Almost decentralized model predictive control of power networks

Stable operation of the electrical power grid in the future will require novel, advanced control techniques for supply and demand matching, as a consequence of the liberalization and decentralization of electrical power generation. Currently, there is an increasing interest for using model predictive control (MPC) for power balancing. However, a centralized implementation of MPC is hampered by the large scale and complexity of power networks. Non-centralized, scalable control schemes are more suited for future application. In this paper we therefore propose a novel almost-decentralized Lyapunov-based predictive control algorithm for power balancing, i.e., for asymptotic stabilization of the network frequency. The algorithm is particularly suited for large-scale power networks, as it requires only local information and limited communication between directly-neighboring control areas to provide a stabilizing control action. We assess the suitability of this scheme and compare it with state-of-the-art non-centralized MPC in a benchmark case study.