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This paper deals with the computational efficiency evaluation of a hierarchical DMPC (distributed model predictive control) framework for resource sharing problems which has been established in the context of smart district energy management. The provided DMPC framework is based on a dual decomposition of the centralized open-loop controller which is decomposed into several subproblems and one coordinator...
This paper deals with energy management in smart districts using distributed model predictive control (DMPC). We investigate two decomposition methods, primal and dual decomposition, for problems where a shared resource has to be distributed optimally amongst sub systems. The objective is to compare these two decomposition methods with a focus on how well they are suited in the context of smart district...
This paper presents a distributed Model Predictive Control framework based on a primal decomposition and a bundle method to control the indoor environmental conditions in a multisource/multizone building. The control aims to minimize the total energy cost under restrictions on global power consumption and local constraints on comfort and saturations on actuators. Moreover, each power source is supposed...
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