Hierarchical Control for Smart Grids

This paper deals with hierarchical model predictive control (MPC) of smart grid systems. The design consists of a high level MPC controller, a second level of so-called aggregators, which reduces the computational and communication-related load on the high-level control, and a lower level of autonomous consumers. The control system is tasked with balancing electric power production and consumption within the smart grid, and makes active use of the flexibility of a large number of power producing and/or power consuming units. The objective is to accommodate the load variation on the grid, arising on one hand from varying consumption, and on the other hand by natural variations in power production e.g. from wind turbines. The high-level MPC problem is solved using quadratic optimisation, while the aggregator level can either involve quadratic optimisation or simple sorting-based min-max solutions. In this paper we compare the performance and computational complexity of these two solutions and find that the performance of the two algorithms are very similar, whereas the sorting-based algorithm is much faster than the quadratic optimisation-based algorithm, thus allowing to handle vastly larger numbers of consumers.