Distributed model predictive control of a nonlinear building energy system using consensus ADMM

2006 14th Mediterranean Conference on Control and Automation, pp. 902–907

Abstract

Model predictive control (MPC) is a promising approach to reduce energy usage in buildings and provide grid flexibility. However, MPC in buildings requires a high modeling effort and struggles with reliability and scalability. Distributed control architectures can help to alleviate these problems. Existing work for distributed building MPC mainly focuses on the consumer side, neglecting the producer dynamics. In this work, we investigate a distributed MPC scheme based on the alternating direction method of multipliers (ADMM), considering the dynamics of producers and consumers simultaneously. The control agents employ nonlinear plant models and are directly coupled through their states. Consequently, a distributed, non-convex optimization problem needs to be solved. We test the distributed control algorithm on an energy system consisting of two thermal zones, a stratified hot water tank, a boiler, and a CHP. In a simulation, we demonstrate that the closed-loop system can be stabilized using the distributed MPC scheme, given a sufficient number of iterations.

Authors 4

1. Steffen Eser Aachen E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate,Aachen,Germany

   E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate, RWTH Aachen University, Aachen, Germany

2. Phillip Stoffel Aachen E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate,Aachen,Germany

   E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate, RWTH Aachen University, Aachen, Germany

3. Alexander Kümpel Aachen E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate,Aachen,Germany

   E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate, RWTH Aachen University, Aachen, Germany

4. Dirk Müller Aachen E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate,Aachen,Germany

   E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate, RWTH Aachen University, Aachen, Germany