Quasi-First-Principle Based Grey-Box Modelling of Microscale Trigeneration Systems for Application in Automatic Control

Sawant, Parantapa; Pfafferott, Jens; Felsmann, Clemens

doi:10.1016/j.ifacol.2018.11.785

Cited by 1 publication

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In reality the construction of a storage tank may have hydraulic connections at different heights of the tank and the user must have the possibility to define the respective layers of entry or exit of water. This will greatly improve the capability of the model to simulate the temperature distribution accurately and adapt the model to different constructions (Sawant et al, 2018). Here, we modified the formulation of the energy balance over the length of the tank by introducing a user-defined parameter "Load_layer".…”

Section: Thermal Energy Storage (Htes and Ctes) Modelmentioning

confidence: 99%

Development and experimental evaluation of grey-box models of a microscale polygeneration system for application in optimal control

Sawant

Bürger

Doan

et al. 2020

Energy and Buildings

Self Cite

View full text Add to dashboard Cite

With the need for optimisation based supervisory controllers for complex energy systems, comes the need for reduced order system models representing not only the non-linear characteristics of the components, but also certain unknown process dynamics like their internal control logic. We present in this paper an extensive literature study of existing methods and a rational modelling procedure based on the grey-box methodology that satisfies the necessary characteristics for models to be applied in an economic-MPC of a real-world polygeneration system at the Offenburg University of Applied Sciences. The engineering application of the models and their fitting coefficients are shared in this paper. Finally, the models are evaluated against experimental data and the efficacy of the methodology is discussed based on quantitative and qualitative arguments.

show abstract

Section: Thermal Energy Storage (Htes and Ctes) Modelmentioning

confidence: 99%