@article{eprints1264,
          author = {Ralph M. Hermans and Andrej Jokic and Mircea Lazar and Alessandro Alessio and Paul Van den bosch and Ian Hiskens and Alberto Bemporad},
       publisher = {Taylor \& Francis},
            note = {Available online: 20 Apr 2012},
         journal = {International Journal of Control },
          number = {8},
           pages = {1162--1177},
           month = {April},
          volume = {85},
            year = {2012},
           title = {Assessment of non-centralised model predictive control techniques for electrical power networks},
        keywords = {Model Predictive Control, decentralised control, distributed control, power systems},
             url = {http://eprints.imtlucca.it/1264/},
        abstract = { Model predictive control (MPC) is one of the few advanced control methodologies that have proven to be very successful in real-life applications. An attractive feature of MPC is its capability of explicitly taking state and input constraints into account. Recently, there has been an increasing interest in the usage of MPC schemes to control electrical power networks. The major obstacle for implementation lies in the large scale of these systems, which is prohibitive for a centralised approach. In this article, we therefore assess and compare the suitability of several non-centralised predictive control schemes for power balancing, to provide valuable insights that can contribute to the successful implementation of non-centralised MPC in the real-life electrical power system. }
}