Prof. Dr. Stefanie Meilinger
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Departments, institutes and facilities
- Internationales Zentrum für Nachhaltige Entwicklung (IZNE) (66)
- Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) (65)
- Fachbereich Ingenieurwissenschaften und Kommunikation (63)
- Fachbereich Informatik (6)
- Fachbereich Wirtschaftswissenschaften (4)
- Institut für KI und Autonome Systeme (A2S) (2)
- Zentrum für Innovation und Entwicklung in der Lehre (ZIEL) (1)
Document Type
- Conference Object (36)
- Article (33)
- Report (5)
- Dataset (3)
- Preprint (3)
- Working Paper (3)
- Part of a Book (2)
- Lecture (2)
- Contribution to a Periodical (1)
- Diploma Thesis (1)
Year of publication
Keywords
- West Africa (7)
- energy meteorology (5)
- Ghana (4)
- Global horizontal irradiance (3)
- Solar energy (3)
- AOD (2)
- COD (2)
- Distribution grid management (2)
- Energiemeteorologie (2)
- Erzeugungsprognose (2)
Germany aims to achieve a national climate-neutral energy system by 2045. The residential sector still accounts for 29% of end energy consumption, with 74% attributed to the direct use of fossil fuels for heating and hot water. In order to reduce fossil energy use in the household sector, great efforts are being made to design new energy concepts that expand the use of renewable energies to supply electricity and heat. One possibility is to convert parts of the natural gas grid to a hydrogen-based gas grid to deliver and store energy for urban quarters of buildings, especially with older building stock where electrification of heat via heat pumps is difficult due to technical, acoustical, and economic reasons. A comprehensive dataset was generated by a bottom-up analysis with open governmental and statistical data to determine regional building types regarding energy demand, solar potential, and existing grid infrastructure. The buildings’ connections to the electricity, gas, and district heating networks are considered. From this, a representative sample dataset was chosen as input for a newly developed energy system model based on energy flow simulation. The model simulates the interaction of hydrogen generation (HG) (from excess solar energy by electrolysis), storage in a metal-hydride storage (MHS) tank, and hydrogen use in a connected fuel cell (FC), forming a local PVPtGtHP (Photovoltaic Power-to-Gas-to-Heat-and-Power) network. Next to the seasonal hydrogen storage path (HSP), a battery will complete the system to form a hybrid energy storage system (HESS). Paired with seasonal time series for PV power, electricity and heat demand, and a model for connection to grid infrastructure, the simulation of different hydrogen applications and MHS placements aims to analyze operating times and energy share of the systems’ equipment and existing infrastructure. The method to obtain the data set together with the simulation model presented can be used by energy planners for cities, communities, and building developers to analyze the potentials of a quarter or region and plan a transition towards a more energy-efficient and sustainable energy system.
In Ghana, unreliable public grid infrastructure greatly impacts rural healthcare, where diesel generators are commonly used despite their high financial and environmental costs. Photovoltaic (PV)-hybrid systems offer a sustainable alternative, but require robust, predictive control strategies to ensure reliability. This study proposes a sector-specific Model Predictive Control (MPC) approach, integrating advanced load and meteorological forecasting for optimal energy dispatch. The methodology includes a long-short-term memory (LSTM)-based load forecasting model with probabilistic Monte Carlo dropout, a customized Numerical Weather Prediction (NWP) model based on the Weather Research and Forecasting (WRF) framework, and deep learning-based All-Sky Imager (ASI) nowcasting to improve short-term solar predictions. By combining these forecasting methods into a seamless prediction framework, the proposed MPC optimizes system performance while reducing reliance on fossil fuels. This study benchmarks the MPC against a traditional rule-based dispatch system, using data collected from a rural health facility in Kologo, Ghana. Results demonstrate that predictive control greatly reduces both economic and ecological costs. Compared to rule-based dispatch, diesel generator operation and fuel consumption are reduced by up to 61.62% and 47.17%, leading to economical and ecological cost savings of up to 20.7% and 31.78%. Additionally, system reliability improves, with battery depletion events during blackouts decreasing by up to 99.42%, while wear and tear on the diesel generator and battery are reduced by up to 54.93% and 37.34%, respectively. Furthermore, hyperparameter tuning enhances MPC performance, introducing further optimization potential. These findings highlight the effectiveness of predictive control in improving energy resilience for critical healthcare applications in rural settings.
