TY - RPRT A1 - Klemm, Christian A1 - Vennemann, Peter T1 - Optimization of resource efficiency in mixed-use quarters Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-110361 ER - TY - RPRT A1 - Klemm, Christian A1 - Vennemann, Peter T1 - Optimierung der Energieeffizienz von Stadtquartieren KW - Energiesystem KW - Modellierung KW - energy system model Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-110401 ER - TY - JOUR A1 - Klemm, Christian A1 - Vennemann, Peter T1 - Modeling and optimization of multi-energy systems in mixed-use districts: A review of existing methods and approaches JF - Renewable and Sustainable Energy Reviews N2 - About 75% of the world's energy consumption takes place in cities. Although their large energy consumption attracts a large number of research projects, only a small fraction of them deal with approaches to model energy systems of city districts. These are particularly complex due to the existence of multiple energy sectors (multi-energy systems, MES), different consumption sectors (mixed-use), and different stakeholders who have many different interests. This contribution is a review of the characteristics of energy system models and existing modeling tools. It evaluates current studies and identifies typical characteristics of models designed to optimize MES in mixed-use districts. These models operate at a temporal resolution of at least 1 h, follow either bottom-up or hybrid analytical approaches and make use of mixed-integer programming, linear or dynamic. These characteristics were then used to analyze minimum requirements for existing modeling tools. Thirteen of 145 tools included in the study turned out to be suitable for optimizing MES in mixed-use districts. Other tools where either created for other fields of application (12), do not include any methodology of optimization (39), are not suitable to cover city districts as a geographical domain (44), do not include enough energy or demand sectors (20), or operate at a too coarse temporal resolution (17). If additional requirements are imposed, e.g. the applicability of non-financial assessment criteria and open source availability, only two tools remain. Overall it can be stated that there are very few modeling tools suitable for the optimization of MES in mixed-use districts. KW - energy system modeling KW - optimization KW - multi-energy system (MES) KW - urban districts KW - modeling tools Y1 - 2021 U6 - http://dx.doi.org/10.1016/j.rser.2020.110206 IS - 135 SP - nn EP - nn ER - TY - JOUR A1 - Söfker-Rieniets, Anne A1 - Hörnschemeyer, Birgitta A1 - Kleckers, Jonas A1 - Klemm, Christian A1 - Stretz, Celestin ED - Ziegler, Christine T1 - Mit Nutzenstiftung zu mehr Ressourceneffizienz im Quartier JF - Transforming Cities N2 - Im Rahmen des Forschungsprojekts „Ressourcenplan im Quartier – R2Q“ startete im Frühjahr 2019 ein großer Forschungsverbund aus Hochschulen, wissenschaftlichen Instituten, Praxispartnern und einer Kommune, um die Verwendung der Ressourcen Wasser, Fläche, Baustoffe und Energie in Quartieren zu bilanzieren und zu bewerten, damit ihre effiziente Verwendung im Quartier mit Hilfe neuer rechtlicher Festsetzungen zukünftig gewährleistet werden kann. KW - Ressourceneffizienz KW - Quartier KW - Fläche KW - Wasser KW - Energie Y1 - 2020 UR - https://www.transforming-cities.de/ausgabe-4-2020-staedtische-ressourcen/ SN - 2366-3723 VL - 2020 IS - 04 SP - 42 EP - 46 ER - TY - CHAP A1 - Klemm, Christian A1 - Vennemann, Peter T1 - Modellierung und Optimierung urbaner Energiesysteme im Projekt R2Q T2 - 4. Regenerative Energietechnik Konferenz in Nordhausen 18. - 19. Februar 2021 / Hrsg. Viktor Wesselak N2 - Rund 75 % des weltweiten Energieverbrauchs findet innerhalb urbaner Energiesysteme statt. Solche Systeme beinhalten mehrere Energiesektoren (Elektrizität, Wärme, Kälte, …), Verbrauchssektoren (Wohnen, Gewerbe, Industrie, Landwirtschaft, Mobilität, …) und Interessensgruppen und sind