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Creating and delivering products and services that promote sustainability is increasingly important in today’s economy. Novel services based on digital technologies and infrastructure can significantly contribute to sustainable development, as demonstrated by digitally enabled car-sharing services where increased asset utilization reduces production-related greenhouse gas emissions. However, there is still limited knowledge on how digital service innovation can purposefully be applied to promote sustainability. To address this gap, we conduct a systematic literature review and perform a qualitative inductive analysis of 50 articles on the impact of digital service innovation on social, environmental, and economic sustainability. We provide a comprehensive overview of real-world applications and identify five underlying mechanisms through which innovation with digital services can drive sustainable development. In doing so, we aim to pave the way to purposefully conceive, design, and implement digital services for sustainability.

Creating and delivering products and services that promote sustainability is increasingly important in today’s economy. Novel services based on digital technologies and infrastructure can significantly contribute to sustainable development, as demonstrated by digitally enabled car-sharing services where increased asset utilization reduces production-related greenhouse gas emissions. However, there is still limited knowledge on how digital service innovation can purposefully be applied to promote sustainability. To address this gap, we conduct a systematic literature review and perform a qualitative inductive analysis of 50 articles on the impact of digital service innovation on social, environmental, and economic sustainability. We provide a comprehensive overview of real-world applications and identify five underlying mechanisms through which innovation with digital services can drive sustainable development. In doing so, we aim to pave the way to purposefully conceive, design, and implement digital services for sustainability.

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur. It contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power. Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation. This is the full data bundle to be used for rigorous research. It includes large bathymetry and natural protection area datasets. While the code in PyPSA-Eur is released as free software under the MIT, different licenses and terms of use apply to the various input data, which are summarised below: corine/* CORINE Land Cover (CLC) database Source: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/ Terms of Use: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata natura/* Natura 2000 natural protection areas Source: https://www.eea.europa.eu/data-and-maps/data/natura-10 Terms of Use: https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata gebco/GEBCO_2014_2D.nc GEBCO bathymetric dataset Source: https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/ Terms of Use: https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf je-e-21.03.02.xls Population and GDP data for Swiss Cantons Source: https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html Terms of Use: https://www.bfs.admin.ch/bfs/en/home/fso/swiss-federal-statistical-office/terms-of-use.html https://www.bfs.admin.ch/bfs/de/home/bfs/oeffentliche-statistik/copyright.html nama_10r_3popgdp.tsv.gz Population by NUTS3 region Source: http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en Terms of Use: https://ec.europa.eu/eurostat/about/policies/copyright GDP_per_capita_PPP_1990_2015_v2.nc Gross Domestic Product per capita (PPP) from years 1999 to 2015 Rectangular cutout for European countries in PyPSA-Eur, including a 10 km buffer Kummu et al. "Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015" Source: https://doi.org/10.1038/sdata.2018.4 and associated dataset https://doi.org/10.1038/sdata.2018.4 ppp_2019_1km_Aggregated.tif The spatial distribution of population in 2020: Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution. Rectangular cutout for non-NUTS3 countries in PyPSA-Eur, i.e. MD and UA, including a 10 km buffer WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00647 Source: https://data.humdata.org/dataset/worldpop-population-counts-for-world and https://hub.worldpop.org/geodata/summary?id=24777 License: Creative Commons Attribution 4.0 International Licens data/bundle/era5-HDD-per-country.csv - Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875- License: CC-BY 4.0- Contains country-level heating degree days in Europe for 1941-2023. Used for rescaling heat demand in weather years not covered by energy balance statistics. data/bundle/era5-runoff-per-country.csv - Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875- License: CC-BY 4.0- Contains country-level daily sum of runoff in Europe for 1941-2023. Used for rescaling hydro-electricity availability in weather years not covered by EIA hydro-generation statistics. shipdensity_global.zip Global Shipping Traffic Density Creative Commons Attribution 4.0 https://datacatalog.worldbank.org/search/dataset/0037580/Global-Shipping-Traffic-Density seawater_temperature.nc Global Ocean Physics Reanalysis Link: https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/services License: https://marine.copernicus.eu/user-corner/service-commitments-and-licence

