Weight optimisation for iterative distributed model predictive control applied to power networks

Authors:
Paul Mc Namara;Rudy R. Negenborn;Bart De Schutter;Gordon Lightbody
Affiliations:
Control and Intelligent Systems Group, Electrical and Electronic Engineering Department, University College Cork, Cork, Ireland;Department of Marine and Transport Technology, Delft University of Technology, Delft, The Netherlands;Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands;Control and Intelligent Systems Group, Electrical and Electronic Engineering Department, University College Cork, Cork, Ireland
Venue:
Engineering Applications of Artificial Intelligence
Year:
2013

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Abstract

This paper presents a weight tuning technique for iterative distributed Model Predictive Control (MPC). Particle Swarm Optimisation (PSO) is used to optimise both the weights associated with disturbance rejection and those associated with achieving consensus between control agents. Unlike centralised MPC, where tuning focuses solely on disturbance rejection performance, iterative distributed MPC practitioners must concern themselves with the trade off between disturbance rejection and the overall communication overhead when tuning weights. This is particularly the case in large scale systems, such as power networks, where typically there will be a large communication overhead associated with control. In this paper a method for simultaneously optimising both the closed loop performance and minimising the communications overhead of iterative distributed MPC systems is proposed. Simulation experiments illustrate the potential of the proposed approach in two different power system scenarios.