The electric power grid is a national critical infrastructure that is increasing vulnerable to malicious physical and cyber attacks. As a result, detailed data describing grid topology and components is considered highly sensitive information that can be shared only under strict non-disclosure agreements. There is also increasing need to foster cooperation among the growing number of participants in microgrid-enabled electric marketplace. However, to maintain their economic competitiveness, the market participants are not inclined to share sensitive information about their grid with other participants. Motivated by this need for increased cyber-physical security and economic confidentiality, the project is developing techniques to obfuscate sensitive design information in power system models without jeopardizing the quality of the solutions obtained from such models. Specifically, solution approaches have been developed to hide sensitive structural information in Direct Current (DC) Optimal Power Flow models. These approaches are currently being extended to Alternating Current (AC) Optimal Power Flow models. The project is also developing secure multi-party methods where the market participants collectively optimize the grid operation while only sharing encrypted private sensitive information. Finally, the project is incorporating secure market operations in jointly solving the Optimal Power Dispatch problem without revealing sensitive private information from each participant to other participants. The techniques developed in this project have the potential to broadly impact areas beyond power systems. The general principles developed in the project can be used to mask sensitive information in many problems that can be formulated as a linear or non-linear programming optimization.

Agency
National Science Foundation (NSF)
Institute
Division of Computer and Network Systems (CNS)
Type
Standard Grant (Standard)
Application #
1329452
Program Officer
Ralph Wachter
Project Start
Project End
Budget Start
2013-10-01
Budget End
2017-09-30
Support Year
Fiscal Year
2013
Total Cost
$678,376
Indirect Cost
Name
University of Wisconsin Madison
Department
Type
DUNS #
City
Madison
State
WI
Country
United States
Zip Code
53715