Distributed Optimization Over Networks With Application to Energy Systems

Abstract

The well-functioning of our modern society rests on the reliable and uninterrupted operation of large scale complex infrastructures, which are more and more exhibiting a network structure with a high number of interacting components/agents. Energy and transportation systems, communication and social networks are a few, yet prominent, examples of such large scale multi-agent networked systems. Depending on the specific case, agents may act cooperatively to optimize the overall system performance or compete for shared resources. Based on the underlying communication architecture, and the presence or not of a central regulation authority, either decentralized or distributed decision making paradigms are adopted. In this seminar, we address the interacting and distributed nature of cooperative multi-agent systems arising in the energy application domain. More specifically, we present our recent results on the development of a unifying distributed optimization framework to cope with the main complexity features that are prominent in such systems, i.e.: heterogeneity, as we allow the agents to have different objectives and physical/technological constraints; privacy, as we do not require agents to disclose their local information; uncertainty, as we take into account uncertainty affecting the agents locally and/or globally; and combinatorial complexity, as we address the case of discrete decision variables. (This is a joint work with Alessandro Falsone, Simone Garatti, and Kostas Margellos.)