List of talks
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Both the program and the order of the talks are now frozen.
Summary table
The following table summarizes the presentations and indicates start times based on 10 minute intervals.
Talks 4 and 7 will not be uploaded to YouTube.
Times in CEST +0200.
| No | Start | Presenter | Title |
|---|---|---|---|
| — | 15:00 | Jan Unnewehr, Robbie Morrison | Introduction |
| 1 | 15:10 | Maarten Brinkerink | Building and Calibrating a Country-Level Detailed Global Electricity Model Based on Public Data |
| 2 | 15:20 | Sylvain Quoilin | The Dispa‑SET Africa model |
| 3 | 15:30 | Dan Stowell | A harmonised, high-coverage, open dataset of solar photovoltaic installations in the UK |
| 4 | 15:40 | Christopher Arderne | Predictive mapping of the global power system using open data |
| 5 | 15:50 | Leon Schwenk‑Nebbe | Dataset: A proxy for historical CO2 emissions related to centralised electricity generation in Europe |
| — | 16:00 | — | Coffee break |
| 6 | 16:10 | Jan Diettrich | Open European gas transmission network data set (SciGRID_gas) |
| 7 | 16:20 | Jacques de Chalendar | Tracking emissions in the US electricity system |
| 8 | 16:30 | Jan Unnewehr, Mirko Schäfer | Carbon intensity of electricity production — new approach for dynamic grid emission factors |
| 9 | 16:40 | Oleg Lugovoy, Shuo Gao | merra2ools: MERRA‑2 subset and R‑package for evaluation of renewables on 0.5° lat × 0.625° lon global grid, 1980–2020 hourly |
| 10 | 16:50 | Fabian Hofmann | atlite: A Lightweight Python Package for Calculating Renewable Power Potentials and Time Series |
| — | 17:00 | Jan Unnewehr | Feedback |
Full submissions
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Maarten Brinkerink. “Building and Calibrating a Country-Level Detailed Global Electricity Model Based on Public Data”. An introduction to PLEXOS-World - a detailed global power system model - and the associated datasets. The presentation will consist of a brief overview of the model development and some application examples. I’ll furthermore touch upon how we deal with openness of data, methods and model. Paper in Energy Strategy Reviews. Data set and Model. Brinkerink et al (2021).
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Sylvain Quoilin. “The Dispa-SET Africa model”. This recently-released open-source, open-data power system model covers the whole African continent. The presentation will focus on the main challenges with regard to data availability, the modeling approach and the potential for future developments and collaborative research. Paper in Energy. Pavičević et al (2021).
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Dan Stowell. “A harmonised, high-coverage, open dataset of solar photovoltaic installations in the UK”. We present the results of a major crowd-sourcing campaign to create open geographic data for over 260,000 solar PV installations across the UK, covering an estimated 86% of the capacity in the country. Our approach is designed to support high-resolution solar power forecasting to reduce the carbon impact of electricity grids. The method is applied to the UK but applicable worldwide. We will describe the dataset as well as the community process we used (OpenStreetMap). Paper in Nature Scientific Data. Stowell et al (2020).
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Christopher Arderne. “Predictive mapping of the global power system using open data”. In many regions, electrical grid data is hard to find, outdated or inaccurate. This presented challenges for government and private planners, and anyone interested in understanding the distribution of infrastructure and the needs for on- and off-grid investments. Using satellite imagery and some big assumptions, we can make estimates about where the grid is likely to be. It’s only accurate enough for high-level investigation, but it’s just useful enough while we wait for ground-truth data to catch up. Paper in Nature Scientific Data. - Gridfinder, Github. Arderne et al (2020).
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Leon J. Schwenk-Nebbe. “Dataset: A proxy for historical CO2 emissions related to centralised electricity generation in Europe”. Paper in Data in Brief. Schwenk‑Nebbe et al (2021).
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Jan C. Diettrich. “Open European gas transmission network data set (SciGRID_gas)”. We present the results of our three year project that aimed at generating an open data set of the European gas transmission network. Component types include pipelines, compressors, storages and LNG terminals for static gas flow modelling. A large number of different data sources had to be gathered and merged. As not all data sources were publicly available, those data set locations and our soon to be open SciGRID_gas Python code will also be discussed briefly, so that anyone can generate the best possible network data set, including copy right restricted data. Data.
