text-only page produced automatically by Usablenet Assistive Skip all navigation and go to page content Skip top navigation and go to directorate navigation Skip top navigation and go to page navigation
National Science Foundation
Awards
design element
Search Awards
Recent Awards
Presidential and Honorary Awards
About Awards
Grant Policy Manual
Grant General Conditions
Cooperative Agreement Conditions
Special Conditions
Federal Demonstration Partnership
Policy Office Website



Award Abstract #1331426

CyberSEES: Type 1: Dynamic Robust Optimization for Emerging Energy Systems

NSF Org: CCF
Division of Computing and Communication Foundations
divider line
Initial Amendment Date: August 7, 2013
divider line
Latest Amendment Date: August 7, 2013
divider line
Award Number: 1331426
divider line
Award Instrument: Standard Grant
divider line
Program Manager: Richard Brown
CCF Division of Computing and Communication Foundations
CSE Direct For Computer & Info Scie & Enginr
divider line
Start Date: August 1, 2013
divider line
End Date: July 31, 2017 (Estimated)
divider line
Awarded Amount to Date: $300,000.00
divider line
Investigator(s): Shabbir Ahmed sahmed@isye.gatech.edu (Principal Investigator)
Santiago Grijalva (Co-Principal Investigator)
Xu Sun (Co-Principal Investigator)
divider line
Sponsor: Georgia Tech Research Corporation
Office of Sponsored Programs
Atlanta, GA 30332-0420 (404)894-4819
divider line
NSF Program(s): CyberSEES
divider line
Program Reference Code(s):
divider line
Program Element Code(s): 8211

ABSTRACT

This project develops and analyzes dynamic robust optimization models and solution methods for electric power systems operation under uncertainty. In particular, this project will develop multi-stage robust optimization models and algorithms for the unit commitment problem, a critical decision-making tool for daily scheduling of power systems. In a multi-stage robust optimization model, both the generation commitment and power dispatch decisions are dynamically adaptive to sequential observation of uncertainty, as opposed to existing models where the decisions are either static or noncausal. Such models involve a large number of mixed integer variables and decision policies in infinite dimensional space. Finding an exact solution for these models is computationally intractable. The challenge is to develop theory and algorithms for large-scale problems along with performance guarantees. Our approach is to develop and apply finitely adaptive decision policies in properly designed multi-stage uncertainty models, and develop efficient decomposition methods for their resolution.

As an effort toward building a sustainable national energy system, electric power systems in the US are experiencing fundamental changes, with large-scale integration of renewable energy resources and the deployment of demand response technologies. As a result, both the energy supply and demand sides have significantly increased uncertainty. Reliable operation of power systems under these uncertainties is critical. The results of this project, if successful, will significantly advance the state of the art in power systems operations under uncertainty, and will also significantly advance the techniques in solving general multi-stage robust optimization problems. Substantial effort will be committed to educating next generation academics and researchers who are proficient in advanced analytics and devoted to improve the national energy sustainability.


PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

Note:  When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).

Some links on this page may take you to non-federal websites. Their policies may differ from this site.


H. Chen, X. Sun, and S. Deng. "Demand Response Portfolio Management via Robust Optimization," IEEE Transactions on Smart Grid, 2013.

 

Please report errors in award information by writing to: awardsearch@nsf.gov.

 

 

Print this page
Back to Top of page
  FUNDING   AWARDS   DISCOVERIES   NEWS   PUBLICATIONS   STATISTICS   ABOUT NSF   FASTLANE  
Research.gov  |  USA.gov  |  National Science Board  |  Recovery Act  |  Budget and Performance  |  Annual Financial Report
Web Policies and Important Links  |  Privacy  |  FOIA  |  NO FEAR Act  |  Inspector General  |  Webmaster Contact  |  Site Map
National Science Foundation Logo
The National Science Foundation, 4201 Wilson Boulevard, Arlington, Virginia 22230, USA
Tel: (703) 292-5111, FIRS: (800) 877-8339 | TDD: (800) 281-8749
  Text Only Version