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This paper deals with hierarchical model predictive control (MPC) of smart grid systems. The objective is to accommodate load variations on the grid, arising from varying consumption and natural variations in the power production e.g. from wind turbines. This balancing between supply and demand is performed by distributing power to consumers in an optimal manner, subject to the requirement that each consumer receives the specific amount of energy the consumer is entitled to within a specific time horizon. However, in order to do so, the high-level controller requires knowledge of how much energy the consumers can receive within a given time horizon. In this paper, we present a method for computing these bounds as convex constraints that can be used directly in the optimisation. The method is illustrated on a simulation example that uses actual wind data as load variation, and fairly realistic consumer models. The example illustrates that the exact bounds computed by the proposed method leads to a better power distribution than a conventional, conservative approach in case of fast changes in the load.