deshalb besonders komplex. Durch den Einsatz von Methoden der Energiesystemmodellierung können diese komplexen Systeme simuliert, analysiert und optimiert werden. Mit Simulationsmodellen können Kosten, Emissionen und verschiedene andere Systemparameter prognostiziert werden. Mithilfe von Optimierungsalgorithmen können Technologien miteinander verglichen, Anlagen dimensioniert und Betriebsweisen optimiert werden. Die Erkenntnisse aus Energiesystemmodellen können zur Einhaltung verschiedener politischer und sozialer Ziele, wie beispielsweise die Reduktion von Treibhausgasemissionen, der Bedarf nach kostengünstiger Energieversorgung oder auch die Stärkung der regionalen Wirtschaft, beitragen. Im Projekt R2Q werden Ansätze der Energiesystemmodellierung für den Einsatz in der Planung urbaner Energiesysteme aufgearbeitet, angepasst und für städteplanerische Prozesse verfügbar gemacht. In ersten Modelldurchläufen für ein Testgebiet in Herne konnte durch die Kombination verschiedener Technologien eine rechnerische Minimierung der monetären Kosten um 19 % bei gleichzeitiger Reduktion der CO2-Emissionen um 36 % ermittelt werden. Durch ein emissionsoptimiertes Szenario können die CO2-Emissionen um 47 % reduziert werden, was jedoch mit einer Steigerung der Kosten um 29 % einhergeht. KW - Energiesystemmodellierung KW - urbane Energiesysteme KW - Multi-Energiesystem Y1 - 2021 UR - https://www.hs-nordhausen.de/fileadmin/daten/fb_ing/inret/PDFs/tagungsband_retcon21_web_aa3__1_.pdf SN - 978-3-940820-17-4 SP - 177 EP - 188 CY - Nordhausen ER - TY - JOUR A1 - Klemm, Christian A1 - Wiese, Frauke T1 - Indicators for the optimization of sustainable urban energy systems based on energy system modeling JF - Energy, Sustainability and Society N2 - Background: Urban energy systems are responsible for 75 % of the world's energy consumption and for 70 % of the worldwide greenhouse gas emissions. Energy system models are used to optimize, benchmark and compare such energy systems with the help of energy sustainability indicators. We discuss several indicators for their basic suitability and their response to changing boundary conditions, system structures and reference values. The most suitable parameters are applied to four different supply scenarios of a real-world urban energy system. Results: There is a number of energy sustainability indicators, but not all of them are suitable for the use in urban energy system optimization models. Shortcomings originate from the omission of upstream energy supply chains (secondary energy efficiency), from limited capabilities to compare small energy systems (energy productivity), from excessive accounting expense (regeneration rate), from unsuitable accounting methods (primary energy efficiency), from a questionable impact of some indicators on the overall system sustainability (self-sufficiency), from the lack of detailed information content (share of renewables), and more. On the other hand, indicators of absolute greenhouse gas emissions, energy costs, and final energy demand are well suitable for the use in optimization models. However, each of these indicators only represents partial aspects of energy sustainability; the use of only one indicator in the optimization process increases the risk that other important aspects will deteriorate significantly, eventually leading to suboptimal or even unrealistic scenarios in practice. Therefore, multi-criteria approaches should be used to enable a more holistic optimization and planning of sustainable urban energy systems. Conclusion: We recommend multi-criteria optimization approaches using the indicators of absolute greenhouse gas emissions, absolute energy costs, and absolute energy demand. For benchmarking and comparison purposes, specific indicators should be used and therefore related to the final energy demand, respectively the number of inhabitants. Our example scenarios demonstrate modeling strategies to optimize