Energy management can play a significant role in energy savings and temperature control of buildings, which consume a major share of energy resources worldwide. Model predictive control (MPC) has become a popular technique for energy management, arguably for its ability to cope with complex dynamics and system constraints. The MPC algorithms found in the literature are mostly centralized, with a single controller collecting signals and performing the computations. However, buildings are dynamic systems obtained by the interconnection of subsystems, with a distributed structure which is not necessarily explored by standard MPC. To this end, this work proposes hierarchical decompositions to split the computations between a master problem (centralized component) and a set of decoupled subproblems (distributed components) which brings about organizational flexibility and distributed computation. Three general methods are considered for hierarchical control and optimization, namely bilevel optimization, Benders and Lagrangean decomposition. Results are reported from a numerical analysis of the decompositions and a simulated application to the energy management of a building, in which a limited source of chilled water is distributed among HVAC units.

能源管理可以在建筑物的节能和温度控制中发挥重要作用，这些建筑物消耗了全球能源的很大一部分。模型预测控制（MPC）已成为能源管理中的一种流行技术，可以说是因为它具有应付复杂动力学和系统约束的能力。文献中发现的MPC算法大部分是集中式的，只有一个控制器收集信号并执行计算。但是，建筑物是通过子系统的互连而获得的动态系统，其分布式结构不一定是标准MPC所探索的。为此，这项工作提出了层次分解法，以将计算拆分成一个主问题（集中组件）和一组解耦子问题（分布式组件），从而带来组织灵活性和分布式计算。考虑了三种用于分层控制和优化的通用方法，即双层优化，Benders和Lagrangean分解。通过分解的数值分析和在建筑物的能源管理中的模拟应用报告了结果，在建筑物中，有限的冷冻水源分布在HVAC单元之间。