Siebel Energy Institute launches with major Berkeley presence
BERKELEY – The Siebel Energy Institute, a global university consortium focused on smart energy, marked its debut today by announcing 24 research grants nearing $1 million. The winning proposals, many of them led by Berkeley faculty, will accelerate improved performance in modern energy systems.
The institute is a consortium of eight research institutions: Carnegie Mellon University, École Polytechnique, Massachusetts Institute of Technology, Politecnico di Torino, Princeton University, University of California, Berkeley, University of Illinois at Urbana-Champaign and University of Tokyo. The Thomas and Stacey Siebel Foundation established the institute with a $10 million grant.
“We created the Siebel Energy Institute to stimulate the best minds in engineering and computer science to work collaboratively on the science of smart energy,” said founder Thomas M. Siebel. “Our goal is to advance innovations in data analytics and machine learning to improve the safety, cybersecurity, reliability, efficiency and environmental integrity of the advanced smart grid.”
Throughout worldwide energy systems, smart-connected devices generate massive amounts of information. Statistical algorithms are necessary to integrate the data, create statistical models with predictive power and extract value from this otherwise incomprehensible stream of information.
“Leading universities are beginning to dedicate research teams to this area, but we have the opportunity to accelerate innovation,” said S. Shankar Sastry, dean of the College of Engineering at UC Berkeley and director of the new institute. “The grants we announced today are a catalyst for research that could ultimately break new ground in energy systems analytics.”
UC Berkeley faculty are lead researchers for half of the institute’s 24 inaugural research grants, including eight projects led by Berkeley Engineering professors:
GridWatch: Using Unmodified Smartphones to Monitor Power Grids – Eric Brewer (EECS) will investigate a low-cost crowd-sourced method of detecting power outages and restorations by monitoring the charging state of smartphones.
Robust Optimization for Local and Global Energy Management – Laurent El Ghaoui (EECS and IEOR), working with the Fortune 500 company EDF, will seek ways to optimize the generation and distribution of energy that reduce costs and increase efficiency.
Understanding the Impact of Electric Vehicle Charging on the Power Grid: An Urban Mobility Perspective – Scott Moura (Civil & Environmental Engineering) will study the interactions between electric vehicle charging, human mobility needs, the transportation network and power infrastructure.
High Performance Computational Methods for Maximizing Efficiency, Reliability and Resiliency of Power Systems – Shmuel Oren (IEOR) and Javad Lavaei (IEOR) aim to address computational challenges to energy efficiency by developing high-performance optimization techniques that can be applied
to a broad set of non-convex energy problems.
Algorithms for Demand Response and Renewable Energy Integration – Claire Tomlin (EECS) will explore new ways to manage electric loads on power networks that can limit the financial risks of energy utilities and customers seeking to utilize clean renewable energy sources.
Improving Reliability and Efficiency of Electrical Distribution Infrastructures (REEDING) – Stephen Mahin (Civil & Environmental Engineering, director of the Pacific Earthquake Engineering Research Center) plans to develop a model to assess the vulnerability of power networks to cascading failures.
Data-Driven Techniques for Assessing Current and Future Grid Reliability – Scott Moura (Civil & Environmental Engineering) and Laurel Dunn (Civil & Environmental Engineering Ph.D. candidate) will identify the specific technology, environmental and human factors that drive power grid reliability, aiming to forecast when, where and why outages are likely to occur in future climate and grid modernization scenarios.
Data Analytics to Assess Energy Efficiency Opportunities in Commercial Buildings – Kameshwar Poolla (EECS and Mechanical Engineering) seeks to develop advanced data analytics methods to identify commercial buildings that have poor operational energy efficiency relative to their peer group, so that they can be targeted for conservation measures.
For more information: