Grouped Charging of Decentralised Storage to Efficiently Control Collective Heating Systems: Limitations and Opportunities

Stef Jacobs (), Margot De Pauw, Senne Van Minnebruggen, Sara Ghane, Thomas Huybrechts, Peter Hellinckx and Ivan Verhaert
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Stef Jacobs: EMIB, Faculty of Applied Engineering, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium
Margot De Pauw: Kenniscentrum Energie, Thomas More Kempen, Kleinhoefstraat 4, 2440 Geel, Belgium
Senne Van Minnebruggen: EMIB, Faculty of Applied Engineering, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium
Sara Ghane: IDLab, Faculty of Applied Engineering, University of Antwerp-imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium
Thomas Huybrechts: IDLab, Faculty of Applied Engineering, University of Antwerp-imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium
Peter Hellinckx: Faculty of Applied Engineering–Electronics ICT, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium
Ivan Verhaert: EMIB, Faculty of Applied Engineering, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium

Energies, 2023, vol. 16, issue 8, 1-28

Abstract: Collective heating systems have multiple end-users with time-varying, often different temperature demands. There are several concepts catering to this, e.g., multi-pipe networks and 2-pipe networks with or without decentralised booster systems. In this study, we focus on 2-pipe networks with a changing supply temperature by smart use of decentralised storage. By grouping high-temperature demands, the average supply temperature can be lowered during large parts of the day, which is beneficial for system efficiency. The actual energy-saving potential, however, can be case-specific and is expected to depend on design choices and implemented control strategies. In this paper, these dependencies are assessed and identified by implementing two optimised rule-based control strategies, providing in such a way a bench-mark for other control strategies. The results show that grouping yields energy savings of up to 36% at similar peak demand as with conventional control strategies. The energy-saving potential is greatest for large storage volumes and small networks, but large networks with large storage and proper control choices can also achieve around 30% energy savings. Moreover, high-temperature time can easily be reduced to less than 40% of the day, which could make space cooling without decentralised booster heat pumps possible, but this requires further research.

Keywords: domestic hot water; DHW; decentralised storage; combined heat distribution; collective heating; temperature control; demand-based control; design impact (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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