Decentralised Scheduling of Power Consumption in Micro-grids: Optimisation and Security

Weldehawaryat, Goitom Kahsay; Ambassa, Pacome L.; Marufu, Anesu M. C.; Wolthusen, Stephen D.; Kayem, Anne V. D. M.

doi:10.1007/978-3-319-61437-3_5

Cited by 2 publications

(1 citation statement)

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“…Some of these techniques are beginning to also be deployed in smartgrid optimization problems: [24] demonstrates the hurdle which compromised nodes create for scheduling, much like [25] does for generalized consensus problems.…”

Section: Background Literaturementioning

confidence: 99%

Cybersecurity in Distributed and Fully-Decentralized Optimization: Distortions, Noise Injection, and ADMM

Munsing,

Moura

2018

Preprint

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As problems in machine learning, smartgrid dispatch, and IoT coordination problems have grown, distributed and fully-decentralized optimization models have gained attention for providing computational scalability to optimization tools. However, in applications where consumer devices are trusted to serve as distributed computing nodes, compromised devices can expose the optimization algorithm to cybersecurity threats which have not been examined in previous literature. This paper examines potential attack vectors for generalized distributed optimization problems, with a focus on the Alternating Direction Method of Multipliers (ADMM), a popular tool for convex optimization. Methods for detecting and mitigating attacks in ADMM problems are described, and simulations demonstrate the efficacy of the proposed models. The weaknesses of fully-decentralized optimization schemes, in which nodes communicate directly with neighbors, is demonstrated, and a number of potential architectures for providing security to these networks is discussed.

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Section: Background Literaturementioning

confidence: 99%