sustainability of urban energy systems. KW - energy system modeling KW - urban energy systems KW - Multi-objective optimization KW - energy sustainability KW - Multi-energy systems Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-145136 VL - 12 IS - 3 SP - 1 EP - 20 PB - Springer Nature ER - TY - JOUR A1 - Hörnschemeyer, Birgitta A1 - Söfker-Rieniets, Anne A1 - Niesten, Jan A1 - Arendt, Rosalie A1 - Kleckers, Jonas A1 - Klemm, Christian A1 - Stretz, Celestin Julian A1 - Reicher, Christa A1 - Grimsehl-Schmitz, Winona A1 - Wirbals, Daniel A1 - Bach, Vanessa A1 - Finkbeiner, Matthias A1 - Haberkamp, Jens A1 - Budde, Janik A1 - Vennemann, Peter A1 - Walter, Gotthard A1 - Flamme, Sabine A1 - Uhl, Mathias T1 - The ResourcePlan — An Instrument for Resource-Efficient Development of Urban Neighborhoods JF - Sustainability N2 - In Germany, the current sectoral urban planning often leads to inefficient use of resources, partly because municipalities lack integrated planning instruments and argumentation strength toward politics, investors, or citizens. The paper develops the ResourcePlan as (i) legal and (ii) a planning instrument to support the efficient use of resources in urban neighborhoods. The integrative, multi-methodological approach addresses the use of natural resources in the building and infrastructural sectors of (i) water (storm- and wastewater) management, (ii) construction and maintenance of buildings and infrastructure, (iii) urban energy system planning, and (iv) land-use planning. First, the development as legal instrument is carried out, providing (i) premises for integrating resource protection at all legal levels and (ii) options for implementing the ResourcePlan within German municipal structures. Second, the evaluation framework for resource efficiency of the urban neighborhoods is set up for usage as a planning instrument. The framework provides a two-stage process that runs through the phases of setting up and implementing the ResourcePlan. (Eco)system services are evaluated as well as life cycle assessment and economic aspects. As a legal instrument, the ResourcePlan integrates resource protection into municipal planning and decision-making processes. The multi-methodological evaluation framework helps to assess inter-disciplinary resource efficiency, supports the spatial identification of synergies and conflicting goals, and contributes to transparent, resource-optimized planning decisions. KW - resource efficiency KW - resource management KW - urban neighborhood KW - urban planning KW - urban development Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-148545 VL - 14 IS - 3 PB - MDPI ER - TY - JOUR A1 - Quest, Gemina A1 - Arendt, Rosalie A1 - Klemm, Christian A1 - Bach, Vanessa A1 - Budde, Janik A1 - Vennemann, Peter A1 - Finkbeiner, Matthias T1 - Integrated Life Cycle Assessment (LCA) of Power and Heat Supply for a Neighborhood: A Case Study of Herne, Germany JF - energies N2 - (1) The use of renewable energy for power and heat supply is one of the strategies to reduce greenhouse gas emissions. As only 14% of German households are supplied with renewable energy, a shift is necessary. This shift should be realized with the lowest possible environmental impact. This paper assesses the environmental impacts of changes in energy generation and distribution, by integrating the life cycle assessment (LCA) method into energy system models (ESM). (2) The integrated LCA is applied to a case study of the German neighborhood of Herne, (i) to optimize the energy supply, considering different technologies, and (ii) to determine the environmental impacts of the base case (status quo), a cost-optimized scenario, and a CO2-optimized scenario. (3) The use of gas boilers in the base case is substituted with CHPs, surface water heat pumps and PV-systems in the CO2-optimized scenario, and five ground-coupled heat pumps and PV-systems for the cost-optimized scenario. This technology