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15 results for “Calliope”
Auxiliary Euro-Calliope datasets: Spatio-temporal data representing national cooking demand and electric vehicle characteristic profiles in Europe
<p>Output generated by the <a href="https://github.com/RAMP-project/">RAMP engine</a> for use in the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope model</a>. The three datasets in this repository are described briefly here and in more detail in the accompanying README files. Each dataset has an hourly temporal resolution spanning the years 2000 - 2018 (inclusive) and a national spatial resolution spanning 26* - 28** countries in Europe. All datasets are dimensionless; only the profile shapes are used in Euro-Calliope.</p> <ul> <li>Cooking energy demand profiles (<em>ramp-cooking-profiles</em>): Profiles of heat energy demand for cooking in buildings in Europe, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP">RAMP model</a> [1]. These profiles are used to distribute annual cooking energy demand in the Euro-Calliope workflow. This dataset covers 28 European countries**.</li> <li>Electric vehicle plug-in profiles (<em>ramp-ev-plugin-profiles</em>): Profiles of the percentage of parked electric vehicles, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-Mobility model</a> [2]. These profiles are used in Euro-Calliope to define the maximum number of electric vehicles that could be plugged in and therefore available to be charged at any given time, assuming controlled (or "smart") charging. This dataset covers 26 European countries*.</li> <li>Electric vehicle energy consumption profiles (<em>ramp-ev-consumption-profiles</em>): Profiles of the electricity consumption of electric vehicles, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-Mobility model</a> [2]. These profiles are aggregated in Euro-Calliope to provide a required percentage of total vehicle electricity demand that must be met in each month. This dataset covers 26 European countries*.</li> </ul> <p>* AUT, BEL, CHE, CZE, DEU, DNK, ESP, EST, FIN, FRA, GBR, HRV, HUN, IRL, ITA, LTU, LUX, LVA, NLD, NOR, POL, PRT, ROU, SVK, SVN, SWE</p> <p>** (*) + BGR, SRB</p> <p>*** ALB, MKD, GRC, CYP, BIH, MNE, ISL</p> <p>[1] Lombardi, Francesco, Sergio Balderrama, Sylvain Quoilin, and Emanuela Colombo. 2019. ‘Generating High-Resolution Multi-Energy Load Profiles for Remote Areas with an Open-Source Stochastic Model’. <em>Energy</em> 177 (June): 433–44. https://doi.org/10.1016/j.energy.2019.04.097.</p> <p>[2] Mangipinto, Andrea, Francesco Lombardi, Francesco Davide Sanvito, Matija Pavičević, Sylvain Quoilin, and Emanuela Colombo. 2022. ‘Impact of Mass-Scale Deployment of Electric Vehicles and Benefits of Smart Charging across All European Countries’. <em>Applied Energy</em> 312 (April): 118676. https://doi.org/10.1016/j.apenergy.2022.118676.</p>
Dictionary of ENBIOS processors, Euro-Calliope inputs and ecoinvent .spold filenames
<p>Provides full details of ENBIOS structural processor structure, input sources from Euro-Calliope files, processor names and energy carrier details, and the names of the corresponding life cycle inventory (LCI) data files in ecoinvent .spold format. Note that the LCI .spold files referenced are those for the allocation at point of substitution (APOS) approach within version 3.8 of the ecoinvent database.</p> <p>New version (18 February 2022) includes new listings for biomass and coal as industrial fuels, as now included in ENBIOS base file.</p>
Calliope-ENBIOS process Dictionary
<p>This is a dictionary to connect Carriers and technologies from the Calliope Software with the processors of the ENBIOS tool</p>
Auxiliary Euro-Calliope datasets: QTDIAN storyline-specific spatial data to represent a European energy system model at several spatial resolutions
<p>Custom output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow.</p> <p>This output provides similar data to <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with three additional land area scenarios.</p> <p>These scenarios are in line with three storylines from the <a href="https://zenodo.org/record/5834010">QTDIAN toolbox</a> and are based on updating the `possibility-for-electricity-autarky` workflow configuration to include the following parameters (also included in `config.yaml`):</p> <p> </p> <pre><code> scenarios: people-powered: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 0.2 # agro pv share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 0.1 share-rooftop-used: 1.0 government-directed: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0 market-driven: use-of-protected areas: true pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 1.0 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0</code></pre> <p> </p> <p>This dataset includes different spatial resolutions of land availability. For more information on the `ehighways` resolution, see <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a>.</p> <p>This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.</p> <p> </p>
