A load-complementarity combined flexible clustering approach for large-scale urban energy-water nexus optimization

Wang, Wei, Jing, Rui, Zhao, Yingru, Zhang, Chuan and Wang, Xiaonan 2020. A load-complementarity combined flexible clustering approach for large-scale urban energy-water nexus optimization. Applied Energy 270 , 115163. 10.1016/j.apenergy.2020.115163

Full text not available from this repository.

Abstract

Modeling and optimization of a large-scale urban energy-water nexus system with sufficient spatial resolutions is a complex challenge. By proper clustering technique, a large-scale problem could possibly be divided into small ones with high spatial resolution and accuracy. Existing literature tends to lower the complexity of large-scale urban energy system problem by accumulating demand profiles on the spatial dimension. This study proposes a flexible clustering approach based on density clustering method with combined index assessment process. The flexible approach considers not only the spatial dimensions but also the complementarity effect of different demand profile and control the computational time of system design and optimization. The approach can increase the clustering flexibility by providing more clustering options than conventional method, take advantages of complementarity effect to further improve the system economic performance and control the solving time in an acceptable range. The proposed approach is evaluated by a case study of a new business district in Shanghai, China with a proposed future energy-water nexus system. After three combined index assessment, 45 new clustering maps are generated by the flexible clustering approach and the final optimal solution obtained by the proposed approach can further obtain 6.74% cost savings compared with conventional clustering approach.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0306-2619
Date of Acceptance:	6 May 2020
Last Modified:	04 Aug 2022 02:11
URI:	https://orca.cardiff.ac.uk/id/eprint/137672

Citation Data

Cited 16 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item