• Energy Research

A model of the European power system built using Calliope. This repository contains the workflow routines that automatically build the model from source data. Alternatively to building models yourself, you can use pre-built models that run out-of-the-box. See README.md for further information. If you use euro-calliope in an academic publication, please cite the following article: Tr��ndle, T., Lilliestam, J., Marelli, S., Pfenninger, S., 2020. Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe. Joule.

Code to reproduce results of Tröndle et al (2019) --- "Home-made or imported: on the possibility for renewable electricity autarky on all scales in Europe". This repository contains the entire research project, including code and report. The philosophy behind this repository is that no intermediary results are included, but all results are computed from raw data and code. To learn how results can be replicated see ./README.md. This repository is released under the MIT license, see ./LICENSE.

This dataset contains statistics of the sonnendach.ch dataset at the national level. See README.md for more information.

pre-built Euro-Calliope Ready to use models of the European electricity system built using Calliope. Models are available on three different spatial resolutions: continental, national, and regional. In addition, euro-calliope models can be built manually which adds more configuration options. To build euro-calliope manually, head over to GitHub. At a glance 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. On each node, 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 which is based on historic data. Locations are connected through transmission lines of unrestricted capacity. Using Calliope, the model is formulated as a linear optimisation problem with total monetary cost of all capacities as the minimisation objective. The pre-built models can be manipulated by updating any of the files. In addition to the pre-built models, models can be built manually. Manual builds provide more flexibility in adapting and configuring the model. To build euro-calliope manually, head over to GitHub. Get ready to run the models You need a Gurobi license installed on your computer. You may as well choose another solver than Gurobi. See Calliope���s documentation to understand how to switch to another solver. You need to have Calliope and Gurobi installed in your environment. The easiest way to do so is using conda. Using conda, you can create a conda environment from within you can build the model: conda env create -f environment.yaml conda activate euro-calliope Run the models There are three models in this directory ��� 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: $ calliope run ./continental/example-model.yaml For more information on how to use and modify Calliope models, see Calliope���s documentation. Manipulating the model using overrides Calliope overrides allow to easily manipulate models. An override named freeze-hydro-capacities can be used for example in this way: calliope run build/model/continental/example-model.yaml --scenario=freeze-hydro-capacities You can define your own overrides to manipulate any model component. The following overrides are built into euro-calliope: directional-rooftop-pv By default, euro-calliope contains a single technology for rooftop PV. This technology comprises the total rooftop PV potential in each location, in particular including east-, west-, and north-facing rooftops. While this allows to fully exploit the potential of rooftop PV, it leads to less than optimal capacity factors as long as the potential is not fully exploited. That is because, one would likely first exploit all south-facing rooftop, then east- and west-facing rooftops, and only then ��� if at all ��� north-facing rooftops. By default, euro-calliope cannot model that. When using the directional-rooftop-pv override, there are three instead of just one technologies for rooftop PV. The three technologies comprise (1) south-facing and flat rooftops, (2) east- and west-facing rooftops, and (3) north-facing rooftops. This leads to higher capacity factors of rooftop PV as long as the potential of rooftop PV is not fully exploited. However, this also increases the complexity of the model. freeze-hydro-capacities By default, euro-calliope allows capacities of run-of-river hydro, reservoir hydro, and pumped storage hydro capacities up to today���s levels. Alternatively, it���s possible to freeze these capacities to today���s levels using the freeze-hydro-capacities override. Model components The models contain the following files. All files in the root directory are independent of the spatial resolution. All files that depend on the spatial resolution are within subfolders named by the resolution. ��������� {resolution} <- For each spatial resolution an individual folder. ��� ��������� capacityfactors-{technology}.csv <- Timeseries of capacityfactors of all renewables. ��� ��������� directional-rooftop.yaml <- Override discriminating rooftop PV by orientation. ��� ��������� electricity-demand.csv <- Timeseries of electricity demand on each node. ��� ��������� example-model.yaml <- Calliope model definition. ��� ��������� link-all-neighbours.yaml <- Connects neighbouring locations with transmission. ��� ��������� locations.csv <- Map from Calliope location id to name of location. ��� ��������� locations.yaml <- Defines all locations and their max capacities. ��������� build-metadata.yaml <- Metadata of the build process. ��������� demand-techs.yaml <- Definition of demand technologies. ��������� environment.yaml <- Conda file defining an environment to run the model in. ��������� interest-rate.yaml <- Interest rates of all capacities. ��������� link-techs.yaml <- Definition of link technologies. ��������� README.md <- The file you are currently looking at. ��������� renewable-techs.yaml <- Definition of supply technologies. ��������� storage-techs.yaml <- Definition of storage technologies. Units of quantities The units of quantities within the models are the following: power: 100,000 MW energy: 100,000 MWh area: 10,000 km2 monetary cost: 1e+09 EUR These units were chosen in order to minimise numerical issues within the optimisation algorithm. License and attribution euro-calliope has been developed and is maintained by Tim Tr��ndle, IASS Potsdam. If you use euro-calliope in an academic publication, please cite the following article: Tr��ndle, T., Lilliestam, J., Marelli, S., Pfenninger, S., 2020. Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe. Joule. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. 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. Contains modified data from Renewables.ninja. Contains modified data from Open Power System Data.

