Shu Hung Henry Chung

Modeling and simulating communications of Multiagent Systems in Smart Grid

Multiagent Systems (MAS) are used extensively in Smart Grid research as a means of developing distributed control systems comprised of a network of communicating units. Many previous works have made implicit or explicit assumptions that lead to simplified modeling and simulation of the underlying communication systems. While these assumptions are valid under some cases, they may not hold true when system size and complexity scale up exponentially, which can be anticipated given the rapid development of smart grid technologies. In this paper, we present a more comprehensive modeling and simulation approach that accounts for the MAS related protocols as described in the FIPA (Foundation for Intelligent Physical Agents) specification; together with a co-simulation platform for the investigation of multi-disciplinary interdependencies between MAS, communication, and electrical systems. To show the benefits of comprehensive modeling and simulation, we demonstrate how different network configurations affect system performance in a Fault Location, Isolation and Service Restoration (FLISR) scenario.

Modeling and co-simulation of IEC61850-based microgrid protection

Microgrid protection schemes are different from those used in conventional distribution networks. With the incorporation of distributed generation (DG), and the ability for bi-directional power flow and island operation; the fault current and delay parameters of protective relays need to be recalculated and updated in response to the dynamic operation of microgrid. Performance evaluation (e.g. fault clearance time and recovery time) and parameter estimation (fault current and trip delay) for such a protection scheme have been difficult because they require comprehensive modeling of electrical network, IED behaviors, and IEC61850-based communication. In this paper, we present a centralized scheme with detailed modeling and co-simulation of all these aspects. We also demonstrate how the analysis of the simulation results helps overcoming the forth-mentioned difficulties.