The transition to renewable energy is essential for reducing greenhouse gas emissions, particularly in the heating sector, which relies on fossil fuels. This study examines the integration of residential heat pumps (HPs) in German municipalities and their role in the country’s energy transition. Using a spatially explicit model, the research analyzes local energy consumption, renewable power production, and HP suitability. Results highlight regional disparities in HP adoption potential and renewable power generation. Rural areas show higher proportional energy savings in residential heating, while urban centers offer greater absolute savings. However, increased electricity demand for HP operation presents challenges due to variable renewable generation. The study finds that HPs could reduce residential heating energy consumption by 55%, but electricity demand could rise by 100 TWh annually. Regions with high renewable energy production, like northern Germany, are better positioned to meet this demand, while urban areas remain dependent on grid electricity. Seasonal fluctuations in renewable energy complicate HP integration, emphasizing the need for enhanced grid infrastructure, energy storage, and demand-side management strategies. This research provides a framework for evaluating HP deployment at the municipal level, offering insights into energy transition dynamics and informing policy decisions for sustainable decarbonization of the heating sector.

Germany has made significant progress towards a renewable and climate-neutral energy system at the federal and state levels, but there is a lack of information on the local use of renewable electricity. To assess the decentralized contributions of renewable electricity to local and total gross electricity consumption, and to gain insight into how the renewable electricity landscape are evolving on the ground, a method was developed to balance renewable electricity generation and gross electricity consumption at the municipal level on an annual basis. A rural-urban and a north-south divide was identified in renewable electricity generation and gross electricity consumption. Municipalities with a population density of more than 100 inhabitants per km2, which represent about 88% of the total population in Germany, will cover only 22% of their gross electricity consumption with locally generated renewable electricity in 2019. The majority of renewable electricity is produced in sparsely populated regions, mostly in the northern and eastern parts of Germany, far from the southern and western centers of electricity consumption. The approach developed thus provides comprehensive insights into local contributions to the energy transition and enables detailed monitoring and assessment of renewable energy development and utilization based on population and land area data.

Since 2009, Germany's Renewable Energy Sources Act has promoted the erection of ground-mounted photovoltaic (PV) plants next to transport routes (railways, federal roads and federal highways) as these areas are considered to be socially, economically and ecologically less valuable. Recent amendments to the act have gradually expanded this strip next to transport routes from 110 m in 2009, to 200 m in 2021 and 500 m in 2023. Our study investigated the effect of these amendments by analysing the development of ground-mounted PV systems next to transport routes between 2000 and 2023 using geo-information data on plant sites, transport networks and site properties. The area data for ground-mounted PV plants indicate that more and more PV plants are being built on land next to transport routes. Currently, 39 % of all ground-mounted PV plants (area) are located within 500 m of a transport route (6919.5 MW). Our analysis shows that the introduction of subsidies has stimulated the expansion of such installations along transport routes, however there has also been a general expansion of ground-mounted PV systems. Our research also reveals that this expansion did not start from zero, as it was already common practice that PV plants were being built on the 500-m strip many years before the subsidy was introduced. In terms of land use patterns and soil quality, the areas covered by PV plants next to transport routes are mostly agricultural areas with all levels of yield potential.

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While storytelling and visualization have always been recognized as invaluable techniques for imparting knowledge across generations, their importance has become even more evident in the present information age as the abundance of complex data grows exponentially. These techniques can simplify convoluted concepts and communicate them in a way to be intelligible for diverse audiences, bringing together heterogeneous stakeholders and fostering collaboration. In the field of energy and climate research, there is an increasing demand to make sophisticated models and their outcomes explainable and comprehensible for an audience of laypersons. Unfortunately, traditional tools and methods may be inefficient to provide meaning for input and output values; therefore, in this study, we employ a storytelling tool, the so-called Academic Presenter, to digest various datasets and visualize the extended BioENergy OPTimization model (BENOPTex) outcomes in different online and offline formats. The developed tool facilitates communications among collaborators with a broad spectrum of backgrounds by transforming outcomes into visually appealing stories. Although this study focuses on designing an ideal user interface for BENOPTex, the developed features and the learned lessons can be replicated for other energy system models.

Information on geo-locations of renewable energy installations is very useful to investigate spatial, social or environmental questions on their impact at local and national level. However, existing data sets do not provide a sufficiently accurate representation of these installations in Germany over space and time. This work provides a valid approach on how a data set of wind power plants, photovoltaic field systems, bioenergy plants and hydropower plants can be created for Germany based on a data extract from the Core Energy Market Data Register (CEMDR) and publicly available data. Established methods were used (e.g., random forest, image recognition), but new techniques were also developed to fill data gaps or locate misplaced renewable energy installations. In this way, a substantial part of the CEMDR data could be corrected and processed in such a way that it can be freely used in a GIS software by any scientific and non-scientific discipline.

