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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.

NFDI4Ing is a consortium from the first round of applications of Germany’s National Research Data Infrastructure (Nationale Forschungsdateninfrastruktur – NFDI). NFDI4Ing’s goal is to make the data accruing throughout the entire engineering research process sustainably accessible, which means in a citable form according to FAIR principles and under licenses that are as free as possible. The funding programme has started as of October 1st 2020, and after the initial kick-off, our Task Areas (TAs) now look back on their accomplishments from 2021. For détails regarding the work programme, please refer to our published proposal.

NFDI4Ing is a consortium from the first round of applications of Germany’s National Research Data Infrastructure (Nationale Forschungsdateninfrastruktur – NFDI). NFDI4Ing’s goal is to make the data accruing throughout the entire engineering research process sustainably accessible, which means in a citable form according to FAIR principles and under licenses that are as free as possible. The funding programme has started as of October 1st 2020, and after looking back on their accomplishments from 2021 (link), our Task Areas (TAs) now look back on their accomplishments from 2022. For détails regarding the work programme, please refer to our published proposal.

Existing planning approaches for onshore wind farm siting and network integration often do not meet minimum cost solutions or social and environmental considerations. In this paper, we develop an approach for the multi-objective optimization of turbine locations and their network connection using a Quota Steiner tree problem. Applying a novel transformation on a known directed cut formulation, reduction techniques, and heuristics, we design an exact solver that makes large problem instances solvable and outperforms generic MIP solvers. Although our case studies in selected regions of Germany show large trade-offs between the objective criteria of cost and landscape impact, small burdens on one criterion can significantly improve the other criteria. In addition, we demonstrate that contrary to many approaches for exclusive turbine siting, network integration must be simultaneously optimized in order to avoid excessive costs or landscape impacts in the course of a wind farm project. Our novel problem formulation and the developed solver can assist planners in decision making and help optimize wind farms in large regions in the future.

Lithium is a critical material for the energy transition, but conventional procurement methods have significant environmental impacts. In this study, we utilize regional energy system optimizations to investigate the techno-economic potential of the low-carbon alternative of direct lithium extraction in deep geothermal plants. We show that geothermal plants will become cost-competitive in conjunction with lithium extraction, even under unfavorable conditions and partially displace photovoltaics, wind power, and storage from energy systems. Our analysis indicates that if 10% of municipalities in the Upper Rhine Graben area in Germany constructed deep geothermal plants, they could provide enough lithium to produce about 1.2 million electric vehicle battery packs per year, equivalent to 70% of today`s annual electric vehicle registrations in the European Union. This approach could offer significant environmental benefits and has high potential for mass application also in other countries, such as the United States, United Kingdom, France, and Italy, highlighting the importance of further research and development of this technology.

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.

Advances in applied energy 11, 100148 - (2023). doi:10.1016/j.adapen.2023.100148, Published by Elsevier ScienceDirect, [Amsterdam]

The NFDI4Ing Conference 2022 took place online on 26 and 27 October2022. The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number 442146713. If not stated otherwise within the respective document, the files go by CC-BY 4.0 license. Please refer to the license comments in the resprective slide decks.

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.

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., Renewable and Sustainable Energy Reviews, 183, ISSN:18790690, ISSN:1364-0321

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.

While the importance of peer influences has been demonstrated in several studies, little is known about the underlying mechanisms of active peer effects in residential photovoltaic (PV) diffusion. Empirical evidence indicates that the impacts of inter-subjective exchanges are dependent on the subjective mutual evaluation of the interlocutors. This paper aims to quantify, how subjective evaluations of peers affect peer effects across different stages of PV adoption decision-making. The findings of a survey among potential and current adopters in Germany(N=1,165)confirm two hypotheses. First, peer effects play a role in residential PV adoption: the number of peer adopters in the decision-maker's social circle has a positive effect on the decision-maker's belief that their social network supports PV adoption; their ascription of credibility on PV-related topics to their peers; and their interest in actively seeking information from their peers in all decision-making stages. Second, there is a correlation between the perceived positive attributes of a given peer and the reported influence of said peer within the decision-making process, suggesting that decision-makers' subjective evaluations of peers play an important role in active peer effects. Decision-makers are significantly more likely to engage in and be influenced by interactions with peers who they perceive as competent, trustworthy, and likeable. In contrast, attributes such as physical closeness and availability have a less significant effect. From a policymaking perspective, this study suggests that the density and quality of peer connections empower potential adopters. Accordingly, peer consultation and community-led outreach initiatives should be promoted to accelerate residential PV adoption.

