Optimization models can support decision-makers in the synthesis and operation of multi-sector energy systems. To identify the optimal design and operation of a low-carbon system, we need to consider high temporal and spatial variability in the electricity supply, sector coupling, and environmental impacts over the whole life cycle. Incorporating such aspects in optimization models is demanding. To avoid redundant research efforts and enhance transparency, the developed models and used data sets should be shared openly. In this work, we present the SecMOD framework for multi-sector energy system optimization incorporating life-cycle assessment (LCA). The framework allows optimizing multiple sectors jointly, ranging from industrial production and their linked energy supply systems to sector-coupled national energy systems. The framework incorporates LCA to account for environmental impacts. We hence provide the first open-source framework to consistently include a holistic life-cycle perspective in multi-sector optimization by a full integration of LCA. We apply the framework to a case-study of the German sector-coupled energy system. Starting with few base technologies, we demonstrate the modular capabilities of SecMOD by the stepwise addition of technologies, sectors and existing infrastructure. Our modular open-source framework SecMOD aims to accelerate research for sustainable energy systems by combining multi-sector energy system optimization and life-cycle assessment.

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Currently, reducing energy consumption and fossil fuel emissions are key factors placed in the first position on the European agenda. District heating technology is an attractive solution, able to satisfy the energy and environmental goals of policymakers and designers. In line with this, a different approach to planning a district heating grid based on the optimization of building clusters is presented. The case study is Wilhelmsburg, a district of Hamburg city. This approach also investigates the usage of industrial waste heat as the grid’s heat source, which is CO2-neutral. First, the data acquisition regarding the buildings’ location and heat demand are described in detail. Based on the derived data and the source of the industrial waste heat, the district heating grid is created by clustering the buildings and connecting the obtained nodes. Furthermore, the grid’s efficiency is improved by eliminating nodes, which are too distant from the heat source, or have lower heat demand. Finally, a single building is simulated in Matlab/Simulink, showing the energy-savings and ecological results. The usage of the district heating grid saves 97.32 GWh annually, which results in financial savings of €5.83 million, and avoided CO2 emissions of 19,585 tCO2.