An analytical optimization model for holistic multiobjective district energy management - a case study approach

Jayan, Bejay, Li, Haijiang ORCID: https://orcid.org/0000-0001-6326-8133, Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400, Hippolyte, Jean-Laurent ORCID: https://orcid.org/0000-0002-5263-2881 and Howell, Shaun Kevin 2016. An analytical optimization model for holistic multiobjective district energy management - a case study approach. International Journal of Modeling and Optimization 6 (3) , pp. 156-165. 10.7763/IJMO.2016.V6.521

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Abstract

Efficient management during the operational phase of district energy systems has become increasingly complex due to the various static and dynamic factors involved. Existing deterministic algorithms which are largely based on human experience acquired from specific domains, normally fail to consider the overall efficiency of district energy systems in a holistic way. This paper looks into taking a black box approach by using genetic algorithms (GA) to solve a multiobjectiveoptimization problem conforming to economic, environmental and efficiency standards. This holistic optimization model, takes into account both heat and electricity demand profiles, and was applied in Ebbw Vale district, in Wales. The model helps compute optimized daily schedules for the generation mix in the district and different operational strategies are analyzed using deterministic and genetic algorithm (GA) based combined optimization methods. The results evidence that GA can be used to define an optimum strategy behind heat production leading to an increase in profit by 32% and reduction in CO2 emissions by 36% in the 24 hour period analyzed. This research fits in well with future district energy systems which give priority to integrated and systematic management.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords:	Analytical model, district energy management, energy efficiency, genetic algorithms, multiobjective optimization.
ISSN:	2010-3697
Date of First Compliant Deposit:	14 July 2016
Date of Acceptance:	21 June 2016
Last Modified:	01 Nov 2022 10:41
URI:	https://orca.cardiff.ac.uk/id/eprint/92569

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