TY - DATA T1 - Data and Optimisation model for space heating and committed emissions for the built environment PY - 2023/05/31 AU - Chelsea Kaandorp AU - Jeroen Verhagen AU - Edo Abraham AU - Tes Miedema AU - Nick van de Giesen UR - https://data.4tu.nl/articles/software/Address_Gurobi_scenario_loop_5y_timestep_py/22256668 DO - 10.4121/22256668.v1 KW - Urban heating systems KW - Committed carbon emissions KW - Retro-fitting of the building stock KW - Electrification of heating KW - Carbon lock-in KW - Mixed-integer non-linear programming KW - Heat and carbon emissions model for Amsterdam N2 -

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. 

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