Assessment of decentralized model predictive control techniques for power networks

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 cen- tralized approach. In this paper we critically assess and com- pare the suitability of three model predictive control schemes for controlling power networks. These techniques are ana- lyzed with respect to the following relevant characteristics: the performance of the closed-loop system, which is eval- uated 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 measure- ments and data communication, type of cost function and the computational time required by each algorithm to ob- tain 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 decen- tralized implementation of MPC in real-life electrical power networks.