Skip to main content

Special Issue on “Data-driven Analytics in Power System Modeling, Analysis and Control”

Modern power systems are faced with challenges brought by the system scale expansion, uncertainty increase, power electronic equipment integration and strict operation constraints. There is a pressing need for more effective methodologies to tackle these challenges. With the development of information and communication technology (ICT), it is more convenient to access various power system operation data, e.g., recorded historical data, periodically updated system external data, real-time system state measurement data and so on. Proper utilization of these data enables the ‘smart’ management and control of power systems. In recent years, how to take advantage of data-driven methods, e.g. statistical analysis and machine learning techniques, to solve the difficulties in power system modeling, analysis and control becomes a very popular and timely research topic around the world.

This special issue aims to call for state-of-the-art research works on the application of data-driven methods in power system modeling, analysis and control. Topics of research papers include but are not limited to:

Data-driven power system modeling techniques, including renewable energy modeling, load pattern recognition and aggregating modelingData-driven power system stability assessment and auxiliary decision-making techniquesData-driven enhanced power system situation awareness and risk assessment, including state estimation and fault diagnosisData-driven cyber security analysis method with comprehensive measurement data from power and communication systemsData-driven electricity market construction and economics transactions decision-making techniquesProper and feasible approaches for data-driven and model-driven method integration in power system operation and control

Submission Guidelines

This special issue solicits any original work that is not under consideration for publication in other journals and magazines. Authors should refer to or for more information about the content and formatting of submissions.

Special issue Guest Editors-in-Chief

Prof. Yi Tang, Southeast University, Nanjing, China

Prof. Le Xie, Texas A&M University, US

Associate Prof. Siqi Bu, The Hong Kong Polytechnic University, Hong Kong, China