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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.
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.
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.
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.
Mechanical ventilation of buildings is generally based on steadily operating systems. This field is well known and established. But, an approach based on time-varied supply flow rates might improve indoor air quality, comfort, and energy consumption. Typical time-scales of the variation are in the order of seconds or minutes. Until now, the effects of unsteady ventilation scenarios are not fully described and so, reliable dimensioning rules are missing. Hence, with a better understanding of the flow in unsteady ventilation, systems can be calculated and optimised. To understand the effective mechanisms and derive functional relations between the flow field and variation parameters, full-field optical flow measurements are executed with a particle image velocimetry (PIV) system. Experiments are conducted under isothermal conditions in water in a small-scale room model (1.00 m × 0.67 m × 0.46 m) with two swirl ceiling diffusers, Reynolds-scaling assures similarity. In a series of experiments, the effects of different unsteady ventilation strategies on the flow fields are investigated and compared to steady conditions with the same mean exchange rate. Mean exchange rates, signal types, periods, and amplitudes are varied. Time-averaged normalised velocity fields already indicate notable differences between steady and unsteady cases especially for lower exchange rates: the distribution is more homogeneous in unsteady scenarios compared to steady conditions, and low-velocity areas are reduced while the mean velocity of the room increases. So, unsteady ventilation might be beneficial in terms of improved ventilation and energy savings in partial-load operation. Fast Fourier Transformation (FFT) analyses of the mean velocity for each field over the whole series detect the main frequency of the volume flow variation. By dividing the velocity field into smaller areas, this main frequency is still detected especially in the upper part of the room, but side frequencies play a role in the room as well.