Onshore wind development has historically focused on cost-efficiency, which may lead to inequitable turbine distributions and public resistance due to landscape impacts. Using a multi-criteria planning approach, we show how onshore wind capacity targets can be achieved by 2050 in a cost-efficient, equitable and publicly acceptable way. For the case study of Germany, we build on the existing turbine stock and use open data on technically feasible turbine locations and scenicness of landscapes to plan the optimal expansion. The analysis shows that while the trade-off between cost-efficiency and public acceptance is rather weak with about 15% higher costs or scenicness, an equitable distribution has a large impact on these criteria. Although the onshore wind capacity per inhabitant could be distributed about 220% more equitably through the expansion, equity would severely limit planning flexibility by 2050. Our analysis assists stakeholders in resolving the onshore wind expansion trilemma.

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 roughly 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., arXiv

In the context of the energy transition, energy system planning is becoming increasingly relevant for decentralized systems. Motivated by the strong increase of publications on municipal energy system planning (MESP), these studies are subjected to a bibliometric analysis in this paper. The 1235 articles, which are based on the Web of Science database, are examined using the R-tool bibliometrix. The study shows that China is the most important contributor with 225 articles, followed by the USA (205) and Germany (120). Most cross-country collaborations were conducted between China and Canada, focusing mainly on uncertainties in MESP. Among the institutions, the North China Electric Power University is responsible for most articles (42). The core journals on MESP are Energy, Applied Energy, Energy Policy, Energies and Renewable Energy, which published 37% of the 1235 articles. Publications on MESP focus on renewable energies, optimization and hybrid energy systems. Furthermore, district heating seems to be a core topic in MESP and is addressed in three of the top five most cited articles. The demonstration of global research trends in MESP can support researchers in identifying the relevant issues regarding this expanding and transforming research area.

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 personal transport, industrial and commercial sectors. A machine learning approach identifies a regression model among 19 methods, which is best suited for the transfer of individual optimization results to all municipalities. The resulting levelized cost of energy (LCOE) from the optimization of 15 case studies are transferred using a stepwise linear regression model. The regression model shows a mean absolute percentage error of 12.5%. 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 appear to have the greatest influence on the LCOEs. 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.

Real-world problems are becoming highly complex and, therefore, have to be solved with combinatorial optimisation (CO) techniques. Motivated by the strong increase of publications on CO, 8,393 articles from this research field are subjected to a bibliometric analysis. The corpus of literature is examined using mathematical methods and a novel algorithm for keyword analysis. In addition to the most relevant countries, organisations and authors as well as their collaborations, the most relevant CO problems, solution methods and application areas are presented. Publications on CO focus mainly on the development or enhancement of metaheuristics like genetic algorithms. The increasingly problem-oriented studies deal particularly with real-world applications within the energy sector, production sector or data management, which are of increasing relevance due to various global developments. The demonstration of global research trends in CO can support researchers in identifying the relevant issues regarding this expanding and transforming research area.

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 personal transport, industrial and commercial sectors. A machine learning approach identifies a regression model among 19 methods, which is best suited for the transfer of individual optimization results to all municipalities. The resulting levelized cost of energy (LCOE) from the optimization of 15 case studies are transferred using a stepwise linear regression model. The regression model shows a mean absolute percentage error of 12.5%. 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 appear to have the greatest influence on the LCOEs. 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.

