Energy management for building district cooling: a distributed approach to resource sharing

TL;DR

This paper presents a distributed energy management algorithm for building district cooling that optimizes shared resource use while preserving privacy, overcoming communication and computational challenges of centralized systems.

Contribution

It introduces a privacy-preserving, iterative distributed algorithm based on proximal minimization, extended theoretically for energy management problems with specific structural properties.

Findings

Algorithm effectively manages shared resources in simulations.

Works across different network topologies.

Preserves building privacy during optimization.

Abstract

This paper deals with energy management in a district where multiple buildings can communicate over a time-varying network and aim at optimizing the use of shared resources like storage systems. We focus on building cooling, and propose an iterative, distributed algorithm that accounts for information privacy, since buildings are not required to disclose information about their individual utility functions and constraint sets encoding, e.g., their consumption profiles, and overcomes the communication and computational challenges imposed by centralized management paradigms. Our approach relies on a methodology based on proximal minimization that has recently appeared in the literature. Motivated by the structure of the considered energy management optimization program, we provide a theoretical extension of this novel methodology, that is applicable to any problem that exhibits such structural properties. The efficacy of the resulting energy management algorithm is illustrated by means of a detailed simulation based study, considering different network topologies.