@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{KlemmWieseVennemann2023,
  author    = {Klemm, Christian and Wiese, Frauke and Vennemann, Peter},
  title     = {Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution},
  series = {Applied Energy},
  volume    = {334},
  journal   = {Applied Energy},
  issn      = {0306-2619},
  doi       = {10.1016/j.apenergy.2022.120574},
  pages     = {120574},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@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}
}