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A novel approach for ventilation systems is a periodically varying supply air flow rate, the so-called unsteady mode of operation. So far, useful effects of this unsteady operating mode have been observed, but the effect mechanisms are still unknown. In this manuscript, simulations using the recently proposed k-ω-ζ - f model implemented in a sensitized RANS computational framework for a cuboid room with swirl diffusers are compared and validated with PIV measurements.
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.
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.
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.
(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.
The effects of different unsteady ventilation strategies on flow-structures in a room are investigated and compared to steady ventilation with the same mean exchange rate. For this, whole-field optical flow measurements were executed by means of a particle image velocimetry system (PIV) in a Reynolds-scaled room model in water. In a first series of experiments, sinusoidal varied supply flows with different frequencies were analysed; two equally supplied simple nozzles in the ceiling were used as inlets. The setup was validated by comparing jet velocities with literature values.
Typically, room airflows are investigated with punctual measurement techniques (e.g.
anemometers), which have an impact on the flow field, or with smoke gas experiments. By using PIV, the flow can be analysed without any influence of sensors or stands/traverses and whole-field measurement data with high spatial resolution and detailed information on the flow field can be collected.
Local and time-averaged velocities and standard deviations were calculated for all scenarios. Unsteady conditions were created by a sinusoidal variation of the supply flow rate with frequencies between 0.025 1/s and 0.050 1/s, an offset of about 1.1 m3/h and an amplitude of about ±1.0 m3/h, which leads to a mean exchange rate of 3.5 1/h. Although averaged velocity fields only show slight differences between steady and unsteady conditions, single pictures vary widely. First effects of unsteady ventilation on flow structures can be recognized. Steady structures are destroyed, and velocities change rapidly.
The inlets will be changed to small-scale ceiling-diffusors in future experiments to create more realistic room ventilation conditions. Other types of unsteady supply flows will be implemented, and parameters will be varied. The results of the PIV-measurements can be used to validate CFD simulations and to derive dimensioning rules and application recommendations.
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.
Unsteady ventilation is being discussed to improve indoor air quality and comfort by simultaneously reducing the energy consumption. But until now, neither any standard system has been established nor are there any design parameters and clear specifications in standards. One reason for this lack of standardisation might be the ver-satile approaches for creating unsteady room airflows: sinusoidal variations, intermittent flow rates, and simu-lated natural wind or temperature variations in forced convection and the use of chaotic structures in free convection. Thus, this article gives an overview of systems available in Germany/Europe, international research and unresolved issues. It allows a clear focus for future investigations to be developed so that unsteady venti-lation concepts will be successfully utilised in practice. Experiments showed positive effects on indoor air quality (IAQ), comfort and energy consumption. However, two main questions have not been answered, yet: what are the fluid mechanical reasons for the observed effects and what are the best parameters to create unsteady room airflows?