Taylor, Michael, Long, Sebastian, Marjanovic, Ognjen and Parisio, Alessandra
2021.
Model predictive control of smart districts with fifth generation heating and cooling networks.
IEEE Transactions on Energy Conversion
36
(4)
, pp. 2659-2669.
10.1109/TEC.2021.3082405
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Abstract
Fifth Generation District Heating and Cooling (5GDHC) networks, in which low temperature water is distributed to water-source heat pumps (WSHPs) in order to meet thermal demands, are expected to have a significant impact on the decarbonisation of energy supply. Thermal storage installed in these networks offers operational flexibility that can be leveraged to integrate renewable electrical and thermal energy sources. Thus, when considered as part of a smart multi-energy district, 5GDHC substation devices (e.g., WSHPs, storage) may be optimally operated using Model Predictive Control (MPC) in order to match demand with low-cost supply of electricity. However, the application of MPC requires the ability to model 5GDHC networks within the context of a multi-energy system. Hence, this paper extends an existing, generalised control-oriented modelling framework for multi-energy systems to accommodate 5GDHC networks. Additions include the ability to represent hydraulic pumps, thermodynamic cycle devices (such as WSHPs) and multi-energy networks within the framework. Furthermore, an economic MPC (eMPC) scheme is proposed for energy management of 5GDHC-based smart districts. Finally, a case study is presented in which the proposed eMPC controller is compared with rule-based control for economic operation of a smart district.
Item Type: | Article |
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Date Type: | Publication |
Status: | Published |
Schools: | Engineering |
Additional Information: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
Publisher: | Institute of Electrical and Electronics Engineers |
ISSN: | 0885-8969 |
Date of First Compliant Deposit: | 28 July 2022 |
Date of Acceptance: | 15 April 2021 |
Last Modified: | 10 Feb 2024 02:08 |
URI: | https://orca.cardiff.ac.uk/id/eprint/151468 |
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