Predictive resource allocation for flexible loads with local QoS

TL;DR

This paper develops a distributed algorithm for resource allocation of flexible loads in power grids, accounting for time-varying conditions and QoS constraints, enhancing grid management efficiency.

Contribution

It introduces a novel algorithm that handles the complexities of time-varying convex optimization with QoS metrics for flexible loads.

Findings

Algorithm effectively manages dynamic load resources.

Improves privacy-preserving distributed optimization.

Addresses realistic time-varying grid conditions.

Abstract

Loads that can vary their power consumption without violating their Quality of service (QoS), that is flexible loads, are an invaluable resource for grid operators. Utilizing flexible loads as a resource requires the grid operator to incorporate them into a resource allocation problem. Since flexible loads are often consumers, for concerns of privacy it is desirable for this problem to have a distributed implementation. Technically, this distributed implementation manifests itself as a time varying convex optimization problem constrained by the QoS of each load. In the literature, a time invariant form of this problem without all of the necessary QoS metrics for the flexible loads is often considered. Moving to a more realistic setup introduces additional technical challenges, due to the problems' time-varying nature. In this work, we develop an algorithm to account for the challenges introduced when considering a time varying setup with appropriate QoS metrics.