DOI: 10.5281/zenodo.3988921
title: dsmlib - region4FLEX example (Supplementary Material for the manuscript: Assessment of the regionalised demand response potential in Germany using an open source tool and dataset)

description: This is the supplementary material for the manuscript:<br> Heitkoetter, Wilko, et al. "Assessment of the regionalised demand response potential in Germany using an open source tool and dataset." <em>Advances in Applied Energy</em> (2020): 100001.<br> Article DOI (open access): https://doi.org/10.1016/j.adapen.2020.100001 <strong>REPOSITORY CONTENT</strong><br> This repository contains the dsmlib python tool for calculating regionalised load shifting potentials and cost-potential curves.<br> Further, the input data and load shifting potential results of the region4FLEX example are provided (region4FLEX model description). In the region4FLEX example dsmlib is applied to the 401 German administrative districts (NUTS-3 regions) considering multiple demand sectors and technologies (see Metadata). <strong>quick_start_dsmlib_region4FLEX.zip:</strong><br> This file contains the python dsmlib source code and the resulting average and maximum load shifting potential values per administrative district for all technologies (e.g.: see "/results/2030/extreme_values/av_values_p_max.csv" for average load increase potentials in 2030). Unless otherwise stated the units of the results are in MW for power and MWh for energy. (For more information on the results, please refer to \dsmlib-zenodo\examples\region4FLEX_dsm_potential\README.md --&gt; section Results). The resulting time series and large-scale input data are not contained, to allow for a fast download. <strong>full_download_dsmlib_region4FLEX.zip:</strong><br> This file contains the python dsmlib source code and the full set of input and result data.<br> For more information refer to /dsmlib/examples/region4FLEX_dsm_potential/README.md <strong>LICENSES</strong><br> The developed source code is licensed under the GPL v3 License. All result data are licensed under the CC-BY 4.0 License.<br> The input data are licensed under different open licenses. For more information refer to the provided README files, LICENSE files and input_data_overview files. <strong>METADATA</strong><br> <strong>Demand sectors:</strong> Residential, commercial trade and services, industry, power-to-heat, power-to-gas, e-mobility<br> <strong>Technologies:</strong> Washing, drying, cooling, ventilation, AC, air separation, cement production, pulping, paper production,<br> recycled paper production, process heat, heat pumps, resistive space heating, resistive DHW heating, power-to-heat in district heating,<br> power-to-methane, power-to-hydrogen, e-mobility <br> <strong>Energy sectors:</strong> Electricity (+ interfaces to heat, gas and transport sector)<br> <strong>Geographical scope:</strong> Germany<br> <strong>Geographical resolution:</strong> Administrative districts (NUTS-3)<br> <strong>Temporal scope:</strong> 2018, 2030<br> <strong>Temporal resolution:</strong> 15min <strong>NEWS AND CONTACT</strong><br> This dataset will be used as part of the region4FLEX model. If you wish to receive news or have general questions please contact: wheitkoetter(at)gmail.com