Energy meteorology is an applied research field of meteorology that focuses on the study and prediction of weather conditions and events that affect energy production and use. This field has become increasingly important as the energy industry has become more dependent on weather conditions, especially in the areas of renewable energy sources such as wind energy, solar energy, and hydropower. The following paper has been written by experts of the Committee on Energy Meteorology of the German Meteorological Society summarizing their more than 30 years of experience and lessons learnt. It gives an overview of activities in energy meteorology that are already essential for the transformation of energy systems to systems with high shares of renewable energies. Building on this, the experts have created a vision of future topics that describe the future research landscape of energy meteorology. The authors explain that work in energy meteorology in recent years has primarily been concerned with the physically based modeling of wind and solar power generation and the development of short-term forecasting systems. In future years, a significant expansion of work in the areas of energy system modeling, digitalization, and climate change is expected. This includes the detailed consideration of regionally specified spatiotemporal variability for system design, the integration of artificial intelligence skills, the development of weather-related consumption based on smart meters, and the mapping of the effects of climate change on the energy system in planning and operating processes.
Optimal placement and upgrade of solar PV integration in a grid-connected solar photovoltaic system
(2024)
The shift towards renewable energy sources has heightened the interest in solar photovoltaic (SPV) systems, particularly in grid-connected configurations, to enhance energy security and reduce carbon emissions. Grid-tied SPVs face power quality challenges when specific grid codes are compromised. This study investigates and upgrades an integrated 90 kWp solar plant within a distribution network, leveraging data from Ghana's Energy Self-Sufficiency for Health Facilities (EnerSHelF) project. The research explores four scenarios for SPV placement optimization using dynamic programming and the Conditional New Adaptive Foraging Tree Squirrel Search Algorithm (CNAFTSSA). A Python-based simulation identifies three scenarios, high load nodes, voltage drop nodes, and system loss nodes, as the points for placing PV for better performance. The analysis revealed 85 %, 82.88 %, and 100 % optimal SPV penetration levels for placing the SPV at high load, voltage drop, and loss nodes. System active power losses were reduced by 72.97 %, 71.52 %, and 70.15 %, and reactive power losses by 73.12 %, 71.86 %, and 68.11 %, respectively, by placing the SPV at the above three categories of nodes. The fourth scenario applies to CNAFTSSA, achieving 100 % SPV penetration and reducing active and reactive power losses by 72.33 % and 72.55 %, respectively. This approach optimizes the voltage regulation (VR) from 24.92 % to 4.16 %, outperforming the VR of PV placement at high load nodes, voltage drop nodes, and loss nodes, where the voltage regulations are 5.25 %, 9.36 %, and 9.64 %, respectively. The novel CNAFTSSA for optimal SPV placement demonstrates its effectiveness in achieving higher penetration levels and improving system losses and VR. The findings highlight the effectiveness of strategic SPV placement and offer a comprehensive methodology that can be adapted for similar power distribution systems.
Analyzing the consequences of power factor degradation in grid-connected solar photovoltaic systems
(2024)
This study examines the impact of integrating solar photovoltaic (PV) systems on power factor (PF) within low-voltage radial distribution networks, using empirical data from the Energy Self-Sufficiency for Health Facilities in Ghana (EnerSHelF) project sites in Ghana. The research included simulations focusing on optimal PV integration, with and without PF considerations, and the strategic placement of PV and shunt capacitors (SC). Three scenarios evaluated PV injection at high-load demand nodes, achieving penetration levels of 85.00 percent, 82.88 percent with high voltage drop, and 100.00 percent with high loss nodes. Additionally, three scenarios assessed SC allocation methods: proportional to the node's reactive power demand (Scenario I), even distribution (Scenario II), and proportional to installed PV capacity at PV nodes (Scenario III).
The analysis used a twin-objective index (TOI), combining voltage deviations and power factor degradation. Results showed significant PV curtailment was necessary to achieve standard PF. Optimal penetration levels, considering TOI, reduced PV penetration from 85.00 percent to 63.75 percent, 82.88 percent to 57.38 percent, and 100.00 percent to 72.50 percent for high load, high voltage drops, and high loss nodes, respectively. Notably, all scenarios showed a concerning PF of 0.00 at dead-end nodes (P20, P21, P22).