shift led to a reduction in greenhouse gas emissions of almost 40% in the cost-optimized, and more than 50% in the CO2-optimized, scenario. However, technology shifts, e.g., due to oversized battery storage, risk higher impacts in other categories, such as terrestrial eco toxicity, by around 22%. Thus, it can be recommended to use smaller battery storage systems. (4) By combining ESM and LCA, additional environmental impacts beyond GHG emissions can be quantified, and therefore trade-offs between environmental impacts can be identified. Furthermore, only applying ESM leads to an underestimation of greenhouse gas emissions of around 10%. However, combining ESM and LCA required significant effort and is not yet possible using an integrated software. KW - LCA KW - life cycle assessment KW - energy system model KW - ILCA KW - urban energy system Y1 - 2022 UR - https://www.mdpi.com/1996-1073/15/16/5900 U6 - http://dx.doi.org/10.3390/en15165900 SN - 1996-1073 VL - 15 IS - 16 SP - 5900 ER - TY - JOUR A1 - Becker, Gregor A1 - Klemm, Christian A1 - Vennemann, Peter T1 - Open Source District Heating Modeling Tools—A Comparative Study JF - energies N2 - Heating networks are highly relevant for the achievement of climate protection goals of urban energy systems. This is due to their high renewable energy potential combined with high plant efficiency and utilization rates. For the optimal integration and sector coupling of heating networks in holistic urban energy systems, open source energy system modeling tools are highly recommended. In this contribution, two open source approaches (the "Spreadsheet Energy System Model Generator"-integrated DHNx-Python module (DHNx/SESMG) and Thermos) are theoretically compared, and practically applied to a real-world energy system. Deviations within the results can be explained by incorrectly pre-defined parameters within Thermos and cannot be adjusted by the modeler. The simultaneity is underestimated in the case study by Thermos by more than 20%. This results in undersized heating plant capacities and a 50% higher number of buildings connected to the network. However, Thermos offers a higher end-user usability and over 100 times faster solving. DHNx/SESMG, in contrast, offers the possibility to adjust more model parameters individually and consider multiple energy sectors. This enables a holistic modeling of urban energy systems and the model-based optimization of multi-sectoral synergies. KW - district heating KW - modeling tools KW - energy system modeling KW - urban energy systems KW - oemof Y1 - 2022 U6 - http://dx.doi.org/10.3390/en15218277 SN - 1996-1073 VL - 15 IS - 8277 ER - TY - RPRT A1 - Hörnschemeyer, Birgitta A1 - Söfker-Rieniets, Anne A1 - Niesten, Jan A1 - Arendt, Rosalie A1 - Kleckers, Jonas A1 - Stretz, Celestin A1 - Klemm, Christian A1 - Budde, Janik A1 - Wagner, Rüdiger A1 - Vonhoegen, Laura A1 - Reicher, Christa A1 - Grimsehl-Schmitz, Winona A1 - Wirbals, Daniel A1 - Stieglitz-Broll, Eva-Maria A1 - Agatz, Kerstin A1 - Bach, Vanessa A1 - Finkbeiner, Matthias A1 - Lewe, Mareike A1 - Henrichs, Malte A1 - Haberkamp, Jens A1 - Walter, Gotthard A1 - Flamme, Sabine A1 - Vennemann, Peter A1 - Zamzow, Malte A1 - Seis, Wolfgang A1 - Matzinger, Andreas A1 - Sonnenberg, Hauke A1 - Rouault, Pascale A1 - Maßmann, Stefanie A1 - Fuchs, Lothar A1 - Plogmeier, Christoph A1 - Steinkamp, Arne A1 - Şereflioğlu, Şenay A1 - Müller, Claus A1 - Spital, Matthias A1 - Uhl, Mathias T1 - Leitfaden RessourcenPlan – Teil 1: Konzeption RessourcenPlan. Ergebnisse des Projekts R2Q RessourcenPlan im Quartier T3 - Leitfaden RessourcenPlan. Ergebnisse des Projekts R2Q RessourcenPlan im Quartier - 1 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-157463 PB - FH Münster CY - Münster ER - TY - RPRT A1 - Klemm, Christian A1 - Budde, Janik A1 - Becker, Gregor A1 - Arendt, Rosalie A1 - Bach, Vanessa A1 - Finkbeiner, Matthias A1 - Vennemann, Peter T1 - Leitfaden RessourcenPlan – Teil 2.4: Ressourcenmanagement Energie. Ergebnisse des Projekts R2Q RessourcenPlan im Quartier T3 - Leitfaden RessourcenPlan. Ergebnisse des Projekts R2Q RessourcenPlan im Quartier - 2.4 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-157560 ER - TY - RPRT A1 - Hörnschemeyer, Birgitta A1 - Kleckers, Jonas A1 - Stretz, Celestin A1 - Klemm, Christian A1 - Budde, Janik A1 - Söfker-Rieniets, Anne A1 - Vonhoegen, Laura A1 - Zamzow, Malte A1 - Matzinger, Andreas A1 - Maßmann, Stefanie A1 - Plogmeier, Christoph T1 - Leitfaden RessourcenPlan – Teil 3.1: Kurzanleitung RessourcenPlan. Ergebnisse des Projekts R2Q RessourcenPlan im Quartier T3 - Leitfaden RessourcenPlan. Ergebnisse des Projekts R2Q RessourcenPlan im Quartier - 3.1 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-157581 ER - TY - RPRT A1 - Söfker-Rieniets, Anne A1 - Vonhoegen, Laura A1 - Klemm, Christian A1 - Budde, Janik A1 - Hörnschemeyer, Birgitta A1 - Lewe, Mareike A1 - Kleckers, Jonas A1 - Stretz, Celestin T1 - Leitfaden RessourcenPlan – Teil 3.2: Lernen von anderen – Booklet „Best-Practice“. Ergebnisse des Projekts R2Q RessourcenPlan im Quartier T3 - Leitfaden RessourcenPlan. Ergebnisse des Projekts R2Q RessourcenPlan im Quartier - 3.2 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-157595 ER - TY - RPRT A1 - Hörnschemeyer, Birgitta A1 - Kleckers, Jonas A1 - Stretz, Celestin A1 - Klemm, Christian A1 - Budde, Janik A1 - Arendt, Rosalie A1 - Lewe, Mareike A1 - Albers, Flemming T1 - Leitfaden RessourcenPlan – Teil 3.3: Maßnahmen des Quartiersmanagements: Maßnahmensteckbriefe. Ergebnisse des Projekts R2Q RessourcenPlan im Quartier T3 - Leitfaden RessourcenPlan. Ergebnisse des Projekts R2Q RessourcenPlan im Quartier - 3.3 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-157603 ER - TY - CHAP A1 - Budde, Janik A1 - Klemm, Christian A1 - Tockloth, Jan N. A1 - Becker, Gregor A1 - Vennemann, Peter T1 - Automatisierte Modellierung und Optimierung urbaner Energiesysteme T2 - 6. Regenerative Energietechnik Konferenz in Nordhausen 09. - 10. Februar 2023 N2 - Traditionelle, lineare Energiesysteme werden zunehmend zu vernetzten, regenerativen Energiesystemen transformiert. Mit dem auf dem „Open Energy Modelling Framework” (oemof) basierenden „Spreadsheet Energy System Model Generator” (SESMG) wurde ein Tool entwickelt, welches die Komplexität und Wechselwirkungen moderner Energiesysteme auf urbaner Ebene automatisiert abbildet. Zur Erstellung individueller Energiesystemmodelle sind ausschließlich quartiersspezifische Parameter notwendig, technische und wirtschaftliche Parameter sind standardmäßig hinterlegt. Mit Hilfe von Algorithmen werden Energieversorgungsszenarien identifiziert, welche individuell definierte Zielgrößen (z. B. monetäre Kosten oder Treibhausgasemissionen) minimieren. Durch die implementierten Methoden zur Modellvereinfachungen können auch mit begrenzten Rechenressourcen (insb. Rechenzeit und Arbeitsspeicherbedarf) große Systeme modelliert und optimiert werden. Die Zielszenarien werden als Diagramme und für die Weiterverarbeitung mit Geoinformationssystemen aufbereitet, sodass die Ergebnisse analysiert, plausibilisiert und präsentiert werden können. Y1 - 2023 UR - https://www.hs-nordhausen.de/fileadmin/Dateien/Forschung/2021/Tagungsband_RETCon_2023_Web.pdf SN - 978-3-940820-21-1 SP - 150 EP - 159 ER - TY - JOUR A1 - Klemm, Christian A1 - Wiese, Frauke A1 - Vennemann, Peter T1 - Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution JF - Applied Energy N2 - Local and regional energy systems are becoming increasingly entangled. Therefore, models for optimizing these energy systems are becoming more and more complex and the required computing resources (run-time and random access memory usage) are increasing rapidly. The computational requirements can basically be reduced solver-based (mathematical optimization of the solving process) or model-based (simplification of the real-world problem in the