Probabilistic modelling of seasonal energy demand patterns in the transition from natural gas to hydrogen for an urban energy district
Probabilistic modelling of seasonal energy demand patterns in the transition from natural gas to hydrogen for an urban energy district
The transition to a low-carbon energy system can be depicted as a “great reconfiguration” from a socio-technical perspective that carries the risk of impact shifts. Electrification with the objective of achieving rapidly deep decarbonisation must be accompanied by effective efficiency and flexibility measures. Hydrogen can be a preferred option in the decarbonisation process where electrification of end-uses is difficult or impractical as well as for long-term storage in energy infrastructure characterised by a large penetration of renewable energy sources. Notwithstanding the current uncertainties regarding costs, environmental impact and the inherent difficulties of increasing rapidly supply capacity, hydrogen can represent a solution to be used in multi-energy systems with combined heat and power (CHP), in particular in urban energy districts. In fact, while achieving carbon savings with natural gas fuelled CHP is not possible when low grid carbon intensity factors are present, it may still be possible to use it to provide flexibility services and to reduce emissions further with switch from natural gas to hydrogen. In this paper, a commercially established urban district energy scheme located in Southampton (United Kingdom) is analysed with the goal of exploring potential variations in its energy demand. The study proposes the use of scalable data-driven methods and probabilistic simulation to generate seasonal energy demand patterns representing the potential short-term and long-term evolution of the energy district.
Data-driven methods, Energy transition, Hydrogen, Probabilistic modelling, Regression-based approaches, Urban energy modelling
398-411
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Gonzalez-Carreon, Karla M.
83974d01-7128-4e9a-9895-edff89cd9a31
Bahaj, Abu Bakr S.
a64074cc-2b6e-43df-adac-a8437e7f1b37
2 January 2024
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Gonzalez-Carreon, Karla M.
83974d01-7128-4e9a-9895-edff89cd9a31
Bahaj, Abu Bakr S.
a64074cc-2b6e-43df-adac-a8437e7f1b37
Manfren, Massimiliano, Gonzalez-Carreon, Karla M. and Bahaj, Abu Bakr S.
(2024)
Probabilistic modelling of seasonal energy demand patterns in the transition from natural gas to hydrogen for an urban energy district.
International Journal of Hydrogen Energy, 51, .
(doi:10.1016/j.ijhydene.2023.05.337).
Abstract
The transition to a low-carbon energy system can be depicted as a “great reconfiguration” from a socio-technical perspective that carries the risk of impact shifts. Electrification with the objective of achieving rapidly deep decarbonisation must be accompanied by effective efficiency and flexibility measures. Hydrogen can be a preferred option in the decarbonisation process where electrification of end-uses is difficult or impractical as well as for long-term storage in energy infrastructure characterised by a large penetration of renewable energy sources. Notwithstanding the current uncertainties regarding costs, environmental impact and the inherent difficulties of increasing rapidly supply capacity, hydrogen can represent a solution to be used in multi-energy systems with combined heat and power (CHP), in particular in urban energy districts. In fact, while achieving carbon savings with natural gas fuelled CHP is not possible when low grid carbon intensity factors are present, it may still be possible to use it to provide flexibility services and to reduce emissions further with switch from natural gas to hydrogen. In this paper, a commercially established urban district energy scheme located in Southampton (United Kingdom) is analysed with the goal of exploring potential variations in its energy demand. The study proposes the use of scalable data-driven methods and probabilistic simulation to generate seasonal energy demand patterns representing the potential short-term and long-term evolution of the energy district.
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More information
Accepted/In Press date: 30 May 2023
e-pub ahead of print date: 23 June 2023
Published date: 2 January 2024
Additional Information:
Funding Information:
This work is part of the activity of the Energy & Climate Change Division, Sustainable Energy Research Group ( https://energy.soton.ac.uk/ ) at University of Southampton. The research was partially funded by the Southampton Marine and Maritime Institute (SMMI) HEIF research collaboration stimulus fund 2022-23 and is also part of the Sustainability Strategy 2020-2025 of University of Southampton ( https://www.southampton.ac.uk/susdev/our-approach/sustainability-strategy.page ).
Publisher Copyright:
© 2023 The Authors
Keywords:
Data-driven methods, Energy transition, Hydrogen, Probabilistic modelling, Regression-based approaches, Urban energy modelling
Identifiers
Local EPrints ID: 481156
URI: http://eprints.soton.ac.uk/id/eprint/481156
ISSN: 0360-3199
PURE UUID: 8e164306-be5b-4f96-9e9a-24d5dc9a5990
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Date deposited: 16 Aug 2023 16:50
Last modified: 18 Mar 2024 03:40
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Author:
Karla M. Gonzalez-Carreon
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