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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.

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.

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.

The reuse of research software is central to research efficiency and academic exchange. The application of software enables researchers with varied backgrounds to reproduce, validate, and expand upon study findings. Furthermore, the analysis of open source code aids in the comprehension, comparison, and integration of approaches. Often, however, no further use occurs because relevant software cannot be found or is incompatible with existing research processes. This results in repetitive software development, which impedes the advancement of individual researchers and entire research communities. In this article, the DataDesc ecosystem is presented, an approach to describing data models of software interfaces with detailed and machine-actionable metadata. In addition to a specialized metadata schema, an exchange format and support tools for easy collection and the automated publishing of software documentation are introduced. This approach practically increases the FAIRness, i.e., findability, accessibility, interoperability, and so the reusability of research software, as well as effectively promotes its impact on research.

In recent years, large language models have achieved breakthroughs on a wide range of benchmarks in natural language processing and continue to increase in performance. Recently, the advances of large language models have raised interest outside the natural language processing community and could have a large impact on daily life. In this paper, we pose the question: How will large language models and other foundation models shape the future product development process? We provide the reader with an overview of the subject by summarizing both recent advances in natural language processing and the use of information technology in the engineering design process. We argue that discourse should be regarded as the core of engineering design processes, and therefore should be represented in a digital artifact. On this basis, we describe how foundation models such as large language models could contribute to the design discourse by automating parts thereof that involve creativity and reasoning, and were previously reserved for humans. We describe how simulations, experiments, topology optimizations, and other process steps can be integrated into a machine-actionable, discourse-centric design process. Finally, we outline the future research that will be necessary for the implementation of the conceptualized framework.

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., Energy & Industry

The electrification of heating, cooling, and transportation to reach decarbonization targets calls for a rapid expansion of renewable technologies. Due to their decentral and intermittent nature, these technologies require robust planning that considers non-technical constraints and flexibility options to be integrated effectively. Energy system models (ESMs) are frequently used to support decision-makers in this planning process. In this study, 116 case studies of local, integrated ESMs are systematically reviewed to identify best-practice approaches to model flexibility and address non-technical constraints. Within the sample, storage systems and sector coupling are the most common types of flexibility. Sector coupling with the transportation sector is rarely considered, specifically with electric vehicles even though they could be used for smart charging or vehicle-to-grid operation. Social aspects are generally either completely neglected or modeled exogenously. Lacking actor heterogeneity, which can lead to unstable results in optimization models, can be addressed through building-level information. A strong emphasis on cost is found and while emissions are also frequently reported, additional metrics such as imports or the share of renewable generation are nearly entirely absent. To guide future modeling, the paper concludes with a roadmap highlighting flexibility and robustness options that either represent low-hanging fruit or have a large impact on results.

Little is known about the role of active peer effects (interpersonal contact) compared to passive peer effects (noticing or seeing) regarding residential photovoltaic (PV) diffusion. Recent literature suggests that peer effects are determined by the perceived credibility of one's active peer connections regarding PV and passive peer effects. Utilising data from a survey of German house owners who either own a PV or indicate purchase intention (N = 1,165), this paper explores the underlying mechanisms of active peer effects during different stages of PV adoption decision-making. Our findings suggest that the perceived credibility of peers on PV related issues is positively associated with the reported number of adopters in the decision-maker's peer group (passive peer effect) and also with the progress in the PV adoption decision process. Furthermore, we find a relationship between the perceived credibility of peers regarding PV and the reported influence strength of peer interactions throughout the decision-making processes of German householders, suggesting that subjective evaluations of peers play a role in active peer effects. Finally, we observe an association between the rate of self-initiated peer interactions about PV for respondents with a higher reported number of peers that have adopted PV, suggesting that passive effects (knowing peer PV adopters) play a role in initiating active peer interactions. These results are significant for all sub-groups, regardless of the decision progress. From a policy-making perspective, “bottom-up” efforts such as peer consultation and community-led outreach should be promoted in Germany to empower potential adopters.

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., Energie and Industrie

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 relating to 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.

