for residential solar and wind control;
• Advanced control and modern AI-based approaches for forecasting of renewable energy sources, loads, and electricity prices;
• Advanced control and modern AI methods for state evaluation and fault detection of SG equipment;
• Applications of advanced control and modern AI techniques in the SG market to accommodate the increasing penetration levels of renewable energy sources;
• Applications of Machine Learning for predictive maintenance and asset management.
OriginalsprogEngelsk
TidsskriftFrontiers in Energy Research
StatusUdgivet - 2021

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