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

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' constraints. 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 à 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.

Flexibility Disaggregation under Forecast Conditions

“…They can be differentiated according to the time horizon, for which a flexibility request is scheduled. While most disaggregate a profile comprised of multiple time slots [4,23,33,37,42,44], few papers schedule a single time slot [34,35,40], which is similar to our approach.…”

“…A shortcoming of their disaggregation techniques based on linear optimization is the exposure of all flexible units to the aggregator, which raises scaling and privacy concerns. Authors of [23] propose a disaggregation scheme that is based on an alternating projection method. This privacy-preserving technique, however, is not designed to provide optimal local solutions for prosumers, but just feasible ones.…”

Optimal Incentives for Electric Vehicles at e-Park & Ride Hub with Renewable Energy Source

“…Here, instead of solving this optimal scheduling problem with the per-EV profiles-which is a topic in itself, see e.g., ref. [18], an aggregate version of this problem is tackled. It consists in considering an optimization problem in which the variable is the aggregated charging profile, i.e., the sum of the individual ones.…”

A decentralized algorithm for a mean field control problem of piecewise deterministic Markov processes

“…The problem consists in optimizing the strategies of a large number of agents, in a random environment, while the decisions of all the agents interact via the objective function because the latter depends on an "aggregative" term as the sum of the decisions of all the agents. This type of problem occurs naturally in several fields of application such as energy management [19], telecommunications [23], portfolio management [21], robot coordination [6] among others.…”