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    <publishedYear>2024</publishedYear>
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    <language>eng</language>
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    <title language="eng">Sensitivity analysis of the energy transition path in the Berlin-Brandenburg area to uncertainties in operational and investment costs of diverse energy production technologies</title>
    <abstract language="eng">The investigation of energy transition paths toward a sustainable and decarbonized future under uncertainty is a critical aspect of contemporary energy planning and policy development. There are numerous methods for analysing uncertainties and sensitivities and many studies on sustainable transformation paths, but there is a lack of combined application to relevant use-cases.&#13;
In this study, we investigate the sensitivity of energy transition paths to uncertainties in operational and investment costs of power plants in the metropolitan area of Berlin and its rural surroundings.&#13;
By employing the linear programming energy system model oemof-B3, we extensively focus on the system's energy technologies, such as wind turbines, photovoltaics, hydro and combustion plants, and energy storages. Greenhouse gas reduction and electrification rates per commodity are realized by selected constraints.&#13;
&#13;
Our research aims to discern how investments in energy production capacities are influenced by uncertainties of other energy technologies' investment and operational costs in the system. We apply a quantitative approach to investigate such interdependencies of cost variations and their impact on long-term energy planning. Thus, the analysis sheds light on the robustness of energy transition paths in the face of these uncertainties.&#13;
&#13;
The region Berlin-Brandenburg serves as a case study and thus reflects on the present space conflicts to meet energy demands in urban and suburban areas and their rural surroundings. An electricity-intensive scenario is selected that assumes a 100 % reduction in greenhouse gas emissions by 2050. With the results of the case study, we show how our approach enables rural and metropolitan decision-makers to collaborate in achieving sustainable energy.&#13;
&#13;
Decision-making in long-term energy planning can be made more robust and flexible by acknowledging the identified sensitivities and enable such regions better to navigate challenges and uncertainties associated with sustainable energy planning.</abstract>
    <parentTitle language="eng">37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2024)</parentTitle>
    <identifier type="doi">10.52202/077185-0115</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2024-05-15</enrichment>
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    <author>Christoph Muschner</author>
    <submitter>Janina Zittel</submitter>
    <author>Inci Yüksel-Ergün</author>
    <author>Marie-Claire Gering</author>
    <author>Karolina Bartoszuk</author>
    <author>Sabine Haas</author>
    <author>Janina Zittel</author>
    <collection role="persons" number="zittel">Zittel, Janina</collection>
    <collection role="persons" number="yueksel-erguen">Yüksel-Ergün, Inci</collection>
    <collection role="institutes" number="aim">Applied Algorithmic Intelligence Methods</collection>
    <collection role="projects" number="Stadt-Land-Energie">Stadt-Land-Energie</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
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  <doc>
    <id>9798</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
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    <language>eng</language>
    <pageFirst>248</pageFirst>
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    <completedDate>2025-08-12</completedDate>
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    <title language="eng">Warm-starting modeling to generate alternatives for energy transition paths in the Berlin-Brandenburg area</title>
    <abstract language="eng">Energy system optimization models are key to investigate energy transition paths towards a decarbonized future. Since this approach comes with intrinsic uncertainties, it is insufficient to compute a single optimal solution assuming perfect foresight to provide a profound basis for decision makers. The paradigm of modeling to generate alternatives enables to explore the near-optimal solution space to a certain extent. However, large-scale energy models require a non-negligible computation time to be solved. We propose to use warm start methods to accelerate the process of finding close-to-optimal alternatives. In an extensive case study for the energy transition of the Berlin-Brandenburg area, we make use of the sector-coupled linear programming oemof-B3 model to analyze a scenario for the year 2050 with a resolution of one&#13;
&#13;
hour and 100% reduction of greenhouse gas emissions. We demonstrate that we can actually achieve a significant computational speedup.</abstract>
    <parentTitle language="eng">Operations Research Proceedings 2024. OR 2024</parentTitle>
    <identifier type="doi">10.1007/978-3-031-92575-7_35</identifier>
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    <enrichment key="AcceptedDate">2024-10-07</enrichment>
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    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-97835</enrichment>
    <enrichment key="Series">Lecture Notes in Operations Research</enrichment>
    <author>Niels Lindner</author>
    <submitter>Niels Lindner</submitter>
    <author>Karolina Bartoszuk</author>
    <author>Srinwanti Debgupta</author>
    <author>Marie-Claire Gering</author>
    <author>Christoph Muschner</author>
    <author>Janina Zittel</author>
    <collection role="persons" number="zittel">Zittel, Janina</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="lindner">Lindner, Niels</collection>
    <collection role="institutes" number="aim">Applied Algorithmic Intelligence Methods</collection>
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  <doc>
    <id>10026</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <pageLast/>
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    <title language="eng">Demand Uncertainty in Energy Systems: Scenario Catalogs vs. Integrated Robust Optimization</title>
    <abstract language="eng">Designing efficient energy systems is indispensable for shaping a more sustainable society. This involves making infrastructure investment decisions that must be valid for a long-term time horizon. While energy system optimization models constitute a powerful technique to support planning decisions, they need to cope with inherent uncertainty. For example, predicting future demand on a scale of decades is not only an intricate challenge in itself, but small fluctuations in such a forecast might also largely impact the layout of a complex energy system.&#13;
In this paper, we compare two methodologies of capturing demand uncertainty for linear-programming based energy system optimization models. On one hand, we generate and analyze catalogs of varying demand scenarios, where each individual scenario is considered independently, so that the optimization produces scenario-specific investment pathways. On the other hand, we make use of robust linear programming to meet the demand of all scenarios at once. Since including a multitude of scenarios increases the size and complexity of the optimization model, we will show how to use warm-starting approaches to accelerate the computation process, by exploiting the similar structure of the linear program across different demand inputs. This allows to integrate a meaningful number of demand scenarios with fully-fledged energy system models.&#13;
We demonstrate the practical use of our methods in a case study of the Berlin-Brandenburg area in Germany, a region that contains both a metropolitan area and its rural surroundings. As a backbone, we use the open-source framework oemof to create a sector-coupled optimization model for planning an energy system with up to 100% reduction of greenhouse gas emissions. This model features a fine-grained temporal resolution of one hour for the full year 2050. We consider uncertainty in demand for electricity, hydrogen, natural gas, central, and decentral heat.&#13;
Based on our computations, we analyze the trade-offs in terms of quality and computation time for scenario catalogs and the robust optimization approach. We further demonstrate that our procedure provides a valuable strategy for decision makers to gain insight on the robustness and sensitivity of solutions regarding demand variability.</abstract>
    <parentTitle language="eng">Proceedings of the 38th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="SubmissionStatus">accepted for publication</enrichment>
    <enrichment key="AcceptedDate">2025-05-26</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-102404</enrichment>
    <author>Niels Lindner</author>
    <submitter>Janina Zittel</submitter>
    <author>Lukas Mehl</author>
    <author>Karolina Bartoszuk</author>
    <author>Sarah Berendes</author>
    <author>Janina Zittel</author>
    <collection role="persons" number="zittel">Zittel, Janina</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="lindner">Lindner, Niels</collection>
    <collection role="institutes" number="aim">Applied Algorithmic Intelligence Methods</collection>
    <collection role="institutes" number="neo">Network Optimization</collection>
    <collection role="projects" number="MODAL-EnergyLab">MODAL-EnergyLab</collection>
    <collection role="projects" number="Stadt-Land-Energie">Stadt-Land-Energie</collection>
    <collection role="persons" number="mehl">Mehl, Lukas</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
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