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The local generation of renewable electricity through roof-mounted photovoltaic (PV) systems on buildings in urban areas provides huge potentials for the mitigation of greenhouse gas emissions. This contribution presents a new method to provide local decision makers with tools to assess the remaining PV potential within their respective communities. It allows highly detailed analyses without having to rely on 3D city models, which are often not available. This is achieved by a combination of publicly available geographical building data and aerial images that are analyzed using image recognition and machine learning approaches. The method also employs sophisticated algorithms for irradiance simulation and power generation that exhibit a higher accuracy than most existing PV potential studies. The method is demonstrated with an application to the city of Freiburg, for which a technical PV electricity generation potential of about 524 GWh/a is identified. A validation with a 3D city model shows that the correct roof azimuth can be determined with an accuracy of about 70% and existing solar installations can be detected with an accuracy of about 90%. This demonstrates that the method can be employed for spatially and temporally detailed PV potential assessments in arbitrary urban areas when only public geographical building data is available instead of exact 3D city model data. Future work will focus on methodological improvements as well as on the integration of the method within an urban energy system modeling framework

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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. Highlights  Analysis of hydrothermal potential in German municipalities  Optimisation of simultaneous geothermal heat and electricity generation  Drilling depth and hydrothermal temperature are implemented endogenously  Integration of geothermal plants in a holistic energy system optimisation  Geothermal plants reveal a potential for cost reduction in off-grid municipalities

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 1x10 7 % more time than the heuristic. The resulting deviations in the calculated district heating system total investment 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 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.