This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous units.
The approach is inspired by smart-grid electric power production and consumption systems, where the flexibility of a large number of power producing and/or power consuming units can be exploited in a smart-grid solution. The objective is to accommodate the load variation on the grid, arising on one hand from varying consumption, on the other hand by natural variations in power production e.g. from wind turbines.
The approach presented is based on quadratic optimization and possess the properties of low algorithmic complexity and of scalability. In particular, the proposed design methodology facilitates plug-and-play addition of subsystems without redesign of any controllers.
The method is supported by a number of simulations featuring a three-level smart-grid power control system for a small isolated power grid.