Scenario I achieved PF ranges of -0.26 to 1.00 with PV at high load, -0.69 to 1.00 with PV at high voltage drop, and 0.95 to 1.00 with PV at high loss nodes. Scenario II produced similar ranges, -0.48 to 1.00, -1.00 to 0.99, and 0.30 to 0.96, with PV placement at high load, voltage drops, and loss nodes, respectively. Scenario III yielded ranges of -0.19 to 0.97 (high load), -0.23 to 1.00 (high voltage drop), and 0.86 to 0.96 (high losses).
The study concluded that the most effective strategy involves installing PVs at high-loss nodes and distributing SCs proportionally to the node's reactive power demand (Scenario I). This approach achieved a more uniform PF pattern throughout the network, highlighting the practical implications of strategic PV placement and targeted reactive power compensation for maintaining a healthy and efficient distribution system with solar PV integration.
A building’s energy storage demand depends on a variety of factors related to the specific local conditions such as building type, self-sufficiency-rate, and grid connection. Here, a newly developed bottom-up procedure is presented for classifying buildings in an urban building portfolio according to specific criteria. The algorithm uses publicly available building data such as building use, ground floor area, roof ridge height, solar roof potential, and population statistics. In addition, it considers the local gas grid (GG) as well as the district heating (DH) network. The building classification is developed for identifying typical building situations that can be used to estimate the demand for residential energy storage capacity. The developed algorithm is used to identify potential implementation of private photovoltaic(PV)-metal-hydride-storage (MHS) systems, for three scenarios, into the urban infrastructure for the city of Cologne. As result the statistical confidence interval of all analyzed buildings regarding their classification as well as corresponding maps is shown. Since similar data sets as used are available for many German or European metropolitan areas, the method developed with the assumptions presented in this work, can be used for classification of other urban and semi-urban areas including the assessment of their grid infrastructure.
Interdisciplinary research (IDR) is a widely applied research approach, combing the efforts of multiple academic disciplines to work on complex problems. Within transdisciplinary research (TDR), non-academic stakeholders participate in the project and offer hands-on experience to the research. These integrative approaches are praised for the ability for addressing ‘wicked problems’ and can lead to new perspectives on relevant contemporary challenges. This working paper is analysing the cooperation and exchange of involved disciplines in the German-Ghanaian interdisciplinary research project Energy-Self-Sufficiency for Health Facilities in Ghana (EnerSHelF). The results are presented in a Collaboration Frequency Network (CFN) as well as qualitatively examined to unravel the level of interaction and perspectives on chances and challenges of IDR and TDR. The analysis shows that disciplinary closeness, data collection and exchange, and individual effort are affecting the level of collaboration among other reasons. Concluding the authors develop recommendations for future IDR and TDR projects.
Accurate global horizontal irradiance (GHI) forecasting is critical for integrating solar energy into the power grid and operating solar power plants. The Weather Research and Forecasting model with its solar radiation extension (WRF-Solar) has been used to forecast solar irradiance in different regions around the world. However, the application of the WRF-Solar model to the prediction of GHI in West Africa, particularly Ghana, has not yet been investigated. The aim of this study is to evaluate the performance of the WRF-Solar model for predicting GHI in Ghana, focusing on three automatic weather stations (Akwatia, Kumasi and Kologo) for the year 2021. We used two one-way nested domains (D1 = 15 km and D2 = 3 km) to investigate the ability of the fully coupled WRF-Solar model to forecast GHI up to 72-hour ahead under different atmospheric conditions. The initial and lateral boundary conditions were taken from the ECMWF high-resolution operational forecasts. Our findings reveal that the WRF-Solar model performs better under clear skies than cloudy skies. Under clear skies, Kologo performed best in predicting 72-hour GHI, with a first day nRMSE of 9.62 %. However, forecasting GHI under cloudy skies at all three sites had significant uncertainties. Additionally, WRF-Solar model is able to reproduce the observed GHI diurnal cycle under high AOD conditions in most of the selected days. This study enhances the understanding of the WRF-Solar model’s capabilities and limitations for GHI forecasting in West Africa, particularly in Ghana. The findings provide valuable information for stakeholders involved in solar energy generation and grid integration towards optimized management in the region.
