Energie · Gebäude · Umwelt (EGU)
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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.
Indicators for the optimization of sustainable urban energy systems based on energy system modeling
(2022)
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.