M. Hashem Nehrir, Ph.D., IEEE Life Fellow
Professor, Electrical and Computer Engineering Department
Montana State University-Bozeman
Dr. Nehrir received his BS, MS, and Ph.D. all in electrical engineering from Oregon State University in 1969, 1971, and 1978, respectively. He started his educational career in 1971 and joined The Montana State University (MSU) Electrical Engineering faculty in 1987. He has taught a variety of courses on electric power systems, alternative energy power generation, electric machinery, electric circuits, and control. Dr. Nehrir's active research include modeling, control, and energy management of alternative energy distributed generation (DG) sources and microgrids with multiple alternative energy and conventional DG sources, and smart grid functions including demand response and application of intelligent control and multiagent systems to power systems. His research has been supported by the following sources: The National Science Foundation, NSF-EPSCoR, USDOE, DOE-EPSCoR, Pacific Northwest National Laboratory, Electric Power Research Institute, The Montana Power Company (now NorthWestern Energy), Montana Electric Power Cooperatives, and Montana Electric Power Affiliates Program (MEPRA).
Dr. Nehrir is the author of three textbooks and numerous journal and conference publications, all listed in his vitae. His research on fuel cell modeling and control, during 1999-2009, resulted in dynamic models for PEM and solid-oxide fuel cells, suitable for distributed generation application studies. These models are being used around the world. For more information about the models and download for research or educational purposes, go to Fuel Cell Models.
He has lectured on his research activities around the globe, including Australia, Canada, China, Germany, India, Iran, Japan, Poland, and USA. He is an editor of IEEE Transactions on Sustainable Energy, recipient of MSU's Wiley Faculty Award for Meritorious Research in 2010, and a Life Fellow of IEEE for contribution to alternative energy power generation systems modeling an control.