  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.
