Mehrdad Pirnia

Mehrdad Pirnia 

Mehrdad Pirnia, Ph.D.
Graduate Attributes Lecturer, Department of Management Sciences
Applied Operation Research

Contact Info:
Email: mpirnia [at] uwaterloo [dot] ca
Tel: +1 (519) 888-4567, ext 38956
CPH 3671
Department of Management Sciences
University of Waterloo
Waterloo, Ontario, Canada
N2L 3G1


Dr. Mehrdad Pirnia received his Ph.D. degree from University of Waterloo in 2014 in Electrical and Computer Engineering (Power Systems Optimization) under the supervision of Professor Claudio A. CaƱizares and Professor Kankar Bhattacharya. He has also obtained his MASc degree in Management Sciences (Optimization Specialty), under the supervision of Professor J. David Fuller. The main focus of his research during his Ph.D. program was on stochastic modeling and analysis of power systems with intermittent energy sources. Also, during his MASc program he worked on the applications of optimization in electricity markets, focusing on electric generation capacity expansion planning and electricity pricing.

He did an internship in Federal Energy Regulatory Commission (FERC), working with Dr. Richard O'Neill and helped in developing linearization techniques to solve AC-OPF problems. After graduation he worked in California ISO where he oversaw California's day-ahead and real-time electricity market operations. Later, he worked in Alstom Grid, (now GE) where he helped in developing and enhancing optimization engines for several electricity markets. He is the recipient of the Exceptional Teaching Award.

Currently, he is a faculty member at the University of Waterloo, Department of Management Sciences.

Research Interests

His research interests lie at the intersection of operations research, economics and power systems engineering. He employs data-driven models and proposes solution methodologies and tools to build economically efficient and environmentally friendly energy systems. In summary, through his research he investigates on:

  • Solution methodologies and heuristics to solve hard, large scale, non-convex, nonlinear and integer optimization problems in power systems

  • Stochastic formulation of Short-term and long-term models to optimize the power system variables, such as generation, reserve and control variables in the presence of supply and demand uncertainties, while maximizing affordability, reliability and resiliency of the system

  • Data-driven techniques to forecast future trends and prescribe efficient policy mechanisms to promote clean and affordable energy

  • Non-convex pricing in electricity markets