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ERE Seminar: Ram Rajagopal, CEE Stanford University — Data-Driven Models in Power Systems

Date and Time: 
April 16, 2018 - 12:30pm to 1:30pm
Room 104, Green Earth Sciences Building, 367 Panama Street, Stanford
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Event Sponsor: 
Energy Resources Engineering

Data-Driven Models in Power Systems 

Ram Rajagopal
Associate Professor | Civil & Environmental Engineering, Stanford University

Increase in supply-side variability due to increases in renewable generation requires integrating new resources utilizing improved models of the power system to reduce electricity delivery costs. Data from consumers and markets has become broadly available and represent an opportunity to learn better models of market dynamics, consumer behavior, and resource valuation. In this talk, we introduce two problems in statistical learning from data in power networks: learning virtual bidding dynamics and optimal storage placement in networks. In the first part of the talk, we focus on models of wholesale electricity markets developed to understand virtual bidding.  We demonstrate that data can be utilized to test market efficiency and to characterize competitive equilibrium conditions in the presence of virtual bidding utilizing more tractable models. In the second part of the talk, we utilize consumer data to infer customer profiles and quantify the benefits of distributed storage and their coordination. We then analyze an optimal placement strategy for storage that can account for capital and operations costs and is simple to implement in practice.


Ram Rajagopal is an Associate Professor of Civil and Environmental Engineering at Stanford University, where he directs the Stanford Sustainable Systems Lab (S3L), focused on large-scale monitoring, data analytics and stochastic control for infrastructure networks, in particular, power networks. His current research interests in power systems are in the integration of renewables, smart distribution systems, and demand-side data analytics. He holds a Ph.D. in Electrical Engineering and Computer Sciences and an M.A. in Statistics, both from the University of California Berkeley, Masters in Electrical and Computer Engineering from University of Texas, Austin and Bachelors in Electrical Engineering from the Federal University of Rio de Janeiro. He is a recipient of the NSF CAREER Award, Powell Foundation Fellowship, Berkeley Regents Fellowship and the Makhoul Conjecture Challenge award. He holds more than 30 patents and several best paper awards from his work and has advised or founded various companies in the fields of sensor networks, power systems, and data analytics.