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur. It contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power. Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation. This is the full data bundle to be used for rigorous research. It includes large bathymetry and natural protection area datasets. While the code in PyPSA-Eur is released as free software under the MIT, different licenses and terms of use apply to the various input data, which are summarised below: corine/* CORINE Land Cover (CLC) database Source: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/ Terms of Use: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata natura/* Natura 2000 natural protection areas Source: https://www.eea.europa.eu/data-and-maps/data/natura-10 Terms of Use: https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata gebco/GEBCO_2014_2D.nc GEBCO bathymetric dataset Source: https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/ Terms of Use: https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf je-e-21.03.02.xls Population and GDP data for Swiss Cantons Source: https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html Terms of Use: https://www.bfs.admin.ch/bfs/en/home/fso/swiss-federal-statistical-office/terms-of-use.html https://www.bfs.admin.ch/bfs/de/home/bfs/oeffentliche-statistik/copyright.html nama_10r_3popgdp.tsv.gz Population by NUTS3 region Source: http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en Terms of Use: https://ec.europa.eu/eurostat/about/policies/copyright GDP_per_capita_PPP_1990_2015_v2.nc Gross Domestic Product per capita (PPP) from years 1999 to 2015 Rectangular cutout for European countries in PyPSA-Eur, including a 10 km buffer Kummu et al. "Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015" Source: https://doi.org/10.1038/sdata.2018.4 and associated dataset https://doi.org/10.1038/sdata.2018.4 ppp_2019_1km_Aggregated.tif The spatial distribution of population in 2020: Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution. Rectangular cutout for non-NUTS3 countries in PyPSA-Eur, i.e. MD and UA, including a 10 km buffer WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00647 Source: https://data.humdata.org/dataset/worldpop-population-counts-for-world and https://hub.worldpop.org/geodata/summary?id=24777 License: Creative Commons Attribution 4.0 International Licens data/bundle/era5-HDD-per-country.csv - Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875- License: CC-BY 4.0- Contains country-level heating degree days in Europe for 1941-2023. Used for rescaling heat demand in weather years not covered by energy balance statistics. data/bundle/era5-runoff-per-country.csv - Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875- License: CC-BY 4.0- Contains country-level daily sum of runoff in Europe for 1941-2023. Used for rescaling hydro-electricity availability in weather years not covered by EIA hydro-generation statistics. shipdensity_global.zip Global Shipping Traffic Density Creative Commons Attribution 4.0 https://datacatalog.worldbank.org/search/dataset/0037580/Global-Shipping-Traffic-Density seawater_temperature.nc Global Ocean Physics Reanalysis Link: https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/services License: https://marine.copernicus.eu/user-corner/service-commitments-and-licence

We present a modeling and optimization framework to design powertrains for a family of electric vehicles, focusing on the concurrent sizing of their motors and batteries. Whilst tailoring these component modules to each individual vehicle type can minimize energy consumption, it can result in high production costs due to the variety of component modules to be realized for the family of vehicles, driving the Total Costs of Ownership (TCO) high. Against this backdrop, we explore modularity and standardization strategies whereby we jointly design unique motor and battery modules to be installed in all the vehicles in the family, using a different number of these modules when needed. Such an approach results in higher production volumes of the same component module, entailing significantly lower manufacturing costs due to Economy-of-Scale (EoS) effects, and hence a potentially lower TCO for the family of vehicles. To solve the resulting one-size-fits-all problem, we instantiate a nested framework consisting of an inner convex optimization routine which jointly optimizes the modules' sizes and the powertrain operation of the entire family, for given driving cycles and modules' multiplicities. Likewise, we devise an outer loop comparing each configuration to identify the minimum-TCO solution with global optimality guarantees. Finally, we showcase our framework on a case study for the Tesla vehicle family in a benchmark design problem, considering the Model S, Model 3, Model X, and Model Y. Our results show that, compared to an individually tailored design, the application of our concurrent design optimization framework achieves a significant reduction of the production costs for a minimal increase in operational costs, ultimately lowering the family TCO in the benchmark design problem by 3.5\%. 17 pages, 17 figures, 7 tables