model). This paper deals with identifying how the required computational requirements for solving optimization models of multi-energy systems with high spatial resolution change with increasing model complexity and which model-based approaches enable to reduce the requirements with the lowest possible model deviations. A total of 12 temporal model reductions (reduction of the number of modeled time steps), nine techno-spatial model reductions (reduction of possible solutions), and five combined reduction schemes were theoretically analyzed and practically applied to a test case. The improvement in reducing the usage of computational resources and the impact on the quality of the results were quantified by comparing the results with a non-simplified reference case. The results show, that the run-time to solve a model increases quadratically and memory usage increases linearly with increasing model complexity. The application of various model adaption methods have enabled a reduction of the run-time by over 99% and the memory usage by up to 88%. At the same time, however, some of the methods led to significant deviations of the model results. Other methods require a profound prior knowledge and understanding of the investigated energy systems to be applied. In order to reduce the run-time and memory requirements for investment optimization, while maintaining good quality results, we recommend the application of (1) a pre-model that is used to (1a) perform technological pre-selection and (1b) define reasonable technological boundaries, (2) spatial sub-modeling along network nodes, and 3) temporal simplification by only modeling every nth day (temporal slicing), where at least 20% of the original time steps are modeled. Further simplifications such as spatial clustering or larger temporal simplification can further reduce the computational effort, but also result in significant model deviations. KW - energy system model KW - model-based KW - run-time KW - memory usage KW - multi-energy system Y1 - 2023 U6 - http://dx.doi.org/10.1016/j.apenergy.2022.120574 SN - 0306-2619 VL - 334 SP - 120574 ER - TY - JOUR A1 - Klemm, Christian A1 - Becker, Gregor A1 - Tockloth, Jan N. A1 - Budde, Janik A1 - Vennemann, Peter T1 - The Spreadsheet Energy System Model Generator (SESMG): A tool for the optimization of urban energy systems JF - Journal of Open Source Software N2 - The Spreadsheet Energy System Model Generator (SESMG) is a tool for modeling and optimizing energy systems with a focus on urban systems. The SESMG is easily accessible as it comes with a browser-based graphical user interface, spreadsheets to provide data entry, and detailed documentation on how to use it. Programming skills are not required for the installation or application of the tool. The SESMG includes advanced modeling features such as the application of the multi-energy system (MES) approach, multi-objective optimization, model-based methods for reducing computational requirements, and automated conceptualization and result processing of urban energy systems with high spatial resolution. Due to its accessibility and the applied modeling methods, urban energy systems can be modeled and optimized with comparatively low effort. Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-170845 SN - 2475-9066 VL - 8 IS - 89 SP - 5519 ER - TY - JOUR A1 - Klemm, Christian A1 - Vennemann, Peter A1 - Wiese, Frauke T1 - Potential-risk and no-regret options for urban energy system design — A sensitivity analysis JF - Sustainable Cities and Society N2 - This study identifies supply options for sustainable urban energy systems, which are robust to external system changes. A multi-criteria optimization model is used to minimize greenhouse gas (GHG) emissions and financial costs of a reference system. Sensitivity analyses examine the impact of changing boundary conditions related to GHG emissions, energy prices, energy demands, and population