Distributed optimization over networks and its application to multi-building energy management


Dr. Maria Prandini, Associate Professor at Politecnico di Milano

02-05.2017, 11:00, room 4981


Motivated by a multi-building energy management problem, we describe a proximal minimization based algorithm for distributed convex optimization over time-varying multi-agent networks, in the presence of constraints and uncertainty. We first focus on the deterministic case, develop an iterative algorithm and show that agents reach consensus, and in particular, that they convergence to some optimizer of the centralized problem. Our approach is then extended to the case where the agents’ constraint sets are affected by a possibly common uncertainty vector. To tackle this problem we follow a scenario-based methodology and provide probabilistic guarantees regarding the feasibility properties of the resulting solution, thus extending the scenario approach to a distributed set-up. We then illustrate how this data-driven distributed design methodology can be applied to the problem of energy management in building networks affected by stochastic uncertainty. We focus on building cooling 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.