Pre-built Swiss-Calliope sector-coupled energy model
<div> <h3>Swiss-Calliope prebuilt model</h3> <div>The model consists of Switzerland and its neighbours, as described in <a href="https://doi.org/10.1016/j.enconman.2024.118426" target="_blank" rel="noopener">Mellot et al., 2024</a>. Switzerland's heating and transport sectors are modelled on top of its electricity sector.</div> <br> <div>The model is ready to be loaded into Calliope, for 2016--2018. This prebuilt was specifically designed for the study of Switzerland's winter deficit, but is easily modifiable for any other analysis. Refer to Calliope's <a href="https://calliope.readthedocs.io/en/stable/" target="_blank" rel="noopener">documentation</a> for information on how to do this.</div> <br> <div>To run the same scenarios as for the Swiss winter deficit analysis, you need to do the following steps. Note that these scenarios were ran on ETH's Euler cluster which uses the slurm batch system. You can otherwise just adapt the following shell scripts to run the scenarios on other systems.</div> <div>1. Set up the conda environment with the correct version of calliope <code>conda env create -f environment.yaml</code>. On slurm systems you may also need to load gurobi <code>module load new gurobi/9.0.0</code>.</div> <div>2. Run the baseline scenarios, i.e. those corresponding to the EP2050+ configuration, by running <code>sh run_baselines 4</code>, where 4 corresponds to the time resolution.</div> <div>3. Once these runs are finished, run the python file <code>python read_baselines_and_fix_neighbours.py</code>. This will fix Switzerland's neighbouring countries' installed capacities for the next scenarios.</div> <div>4. Then you may run the study's scenarios by running <code>sh run_initial_scenarios.sh 4</code>, and the sensitivity analysis scenarios by running <code>sh run_sensitivies.sh 4</code>.</div> <div> </div> <div>The model's units are GW, GWh, Million euros, and Million kilometers.</div> </div>
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>
Input datasets for Euro-Calliope
<p><a href="https://euro-calliope.readthedocs.io/">Euro-Calliope</a> is a set of models of the European energy sytem and an automatic workflow to generate them. Euro-Calliope is based on a variety of input datasets each of which describes a certain aspect of the energy system, like generation potentials, historical generation, and energy demand. The workflow building all models does not contain data but instead automatically derives input data from their source where possible. For some input datasets this is not possible and this folder includes these datasets.</p>
Euro-Calliope: Pre-built models
<p>Ready to use models of the European electricity system built for use in <em>Calliope</em>. Models are available on three different spatial resolutions: continental, national, and regional.</p> <p>In addition, Euro-Calliope models can be built manually which adds more configuration options. To learn how to build Euro-Calliope manually, head over to <a href="https://euro-calliope.readthedocs.io">Euro-Calliope’s documentation</a>.</p> <p><strong>At a glance</strong></p> <p>Euro-Calliope models the European electricity system with each location representing an administrative unit. It is built on three spatial resolutions: on the continental level as a single location, on the national level with 34 locations, and on the regional level with 497 locations. At each location, renewable generation capacities (wind, solar, bioenergy) and balancing capacities (battery, hydrogen) can be built. In addition, hydro electricity and pumped hydro storage capacities can be built up to the extent to which they exist today. All capacities are used to satisfy electricity demand on all locations where demand is based on historic data. Locations are connected through transmission lines of either unrestricted capacity or projections. Using <a href="https://www.callio.pe">Calliope</a>, the model is formulated as a linear optimisation problem with total monetary cost of all capacities as the minimisation objective. Due to the flexibility of Calliope and the availability of the routines building the model all components can be adapted to the modeller’s needs.</p> <p><strong>Prepare</strong></p> <ol> <li> <p>Install a Gurobi license on your computer (<a href="https://www.gurobi.com/downloads/end-user-license-agreement-academic/">academic license</a> comes at no cost), or <a href="https://euro-calliope.readthedocs.io/en/latest/model/customisation/#manual-changes">choose a different solver</a>.