Data and Optimisation model for space heating and committed emissions for the built environment

DOI:10.4121/22256668.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/22256668

Datacite citation style

Kaandorp, Chelsea; Verhagen, Jeroen; Edo Abraham; Miedema, Tes; Nick van de Giesen (2023): Data and Optimisation model for space heating and committed emissions for the built environment. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/22256668.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Software

This dataset is used to arrive to the results presented in the paper `Reducing committed emissions of heating towards 2050: Analysis of scenarios for the insulation of buildings and the decarbonisation of electricity generation' from Kaandorp et al. (2022). The dataset consists of a Python code together with the input data used to run the code. The code is used to compute which technology mix is to be applied in a neighbourhood to optimally minimise the carbon emissions associated with space heating between 2030 and 2050. The neighbourhoods used in this study are 'Felix Meritis', 'Prinses Irenebuurt', and 'Molenwijk'. The model is run for scenarios which represents different rates of the insulation of buildings and the decarbonisation of electricity production between 2020 and 2050.

The python code requires the following data files (provided in this collection):

- Address_Neigborhood_Heat_Demand.xlsx

- Heat_technology.xlsx (or Heat_teachnology_highEFhydrogen.xlsx to run change the input of the emission factors related to hydrogen).

- Scenario_Settings.xlsx

The data file 'Scenario_Setting.xlsx' is used for a first-order sensitivity analysis).

The code in 'post_processing.py' is used to process the output data from 'Address_Gurobi_scenario_loop_5y_timestep.py' (in this dataset) to facilitate analysis. 

History

  • 2023-05-31 first online, published, posted

Publisher

4TU.ResearchData

Format

python code (*.py); *.xlsx

Funding

  • ENabling LARGE-scale integration of technology hubs to enhance community resiliency via DDS in various urban FWE nexuses (grant code 438-17-407) [more info...] Dutch Research Council

Organizations

TU Delft, Faculty of Civil Engineering and Geoschences, Department of Water Management

DATA

Files (6)