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.

[1]  Chang Wook Ahn,et al.  On the practical genetic algorithms , 2005, GECCO '05.

[2]  Yan Zhang,et al.  Nash-optimization enhanced distributed model predictive control applied to the Shell benchmark problem , 2005, Inf. Sci..

[3]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[4]  J. Rooda,et al.  Augmented Lagrangian coordination for distributed optimal design in MDO , 2008 .

[5]  Stephen J. Wright,et al.  Numerical Optimization (Springer Series in Operations Research and Financial Engineering) , 2000 .

[6]  Brian Birge,et al.  PSOt - a particle swarm optimization toolbox for use with Matlab , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[7]  A. H. Jones,et al.  Genetic auto-tuning of PID controllers , 1995 .

[8]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[9]  A. van Keulen,et al.  Distributed optimization for systems design : an augmented Lagrangian coordination method , 2004 .

[10]  R M Hermans,et al.  Almost decentralized lyapunov-based nonlinear model predictive control , 2010, Proceedings of the 2010 American Control Conference.

[11]  Yoshikazu Fukuyama,et al.  Automatic Tuning of Model Predictive Control Using Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[12]  C. Ocampo‐Martinez,et al.  Partitioning Approach oriented to the Decentralised Predictive Control of Large-Scale Systems , 2011 .

[13]  Robert Eriksson,et al.  Security-centered Coordinated Control in AC/DC Transmission Systems , 2008 .

[14]  Marco Dorigo,et al.  Ant colony optimization theory: A survey , 2005, Theor. Comput. Sci..

[15]  Jan M. Maciejowski,et al.  Tuning MPC using H-infinity loop shaping , 2000 .

[16]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[17]  Bruce H. Krogh,et al.  Distributed model predictive control , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[18]  Peter W. Sauer,et al.  Power System Dynamics and Stability , 1997 .

[19]  Alberto Bemporad,et al.  Model Predictive Control Tuning by Controller Matching , 2010, IEEE Transactions on Automatic Control.

[20]  Adrian Gambier.  Parametric Optimization for Practical Control Systems Design , 2007, 2007 IEEE International Conference on Control Applications.

[21]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[22]  Jay H. Lee,et al.  Tuning of model predictive controllers for robust performance , 1994 .

[23]  Panagiotis D. Christofides,et al.  Sequential and Iterative Architectures for Distributed Model Predictive Control of Nonlinear Process Systems , 2010 .

[24]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[25]  Aswin N. Venkat.  Distributed Model Predictive Control: Theory and Applications , 2006 .

[26]  Zwe-Lee Gaing,et al.  A particle swarm optimization approach for optimum design of PID controller in AVR system , 2004 .

[27]  Bart De Schutter,et al.  Multi-agent model predictive control for transportation networks: Serial versus parallel schemes , 2008, Eng. Appl. Artif. Intell..