Toward data-driven predictive control of multi-energy distribution systems

Publikationstyp
Journal Article
Date Issued
2022-11-01
Sprache
English
Author(s)
Bilgic, Deborah
Koch, Alexander
Pan, Guanru
Faulwasser, Timm  
TORE-URI
https://hdl.handle.net/11420/45648
Journal
Electric power systems research  
Volume
212
Article Number
108311
Citation
Electric Power Systems Research 212: 108311 (2022-11)
Publisher DOI
10.1016/j.epsr.2022.108311
Scopus ID
2-s2.0-85134813122
Publisher
Elsevier
The necessity to obtain, to parametrize, and to maintain models of the underlying dynamics impedes predictive control of energy systems in many real-world applications. To alleviate the need for explicit model knowledge this paper proposes a framework for the operation of distributed multi-energy systems via data-driven predictive control with stochastic uncertainties. Instead of modeling the dynamics of the individual distributed energy resources, our approach relies on measured input–output data of the distributed resources only. Moreover, we combine data-driven predictive control with forecasts of exogenous signals (renewable generations and demands) by Gaussian processes. A simulation study based on realistic data illustrates the efficacy of the proposed scheme to handle mild non-linearities and measurement noise.
Subjects
Data-driven control
Gaussian processes
Multi-energy distribution systems
Predictive control
DDC Class
530: Physics