A Privacy-preserving Disaggregation Algorithm for Non-intrusive\n Management of Flexible Energy Article Swipe
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· 2019
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.1903.03053
· OA: W2919908197
We consider a resource allocation problem involving a large number of agents\nwith individual constraints subject to privacy, and a central operator whose\nobjective is to optimizing a global, possibly non-convex, cost while satisfying\nthe agents'c onstraints. We focus on the practical case of the management of\nenergy consumption flexibilities by the operator of a microgrid. This paper\nprovides a privacy-preserving algorithm that does compute the optimal\nallocation of resources, avoiding each agent to reveal her private information\n(constraints and individual solution profile) neither to the central operator\nnor to a third party. Our method relies on an aggregation procedure: we\nmaintain a global allocation of resources, and gradually disaggregate this\nallocation to enforce the satisfaction of private contraints, by a protocol\ninvolving the generation of polyhedral cuts and secure multiparty computations\n(SMC). To obtain these cuts, we use an alternate projections method \\`a la Von\nNeumann, which is implemented locally by each agent, preserving her privacy\nneeds. Our theoretical and numerical results show that the method scales well\nas the number of agents gets large, and thus can be used to solve the\nallocation problem in high dimension, while addressing privacy issues.\n