</p> </li> <li> <p>Install Calliope and all required dependencies. The easiest way to do so is using <a href="https://conda.io/">conda</a> or <a href="https://mamba.readthedocs.io/">mamba</a>. Using conda, you can install Calliope:</p> </li> </ol> <pre><code>cd pre-built-euro-calliope-v1.1.0 conda env create -f environment.yaml conda activate euro-calliope</code></pre> <p><strong>Run</strong></p> <p>There are three models in the directory of the pre-builts – one for each of the three spatial resolutions continental, national, and regional. You can run all three models out-of-the-box, but you may want to modify the model. By default, the model runs for the first day of January only. To run the example model on the continental resolution type:</p> <pre><code>calliope run ./continental/example-model.yaml</code></pre> <p><strong>Customise</strong></p> <p>The pre-built models are examples and very likely require customisation to fit your purpose. Once you’ve managed to run them, it’s a good point in time to learn about <a href="https://euro-calliope.readthedocs.io/en/latest/model/customisation/">model customisation options in Euro-Calliope</a>.</p> <p><strong>More information</strong></p> <p>For more information on Euro-Calliope and how to use and modify the models, see <a href="https://euro-calliope.readthedocs.io">Euro-Calliope’s documentation</a>.</p> <p><strong>License and attribution</strong></p> <p>Euro-Calliope is developed and maintained within the <a href="https://www.callio.pe">Calliope project</a>.<br> <br> This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p>Contains modified Copernicus Atmosphere Monitoring Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p>Contains modified data from <a href="https://www.renewables.ninja/">Renewables.ninja</a>.</p> <p>Contains modified data from <a href="https://open-power-system-data.org">Open Power System Data</a>.</p>
Auxiliary Euro-Calliope datasets: Spatial data to represent a European energy system model at several spatial resolutions
<p>Main output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow.</p> <p>This output provides similar data to <a href="https://doi.org/10.5281/zenodo.3246302">https://doi.org/10.5281/zenodo.3246302</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with two key differences:</p> <ol> <li>The spatial extent has been expanded to include Iceland.</li> <li>Two new spatial resolutions have been added: `ehighways` and `ehighways_disaggregated`.</li> </ol> <p>`ehighways` defines 98 regions based on the result of work undertaken in the European Commission Seventh Framework Programme project e-HIGHWAY 2050 [1]. The regions cover 35 European countries; 19 are described at a national resolution and the rest at a subnational resolution. Those at a subnational resolution are aggregated from NUTS3-2006 statistical units. `ehighways_disaggregated` provides the data at the resolution of statistical units in Europe, which is then aggregated to produce the data at the `ehighways` resolution. The mapping from statistical units to ehighways regions is defined in `./ehighways/statistical_units_to_ehighways_regions.csv`. `./ehighways/units.png` shows a map of the resulting 98 `ehighways` regions. The region colours are used to help differentiate regions and have no other meaning.</p> <p>This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.</p> <p>[1] Anderski, T., Surmann, Y., Stemmer, S., Grisey, N., Momot, E., Leger, A.-C., Betraoui, B., and van Roy, P. (2014). European cluster model of the Pan-European transmission grid (e-HIGHWAY 2050)</p>
National Data Files for Pre-built Sector-coupled Euro-Calliope Model
<p>National time series data derived from the <a href="https://zenodo.org/record/5774988#.YtUQ9-zP3Ph">Sector-coupled Euro-Calliope Pre-built Model</a></p>
Pre-built Sector-coupled Euro-Calliope Model
<p><strong>Sector-coupled Euro-Calliope subnational-scale pre-built models</strong></p> <p>Built using <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a> commit hash: 6fd0bf3dce2a0799ac9821b50e9b1513fa783018</p> <p>This model is pre-packaged and ready to be loaded into Calliope, based on 2010 - 2018 input data. To run the model you will need to do the following:</p> <p>a. Install a specific conda environment to be working with the correct version of Calliope (<code>conda env create -f requirements.yml</code>)</p> <p>b. Include specific scenarios to pick up the relevant sectors. For the study accompanying this release, The following scenarios were included <code>"industry_fuel_shared,transport,heat,config_overrides,res_2h,gas_storage,link_cap_dynamic,freeze-hydro-capacities,add-biofuel"</code>, where:</p> <ul> <li> <p><code>industry_fuel_shared</code>: Includes all non-electrical industry demands and the necessary technologies to generate those fuels synthetically. This includes e.g. annual methanol requirements for the chemical industry. <code>shared</code> refers to the fact that all regions' annual demand is pooled and can be met across all regions. The other option is to set this to <code>industry_fuel_isolated</code>, where a region must meet its own annual demand by generation of fuel within the region.