This work proposes a novel approach for probabilistic end-to-end all-sky imager-based nowcasting with horizons of up to 30 min using an ImageNet pre-trained deep neural network. The method involves a two-stage approach. First, a backbone model is trained to estimate the irradiance from all-sky imager (ASI) images. The model is then extended and retrained on image and parameter sequences for forecasting. An open access data set is used for training and evaluation. We investigated the impact of simultaneously considering global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI) on training time and forecast performance as well as the effect of adding parameters describing the irradiance variability proposed in the literature. The backbone model estimates current GHI with an RMSE and MAE of 58.06 and 29.33 W m−2, respectively. When extended for forecasting, the model achieves an overall positive skill score reaching 18.6 % compared to a smart persistence forecast. Minor modifications to the deterministic backbone and forecasting models enables the architecture to output an asymmetrical probability distribution and reduces training time while leading to similar errors for the backbone models. Investigating the impact of variability parameters shows that they reduce training time but have no significant impact on the GHI forecasting performance for both deterministic and probabilistic forecasting while simultaneously forecasting GHI, DNI, and DHI reduces the forecast performance.
Accurate forecasting of solar irradiance is crucial for the integration of solar energy into the power grid, power system planning, and the operation of solar power plants. The Weather Research and Forecasting (WRF) model, with its solar radiation (WRF-Solar) extension, has been used to forecast solar irradiance in various regions worldwide. However, the application of the WRF-Solar model for global horizontal irradiance (GHI) forecasting in West Africa, specifically in Ghana, has not been studied. This study aims to evaluate the performance of the WRF-Solar model for GHI forecasting in Ghana, focusing on 3 health centers (Kologo, Kumasi and Akwatia) for the year 2021. We applied a two one-way nested domain (D1=15 km and D2=3 km) to investigate the ability of the WRF solar model to forecast GHI up to 72 hours in advance under different atmospheric conditions. The initial and lateral boundary conditions were taken from the ECMWF operational forecasts. In addition, the optical aerosol depth (AOD) data at 550 nm from the Copernicus Atmosphere Monitoring Service (CAMS) were considered. The study uses statistical metrics such as mean bias error (MBE), root mean square error (RMSE), to evaluate the performance of the WRF-Solar model with the observational data obtained from automatic weather stations in the three health centers in Ghana. The results of this study will contribute to the understanding of the capabilities and limitations of the WRF-Solar model for forecasting GHI in West Africa, particularly in Ghana, and provide valuable information for stakeholders involved in solar energy generation and grid integration towards optimized management of in the region.
Solar photovoltaic power output is modulated by atmospheric aerosols and clouds and thus contains valuable information on the optical properties of the atmosphere. As a ground-based data source with high spatiotemporal resolution it has great potential to complement other ground-based solar irradiance measurements as well as those of weather models and satellites, thus leading to an improved characterisation of global horizontal irradiance. In this work several algorithms are presented that can retrieve global tilted and horizontal irradiance and atmospheric optical properties from solar photovoltaic data and/or pyranometer measurements. The method is tested on data from two measurement campaigns that took place in the Allgäu region in Germany in autumn 2018 and summer 2019, and the results are compared with local pyranometer measurements as well as satellite and weather model data. Using power data measured at 1 Hz and averaged to 1 min resolution along with a non-linear photovoltaic module temperature model, global horizontal irradiance is extracted with a mean bias error compared to concurrent pyranometer measurements of 5.79 W m−2 (7.35 W m−2) under clear (cloudy) skies, averaged over the two campaigns, whereas for the retrieval using coarser 15 min power data with a linear temperature model the mean bias error is 5.88 and 41.87 W m−2 under clear and cloudy skies, respectively.
During completely overcast periods the cloud optical depth is extracted from photovoltaic power using a lookup table method based on a 1D radiative transfer simulation, and the results are compared to both satellite retrievals and data from the Consortium for Small-scale Modelling (COSMO) weather model. Potential applications of this approach for extracting cloud optical properties are discussed, as well as certain limitations, such as the representation of 3D radiative effects that occur under broken-cloud conditions. In principle this method could provide an unprecedented amount of ground-based data on both irradiance and optical properties of the atmosphere, as long as the required photovoltaic power data are available and properly pre-screened to remove unwanted artefacts in the signal. Possible solutions to this problem are discussed in the context of future work.
