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Preserving Privacy of AC Optimal Power Flow Models in Multi-Party Electric Grids

The electric power grid is owned and operated by many competing providers who do not want to reveal their sensitive information to other providers. This competitive environment motivates the use of a third-party (cloud-computing) platform that combines information from each provider and jointly opti...

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Published in:	IEEE transactions on smart grid 2016-07, Vol.7 (4), p.2050-2060
Main Authors:	Wu, Dan, Lesieutre, Bernard C., Ramanathan, Parameswaran, Kakunoori, Bhuvana
Format:	Article
Language:	English
Subjects:	Alternating current Computation Computational modeling Cryptography Electric grid Electric power systems Information dissemination Load flow Mathematical model optimal power flow Optimization Platforms Power flow Privacy privacy and security Security Smart grid
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description	The electric power grid is owned and operated by many competing providers who do not want to reveal their sensitive information to other providers. This competitive environment motivates the use of a third-party (cloud-computing) platform that combines information from each provider and jointly optimizes resources in the entire grid. Since the power system structure and component values are confidential, the third-party computing platform must be either absolutely secure or information needs to be masked before sharing with the third party. In this paper, we propose an approach to obfuscate the sensitive information and solve the multi-party AC optimal power flow problem in a shared computing platform. The initial obfuscation is separately performed by each party so that sensitive information is masked from the third party and all other participants.
doi_str_mv	10.1109/TSG.2016.2544179
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subjects	Alternating current Computation Computational modeling Cryptography Electric grid Electric power systems Information dissemination Load flow Mathematical model optimal power flow Optimization Platforms Power flow Privacy privacy and security Security Smart grid
title	Preserving Privacy of AC Optimal Power Flow Models in Multi-Party Electric Grids
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