Physics-Based Modelling of a Milk Cooling System for Intelligent Energy Management
DOI:
https://doi.org/10.26034/lu.akwi.2024.6231Schlagworte:
Modelling, Optimization, Parameter Identification, Demand Side Management, Renewable EnergyAbstract
Forecast-based energy management can play a large
role in a smarter and more efficient use of renewable
energies based on demand side management. Using
approaches such as model predictive control, individual
consumption devices can be shifted within operation
constraints so that their electricity consumption
optimally matches generation. In agriculture, large
thermal storages make up a sizeable part of electricity
consumption, and offer a potential use in the short term
shifting of demand. Necessary for this are accurate
models to forecast behaviour of such dynamic systems,
so that minimal power demand and fulfilment of operation
constraints can be ensured when computing optimal
controls. This work focuses on the physics-based
modelling of a milk cooling storage through parameter
identification on real measurement data. Emphasized
are the derivation of a suitable model ODE with regards
to available data, and evaluation of the model on
a rolling horizon. All major features of the measurement
data can be recreated by the model forecasts, and
model performance values show errors of around 30%
relative to mean temperature. Model performance is
considered suitable for use in energy management at
least on short forecast horizons, while practicability
on longer horizons is subject to further research.
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Copyright (c) 2024 Lars Kappertz, Christof Büskens
Dieses Werk steht unter der Lizenz Creative Commons Namensnennung 4.0 International.