density. Options that align with both financial and emission reduction and are robust to system changes are called “no-regret” options. Options sensitive to system changes are labeled as “potential-risk” options. There is a conflict between minimizing GHG emissions and financial costs. In the reference case, the emission-optimized scenario enables a reduction of GHG emissions (-93%), but involves higher costs (+160%) compared to the financially-optimized scenario. No-regret options include photovoltaic systems, decentralized heat pumps, thermal storages, electricity exchange between sub-systems and with higher-level systems, and reducing energy demands through building insulation, behavioral changes, or the decrease of living space per inhabitant. Potential-risk options include solar thermal systems, natural gas technologies, high-capacity battery storages, and hydrogen for building energy supply. When energy prices rise, financially-optimized systems approach the least-emission system design. The maximum profitability of natural gas technologies was already reached before the 2022 European energy crisis. KW - sustainable energy KW - urban energy system KW - no-regret KW - sensitivity analysis KW - energy system modeling Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-175686 SN - 2210-6707 VL - 102 SP - 105189 ER - TY - GEN A1 - Tockloth, Jan N. A1 - Klemm, Christian A1 - Becker, Gregor A1 - Blankenstein, Benjamin A1 - Vennemann, Peter T1 - Spreadsheet Energy System Model Generator (SESMG) T2 - 16. Steinfurter Bioenergiefachtagung - Tagungsband N2 - Die Transformation der Energiesysteme im Rahmen der Energiewende macht diese durch zusätzliche Komponenten und Wechselwirkungen immer komplexer. Das ökonomische und ökologische Potenzial, dass sich aus der Nutzung der Synergien dieser Komponenten ergeben kann, erfordert eine gemeinsame Betrachtung des gesamten Energiesystems hinsichtlich sämtlicher Energie- und Verbrauchssektoren. Die Energiesystemmodellierung stellt eine geeignete Methode zur Modellierung und Optimierung dieser urbanen Energiesysteme dar. Mit dem „Spreadsheet Energy System Model Generator“ (SESMG) hat die FH Münster ein Open Source Tool entwickelt, das die Betrachtung urbaner Quartiere ermöglicht. Diese können hinsichtlich verschiedener Zielkriterien wie z. B. monetären Kosten und THG-Emissionen optimiert werden. Die tabellenbasierte Eingabe erfordert keine Programmierkenntnisse. Das implementierte Urban District Upscaling Tool erleichtert die effektive Modellierung auch größerer Systeme. Die automatisierte Ergebnisaufbereitung ermöglicht eine schnelle Analyse der Ergebnisse. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-178209 ER - TY - CHAP A1 - Tockloth, Jan N. A1 - Klemm, Christian A1 - Becker, Gregor A1 - Blankenstein, Benjamin A1 - Vennemann, Peter T1 - Der Spreadsheet Energy System Model Generator (SESMG): Ein Tool zur Optimierung urbaner Energiesysteme T2 - 16. Steinfurter Bioenergiefachtagung - Tagungsband N2 - Der Spreadsheet Energy System Model Generator (SESMG) ist ein Werkzeug zur Modellierung und Optimierung von (urbanen) Energiesystemen. Der SESMG hat eine browserbasierte grafische Benutzeroberfläche, eine tabellenbasierte Dateneingabe und eine ausführliche Dokumentation, was einen einfachen Einstieg ermöglicht. Zudem erfordern die Installation und Anwendung keine Programmierkenntnisse. Im SESMG sind verschiedene Modellierungsmethoden implementiert, wie z. B. die Anwendung des Multi-Energie-System-Ansatzes, die multikriteriale Optimierung, modellbasierte Methoden zur Reduktion des Rechenaufwands sowie die automatisierte Erstellung von räumlich hoch aufgelösten Energiesystemmodellen. Somit können urbane Energiesysteme mithilfe des SESMGs mit vergleichsweise geringem Aufwand, aber unter Berücksichtigung einer Vielzahl von Parametern und Randbedingungen, modelliert und optimiert werden. Y1 - 2024 UR - https://doi.org/10.25974/fhms-17789 SP - 18 EP - 19 ER -