Cost-efficiency and public acceptance are competing objectives for onshore wind locations. We quantify the link between economic wind resources and beautiful landscapes with over 1.5 million ‘scenicness’ ratings of around 200,000 geotagged photographs from across Great Britain. We find statistically significant evidence that planning applications for onshore wind are more likely to be rejected when proposed in more scenic areas. Compared to the technical potential of onshore wind of 1700 TWh at total costs of £280 billion, removing the 10% most scenic areas implies about 18% lower generation potential and 8-26% higher costs. We consider connection distances to the nearest electricity network transformer for the first time, showing that the connection costs constitute up to half of the total costs. The results provide a quantitative framework for researchers and policymakers to consider the trade-offs between cost-efficiency and public acceptance for onshore wind.

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 roughly 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.

Attention on decentralised autonomous energy systems has increased exponentially in the past three decades, as demonstrated by the absolute number of real-world projects and the share of publications in the corpus of scientific literature. This is due to the energy transition and the related environmental awareness as well as the desire of citizens to play an active role in energy supply and to be less dependent on central markets and structures. However, local decision-makers, who often lack the necessary expertise, need decision support in energy system planning. To this end, this thesis follows the objective to develop novel methods for the technical, economic and environmental assessment of a large number of completely energy autonomous municipalities and their impacts on the overall energy system. Completely energy autonomous municipalities are disconnected from the gas and electricity grid and supply themselves with energy from plants owned and operated by the municipality. Novel methods of energy system analysis were developed in this thesis as part of seven original research articles. Germany is used as a case study, but the general approach, methods and results are transferable to other contexts. First of all, the 11,131 German municipalities were clustered with regard to their suitability for decentralised energy systems. Based on this municipality typology, representative municipalities were selected to be investigated in an already existing holistic municipal energy system optimisation model (RE³ASON). This model was extended by novel and transferable approaches to design deep geothermal plants and district heating networks. These base-load capable technologies were selected to reduce the storage costs in energy autonomous municipalities. The technical feasibility and economic expenditures of energy autonomy could finally be determined in all 11,131 German municipalities by combining the extended energy system optimisation model with a stepwise linear regression. The energy system optimisations showed that in the case of complete energy autonomy, deep geothermal plants in combination with district heating networks could reduce the total costs by up to 50%. On average, the energy system costs until 2030 in German municipalities increase by about 0.41 €/kWh in the energy autonomous case compared to the optimised reference case with grid connection. While a technical potential to achieve energy autonomy is present in 56% of the German municipalities, there seem to be no economic advantages through energy autonomy compared to the optimised reference energy system. The novel methodological approach of this thesis enabled to obtain optimisation results for a high number of energy systems (6,314 municipalities) with practicable computational expenses. In addition to the original data and planning tools published alongside the articles, the findings of this thesis can also support local decision makers in determining suitable municipal energy systems. In order to increase the realizability of the case study results, some methodological extensions should be investigated in future studies such as other perspectives than that of a central planner, higher temporal model resolutions or social aspects like consumer acceptance of specific technologies or a security of supply below 100%.

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 roughly 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 cost-efficient and sustainable energy transition requires reliable information about the distribution of renewable energy resources. Here we draw on over 1.5 million scenicness ratings of around 200,000 geotagged photographs from Scenic-Or-Not to quantify the aesthetic value of the landscapes in which onshore wind energy installations could be situated in Great Britain. An analysis of planning applications provides quantitative evidence that onshore wind projects are more likely to be rejected when proposed in more scenic areas. Exploiting further open data sources including OpenStreetMap, we build on these findings to generate new estimates of the feasible potential and costs for onshore wind in Great Britain, which we find to be around 1700 TWh and £280 billion respectively. We also uncover a strong spatial correlation between scenicness and the quality of the wind resource, implying inevitable trade-offs between cost-efficiency and public acceptance.

