Customised pre-built Sector-coupled Euro-Calliope Model - Focus on the power sector and additional SPORES options

<strong>Customised pre-built Sector-coupled Euro-Calliope Model - Focus on the power sector and additional SPORES options</strong> <em>Based on the pre-built Sector-coupled Euro-Calliope model developed by Bryn Pickering</em> This model is pre-packaged and ready to be loaded into Calliope, based on 2015 input data. To run the model as done in the associated publication you will need to do the following: Install a specific conda environment to be working with the correct version of Calliope ( <code>conda env create -f requirements.yml</code> ) Run the model including only those scenarios that relate to the power sector and SPORES <strong>Main and parallel batches of SPORES</strong> To facilitate this second point and the reproduction of results, you'll find some pre-packaged python script with all and only those model scenarios that allow you to run either the "main batch" of SPORES (<code>spores_model_run.py</code>) or any of the "parallel batches" of SPORES (e.g., <code>excl_bio</code> and <code>max_bio</code>, which generate SPORES while minimising and, respectively, maximising bioenergy deployment). <strong>Strength of the anchoring to extremes of the decision space</strong> To tweak the strength of the anchoring to a specific technology feature, as we do in the paper, you need to modify the <code>euro_calliope/spores.yaml</code> override file. More precisely, you need to change the <code>excl_score</code> parameter in the objective function at the end of the file: <pre><code class="language-bash">max_mode.run.spores_options.objective_cost_class: {'spores_score': 1, 'monetary': 0, 'excl_score': -1} excl_mode.run.spores_options.objective_cost_class: {'spores_score': 1, 'monetary': 0, 'excl_score': 1}</code></pre> A value of 1 (for maximisation) or -1 (for minimisation) is the default by which we generate the primary results in the paper. By changing it to 0.1, you can reproduce as well the secondary results that we use as a sensitivity for a "weaker anchoring" to extreme technology features of the decision space. <br> <strong>Weight-assignment method</strong> Finally, to change the weight-assignment method, you need to modify the <code>euro_calliope/eurospores/model.yaml</code> file. More precisely, the <code>scoring_method</code> parameter, which can be one of the following: <code>integer</code>, <code>relative_deployment</code>, <code>random</code> or <code>evolving_average</code>. <pre><code class="language-bash">run.spores_options.scoring_method: integer</code></pre> <strong>Hard-coded changes to be aware of</strong> The files in this model theoretically allow accounting for all energy sectors (power, heat, transport, industry). Yet, we subset the analysis in the associated publication to only the power sector. To this end, we have modified the original electricity demand file (<code>euro_calliope/eurospores/electricity-demand.csv</code>). In fact, the original file did not account for the fraction of electricity associated with heat, transport or industry consumption, which was instead allocated to sector-specific demand files. In such a way, the model was free to decide whether to electrify these sectoral demands or not. In the present study, instead, we wanted to run our analysis based on the current electricity demand, inclusive of the currently electrified sector-specific demand. Therefore, we have replaced the original file with a new one that includes the present-day electricity demand, with no subtractions. If you want to run the analysis for all sectors, unlike we do in the study, you'll first need to recover the original file. You'll quickly find it in the same folder, named as <code>__electricity-demand.csv</code>. <br> <strong>Summary of results from the paper</strong> The folder <code>paper_summary_results</code> features some CSV files that summarise the results we obtained for our study across all the different tested search strategies.