%0 Conference Paper
%A Damoiseaux, Armand
%A Jokic, Andrej
%A Lazar, Mircea
%A Alessio, Alessandro
%A Van den bosch, Paul
%A Hiskens, Ian
%A Bemporad, Alberto
%B 16th Power Systems Computation Conference
%C 14th - 16th July 2008
%D 2008
%F eprints:616
%K Model predictive control; Decentralized control; Distributed control; Power systems
%T Assessment of decentralized model predictive control techniques for power networks
%U http://eprints.imtlucca.it/616/
%X Model predictive control (MPC) is one of the few advanced control methodologies that have proven to be very successful in real-life control applications. MPC has the capability to guarantee optimality with respect to a de- sired performance cost function, while explicitly taking con- straints into account. Recently, there has been an increas- ing interest in the usage of MPC schemes to control power networks. The major obstacle for implementation lies in the large scale of power networks, which is prohibitive for a centralized approach. In this paper we critically assess and compare the suitability of three model predictive control schemes for controlling power networks. These techniques are analyzed with respect to the following relevant characteristics: the performance of the closed-loop system, which is evaluated and compared to the performance achieved with the classical automatic generation control (AGC) structure; the decentralized implementation, which is investigated in terms of size of the models used for prediction, required measurements and data communication, type of cost function and the computational time required by each algorithm to obtain the control action. Based on the investigated properties mentioned above, the study presented in this paper provides valuable insights that can contribute to the successful decentralized implementation of MPC in real-life electrical power networks.