Distributed MPC for Demand Side Management using Smart Homes
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2022-08
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Master Thesis
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Abstract
Building comfort control contributes a substantial quantity of greenhouse gas emissions. Two approaches can be combined to decrease this carbon footprint using model predictive control (MPC) strategies. First, the energy consumption of already-installed convectors should be reduced while maintaining thermal comfort. Secondly, it may be advantageous for the distribution grid to incorporate more renewable energy sources and to limit the aggregated load consumption. These two methods can be combined to design a centralized MPC strategy. To efficiently apply this control strategy to largescale systems, we design an algorithm to solve the problem in a distributed manner that is significantly less computationally demanding than centralized implementations. Through extensive numerical studies, we demonstrate that the proposed distributed MPC controller retains the same control performances of the centralized ones. Furthermore, we demonstrate that aggregated load constraints can be satisfied and consumer costs can be reduced relative to industry standard approaches such as hysteresis-based (or Bang-Bang) controllers. Finally, we demonstrate the scalability of the distributed MPC controller to illustrate its applicability to large-scale systems.
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Examiner: Hudoba de Badyn, Mathias
Examiner: Belgioioso, Giuseppe
Examiner : Liao-McPherson, Dominic
Examiner : Flamm, Benjamin
Examiner: Hall, Sophie
Examiner: Lygeros, John
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ETH Zurich
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03751 - Lygeros, John / Lygeros, John