Dan Lu

Associate Professor of Renewable Energy Engineering
Renewable Energy Engineering
Inamori School of Engineering
Engineering Laboratories
Tue: 10:00 am - 12:00 pm
Thu: 10:00 am - 12:00 pm
Google Scholar Profile


  • PhD: Electrical Engineering, Illinois Institute of Technology, 2017
  • MS: Electrical Engineering, Illinois Institute of Technology, 2013
  • BS: Electrical Engineering, North China Electric Power University, 2007


  • Over 4 years of experience as a practicing Electrical Engineer and 11 years of experience in power system research.
  • 5 years of experience as an Assistant Professor in Renewable Energy Engineering.
  • Specialized in statistical and machine learning techniques to perform historical energy data analysis based on power system background knowledge. Extracting features and reapplying the features to solve power system problems, such as electrical load forecasting and robust Security-Constrained Unit Commitment (SCUC).
  • Specialized in optimal power flow analysis, including load flow dynamic and stability analysis. Have a strong interest in control theory, transient and dynamic simulation.
  • Specialized in applying optimization algorithms to solve secure and economical operation problems in power systems, congestion analysis, generation/transmission expansion planning, and renewable energy integration analysis.

Courses Taught

  • ENGR 113 Explorations in Renewable Energy Engineering
  • RNEW 322 Signals and Systems
  • MECH 422/522 Control Systems
  • RNEW 432 Solar Energy Systems
  • ENGR 306 Engineering Economics
  • ENGR 490 Senior Designs
  • RNEW 450 Independent Study

Research, Publications, & Presentations

Research & Publications

  • Detecting attacks of power system parameters to avoid further erroneous detection of operators based on the optimal power flow analysis, including dynamic and stability analysis.
  • Forecasting short-term load to serve the operations and planning of the day-ahead market.
  • Realistic load sampling to locate robust SCUC by load profiles instead of unrealistic load simulations to cover a big interval of possible load.
  • Long-term planning for the battery energy storage transportation by trains to solve transmission line outages problems in severe weather conditions, congestions at extremely high peak load periods, and the transmission of large capacity of renewable energy (e.g. suburb wind farm)

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