A Privacy-preserving Disaggregation Algorithm for Non-intrusive Management of Flexible Energy

TL;DR

This paper introduces a privacy-preserving algorithm for resource allocation in energy management that ensures agents' private constraints remain confidential while achieving optimal distribution in large-scale microgrids.

Contribution

It proposes a novel aggregation and disaggregation protocol using secure multiparty computations and polyhedral cuts to preserve privacy in resource allocation.

Findings

Algorithm scales well with large numbers of agents.

Maintains privacy of individual constraints and solutions.

Achieves near-optimal resource allocation in microgrid management.

Abstract

We consider a resource allocation problem involving a large number of agents with individual constraints subject to privacy, and a central operator whose objective is to optimizing a global, possibly non-convex, cost while satisfying the agents'c onstraints. We focus on the practical case of the management of energy consumption flexibilities by the operator of a microgrid. This paper provides a privacy-preserving algorithm that does compute the optimal allocation of resources, avoiding each agent to reveal her private information (constraints and individual solution profile) neither to the central operator nor to a third party. Our method relies on an aggregation procedure: we maintain a global allocation of resources, and gradually disaggregate this allocation to enforce the satisfaction of private contraints, by a protocol involving the generation of polyhedral cuts and secure multiparty computations (SMC). To obtain these cuts, we use an alternate projections method \`a la Von Neumann, which is implemented locally by each agent, preserving her privacy needs. Our theoretical and numerical results show that the method scales well as the number of agents gets large, and thus can be used to solve the allocation problem in high dimension, while addressing privacy issues.