We present a modeling and optimization framework to design powertrains for a family of electric vehicles, focusing on the concurrent sizing of their motors and batteries. Whilst tailoring these component modules to each individual vehicle type can minimize energy consumption, it can result in high production costs due to the variety of component modules to be realized for the family of vehicles, driving the Total Costs of Ownership (TCO) high. Against this backdrop, we explore modularity and standardization strategies whereby we jointly design unique motor and battery modules to be installed in all the vehicles in the family, using a different number of these modules when needed. Such an approach results in higher production volumes of the same component module, entailing significantly lower manufacturing costs due to Economy-of-Scale (EoS) effects, and hence a potentially lower TCO for the family of vehicles. To solve the resulting one-size-fits-all problem, we instantiate a nested framework consisting of an inner convex optimization routine which jointly optimizes the modules' sizes and the powertrain operation of the entire family, for given driving cycles and modules' multiplicities. Likewise, we devise an outer loop comparing each configuration to identify the minimum-TCO solution with global optimality guarantees. Finally, we showcase our framework on a case study for the Tesla vehicle family in a benchmark design problem, considering the Model S, Model 3, Model X, and Model Y. Our results show that, compared to an individually tailored design, the application of our concurrent design optimization framework achieves a significant reduction of the production costs for a minimal increase in operational costs, ultimately lowering the family TCO in the benchmark design problem by 3.5\%. 17 pages, 17 figures, 7 tables

Batteries can effectively improve the security of energy systems and mitigate climate change by facilitating wind and solar power. The installed capacity of battery energy storage system (BESS), mainly the lithium ion batteries are increasing significantly in recent years. However, the battery degradation cannot be accurately quantified and integrated into energy management system with existing heuristic battery degradation models. This paper proposed a hierarchical deep learning based battery degradation quantification (HDL-BDQ) model to quantify the battery degradation given scheduled BESS daily operations. Particularly, two sequential and cohesive deep neural networks are proposed to accurately estimate the degree of degradation using inputs of battery operational profiles and it can significantly outperform existing fixed or linear rate based degradation models as well as single-stage deep neural models. Training results show the high accuracy of the proposed system. Moreover, a learning and optimization decoupled algorithm is implemented to strategically take advantage of the proposed HDL-BDQ model in optimization-based look-ahead scheduling (LAS) problems. Case studies demonstrate the effectiveness of the proposed HDL-BDQ model in LAS of a microgrid testbed. 12 pages

Batteries can effectively improve the security of energy systems and mitigate climate change by facilitating wind and solar power. The installed capacity of battery energy storage system (BESS), mainly the lithium ion batteries are increasing significantly in recent years. However, the battery degradation cannot be accurately quantified and integrated into energy management system with existing heuristic battery degradation models. This paper proposed a hierarchical deep learning based battery degradation quantification (HDL-BDQ) model to quantify the battery degradation given scheduled BESS daily operations. Particularly, two sequential and cohesive deep neural networks are proposed to accurately estimate the degree of degradation using inputs of battery operational profiles and it can significantly outperform existing fixed or linear rate based degradation models as well as single-stage deep neural models. Training results show the high accuracy of the proposed system. Moreover, a learning and optimization decoupled algorithm is implemented to strategically take advantage of the proposed HDL-BDQ model in optimization-based look-ahead scheduling (LAS) problems. Case studies demonstrate the effectiveness of the proposed HDL-BDQ model in LAS of a microgrid testbed. 12 pages