Data-driven non-parametric chance-constrained model predictive control for microgrids energy management using small data batches

Leon Babić, Marco Lauricella, Glenn Ceusters, Matthias Biskoping

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This paper presents a stochastic model predictive control approach combined with a time-series forecasting technique to tackle the problem of microgrid energy management in the face of uncertainty. The data-driven non-parametric chance constraint method is used to formulate chance constraints for stochastic model predictive control, while removing the dependency on probability density assumptions of uncertain variables and retaining the linear structure of the resulting optimization problem. The proposed approach is suitable for implementation on systems with limited computational power or limited memory storage, thanks to its simple linear structure and its ability to provide accurate results within pre-defined confidence levels, even when using small data batches. The proposed forecasting and stochastic model predictive control approaches are applied on a numerical example featuring a small grid-connected microgrid with PV generation, a battery storage system, and a non-controllable load, showing the ability to reduce costs by reducing the confidence level, and to satisfy pre-defined confidence levels.
Originele taal-2English
Artikelnummer1237759
Aantal pagina's14
TijdschriftFrontiers in Control Engineering
Volume4
Nummer van het tijdschrift2023
DOI's
StatusPublished - 17 aug 2023

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