Local resistance often hinders renewable energy technology developments, especially for onshore wind. In decentralized energy systems, the landscape impact of wind turbines or transmission lines is a key barrier to public acceptance. By using landscape scenicness as a proxy for public acceptance, we quantify its impact on the optimal energy systems of 11,131 German municipalities. In municipalities with high scenicness, it is likely that onshore wind will be rejected, leading to higher levelized costs of energy by up to about 7 €-cent/kWh. Onshore wind would be replaced mainly by solar photovoltaics and imports, and the cost-optimal energy systems would be associated with higher CO2 emissions of up to about 200 gCO2/kWh compared with an average of around 50 gCO2/kWh. The findings help to identify municipalities where public resistance to onshore wind could be particularly high and support the scientific and policy debate about the location of onshore wind farms.

Decision support systems like computer-aided energy system analysis (ESA) are considered one of the main pillars for developing sustainable and reliable energy strategies. Although today's diverse tools can already support decision-makers in a variety of research questions, further developments are still necessary. Intending to identify opportunities and challenges in the field, we classify modelling topics into modelling capabilities (32), methodologies (15), implementation issues (15) and management issues (7) from an extensive literature review. Based on a quantitative expert survey of energy system modellers (N = 61) mainly working with simulation and optimisation models, the Status of Development and the Complexity of Realisation of those modelling topics are assessed. While the rated items are considered to be more complex than actually represented, no significant outliers are determinable, showing that there is no consensus about particular aspects of ESA that are lacking development. Nevertheless, a classification of the items in terms of a specially defined modelling strategy matrix identifies capabilities like land-use planning patterns, equity and distributional effects and endogenous technological learning as “low hanging fruits” for enhancement, as well as a large number of complex topics that are already well implemented. The remaining “tough nuts” regarding modelling capabilities include non-energy sector and social behaviour interaction effects. In general, the optimisation and simulation models differ in their respective strengths, justifying the existence of both. While methods were generally rated as quite well developed, combinatorial optimisation approaches, as well as machine learning, are identified as important research methods to be developed further for ESA.

The field of Energy System Analysis (ESA) has experienced exponential growth in the number of publications since at least the year 2000. This paper presents a comprehensive bibliometric analysis on ESA by employing different algorithms in Matlab and R. The focus of results is on quantitative indicators relating to number and type of publication outputs, collaboration links between institutions, authors and countries, and dynamic trends within the field. The five and twelve most productive countries have 50% and 80% of ESA publications respectively. The dominant institutions are even more concentrated within a small number of countries. A significant concentration of published papers within countries and institutions was also confirmed by analysing collaboration networks. These show dominant collaboration within the same university or at least the same country. There is also is a strong link among the most successful journals, authors and institutions. The Energy journal has had the most publications in the field, and its editor-in-chief Lund H is the author with most of the publications in the field, as well as the author with most of the highly cited publications in the field. In terms of the dynamics within the field in the past decade, recent years have seen a higher impact of topics related to flexibility and hybrid/integrated energy systems alongside a decline in individual technologies. This paper provides a holistic overview of two decades' research output and enables interested readers to obtain a comprehensive overview of the key trends in this active field., Comment: Energy system analysis, bibliometrics, collaboration networks, renewable energy, paper impact, h-index, gross expenditure on research and development, Scopus analysis, scientific productivity of nations

Research attention on decentralized autonomous energy systems has increased exponentially in the past three decades, as demonstrated by the absolute number of publications and the share of these studies in the corpus of energy system modelling literature. This paper shows the status quo and future modelling needs for research on local autonomous energy systems. A total of 359 studies are investigated, of which a subset of 123 in detail. The studies are assessed with respect to the characteristics of their methodology and applications, in order to derive common trends and insights. Most case studies apply to middle-income countries and only focus on the supply of electricity in the residential sector. Furthermore, many of the studies are comparable regarding objectives and applied methods. Local energy autonomy is associated with high costs, leading to levelized costs of electricity of 0.41 $/kWh on average. By analysing the studies, many improvements for future studies could be identified: the studies lack an analysis of the impact of autonomous energy systems on surrounding energy systems. In addition, the robust design of autonomous energy systems requires higher time resolutions and extreme conditions. Future research should also develop methodologies to consider local stakeholders and their preferences for energy systems.

