A lack of aggregate analysis concerning energy communities exists in the academic literature. The authors utilized a combination of literature reviews and desk research to fill this gap. The existing debate on the classification of energy communities was summarized and aligned. Discovered classifications were used to analyze the status quo of the sector. The authors found nearly 4000 energy communities with 900,000 members in the European Union. On average, there are 844 members per one energy community. Germany, the Netherlands, Denmark, and the United Kingdom are at the forefront of the movement. Different countries have different primary sources of renewable energy utilized by energy communities, and membership structures vary based on the energy source and corporate purpose of the energy community together with the sector’s maturity in a certain country. Predominantly, hydro and biomass are used by energy communities in Alpine countries, solar energy is used in Germany, Spain, and France, wind in the Netherlands and Denmark, and different renewables in the United Kingdom. More members have joined the hydro, biomass, and wind communities than solar communities. Each country has national and regional associations of energy communities. In addition, intermediary actors, researchers, and consultancy agencies have shown a growing interest in the deployment of the movement. Achieving a conformity of business models Europe-wide would probably be impossible and pointless. Distinct geographical, institutional, and policy context-specific conditions stimulate diversity rather than conformity.

Battery-powered electric mobility is currently the most promising technology for the decarbonisation of the transport sector, alongside hydrogen-powered vehicles, provided that the electricity used comes 100% from renewable energy sources. To estimate its electricity demand both nationwide and in individual smaller communities, a calculation based assessment on driving profiles that are as realistic as possible is required. The developed model based analysis presented in this paper for the creation of driving and thus electricity load profiles makes it possible to build different compositions of driving profiles. The focus of this paper lies in the analysis of motorised private transport, which makes it possible to assess future charging and load control potentials in a subsequent analysis. We outline the differences in demand and driving profiles for weekdays as well as for Saturdays, Sundays and holidays in general. Furthermore, the modelling considers the length distribution of the individual trips per trip purpose and different start times. The developed method allows to create individual driving and electric vehicle (EV) demand profiles as well as averaged driving profiles, which can then be scaled up and analysed for an entire country.

Interest in and demand for battery electric vehicles (BEVs) is growing strongly due to the increasing awareness of climate change and specific decarbonization goals. One of the largest challenges remains the provision of large-scale, efficient charging infrastructure in multi-apartment buildings. Successful load management (LM) for BEV charging directly influences the technical requirements and the economic and environmental aspects of charging infrastructure and can prevent costly distribution grid expansion. The main objective of this paper is to evaluate potential LM approaches in multi-apartment buildings to avoid an increase in existing electricity demand peaks with BEV diffusion. Using our model parameters, off-peak charging achieved a 40% reduction in the building’s demand peak at 100% BEV diffusion compared to uncontrolled charging and reduced the correlation between BEV charging and the national share of thermal power generation. The most efficient charging capacity in the private network was achieved at 0.44 kW/BEV. A verification of the model results with the demonstration phase of the “Urcharge” project supports our overall findings. Our results outline the advantages of LM across a large-scale BEV charging network to control the impact on the electricity system along with the diffusion of e-mobility.