In recent years, electricity production from wind turbines and photovoltaic systems has grown significantly in Germany. To determine the multiple impacts of rising variable renewable energies on an increasingly decentralized power supply, spatially and temporally resolved data on the power generation are necessary or, at least, very helpful. Because of extensive data protection regulations in Germany, especially for smaller operators of renewable power plants, such detailed data are not freely accessible. In order to fill this information gap, simulation models employing publicly available plant and weather data can be used. The numerical simulations are performed for the year 2016 and consider an ensemble of almost 1.64 million variable renewable power plants in Germany. The obtained time series achieve a high agreement with measured feed-in patterns over the investigated year. Such disaggregated power generation data are very advantageous to analyze the energy transition in Germany on a spatiotemporally resolved scale. In addition, this study also derives meaningful key figures for such an analysis and presents the generated results as detailed maps at county level. To the best of our knowledge, such highly resolved electricity data of variable renewables for the entire German region have never been shown before.

Wind power has risen continuously over the last 20 years and covered almost 25% of the total German power provision in 2019. To investigate the effects and challenges of increasing wind power on energy systems, spatiotemporally disaggregated data on the electricity production from wind turbines are often required. The lack of freely accessible feed-in time series from onshore turbines, e.g., due to data protection regulations, makes it necessary to determine the power generation for a certain region and period with the help of numerical simulations using publicly available plant and weather data. For this, a new approach is used for the wind power model which utilizes a sixth-order polynomial for the specific power curve of a turbine. After model validation with measured data from a single wind turbine, the simulations are carried out for an ensemble of 25,835 onshore turbines to determine the German wind power production for 2016. The resulting hourly resolved data are aggregated into a time series with daily resolution and compared with measured feed-in data of entire Germany which show a high degree of agreement. Such electricity generation data from onshore turbines can be applied to optimize and monitor renewable power systems on various spatiotemporal scales.

The energy system transformation in Germany is a challenge for society, economy and politics and has several impacts on multiple scales. This paper investigates the effects of the trajectories towards net zero emissions by 2050 through focusing on the spatial dimension of impacts, benefits, and losses for different stakeholders and technologies. Spatial heterogeneity in the energy transition means that regions enjoying benefits from decarbonization might diverge from regions experiencing losses, and that there are different geographical potentials and challenges. The question arising is one of the need for redistribution between benefits and losses, whilst ensuring that all stakeholders remain willing to act as frontrunners in the transformation of the energy system. Inclusion and participation in the process, together with a carefully targeted mixed set of regional energy policy, combining tax solutions and incentives for acceptance of required measures could facilitate a successful, efficient policy-supported energy transition.

Photovoltaics, as one of the most important renewable energies in Germany, have increased significantly in recent years and cover up to 50% of the German power provision on sunny days. To investigate the manifold effects of increasing renewables, spatiotemporally disaggregated data on the power generation from photovoltaic (PV) systems are often mandatory. Due to strict data protection regulations, such information is not freely available for Germany. To close this gap, numerical simulations using publicly accessible plant and weather data can be applied to determine the required spatiotemporal electricity generation. For this, the sunlight-to-power conversion is modeled with the help of the open-access web tool of the Photovoltaic Geographical Information System (PVGIS). The presented simulations are carried out for the year 2016 and consider nearly 1.612 million PV systems in Germany, which have been aggregated into municipal areas before performing the calculations. The resulting hourly resolved time series of the entire plant ensemble are converted into a time series with daily resolution and compared with measured feed-in data to validate the numerical simulations that show a high degree of agreement. Such power production data can be used to monitor and optimize renewable energy systems on different spatiotemporal scales.

The share of wind power in the generation of electricity has increased significantly in recent years and, despite its volatility, variable energy from wind turbines has become an essential pillar for the power supply in many countries around the world. To investigate the effects of increasing variable renewables on power grids, the environment or electricity markets, detailed power generation data from wind turbines with high spatial and temporal resolution are often mandatory. The lack of freely accessible feed-in time series, for example due to data protection regulations, makes it necessary to determine the wind power feed-in for a required region and period with the help of numerical simulations. Our contribution demonstrates how such a numerical simulation can be developed using publicly available wind turbine and weather data. Herein, a novel model approach will be presented for the wind-to-power conversion, which utilizes a sixth-order polynomial for the specific power curve of a wind turbine. After such an analytical representation is derived for a certain turbine, its output power can be easily calculated using the wind speed and air temperature at its hub height. For proof of concept and model validation, measured feed-in time-series of a geographically and technically known wind turbine are compared with the simulated time-series at a high temporal resolution of 10 minutes. In order to determine the power generation for larger regions or an entire country the derived numerical simulation is also carried out for an ensemble of almost 26 thousand onshore wind turbines in Germany with a total capacity of about 44 GW. With this ensemble, first simulation results with municipal and hourly resolution can be presented for an annual period.

The expansion of renewable energy technologies, accompanied by an increasingly decentralized supply structure, raises many research questions regarding the structure, dimension, and impacts of the electricity supply network. In this context, information on renewable energy plants, particularly their spatial distribution and key parameters—e.g., installed capacity, total size, and required space—are more and more important for public decision makers and different scientific domains, such as energy system analysis and impact assessment. The dataset described in this paper covers the spatial distribution, installed capacity, and commissioning year of wind turbines, photovoltaic field systems, and bio- and river hydro power plants in Germany. Collected from different online sources and authorities, the data have been thoroughly cross-checked, cleaned, and merged to generate validated and complete datasets. The paper concludes with notes on the practical use of the dataset in an environmental impact monitoring framework and other potential research or policy settings.