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Jacques A. de Chalendar. “Tracking emissions in the US electricity system” To encourage and guide decarbonization efforts, better tools are needed to monitor real-time electricity system emissions from electricity consumption, production, imports, and exports. Until now, time-intensive, ad-hoc and manual data verification strategies are used to prepare the data for quantitative analysis. As an alternative to existing techniques, this work introduces a physics-informed framework to greatly accelerate and automate such procedures and enable the availability of internally consistent electric system operating data available in real-time, for the benefit of policy makers, private sector actors and researchers. The effectiveness of the framework is demonstrated by applying it to an example data set for the continental United States electricity network; emissions for electricity consumption, production and exchanges are also computed. The method that was developed in this work was implemented in a software system that updates this data set hourly. Paper in PNAS. Github, Visualization. de Chalender et al (2019).
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Jan Unnewehr, Mirko Schäfer. “Carbon intensity of electricity production - new approach for dynamic grid emission factors”. Dynamic grid emission factors provide a temporally resolved signal about the carbon intensity of electricity generation in the power system. Since actual emission measurements are usually lacking, such a signal has to be derived from given system-specific emission factors combined with power generation time series. We present a bottom-up methodology, which allows to derive per country and technology emission factors for European countries based on power plant generation time series and reported emissions from the EU ETS mechanism. The resulting historical per country carbon intensity of electricity generation is compared with corresponding values from a top-down approach, which uses statistical data on emissions and power generation on national scales.
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Oleg Lugovoy, Shuo Gao. “merra2ools: MERRA-2 subset and R-package for evaluation of renewables on 0.5° lat x 0.625° lon global grid, 1980-2020 hourly”. Data in Dryad, R-package.
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Fabian Hofmann. “atlite: A Lightweight Python Package for Calculating Renewable Power Potentials and Time Series”
atliteis an open Python software package for retrieving global historical weather data (ERA5/SARAH-2) and converting it to power generation potentials and time series for renewable energy technologies like wind turbines or solar photovoltaic panels. It further provides weather-dependent output on the demand side like building heating demand and heat pump performance. Usingxarray,daskandrasterio, the software is optimized to aggregate data over multiple large regions with user-defined weightings, e.g. based on land use eligibility constraints. https://github.com/PyPSA/atlite.
Withdrawn submissions
[was and replaced by a short coffee break] 6. Adam Pluta. “A Python Library to Efficiently Extract OpenStreetMap Data”. Paper in Journal of Open Research Software. Puta and Lünsdorf (2020).
Cited papers
A not‑necessarily complete list of cited papers.
Arderne, C, C Zorn, C Nicolas, and EE Koks (15 January 2020). “Predictive mapping of the global power system using open data”. Scientific Data. 7 (1): 19. ISSN 2052-4463. doi:10.1038/s41597-019-0347-4. CC‑BY‑4.0 license.
Brinkerink, Maarten, Brian Ó Gallachóir, and Paul Deane (1 January 2021). “Building and calibrating a country-level detailed global electricity model based on public data”. Energy Strategy Reviews. 33: 100592. ISSN 2211-467X. doi:10.1016/j.esr.2020.100592. CC‑BY‑4.0 license.
de Chalendar, Jacques A, John Taggart, and Sally M Benson (17 December 2019). “Tracking emissions in the US electricity system”. Proceedings of the National Academy of Sciences. 116 (51): 25497–25502. ISSN 0027-8424. doi:10.1073/pnas.1912950116. CC‑BY‑NC‑ND‑4.0 license.
Pavičević, Matija, Matteo De Felice, Sebastian Busch, Ignacio Hidalgo González, and Sylvain Quoilin (1 August 2021). “Water-energy nexus in African power pools – the Dispa-SET Africa model”. Energy. 228: 120623. ISSN 0360-5442. doi:10.1016/j.energy.2021.120623. Closed access.
Pluta, Adam and Ontje Lünsdorf (1 September 2020). “esy-osmfilter – a Python library to efficiently extract OpenStreetMap data”. Journal of Open Research Software. 8 (1): 19. ISSN 2049-9647. doi:10.5334/jors.317. CC‑BY‑4.0 license.
Schwenk‑Nebbe, Leon Joachim, Marta Victoria, and Gorm Bruun Andresen (1 June 2021). “Dataset: a proxy for historical CO2 emissions related to centralised electricity generation in Europe”. Data in Brief. 36: 107016. ISSN 2352-3409. doi:10.1016/j.dib.2021.107016. CC‑BY‑4.0 license.
Stowell, Dan, Jack Kelly, Damien Tanner, Jamie Taylor, Ethan Jones, James Geddes, and Ed Chalstrey (13 November 2020). “A harmonised, high-coverage, open dataset of solar photovoltaic installations in the UK”. Scientific Data. 7 (1): 394. ISSN 2052-4463. doi:10.1038/s41597-020-00739-0. CC‑BY‑4.0 license.