Hierarchical model for design and operation optimization of district cooling networks

Manfredi Neri, Elisa Guelpa, Jun Onn Khor, Alessandro Romagnoli and Vittorio Verda

Applied Energy, 2024, vol. 371, issue C, No S030626192401050X

Abstract: District cooling systems offer a promising alternative to conventional cooling, thanks to overall larger efficiencies and reduced carbon footprint. On the other hand they are characterized by large capital and operational costs that impede their diffusion. Optimizing the design and operation of these systems is therefore fundamental to fully exploit their potential. This paper proposes a novel optimization framework for the simultaneous optimization of design and operation of these systems. The model integrates a genetic algorithm at a master level with Mixed Integer Linear Programming models at a lower level. This approach enables the optimization of various aspects, including network topology, plant locations, supply temperatures and the selection of thermal storage technology. The study showed that in the specific case of Singapore, where the cost for space occupancy is significant, latent heat thermal storages are preferred. In addition, the results highlighted the cost benefits of thermal storage, as district cooling networks lacking it can be from 6.2% to 20.8% more expensive, depending on the scenario. Furthermore, the study demonstrated that the utilization of waste heat through absorption chillers enhances the economic feasibility of district cooling, lowering the payback time by up to 5 years. Lastly, it was observed that 3 °C increase of the indoor set point temperature could reduce the payback time by 3 years and increase the final net present value by 43%, as larger network supply temperatures allow chillers to operate with better performances. The developed model allows to estimate various crucial outcomes with few input parameters, representing a useful tool for both research and planning.

Keywords: District cooling; Thermal network; Genetic algorithm; MILP (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030626192401050X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:371:y:2024:i:c:s030626192401050x

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.123667

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().