District heat network extension to decarbonise building stock: A bottom-up agent-based approach

M. Pagani, P. Maire, W. Korosec, N. Chokani and R.S. Abhari

Applied Energy, 2020, vol. 272, issue C, No S0306261920306899

Abstract: A novel framework, that is comprised of large-scale, agent-based models of residential buildings and the occupants of the buildings, and a bottom-up heat demand model, is developed to assess scenarios related to the extension of a city’s district heat network. The agent-based models account for the characteristics of the individual buildings and the behaviours of the individual occupants. The bottom-up heat demand model accounts for the spatial and temporal differences in heat demand of individual buildings. It is shown that by accounting for the behaviour of building occupants the time-resolved dynamics of heat demand are more accurately captured and the quantitative prediction of the heat demand is improved compared to prior approaches. The novel framework is applied to assess the extension of the district heat network of a mid-sized city in Switzerland, whereby the likelihood of a building to connecting to the extended network can be considered. By accounting both for the profitability of the predicted heat demand and for the likelihood of a building being connected to the extended network, the internal rate of return of the infrastructure can be increased by 25%, compared to an extension of the network where these aspects are not considered. Overall, this novel framework provides insights and cost-effective solutions for policy makers and energy multi-utilities regarding the decarbonisation of building stock.

Keywords: Bottom-up heat model; Agent-based; Predictive logistic regression; Routing algorithm; Decarbonisation (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.apenergy.2020.115177

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