A growing number of German municipalities are striving for energy autonomy. Geothermal plants are increasingly constructed in municipalities in order to exploit the high hydrothermal potential. This paper analyses the potential contribution of simultaneous geothermal power and heat generation in German municipalities to achieving energy autonomy. A linear regression estimates the achievable hydrothermal temperatures and the required drilling depths. Technical restrictions and cost estimations for geothermal plants are implemented within an existing linear optimisation model for municipal energy systems. Novel modelling approaches, such as optimisation with variable drilling depths, are developed. The new approach is validated with data from existing geothermal plants in Germany, demonstrating a Root Mean Squared Error of about 15%. Eleven scenarios show that achieving energy autonomy is associated with at least 4% additional costs, compared to scenarios without it. The crucial role of geothermal plants in providing base load heat and power to achieve energy autonomy is demonstrated. The importance of simultaneous modelling of electricity and heat generation in geothermal plants is also evident, as district heating plants reduce the costs, especially in municipalities with high hydrothermal potential. Further work should focus on the optimal spatial scale of the system boundaries and the impact of the temporal resolution of the analysis on the costs for achieving energy autonomy.

This paper analyses the technical potential for utilising excess heat from biogas plants, in order to supply local settlements through district heating. Based on a survey of around 600 biogas plant operators, the fractions of excess heat from the cogeneration units in these plants are analysed. A heuristic is developed to match biogas plants (heat sources) with local settlements (heat sinks) in order to determine a least-cost district heating supply for residential buildings. Two criteria are employed, namely the CO2 abatement costs and the payback period, which represent the macro- and microeconomic perspectives respectively. Based on the survey, the mean fraction of excess heat is 40%, which is in agreement with other empirical studies. Extrapolating this fraction to the German biogas plant stock, which is selected as a case study, leads to technically feasible CO2 savings of around 2.5 MtCO2/a. Employing the criteria of CO2 abatement costs and payback period yields about 2 MtCO2/a below CO2 abatement costs of 200 €/tCO2 and below a payback period of 9 years. This represents about 0.25% of the total German CO2 emissions in 2016 or around 2.5% of all CO2 in residential buildings. Alternative threshold values of 80 €/tCO2 and 5 years payback period reduce the carbon reduction potential to about 0.5 MtCO2 and 0.75 MtCO2 respectively. These relatively high average costs are related to the typically low population density in rural regions where biogas plants are located. These potentials are concentrated in around 3,500 of 11,400 municipalities, where district heating from biogas plants could reduce CO2 emissions per capita by an average of 250 kgCO2/a and cover 12% of the total heating demand. Apart from a methodology that can be transferred to any country with comparable data availability, the present study demon

Plants increasingly exploit high geothermal energy potentials in German district heating networks. Municipal planners need instruments to design the district heating network for geothermal heat. This paper presents a combinatorial mixed-integer linear optimisation model and a three-stage heuristic to determine the minimum-cost district heating systems in municipalities. The central innovations are the ability to optimise both the structure of the heating network and the location of the heating plant, the consideration of partial heat supply from district heating and the scalability to larger municipalities. A comparison of optimisation and heuristic for three exemplary municipalities demonstrates the efficiency of the latter: the optimisation takes between 500% and 1 × 107% more time than the heuristic. The deviations of the heuristic's calculated total investments for the district heating system compared to the optimisation are in all cases below 5%, and in 80% of cases below 0.3%. The efficiency of the heuristic is further demonstrated by comparison with the Nearest-Neighbour-Heuristic, which is less efficient and substantially overestimates the total costs by up to 80%. The heuristic can also be used to design district heating networks in holistic energy system optimisations due to the novel possibility of connecting an arbitrary number of buildings to the network. Future work should focus on a more precise consideration of heat losses, as well as taking additional geological and topographical conditions into account.

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.