Optimisation of Smart Grid performance using centralised and distributed control techniques

A massive change is currently taking place in the manner in which power networks are operated. Traditionally, power networks consisted of large power stations which were controlled from centralised locations. The trend in modern power networks is for generated power to be produced by a diverse array of energy sources which are spread over a large geographical area. As a result, controlling these systems from a centralised controller is impractical. Thus, future power networks will be controlled by a large number of intelligent distributed controllers which must work together to coordinate their actions. The term Smart Grid is the umbrella term used to denote this combination of power systems, artificial intelligence, and communications engineering. This thesis focuses on the application of optimal control techniques to Smart Grids with a focus in particular on iterative distributed MPC. A novel convergence and stability proof for iterative distributed MPC based on the Alternating Direction Method of Multipliers is derived. Distributed and centralised MPC, and an optimised PID controllers' performance are then compared when applied to a highly interconnected, nonlinear, MIMO testbed based on a part of the Nordic power grid. Finally, a novel tuning algorithm is proposed for iterative distributed MPC which simultaneously optimises both the closed loop performance and the communication overhead associated with the desired control.

Grantor dc:publisher

University College Cork

Year dc:date.issued

2012

Author dc:creator

• McNamara, Paul

Advisor dc:contributor.advisor

• Lightbody, Gordon

• Distributed model predictive control
• Control
• Smart grids
• Multi-agent
• Power systems

Statement dc:rights

• © 2012, Paul Mc Namara

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nc-nd/3.0/

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10468/580

OAI identifier oai:identifier

oai:cora.ucc.ie:10468/580