Tehran Institute for Advanced Studies (TeIAS)

/ Economizing the Uneconomic: Markets for Sustainable, Reliable, and Price Efficient Electricity __ Mohammad Rasouli


Economizing the Uneconomic: Markets for Sustainable, Reliable, and Price Efficient Electricity

June 20, 2018


Khatam University, Building No2.
Address: Mollasadra Blvd., North Shirazi St., East Daneshvar St., No.17. See location on Google map


Dr. Mohammad Rasouli

Postdoctoral Scholar at Stanford University Management Science and Engineering Department


The electricity policy targets aim to provide sustainable and reliable electricity with efficient prices under uncertain demand. Any solution addressing a subset of the policy targets can affect the others. Current electricity markets do not achieve the above-stated targets efficiently, thus, there are debates on the use of carbon markets, capacity markets, and non-market mechanisms such as offer-caps, price-caps and market-monitoring. We focus on implementing all of the above policy targets by developing a framework for designing efficient auctions with constraints. We provide clear answers to the above debates.  This multidisciplinary research is advised by Prof. Teneketzis (Ph.D. advisor) from Michigan EECS, Prof. Paul Milgrom from Stanford Economics, Prof. William Hogan from Harvard Kennedy School, and Prof. Asuman Ozdaglar from MIT EECS. It is also supported by NSF Grant on Foundations of Resilient Cyber-Physical Systems sponsored at UC Berkeley.


Mohammad Rasouli is a Postdoctoral Scholar at Stanford University Management Science and Engineering Department where he collaborates with Stanford Sustainable Systems Lab (S3L) for designing business model for electricity cloud storage. He received his Ph.D. in Electrical Engineering: Systems in 2018 from the University of Michigan where he also received a M.Sc. in Economics. During Ph.D. he interned at Microsoft. He received B.Sc. and M.Sc. in Electrical Engineering: Communications both from Sharif University of Technology. His research interests include stochastic control, mechanism design, and data analytics with applications in power networks.