District heating energy generation optimisation considering thermal storage

Reynolds, Jonathan ORCID: https://orcid.org/0000-0001-9106-9246, Ahmad, Muhammad Waseem ORCID: https://orcid.org/0000-0002-7269-4369 and Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400 2018. District heating energy generation optimisation considering thermal storage. Presented at: International Conference on Smart Energy Grid Engineering (IEEE SEGE 2018), Oshawa, ON, Canada, 12-15 August 2018. 2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE). IEEE, pp. 330-335. 10.1109/SEGE.2018.8499509

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Abstract

Modern, decentralised, multi-energy vector districts have great potential to reduce energy consumption and emissions. However, due to the complex nature of these systems, they require intelligent management to maximise their benefit. Therefore, this paper models the energy generation of a district heating plant for the purpose of hourly, operational optimisation. Crucially, non-linear, part-load efficiency curves, and minimum load percentages are included in the energy generation modelling as well as thermal energy storage. Due to the non-linearities, a genetic algorithm, optimisation approach was utilised. The optimisation framework was applied to a case study district with three distinct thermal energy generation sources, a gas CHP, gas boilers, and biomass boilers. The optimisation controlled the load percentage of each technology as well as varying thermal storage capacity to minimise the cost of meeting the heat demand. The study found that compared to the current, rule-based approach, the optimisation achieved a significant cost saving of 12.7% without any thermal storage. As the thermal storage capacity was increased the potential cost saving was also shown to increase proportionally to 22.6% with 1000 kWh of storage.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	IEEE
ISBN:	9781538664100
ISSN:	2575-2693
Date of First Compliant Deposit:	23 November 2020
Date of Acceptance:	29 June 2018
Last Modified:	09 Nov 2022 09:40
URI:	https://orca.cardiff.ac.uk/id/eprint/136550

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