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Physics-based modelling of a milk cooling system for intelligent energy management

• Lars Kappertz
• Christof Bueskens

(2024). ECMS 2024, 38th Proceedings
Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation.
DOI: http://doi.org/10.7148/2024
ISSN: 2522-2422 (ONLINE)
ISSN: 2522-2414 (PRINT)
ISSN: 2522-2430 (CD-ROM)
ISBN: 978-3-937436-84-5
ISBN: 978-3-937436-83-8 (CD) Communications of the ECMS Volume 38, Issue 1, June 2024, Cracow, Poland June 4th – June 7th, 2024

https://doi.org/10.7148/2024-0401

Lars kappertz, Christof bueskens (2024). Physics-Based Modelling of a Milk Cooling System for Intelligent Energy Management, ECMS 2024, Proceedings Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation. doi:10.7148/2024-0401

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.