The rapid uptake of renewable energy technologies in recent decades has increased the demand of energy researchers, policymakers and energy planners for reliable data on the spatial distribution of their costs and potentials. For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally. A particular thread of this research attempts to go beyond purely technical or spatial restrictions and determine the realistic, feasible or actual potential for wind energy. Motivated by these developments, this paper reviews methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials. We address each of these potentials in turn, including aspects related to land eligibility criteria, energy meteorology, and technical developments of wind turbine characteristics such as power density, specific rotor power and spacing aspects. Economic aspects of potential assessments are central to future deployment and are discussed on a turbine and system level covering levelized costs depending on locations, and the system integration costs which are often overlooked in such analyses. Non-technical approaches include scenicness assessments of the landscape, constraints due to regulation or public opposition, expert and stakeholder workshops, willingness to pay/accept elicitations and socioeconomic cost-benefit studies. For each of these different potential estimations, the state of the art is critically discussed, with an attempt to derive best practice recommendations and highlight avenues for future research. Renewable Energy, 182 ISSN:0960-1481 ISSN:1879-0682

AbstractAs the climate targets tighten and countries are impacted by several crises, understanding how and under which conditions carbon dioxide emissions peak and start declining is gaining importance. We assess the timing of emissions peaks in all major emitters (1965–2019) and the extent to which past economic crises have impacted structural drivers of emissions contributing to emission peaks. We show that in 26 of 28 countries that have peaked emissions, the peak occurred just before or during a recession through the combined effect of lower economic growth (1.5 median percentage points per year) and decreasing energy and/or carbon intensity (0.7) during and after the crisis. In peak-and-decline countries, crises have typically magnified pre-existing improvements in structural change. In non-peaking countries, economic growth was less affected, and structural change effects were weaker or increased emissions. Crises do not automatically trigger peaks but may strengthen ongoing decarbonisation trends through several mechanisms.

Abstract Since Russia’s 2022 invasion, Ukraine’s civilian energy infrastructure has faced systematic attack and requires urgent and strategic reconstruction. This study confronts the dual challenges of rebuilding Ukraine’s energy system rapidly to mitigate civilian and economic disruption while aligning this to long-term goals of sustainability and energy security. We demonstrate that Ukraine can readily meet future energy demands through a fully renewable electrified system at costs comparable to those from fossil fuels and nuclear power. Contrary to previous reliance on high-carbon energy sources, we find a diversified renewable energy portfolio, including significant solar photovoltaic and wind contributions, can efficiently meet growing energy demands and position Ukraine as an energy exporter, capitalising on its geographical advantages. This study’s approach, based on open data and models, extends beyond national borders and offers a model for post-conflict reconstruction that harmonizes immediate recovery with sustainable energy transition.