Holistic modelling techniques for the operational optimisation of multi-vector energy systems

Reynolds, Jonathan ORCID: https://orcid.org/0000-0001-9106-9246, Ahmad, Muhammad ORCID: https://orcid.org/0000-0002-7269-4369 and Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400 2018. Holistic modelling techniques for the operational optimisation of multi-vector energy systems. Energy and Buildings 169 , pp. 397-416. 10.1016/j.enbuild.2018.03.065

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Abstract

Modern district energy systems are highly complex with several controllable and uncontrollable variables. To effectively manage a multi-vector district requires a holistic perspective in terms of both modelling and optimisation. Current district optimisation strategies found in the literature often consider very simple models for energy generation and conversion technologies. To improve upon the state of the art, more realistic and accurate models must be produced whilst remaining computationally and mathematically simple enough to complete within short periods. Therefore, this paper provides a comprehensive review of modelling techniques for common district energy conversion technologies including Power-to-Gas. In addition, dynamic building modelling techniques are reviewed as buildings must be considered active and flexible participants in a district energy system. In both cases, a specific focus is placed on artificial intelligence-based models suitable for implementation in the real-time operational optimisation of multi-vector systems. Future research directions identified from this review include the need to integrate simplified models of energy conversion units, energy distribution networks, dynamic building models and energy storage into a holistic district optimisation. Finally, a future district energy management solution is proposed. It leverages semantic modelling to allow interoperability of heterogeneous data sources to provide added value inferencing from contextually enriched information

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0378-7788
Funders:	Engineering and Physical Sciences Research Council
Date of First Compliant Deposit:	6 April 2018
Date of Acceptance:	26 March 2018
Last Modified:	08 May 2023 14:15
URI:	https://orca.cardiff.ac.uk/id/eprint/110550

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