Geothermal plants have been increasingly constructed in recent years to exploit the high geothermal energy potential in Germany in district heating networks at the municipal level. In order to use this potential economically, municipal planners need instruments for designing the district heating network to supply households with the geothermal heat. This paper presents a combinatorial mixed-integer linear optimisation model and a three-stage heuristic to determine the minimum costs for geothermal district heating systems in municipalities. The central innovations are the ability to optimise both the structure of the district heating network and the location of the district heating plant, the consideration of partial heat supply from district heating and the scalability to many larger municipalities. A comparison of optimisation and heuristic for three exemplary municipalities demonstrates the efficiency of the developed heuristic: the optimisation takes between 500% and 10,000,000% more time than the heuristic. The resulting deviations in the calculated total investment for the district heating from the results of 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 the comparison with simpler heuristics like the Nearest-Neighbour-Heuristic. The latter is not only less efficient, it substantially overestimates the total costs by up to 80% in all cases with less than 100% heat coverage. Future work should focus on a more precise consideration of heat losses in the district heating network, as well as taking additional geological and topological conditions in the municipalities into account.

Geothermal plants have been increasingly constructed in recent years to exploit the high geothermal energy potential in Germany in district heating networks at the municipal level. In order to use this potential economically, municipal planners need instruments for designing the district heating network to supply households with the geothermal heat. This paper presents a combinatorial mixed-integer linear optimisation model and a three-stage heuristic to determine the minimum costs for geothermal district heating systems in municipalities. The central innovations are the ability to optimise both the structure of the district heating network and the location of the district heating plant, the consideration of partial heat supply from district heating and the scalability to many larger municipalities. A comparison of optimisation and heuristic for three exemplary municipalities demonstrates the efficiency of the developed heuristic: the optimisation takes between 500% and 10,000,000% more time than the heuristic. The resulting deviations in the calculated total investment for the district heating from the results of 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 the comparison with simpler heuristics like the Nearest-Neighbour-Heuristic. The latter is not only less efficient, it substantially overestimates the total costs by up to 80% in all cases with less than 100% heat coverage. Future work should focus on a more precise consideration of heat losses in the district heating network, as well as taking additional geological and topological conditions in the municipalities into account.

The recent rapid expansion of renewable energy capacities in Germany has been dominated by decentralised wind, photovoltaic (PV) and bioenergy plants. The spatially disperse and partly unpredictable nature of these resources necessitates an increasing exploitation of integration measures such as curtailment, supply and demand side flexibilities, network strengthening and storage capacities. Indeed, one solution to the large-scale integration of renewable energies could be decentralised autonomous municipal energy systems. The achievement of grid parity for some renewable energy technologies has strengthened the desire of some communities to become independent from central markets. Whilst many communities in Germany already strive for socalled energy autonomy, the vast majority do so only on an annual basis. Several studies have already analysed the technical and economic implications of the mainly decentralised future energy system, but most are restricted in their insights by limited temporal and spatial resolution. The large number (11,131) of German municipalities means that a national analysis at this resolution is not feasible. Hence, this study employs a cluster analysis to develop a municipality typology in order to analyse the techno-economic suitability of these municipalities for autonomous energy systems. A total of 34 socio-technical indicators are employed at the municipal level, with a particular focus on the sectors of Private Households and Transport, and the potentials for decentralised renewable energies. The first step is to scale the indicator values and reduce their number by using a factor analysis. Several alternative methods are weighed against each other, and the most suitable methods for the factor analysis are chosen. Secondly, selected quantitative cluster validation methods are employed alongside qualitative criteria to determine the optimal number of clusters. This results in a total of ten clusters, which show a large variation as well as some overlap with respect to specific indicators. For example, one cluster contains all major German cities and has a low potential for renewable energies. Another cluster, on the other hand, contains the municipalities with a higher potential for renewable energies due to their high hydrothermal potential for geothermal power. An analysis of the municipalities from three German renewable energy projects “Energy Municipalities”, ”Bioenergy Villages” and “100% Renewable Energy Regions” shows that in eight of the ten clusters municipalities are aiming for energy autonomy (in varying degrees). It is challenging to differentiate between the clusters regarding readiness for energy autonomy projects, however, especially if the degree of social acceptance and engagement for such projects is to be considered. To answer the more techno-economical part of this question, future work will employ the developed clusters in the context of an energy system optimisation. Insights gained at the municipal level will then be qualitatively transferred to the national context to assess the implications for the whole energy system.