Estimates of global horizontal irradiance (GHI) from reanalysis and satellite-based data are the most important information for the design and monitoring of PV systems in Africa, but their quality is unknown due to the lack of in situ measurements. In this study, we evaluate the performance of hourly GHI from state-of-the-art reanalysis and satellite-based products (ERA5, CAMS, MERRA-2, and SARAH-2) with 37 quality-controlled in situ measurements from novel meteorological networks established in Burkina Faso and Ghana under different weather conditions for the year 2020. The effects of clouds and aerosols are also considered in the analysis by using common performance measures for the main quality attributes and a new overall performance value for the joint assessment. The results show that satellite data performs better than reanalysis data under different atmospheric conditions. Nevertheless, both data sources exhibit significant bias of more than 150 W/m2 in terms of RMSE under cloudy skies compared to clear skies. The new measure of overall performance clearly shows that the hourly GHI derived from CAMS and SARAH-2 could serve as viable alternative data for assessing solar energy in the different climatic zones of West Africa.
Solar photovoltaic power output is modulated by atmospheric aerosols and clouds and thus contains valuable information on the optical properties of the atmosphere. As a ground-based data source with high spatiotemporal resolution it has great potential to complement other ground-based solar irradiance measurements as well as those of weather models and satellites, thus leading to an improved characterisation of global horizontal irradiance. In this work several algorithms are presented that can retrieve global tilted and horizontal irradiance and atmospheric optical properties from solar photovoltaic data and/or pyranometer measurements. Specifically, the aerosol (cloud) optical depth is inferred during clear sky (completely overcast) conditions. The method is tested on data from two measurement campaigns that took place in Allgäu, Germany in autumn 2018 and summer 2019, and the results are compared with local pyranometer measurements as well as satellite and weather model data. Using power data measured at 1 Hz and averaged to 1 minute resolution, the hourly global horizontal irradiance is extracted with a mean bias error compared to concurrent pyranometer measurements of 11.45 W m−2, averaged over the two campaigns, whereas for the retrieval using coarser 15 minute power data the mean bias error is 16.39 W m−2.
During completely overcast periods the cloud optical depth is extracted from photovoltaic power using a lookup table method based on a one-dimensional radiative transfer simulation, and the results are compared to both satellite retrievals as well as data from the COSMO weather model. Potential applications of this approach for extracting cloud optical properties are discussed, as well as certain limitations, such as the representation of 3D radiative effects that occur under broken cloud conditions. In principle this method could provide an unprecedented amount of ground-based data on both irradiance and optical properties of the atmosphere, as long as the required photovoltaic power data are available and are properly pre-screened to remove unwanted artefacts in the signal. Possible solutions to this problem are discussed in the context of future work.
In her recent article, Bender discusses several aspects of research–practice–collaborations (RPCs). In this commentary, we apply Bender's arguments to experiences in engineering research and development (R&D). We investigate the influence of interaction with practice partners on relevance, credibility, and legitimacy in the special engineering field of product development and analyze which methodological approaches are already being pursued for dealing with diverging interests and asymmetries and which steps will be necessary to include interests of civil society beyond traditional customer relations.
This dataset contains data from two measurement campaigns in autumn 2018 and summer 2019 that were part of the BMWi project "MetPVNet", and serve as a supplement to the paper "Dynamic model of photovoltaic module temperature as a function of atmospheric conditions", published in the special edition of "Advances in Science and Research", the proceedings of the 19th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2019.
Data are resampled to one minute, and include:
PV module temperature
Ambient temperature
Plane-of-array irradiance
Windspeed
Atmospheric thermal emission
The data were used for the dynamic temperature model, as presented in the paper
The electricity grid of the future will be built on renewable energy sources, which are highly variable and dependent on atmospheric conditions. In power grids with an increasingly high penetration of solar photovoltaics (PV), an accurate knowledge of the incoming solar irradiance is indispensable for grid operation and planning, and reliable irradiance forecasts are thus invaluable for energy system operators. In order to better characterise shortwave solar radiation in time and space, data from PV systems themselves can be used, since the measured power provides information about both irradiance and the optical properties of the atmosphere, in particular the cloud optical depth (COD). Indeed, in the European context with highly variable cloud cover, the cloud fraction and COD are important parameters in determining the irradiance, whereas aerosol effects are only of secondary importance.
Intention: Within the research project EnerSHelF (Energy-Self-Sufficiency for Health Facilities in Ghana), i. a. energy-meteorological and load-related measurement data are collected, for which an overview of the availability is to be presented on a poster.
Context: In Ghana, the total electricity consumed has almost doubled between 2008 and 2018 according to the Energy Commission of Ghana. This goes along with an unstable power grid, resulting in power outages whenever electricity consumption peaks. The blackouts called "dumsor" in Ghana, pose a severe burden to the healthcare sector. Innovative solutions are needed to reduce greenhouse gas emissions and improve energy and health access.
