A whole-year simulation study on nonlinear mixed-integer model predictive control for a thermal energy supply system with multi-use components

Adrian Bürger, Markus Bohlayer, Sarah Hoffmann, Angelika Altmann-Dieses, Marco Braun and Moritz Diehl

Applied Energy, 2020, vol. 258, issue C

Abstract: This work presents a whole-year simulation study on nonlinear mixed-integer Model Predictive Control (MPC) for a complex thermal energy supply system which consists of a heat pump, stratified water storages, free cooling facilities, and a large underground thermal storage. For solution of the arising Mixed-Integer Non-Linear Programs (MINLPs) we apply an existing general and optimal-control-suitable decomposition approach. To compensate deviation of forecast inputs from measured disturbances, we introduce a moving horizon estimation step within the MPC strategy. The MPC performance for this study, which consists of more than 50,000 real-time suitable MINLP solutions, is compared to an elaborate conventional control strategy for the system. It is shown that MPC can significantly reduce the yearly energy consumption while providing a similar degree of constraint satisfaction, and autonomously identify previously unknown, beneficial operation modes.

Keywords: Model predictive control; Energy systems; Mixed-integer nonlinear programming (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:258:y:2020:i:c:s0306261919317519

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DOI: 10.1016/j.apenergy.2019.114064

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