Options
Data-driven uncertainty propagation for stochastic predictive control of multi-energy systems
Publikationstyp
Journal Article
Date Issued
2024-06-17
Sprache
English
Author(s)
Özmeteler, M. Batu
Bilgic, Deborah
Koch, Alexander
Journal
Volume
80
Article Number
101066
Citation
European Journal of Control 80: 101066 (2024)
Publisher DOI
Scopus ID
Publisher
Elsevier
Stochastic predictive control schemes that account for epistemic and aleatoric uncertainties, i.e. lack of model knowledge and stochastic disturbances, are of major interest for multi-energy systems. However, there exists a trade-off between model complexity, computational effort, and accuracy of uncertainty quantification. This paper attempts to assess this trade-off by comparing a recently proposed approach combining Willems’ fundamental lemma with polynomial chaos expansion to a model-based scheme that first propagates uncertainty with PCE and then considers chance constraints in the optimization. The simulation results show that the data-driven scheme yields similar performance and computational efficiency compared to the model-based scheme, with the advantage of avoiding the construction of explicit models.
Subjects
Data-driven control | Multi-energy systems | Polynomial chaos | Uncertainty propagation | Uncertainty quantification | Willems’ fundamental lemma
DDC Class
600: Technology