Detail publikace
Optimization of Cooling Utility System with Continuous Self-Learning Performance Models
Peesel, R.H. Schlosser, F. , Meschede, H. Dunkelberg, H. Walmsley, T.G.
Anglický název
Optimization of Cooling Utility System with Continuous Self-Learning Performance Models
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
en
Originální abstrakt
Prerequisite for an efficient cooling energy system is the knowledge and optimal combination of different operating conditions of individual compression and free cooling chillers. The performance of cooling systems depends on their part-load performance and their condensing temperature, which are often not continuously measured. Recorded energy data remain unused, and manufacturers' data differ from the real performance. For this purpose, manufacturer and real data are combined and continuously adapted to form part-load chiller models. This study applied a predictive optimization algorithm to calculate the optimal operating conditions of multiple chillers. A sprinkler tank offers the opportunity to store cold-water for later utilization. This potential is used to show the load shifting potential of the cooling system by using a variable electricity price as an input variable to the optimization. The set points from the optimization have been continuously adjusted throughout a dynamic simulation. A case study of a plastic processing company evaluates different scenarios against the status quo. Applying an optimal chiller sequencing and charging strategy of a sprinkler tank leads to electrical energy savings of up to 43%. Purchasing electricity on the EPEX SPOT market leads to additional costs savings of up to 17%. The total energy savings highly depend on the weather conditions and the prediction horizon.
Anglický abstrakt
Prerequisite for an efficient cooling energy system is the knowledge and optimal combination of different operating conditions of individual compression and free cooling chillers. The performance of cooling systems depends on their part-load performance and their condensing temperature, which are often not continuously measured. Recorded energy data remain unused, and manufacturers' data differ from the real performance. For this purpose, manufacturer and real data are combined and continuously adapted to form part-load chiller models. This study applied a predictive optimization algorithm to calculate the optimal operating conditions of multiple chillers. A sprinkler tank offers the opportunity to store cold-water for later utilization. This potential is used to show the load shifting potential of the cooling system by using a variable electricity price as an input variable to the optimization. The set points from the optimization have been continuously adjusted throughout a dynamic simulation. A case study of a plastic processing company evaluates different scenarios against the status quo. Applying an optimal chiller sequencing and charging strategy of a sprinkler tank leads to electrical energy savings of up to 43%. Purchasing electricity on the EPEX SPOT market leads to additional costs savings of up to 17%. The total energy savings highly depend on the weather conditions and the prediction horizon.
Klíčová slova anglicky
Cooling system; Flexible control technology; Machine learning; Mathematical optimization; Cooling; Energy conservation; Learning systems; Manufacture; OptimizationTanks (containers); Thermoelectric equipment; Condensing temperature; Different operating conditions; Electrical energy savings; Flexible control; Optimal chiller sequencing; Optimal operating conditions; Optimization algorithms;
Vydáno
02.05.2019
Nakladatel
MDPI AG
Místo
MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
ISSN
1996-1073
Ročník
10
Číslo
12
Strany od–do
1926–1935
Počet stran
10
BIBTEX
@article{BUT160811,
author="Timothy Gordon {Walmsley},
title="Optimization of Cooling Utility System with Continuous Self-Learning Performance Models",
year="2019",
volume="10",
number="12",
month="May",
pages="1926--1935",
publisher="MDPI AG",
address="MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND",
issn="1996-1073"
}