%0 Computer Program
%A Kaandorp, Chelsea
%A Verhagen, Jeroen
%A Abraham, Edo
%A Miedema, Tes
%A van de Giesen, Nick
%D 2023
%T Data and Optimisation model for space heating and committed emissions for the built environment
%U https://data.4tu.nl/articles/software/Address_Gurobi_scenario_loop_5y_timestep_py/22256668
%R 10.4121/22256668.v1
%K Urban heating systems
%K Committed carbon emissions
%K Retro-fitting of the building stock
%K Electrification of heating
%K Carbon lock-in
%K Mixed-integer non-linear programming
%K Heat and carbon emissions model for Amsterdam
%X <p>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).&nbsp;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.</p><p></p><p>The python code requires the following data files (provided in this collection):</p><p>-&nbsp;Address_Neigborhood_Heat_Demand.xlsx</p><p>- Heat_technology.xlsx (or Heat_teachnology_highEFhydrogen.xlsx to run change the input of the emission factors related to hydrogen).</p><p>- Scenario_Settings.xlsx</p><p></p><p>The data file 'Scenario_Setting.xlsx' is used for a first-order sensitivity analysis).</p><p></p><p>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.&nbsp;</p>
%I 4TU.ResearchData