@article{KlemmVennemannWiese2024,
  author    = {Klemm, Christian and Vennemann, Peter and Wiese, Frauke},
  title     = {Potential-risk and no-regret options for urban energy system design — A sensitivity analysis},
  series = {Sustainable Cities and Society},
  volume    = {102},
  journal   = {Sustainable Cities and Society},
  issn      = {2210-6707},
  doi       = {10.25974/fhms-17568},
  url       = {http://nbn-resolving.de/urn:nbn:de:hbz:836-opus-175686},
  pages     = {105189},
  year      = {2024},
  abstract  = {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.},
  language  = {en}
}
@article{BeckerKlemmVennemann2022,
  author    = {Becker, Gregor and Klemm, Christian and Vennemann, Peter},
  title     = {Open Source District Heating Modeling Tools—A Comparative Study},
  series = {energies},
  volume    = {15},
  journal   = {energies},
  number    = {8277},
  issn      = {1996-1073},
  doi       = {10.3390/en15218277},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{KlemmWiese2022,
  author    = {Klemm, Christian and Wiese, Frauke},
  title     = {Indicators for the optimization of sustainable urban energy systems based on energy system modeling},
  series = {Energy, Sustainability and Society},
  volume    = {12},
  journal   = {Energy, Sustainability and Society},
  number    = {3},
  publisher = {Springer Nature},
  doi       = {10.25974/fhms-14513},
  url       = {http://nbn-resolving.de/urn:nbn:de:hbz:836-opus-145136},
  pages     = {1 -- 20},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{KlemmVennemann2021,
  author    = {Klemm, Christian and Vennemann, Peter},
  title     = {Modeling and optimization of multi-energy systems in mixed-use districts: A review of existing methods and approaches},
  series = {Renewable and Sustainable Energy Reviews},
  journal   = {Renewable and Sustainable Energy Reviews},
  number    = {135},
  doi       = {10.1016/j.rser.2020.110206},
  pages     = {nn -- nn},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}