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11 results for “Power sector”
Customised pre-built Sector-coupled Euro-Calliope Model - Focus on the power sector and additional SPORES options
<p><strong>Customised pre-built Sector-coupled Euro-Calliope Model - Focus on the power sector and additional SPORES options</strong></p> <p><em>Based on the <a href="https://zenodo.org/record/5774988#.YqwqYDJByUk">pre-built Sector-coupled Euro-Calliope model</a> developed by Bryn Pickering</em></p> <p>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:</p> <ol> <li>Install a specific conda environment to be working with the correct version of Calliope ( <code>conda env create -f requirements.yml</code> )</li> <li>Run the model including only those scenarios that relate to the power sector and SPORES</li> </ol> <p> </p> <p><strong>Main and parallel batches of SPORES</strong></p> <p>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).</p> <p> </p> <p><strong>Strength of the anchoring to extremes of the decision space</strong></p> <p>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:</p> <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> <p>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.</p> <p><br> <strong>Weight-assignment method</strong></p> <p>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>.</p> <pre><code class="language-bash">run.spores_options.scoring_method: integer</code></pre> <p> </p> <p><strong>Hard-coded changes to be aware of</strong></p> <p>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>).</p> <p>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.</p> <p>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>.</p> <p><br> <strong>Summary of results from the paper</strong></p> <p>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.</p>
Comparing long-term power sector pathways in Viet Nam: a simple cost-optimization approach with OSeMOSYS
<p>Six clicSAND files for OSeMOSYS Modelling in Viet Nam. The six files depict six different scenarios:</p> <p>1. Power Development Plan 7-based scenario</p> <p>2. Power Development Plan 8-based scenario</p> <p>3. Renewable Energy Development Strategy-based scenario</p> <p>4. Renewables-Led Pathway-based scenario</p> <p>5. Net Zero</p> <p>6. Clean Efficient Transition</p> <p>The Reference Energy System (RES) is also included to represent the relationship between estimated energy demands, energy conversion technologies, fuel mixes, and the resources required to satisfy the demands in the study.</p>
Data input for the RegMex model experiment on the power system and flexible sector coupling
<p>This file provides the input data used in the power system flexibility model experiment performed within the RegMex project. Comprehensive information about the project can be found in the project report [Lechtenböhmer2018] (in German, see link in the file). In the experiment performed with the data documented here, three scenarios were considered, labelled "Import", "Decentralized" and "Offshore". This file contains the input for all scenarios. All further information on the model and scenario configuration is available from the project report. Many technology parameter have been derived as own assumptions within previous projects, relying on different sources. Details can be found in the cited PhD and masters theses. In the experiment, Germany was modelled with 18 regions reflecting the transmission grid operator zones (see map in the file).</p>
Identifying cost-effective decarbonisation pathways for South Africa's power sector (EMP-A 2023) - Dataset
<p>Dataset of the project "Identifying cost-effective decarbonisation pathways for South Africa's power sector (EMP-A 2023)".</p>
Data Documentation Erdgas-BRidGE – Input data for modeling the power, building, and gas sector
<p>This data documentation provides data representing parts of the power, the building and gas sector, which have been compiled within the research project Erdgas-BRidGE (Erdgas - Bedeutung und zukünftige Rolle in der deutschen (German) Energiewende). The aim of this documentation is to increase the transparency of input data for energy modeling in the German context.</p> <p>Therefore, the report Erdgas-BRidGE_Data_Documentation_Report (2021).pdf documents the data collected and processed in the course of the project. Furthermore, the data set Erdgas-BRidGE_Dataset (2021).xlsx provides the compiled or further processed data with separate table sheets. The script available under the file name PythonCodeToProcessCapacityBookings.zip has been used to process historical capacity bookings.</p> <p>The modifications of the version 1.1.0 – compared to the previous version 1.0.0 – include the following updates:</p> <ul> <li>Minor formal corrections in the report</li> <li>An adjustment in the calculation basis of the district heating profiles</li> <li>Correction of an error in the calculation of the absolute number of individual type buildings in the database for the German building stock.</li> </ul> <p>Erdgas-BRidGE is a joined effort by the Energiewirtschaftliches Insitut an der Universität zu Köln (ewi) and the Chair of Energy Economics at the Technische Universität Dresden (TUD-EE2). The project was funded by the Federal Ministry for Economic Affairs and Energy through the grant "Erdgas-BRidGE", FKZ: 03ET4055A and FKZ: 03ET4055B.</p> <ul> </ul>
Modeling Decarbonization Pathways in the Power Sector in Developing Countries: The case of Colombia (EMP-LAC 2023) - Dataset
<p>Dataset of the project Modeling Decarbonization Pathways in the Power Sector in Developing Countries: The case of Colombia (EMP-LAC 2023) - Dataset</p>
Code and input data related to "Integrated decarbonization of hard-to-abate industry utilizing biomass reliefs burden on power sector"
<p>Input data and code for the submitted article: "Integrated decarbonization of hard-to-abate industry utilizing biomass reliefs burden on power sector" <br><br>by Alissa Ganter <sup>1,†</sup>, Paula Baumann <sup>1,2,†</sup>, Veis Karbassi <sup>2</sup>, Giovanni Sansavini <sup>1,*</sup></p> <p><sup>1 </sup>Reliability and Risk Engineering, Institute of Process and Energy Engineering, ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland</p> <p><sup>2 </sup>School of Business and Economics, RWTH Aachen University, Kackertstraße 7, 52072 Aachen, Germany</p> <p><sup>† </sup>These authors contributed equally</p> <p><sup>*</sup>Corresponding author: sansavig@ethz.ch</p>
Codes for "Co-firing biomass and coal with retrofitted carbon capture and storage ease China to achieve carbon neutrality and net negative carbon in power sector"
<p>Codes for “Co-firing biomass and coal with retrofitted carbon capture and storage ease China to achieve carbon neutrality and net negative carbon in power sector”</p>
TOURISM IS A STRATEGIC SECTOR OF THE COUNTRY'S ECONOMY AND THE FULL AND EFFECTIVE USE OF ITS POWER
Open the record for dataset details and reuse information.
Source code and data for Ou et al. 2021 (US state-level capacity expansion pathways with improved modeling of the power sector dynamics within a multisector model)
<p>For details, please check the "readme" file.</p>
LCA of Power-to-X processes and applications in the residential sector
<p>This report describes the modelling of life cycle inventories for the provision of heat and electricity for residential purpose, relying on Power-to-X conversion processes. This includes the following energy carriers: hydrogen, synthetic natural gas, and methanol. Several end use technologies are modelled, from boilers to co-generation units and fuel cells.</p> <p>The heat and electricity supply chain specifications and corresponding inventories supplied with this report are available for different LCI databases and software through the datasets included in this repository.</p>
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