Conference paper
Economic Model Predictive Control for Energy Systems in Smart Homes
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Scientific Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark3
CITIES - Centre for IT-Intelligent Energy Systems, Centers, Technical University of Denmark4
In this paper we present simulations of Economic Model Predictive Control (EMPC) for control of the energy system in a smart home. The energy system in a smart home consists of a stationary battery, photo voltaic solar cells on the roof, a heat pump for heating, and an electrical vehicle battery that is charged.
Using weather forecasts, thermal comfort and driving profiles, the EMPC coordinates this energy system and its interaction with the external energy system by purchasing and selling electricity at prices that are announced in advance. The EMPC is a linear program with soft constraints for the output constraints and an objective function that represents the cost of energy.
In contrast to existing methods, the key novelties in the present paper is the use of multi-level soft constraints and an objective function that accounts not only for the cost of energy used during the prediction horizon but also for the value of energy stored at the end of the prediction horizon. We demonstrate by simulation, that these novelties are important for well-behaved closed-loop performance of the EMPC.
Language: | English |
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Publisher: | IEEE |
Year: | 2019 |
Pages: | 598-604 |
Proceedings: | 2019 IEEE Conference on Control Technology and Applications |
ISBN: | 1728127661 , 172812767X , 172812767x , 1728127688 , 9781728127668 , 9781728127675 and 9781728127682 |
Types: | Conference paper |
DOI: | 10.1109/ccta.2019.8920663 |
ORCIDs: | Schlüter, Hjørdis Amanda , Boiroux, Dimitri , Poulsen, Niels Kjølstad , Madsen, Henrik and Jørgensen, John Bagterp |