Senior Scientist and Lecturer (Oberingenieur)
Hamburg University of Technology (TUHH)
Institute of Electrical Power and Energy Technology (ieet)
Harburger Schloßstraße 22a,
21079 Hamburg E-mail: payam.baboli@tuhh.de
Building Palmspeicher, Room 2.001 Phone: +49 40 42878 3013
You can find me also on LinkedIn | ieet | Google Scholar | ORCID | Scopus | WebofScience
Full-version of my CV (in English): here
Dr.-Ing. Payam Teimourzadeh Baboli (S'08-M'15-SM'20) is a senior scientist and lecturer at the Institute for Electrical Power and Energy Technologies (IEET) at Hamburg University of Technology (TUHH). Born in Babol, Iran in 1985, he obtained his B.Sc. degree in Electrical Engineering from the University of Mazandaran (UMZ), Babolsar, Iran in 2007. He continued his academic journey, earning both his M.Sc. and Ph.D. degrees in Electrical Engineering with a specialization in power systems from Tarbiat Modares University (TMU), Tehran, Iran in 2009 and 2014, respectively.
Dr. Baboli served as an assistant professor in electrical engineering at UMZ from February 2015 to July 2019. From August 2019 to December 2023, he held the position of Project Manager and Senior Researcher at the OFFIS - Institute for Information Technology, Oldenburg, Germany. In January 2024, he joined the Hamburg University of Technology as a senior scientist and lecturer, contributing to the Institute for Electrical Power and Energy Technologies.
Renewable Energy Sources
Developing novel approaches for the integration of renewable energy sources in smart grids, including energy storage technologies and microgrids
Studying the impact of weather and climate variability on the performance of renewable energy sources in smart grids
Power System Reliability and Resilience
Developing novel approaches for the modeling and analysis of power system reliability and resilience, including the use of advanced simulation techniques and risk-based approaches
Studying the impact of extreme weather events, cyber-attacks, and other disruptions on power system reliability and resilience
Data Science and Automation
Developing advanced machine learning and artificial intelligence algorithms for the control and automation of smart grids
Investigating the use of big data and cloud computing in the optimization of smart grid operations
Energy Management and Demand Response (DR)
Developing advanced optimization algorithms for energy management and demand response programs in smart grids
Investigating the use of blockchain and other distributed ledger technologies for the implementation of DR and energy efficiency measures in smart grids
Multi-Objective Optimization and Decision Making
Developing advanced multi-objective optimization algorithms for the design and operation of smart grids
Investigating the game theory and other decision-making frameworks for analyzing and designing complex energy systems.
