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An increasing number of municipalities are striving for energy autonomy. This study determines in which municipalities and at what additional cost energy autonomy is feasible for a case study of Germany. An existing municipal energy system optimization model is extended to include the industrial, commercial and personal transport sectors. Multiple regression methods are benchmarked in order to identify the model best suited for the transfer of individual optimization results to a large proportion of German municipalities. The resulting levelized cost of energy (LCOE) from the optimization of representative case study municipalities are transferred using energy-relevant indicators. The study demonstrates that energy autonomy is technically feasible in 6,314 (56%) municipalities. Thereby, the LCOEs increase in the autonomous case on average by 0.41 €/kWh compared to the minimum cost scenario. Apart from energy demand, base-load-capable bioenergy and deep geothermal energy have the greatest influence on the LCOEs. Overall, it appears that municipal energy autonomy is not economically viable under current framework conditions. This study represents a starting point for defining possible scenarios in studies of future national energy system or transmission grid expansion planning, which for the first time consider completely energy autonomous municipalities. [ABSTRACT FROM AUTHOR]

Recent global events emphasize the importance of a reliable energy supply. One way to increase energy supply security is through decentralized off-grid renewable energy systems, for which a growing number of case studies are researched. This review gives a global overview of the levelized cost of electricity (LCOE) for these autonomous energy systems, which range from 0.03 $ 2021 /kWh to over 1.00 $ 2021 /kWh worldwide. The average LCOEs for 100% renewable energy systems have decreased by 9% annually between 2016 and 2021 from 0.54 $ 2021 /kWh to 0.29 $ 2021 /kWh, most likely due to cost reductions in renewable energy and storage technologies. This review identifies and discusses seven key reasons why LCOEs are frequently overestimated or underestimated in research, and how this can be prevented in the future. This overview can be employed to verify findings on off-grid systems, to assess where these systems might be deployed and how costs evolve. [Display omitted] • Global overview of 161 studies on LCOEs of decentralized off-grid energy systems. • Studies using simulation models tend to oversize system components. • LCOEs for 100% renewable energy systems decreased by 9% annually from 2016 to 2021. • Most case studies were conducted for India (22%), Iran (7%) and China (7%). [ABSTRACT FROM AUTHOR]

[Display omitted] • Local energy system transformation in line with national GHG reduction strategies. • Consideration of temporal building stock dynamic and heterogeneity. • Case study for central European city Karlsruhe. • 192 stochastic building stock transformation scenarios. Climate change mitigation and transformation strategies with expansion targets for renewable energy sources are defined at the national level. Due to the decentralised character of these sources, local energy system planning plays an important role. However, local communities often lack the capacity to develop energy concepts and thus exploit local renewable potentials consistently. This study develops a highly transferable methodology for deriving local energy system transformation scenarios in line with national greenhouse gas reduction strategies. Thus, an energy system optimisation model is substantially extended to collectively optimise the transformation of final energy demand in the residential, industry, tertiary and transport sectors, as well as established and niche greenhouse gas reduction technologies. Here, a focus is set on the building stock transformation, and a stochastic model is presented to better grasp and represent the dynamic developments and heterogeneity of the local building stock. Based on superordinate parameters such as retrofit rates and heating technology diffusion, the stochastic model generates informative building stock scenarios that are used as input for the developed energy system optimisation model. Exemplarily, the model is applied to the central European city of Karlsruhe. The results show that an increase of the retrofit rate to 2 %/a and strong electrification of the heat supply in the building sector is economically and environmentally beneficial. Furthermore, an accelerated expansion of photovoltaics compared to the national expansion rate can save costs and CO 2 emissions. Building on the methodology presented, transferable infrastructure models for the electricity, gas and district heating network should be developed that can be used to assess the feasibility of the transformation paths determined by the methodology presented. [ABSTRACT FROM AUTHOR]

In the context of the energy transition, municipalities are increasingly attempting to exploit renewable energies. Socio-energetic data are required as input for municipal energy system analyses. This Data Descriptor provides a compilation of 40 indicators for all 11,131 German municipalities. In addition to census data such as population density, mobility data such as the number of vehicles and data on the potential of renewables such as wind energy are included. Most of the data set also contains public data, the allocation of which to municipalities was an extensive task. The data set can support in addressing a wide range of energy-related research challenges. A municipality typology has already been developed with the data, and the resulting municipality grouping is also included in the data set. Measurement(s) Demographic Data • vehicle • renewable energy Technology Type(s) digital curation Sample Characteristic - Environment city Sample Characteristic - Location Germany Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9933890 [ABSTRACT FROM AUTHOR]

The rapid uptake of renewable energy technologies in recent decades has increased the demand of energy researchers, policymakers and energy planners for reliable data on the spatial distribution of their costs and potentials. For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally. A particular thread of this research attempts to go beyond purely technical or spatial restrictions and determine the realistic, feasible or actual potential for wind energy. Motivated by these developments, this paper reviews methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials. We address each of these potentials in turn, including aspects related to land eligibility criteria, energy meteorology, and technical developments of wind turbine characteristics such as power density, specific rotor power and spacing aspects. Economic aspects of potential assessments are central to future deployment and are discussed on a turbine and system level covering levelized costs depending on locations, and the system integration costs which are often overlooked in such analyses. Non-technical approaches include scenicness assessments of the landscape, constraints due to regulation or public opposition, expert and stakeholder workshops, willingness to pay/accept elicitations and socioeconomic cost-benefit studies. For each of these different potential estimations, the state of the art is critically discussed, with an attempt to derive best practice recommendations and highlight avenues for future research. • Review of over 300 studies on large-scale onshore wind potential assessments. • Analysis of geographic, technical, economic and 'feasible' potentials. • Identify weaknesses in methods and best practice examples. • Methods could improve transparency, validation, and sensitivities. • Further interdisciplinary research required on 'feasible' potentials. [ABSTRACT FROM AUTHOR]

Bottom-up energy system models are often based on hourly time steps due to limited computational tractability or data availability. However, in order to properly assess the rentability and reliability of energy systems by accounting for the intermittent nature of renewable energy sources, a higher level of detail is necessary. This study reviews different methods for increasing the temporal resolutions of time series data for global horizontal and direct normal irradiance for solar energy, and wind speed for wind energy. The review shows that stochastic methods utilizing random sampling and non-dimensional approaches are the most frequently employed for solar irradiance data downscaling. The non-dimensional approach is particularly simple, with global applicability and a robust methodology with good validation scores. The temporal increment of wind speed, however, is challenging due to its spatiotemporal complexity and variance, especially for accurate wind distribution profiles. Recently, researchers have mostly considered methods that draw on the combination of meteorological reanalysis and stochastic fluctuations, which are more accurate than the simple and conventional interpolation methods. This review provides a road map of how to approach solar and wind speed temporal downscaling methods and quantify their effectiveness. Furthermore, potential future research areas in solar and wind data downscaling are also highlighted. • Sub-hourly modeling is relevant for both present and future energy systems. • A higher level of accuracy is obtained with sub-hourly energy system modeling. • A systematic review and analysis of solar and wind downscaling approaches. • Insights into the principles of the approaches, global use, and error measurements. • Future research areas and advancements in solar and wind downscaling approaches. [ABSTRACT FROM AUTHOR]