Background: Germany has set ambitious goals for the reduction of greenhouse gases. The decarbonisation of the energy system has been in focus. An important means to achieve this is the increased utilisation of wind energy. The growth of wind power entails changes not only in the electrical system but also in the landscape and environment. Prospectively, scenarios will have to consider a wide range of aspects, not only economics and technology but also nature conservation and social affairs. The authors are participating in the research study "Szenarien für den Ausbau der erneuerbaren Energien aus Naturschutzsicht", funded by the Federal Agency for Nature Conservation, which examines the possibilities of integrating nature conservation into the development of scenarios. Methods: For aspects of nature conservation to be taken into account in scenario development, a multi-stage methodology has been developed to assess the conflict risk of wind energy and nature conservation throughout Germany. To ensure comparability of the scenarios, all of them are based on the same general framework consisting of fixed excluded areas, the same method of detail allocation and the same overall expected energy output. The "nature conservation" driver is integrated in the form of a nationwide comparative assessment of risk levels. The mapping of spatially differentiated risk levels for wind energy has been achieved in a GIS-based and discursive process. Results: The results show that nature conservation can be addressed properly in scenario-building. Here, the method of multi-criterion scenario-building itself, with its focus on including nature conservation as one of several drivers determining the spatial distribution of wind turbines, is a major result. The authors have developed specific scenarios that mainly address questions of landscape and nature conservation. Out of the four generic scenarios presented for the year 2035, two have nature conservation as their main driver, whereas the other two consider energy-economic drivers only. Examining these scenarios provides insight into the influence of each driver. For example, adding nature conservation as the main driver (highest priority) reduces the specific conflict risk by 26.1%, while at the same time only a relatively small increase in wind turbines is required (+12.5% in numbers, +2.3% in installed power capacity). Conclusion: The methods developed here provide a driver for allocating wind power plants to reduce conflicts in high-risk areas. Furthermore, using the same spatial distribution of risk levels makes it possible to subsequently rate the scenarios from a conservation perspective. The method developed here provides the means to analyse trade-offs between relevant drivers. The "nature conservation" scenarios show a relatively small additional demand for wind turbines but a greater amount of avoided conflict risk.

The electrification of the transport sector together with an increasing share of renewable energies has the potential to reduce CO2 emissions significantly. This transformation requires the rollout of charging infrastructure, which has an impact on power grids. For grid planning and dimensioning purposes, it is crucial to assess this rapidly growing impact. We present an approach using socio-economic data such as income levels together with a model for demographic changes to estimate where electric mobility is likely to be concentrated, especially during the transformation phase. We present a total-cost-of-ownership approach for the ramp-up of electric mobility, considering an increased penetration of renewable energies. With the city of Wiesbaden in Germany as an example for an application area, the possible expansion of vehicle ownership and charging points is modeled on the level of individual buildings. Compared to a simpler approach, the detailed model results in more consistent charging point allocations, higher line/transformer loadings and lower bus voltages for the investigated grids. Predicting future distributions of charging points with such a level of detail in terms of ramp-up and spatial resolution proves potentially beneficial for grid analysis and planning purposes, especially in urban areas, where infrastructure changes are expensive and time-consuming.

Abstract. Considering climate change, it is essential to reduce CO2 emissions. The provision of charging infrastructure in public spaces for electromobility – along with the substitution of conventional power generation by renewable energies – can contribute to the energy transition in the transport sector. Scenarios for the spatial distribution of this charging infrastructure can help to exemplify the need for charging points and their impact, for example on power grids. We model two kinds of demand for public charging infrastructure. First, we model the demand for public charging points to compensate for the lack of home charging points, which is derived from a previously developed and published model addressing electric-vehicle ownership (with and without home charging options) in households. Second, and in the focus of the work presented here, is the demand for public charging infrastructure at points of interest (POIs). Their locations are derived from OpenStreetMap (OSM) data and weighted based on an evaluation of movement profiles from the Mobilität in Deutschland survey (MiD, German for “Mobility in Germany”). We combine those two demands with the available parking spaces and generate distributions for possible future charging points. We use a raster-based approach in which all vector data are rasterized and computations are performed on a municipality's full grid. The presented application area is Wiesbaden, and the methodology is generally applicable to municipalities in Germany. The model is compared with three other models or model variants in a correlation comparison in order to determine the influence of certain model assumptions and input data. The identification of potential charging points in public spaces plays an important role in modeling the future energy system – especially the power grid – as the rapid adoption of electric vehicles will shift locations of electrical demand. With our investigation, we would like to present a new method to simulate future public charging point locations and show the influences of different modeling methods.