Hierarchical Model Predictive Control for Plug-and-Play Resource Distribution

This chapter deals with hierarchical model predictive control (MPC) of distributed systems. A three level hierarchical 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 proposed method can also be applied to supply chain management systems, where the challenge is to balance demand and supply, using a number of storages each with a maximal capacity. The algorithm will then try to balance the risk of individual storages running empty or full with the risk of having overproduction or unsatisfied demand. The approach presented is based on quadratic optimization and possesses 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 verified by a number of simulations featuring a three-level smartgrid power control system for a small isolated power grid.1 The three authors are with: Department of Electronic Systems, Automation and Control, Aalborg University, Fr. Bajers Vej 7C, 9220 Aalborg, Denmark; e-mail: [email protected],[email protected],[email protected] 1 This work is supported by The Danish Research Council for Technology and Production Sciences.