With view to the high share of the transport sector in total energy consumption, e-mobility should play an important role within the transition of the energy systems. Policymakers in several countries consider electric vehicles (EV) as an alternative to fossil-fueled vehicles. In order to allow for the development of EV, the charging infrastructure has to be set up at locations with high charging potential, identified by means of various criteria such as demand density or trip length. Many methodologies for locating charging stations (CS) have been developed in the last few years. First, this paper presents a broad overview of publications in the domain of CS localization. A classification scheme is proposed regarding modeling theory and empirical application; further on, models are analyzed, distinguishing between users, route or destination centricity of the approaches and outcomes. In a second step, studies in the field of explicit spatial location planning are reviewed in more detail, that is, in terms of their target criteria and the specialization of underlying analytical processes. One divergence of these approaches lies in the varying level of spatial planning, which could be crucial depending on the planning requirements. It is striking that almost all CS locating concepts are proposed for urban areas. Other constraints, such as the lack of extensive empirical EV traffic data for a better understanding of the driving behavior, are identified. This paper provides an overview of the CS models, a classification approach especially considering the problem’s spatial dimension, and derives perspectives for further research.

Integrated spatial and energy planning has become a major field of interest to meet the current renewable energy share expansion and CO2 emissions reduction targets. Geographic Information Systems (GIS) play a considerable role in supporting decision making in this field. Solar potential maps are a popular strategy to promote renewable energy generation through photovoltaic (PV) panel installations at city and municipal scales. They indicate the areas of roofs that would provide the maximum amount of energy in kWh per year. These are often used to suggest “optimal locations” for PV-panels and/or recommend system sizes to achieve a certain level of yearly autarchy. This approach is acceptable if PVs have only a minor share in the local energy supply system. However, increased PV-penetration can lead to instability of the local grid, create hazards for the security of the supply, and considerably escalate the storage and system back-up requirements. To obtain a proper understanding of the consequences for the local energy balance when selecting or rejecting a certain installation, examining the hourly and intra-hourly time series of the potential energy generation from PVs is necessary. This paper introduces a GIS-based procedure to estimate the potential PV-electricity generation time series for every roof-top section within a study area using open source software. This procedure is complemented by a series of strategies to select suitable PV-installations considering the time series analysis of supply and demand. Furthermore, thirteen technical indicators are considered to evaluate the PV-installation sets selected with every strategy. The capabilities of the procedure are tested using data from a German rural municipality. The proposed procedure constitutes an efficient and accessible way to assess solar potentials at the municipal scale and to design roof-top PV exploitation plans, which are more appropriate to fulfill the local energy requirements

Independence from the power grid can be pursued by achieving total self-sufficient electricity supply. Such an energy supply model might be particularly interesting for settlements located in rural areas where enough space is available for energy generation installations. This article evaluates how and at what cost electricity demand of residential users across Germany and the Czech Republic could be covered by hybrid renewable energy generation systems consisting of photovoltaics, micro-generation wind turbines and batteries. High-resolution reanalysis data are used to calculate necessary system sizes over a large area by simultaneously accounting for the temporal variability of renewable energy. For every potential location in the research area, the hybrid system requirements for clusters of 50 self-sufficient single-family houses are calculated. The results indicate no general trend regarding the size of the respective technologies, although larger areas where PV-wind power complementarity enables lowering the total system costs and required storage capacities were determined. Assuming that the cluster of households could be constituted and depending on the location, the total installation and operation costs for the proposed systems for a lifetime of 20 years range between EUR 1.8 Million and EUR 5 Million without considering costs of financing. Regions with the lowest costs were identified mainly in the south of Germany.

Wildlife–vehicle collisions (WVCs) cause significant road mortality of wildlife and have led to the installation of protective measures along streets. Until now, it has been difficult to determine the impact of roadside infrastructure that might act as a barrier for animals. The main deficits are the lack of geodata for roadside infrastructure and georeferenced accidents recorded for a larger area. We analyzed 113 km of road network of the district Freyung-Grafenau, Germany, and 1571 WVCs, examining correlations between the appearance of WVCs, the presence or absence of roadside infrastructure, particularly crash barriers and fences, and the relevance of the blocking effect for individual species. To receive infrastructure data on a larger scale, we analyzed 5596 road inspection images with a neural network for barrier recognition and a GIS for a complete spatial inventory. This data was combined with the data of WVCs in GIS to evaluate the infrastructure’s impact on accidents. The results show that crash barriers have an effect on WVCs, as collisions are lower on roads with crash barriers. In particular, smaller animals have a lower collision share. The risk reduction at fenced sections could not be proven as fenced sections are only available at 3% of the analyzed roads. Thus, especially the fence dataset must be validated by a larger sample number. However, these preliminary results indicate that the combination of artificial intelligence and GIS may be used to analyze and better allocate protective barriers or to apply it in alternative measures, such as dynamic WVC risk-warning.