To cover 100% of the energy demand using renewable energies, technologies like small wind turbines (SWT) or hydrogen (H2) storage systems could be integrated into the household energy system. In this study optimal self-sufficient energy supply systems for residential buildings are determined using an MILP optimization model. To consume energy at the time it is generated, flexible devices and optimal charging strategies for electric vehicles are considered. Least cost systems for different regions are identified and compared to a conventional reference system. It is shown that H2 storage systems and SWT can be economically beneficial for self-sufficient household supply.

This paper analyses the current technical potential for utilising excess heat from German 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 in these plants are analysed. A heuristic is developed to match biogas plants (heat sources) with local settlements (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, a mean fraction of 40% excess heat is determined, which is in agreement with other empirical studies. Extrapolating this fraction to the German biogas plant stock 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 respectively. This represents about 0.25% of the total German CO2 emissions in 2016 or around 2.5% of all CO2 in residential buildings. If threshold values of 80 €/tCO2 and 5 years are employed, to reflect the German government’s suggested external cost of carbon and an expected payback period from an investor’s point of view respectively, the carbon reduction potential is about 0.5 MtCO2 and 0.75 MtCO2 respectively. 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 kgCO_2/a and cover 12% of the total heating demand.

This paper analyses the current technical potential for utilising excess heat from German 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 in these plants are analysed. A heuristic is developed to match biogas plants (heat sources) with local settlements (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, a mean fraction of 40% excess heat is determined, which is in agreement with other empirical studies. Extrapolating this fraction to the German biogas plant stock 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 respectively. This represents about 0.25% of the total German CO2 emissions in 2016 or around 2.5% of all CO2 in residential buildings. If threshold values of 80 €/tCO2 and 5 years are employed, to reflect the German government's suggested external cost of carbon and an expected payback period from an investor's point of view respectively, the carbon reduction potential is about 0.5 MtCO2 and 0.75 MtCO2 respectively. 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 kgCO_2/a and cover 12% of the total heating demand.

The recent rapid expansion of renewable energy capacities in Germany has been dominated by decentralised wind, photovoltaic (PV) and bioenergy plants. The spatially disperse and partly unpredictable nature of these resources necessitates an increasing exploitation of integration measures such as curtailment, supply and demand side flexibilities, network strengthening and storage capacities. Indeed, one solution to the large-scale integration of renewable energies could be decentralised autonomous municipal energy systems. The achievement of grid parity for some renewable energy technologies has strengthened the desire of some communities to become independent from central markets. Whilst many communities in Germany already strive for socalled energy autonomy, the vast majority do so only on an annual basis. Several studies have already analysed the technical and economic implications of the mainly decentralised future energy system, but most are restricted in their insights by limited temporal and spatial resolution. The large number (11,131) of German municipalities means that a national analysis at this resolution is not feasible. Hence, this study employs a cluster analysis to develop a municipality typology in order to analyse the techno-economic suitability of these municipalities for autonomous energy systems. A total of 34 socio-technical indicators are employed at the municipal level, with a particular focus on the sectors of Private Households and Transport, and the potentials for decentralised renewable energies. The first step is to scale the indicator values and reduce their number by using a factor analysis. Several alternative methods are weighed against each other, and the most suitable methods for the factor analysis are chosen. Secondly, selected quantitative cluster validation methods are employed alongside qualitative criteria to determine the optimal number of clusters. This results in a total of ten clusters, which show a large variation as well as some overlap with respect to specific indicators. For example, one cluster contains all major German cities and has a low potential for renewable energies. Another cluster, on the other hand, contains the municipalities with a higher potential for renewable energies due to their high hydrothermal potential for geothermal power. An analysis of the municipalities from three German renewable energy projects “Energy Municipalities”, ”Bioenergy Villages” and “100% Renewable Energy Regions” shows that in eight of the ten clusters municipalities are aiming for energy autonomy (in varying degrees). It is challenging to differentiate between the clusters regarding readiness for energy autonomy projects, however, especially if the degree of social acceptance and engagement for such projects is to be considered. To answer the more techno-economical part of this question, future work will employ the developed clusters in the context of an energy system optimisation. Insights gained at the municipal level will then be qualitatively transferred to the national context to assess the implications for the whole energy system.