</p> </li> <li> <p><code>transport</code>: This ensures ICE and EV light and heavy vehicle technologies and annual demands are in the model. It also includes reference to constraints required to make smart-charging of EVs work (e.g. weekly demand requirements).</p> </li> <li> <p><code>heat</code>: This ensures that all heat provision technologies and hourly demands are in the model. Carriers added are <code>heat</code> (space heating and hot water) and <code>cooking</code>. Technologies added can be found in <code>heat-techs.yaml</code>.</p> </li> <li> <p><code>config_overrides</code>: This includes high-level simplifications, such as removal of technologies that are considered redundant (e.g. less interesting combined heat and power technologies).</p> </li> <li> <p><code>res_2h</code>: Sets the model with a 2h resolution. Can be omitted or can be one of <code>res_2h</code>, <code>res_3h</code>, <code>res_6h</code>, <code>res_12h</code>. The full hourly resolution model takes ~2 days to complete.</p> </li> <li> <p><code>gas_storage</code>: Includes underground methane storage facilities, based on latest data on a national level. Can be omitted to remove the option of this technology.</p> </li> <li> <p><code>link_cap_dynamic</code>: Sets a limit on transmission line capacities. The limits are chosen subjectively based on current capacity, such that lines with smaller current capacities can proportionally increase much more (e.g. 100x) than larger lines (e.g. 2x). See <code>national/links.yaml</code> for other override options to apply here.</p> </li> <li> <p><code>freeze-hydro-capacities</code>: Sets hydro capacities to equal "today's" capacities. This seems more reasonable than setting current capacities as upper limits, as this causes the model to install no hydro.</p> </li> <li> <p><code>add-biofuel</code>: Enables a biofuel supply stream with a distinct <code>biofuel</code> carrier, with annual limits on biofuel that can be provided (based on JRC residuals). This differs from Euro-Calliope v1.0 which is a black box technology converting biofuel to electricity directly.</p> </li> </ul> <p>c. decide on a SPORES run to undertake, e.g. the scenario <code>spores_supply</code> will run SPORES for primary energy supply technologies. SPORES scenarios can be found in the file <code>spores.yaml</code>.</p> <p>d. pick your run year, by pointing to the relevant model config file (e.g. <code>model-2018.yaml</code> for the 2018 weather year).</p> <p>e. run the model via the dedicated scripts found in this directory. These scripts include the addition of custom constraints and have been copied directly from the workflow, where they would normally be initiated as part of the internal process. However, you can load and run them in an interactive session / with your own python script to call them:</p> <p><code class="language-python">[1] import create_input </code></p> <p><code class="language-python">[2] create_input.build_model(path_to_model_yaml, scenarios_string, path_to_netcdf_of_model_inputs) </code></p> <p><code class="language-python">[3] import run </code></p> <p><code class="language-python">[4] run.run_model(path_to_netcdf_of_model_inputs, path_to_netcdf_of_results)</code></p> <p> </p> <p><code class="language-python">Note: The only difference between this version and v0.1 is that spurious hidden files specific to MacOS have been removed from the dataset.</code></p>
Euro-Calliope model and results for "Open Source Energiewende" multi-model analysis
<p>Contains the model version of Euro-Calliope applied in the multi-model analysis "Open Source Energiewende" and the aggregated results of six scenarios. See `./README.md` for more information.</p>
Fertility And Sexual Function In CAH: CALLIOPE
ClinicalTrials.gov study NCT07099456. IPD Sharing: Not stated. Countries: 1. Publications: 17.
Data from: Three-dimensional simulation for fast forward flight of a calliope hummingbird
We present a computational study of flapping-wing aerodynamics of a calliope hummingbird (Selasphorus calliope) during fast forward flight. Three-dimensional wing kinematics were incorporated into the model by extracting time-dependent wing position from high-speed videos of the bird flying in a wind tunnel at 8.3 m s−1. The advance ratio, i.e. the ratio between flight speed and average wingtip speed, is around one. An immersed-boundary method was used to simulate flow around the wings and bird body. The result shows that both downstroke and upstroke in a wingbeat cycle produce significant thrust for the bird to overcome drag on the body, and such thrust production comes at price of negative lift induced during upstroke. This feature might be shared with bats, while being distinct from insects and other birds, including closely related swifts.
Data from: Three-dimensional simulation for fast forward flight of a calliope hummingbird
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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