West Africa has great potential for the use of solar energy systems, as it has both a high solar radiation rate and a lack of energy production. West Africa is a very aerosol-rich region, whose effects on photovoltaic (PV) use are due to both atmospheric conditions and existing solar technology. This study reports the variability of aerosol optical properties in the city of Koforidua, Ghana over the period 2016 to 2020, and their impact on the radiation intensity and efficiency of a PV cell. The study used AERONET ground (Giles et al., 2019) and satellite data produced by CAMS (Gschwind, et al., 2019), which both provide aerosol optical depth (AOD) and metrological parameters used for radiative transfer calculations with libRadtran (Emde, et al., 2016). A spectrally resolved PV model (Herman-Czezuch et al., 2022) is then used to calculate the PV yield of two PV technologies: polycrystalline and amorphous silicon. It is observed that for both data sets, the aerosol is mainly composed of dust and organic matter, with a very increased AOD load during the harmattan period (December-February), also due to the fires observed during this period.
The accurate forecasting of solar radiation plays an important role for predictive control applications for energy systems with a high share of photovoltaic (PV) energy. Especially off-grid microgrid applications using predictive control applications can benefit from forecasts with a high temporal resolution to address sudden fluctuations of PV-power. However, cloud formation processes and movements are subject to ongoing research. For now-casting applications, all-sky-imagers (ASI) are used to offer an appropriate forecasting for aforementioned application. Recent research aims to achieve these forecasts via deep learning approaches, either as an image segmentation task to generate a DNI forecast through a cloud vectoring approach to translate the DNI to a GHI with ground-based measurement (Fabel et al., 2022; Nouri et al., 2021), or as an end-to-end regression task to generate a GHI forecast directly from the images (Paletta et al., 2021; Yang et al., 2021). While end-to-end regression might be the more attractive approach for off-grid scenarios, literature reports increased performance compared to smart-persistence but do not show satisfactory forecasting patterns (Paletta et al., 2021). This work takes a step back and investigates the possibility to translate ASI-images to current GHI to deploy the neural network as a feature extractor. An ImageNet pre-trained deep learning model is used to achieve such translation on an openly available dataset by the University of California San Diego (Pedro et al., 2019). The images and measurements were collected in Folsom, California. Results show that the neural network can successfully translate ASI-images to GHI for a variety of cloud situations without the need of any external variables. Extending the neural network to a forecasting task also shows promising forecasting patterns, which shows that the neural network extracts both temporal and momentarily features within the images to generate GHI forecasts.
Fundamentals of Energy Meteorology - Influence of atmospheric parameters on solar energy production
(2015)
An aircraft plume model has been developed on the basis of two coupled trajectory box models. Two boxes, one for plume and one for background conditions, are coupled by means of a mixing parameterization based on turbulence theory. The model considers comprehensive gas phase chemistry for the tropopause region including acetone, ethane and their oxidation products. Heterogeneous halogen, N2O5 and HOx chemistry on various types of background and aircraft-induced aerosols (liquid and ice) is considered, using state-of-the-art solubility dependent uptake coefficients for liquid phase reactions. The microphysical scheme allows for coagulation, gas-diffusive particle growth and evaporation, so that the particle development from 1s after emission to several days can be simulated. Model results are shown, studying emissions into the upper troposphere as well as into the lowermost stratosphere for contrail and non-contrail conditions. We show the microphysical and chemical evolution of spreading plumes and use the concept of mean plume encounter time, tl, to define effective emission and perturbation indices (EEIs and EPIs) for the North Atlantic Flight Corridor (NAFC) showing EEI(NOy) and EPI(O3) for various background conditions, such as relative humidity, local time of emission, and seasonal variations. Our results show a high sensitivity of EEI and EPIs on the exact conditions under which emissions take place. The difference of EEIs with and without considering plume processes indicates that these processes cannot be neglected.
For the winter 1999/2000 transport of air masses out of the vortex to mid-latitudes and ozone destruction inside and outside the northern polar vortex is studied to quantify the impact of earlier winter (before March) polar ozone destruction on mid-latitude ozone.
Nearly 112 000 trajectories are started on 1 December 1999 on 6 different potential temperature levels between 500–600 K and for a subset of these trajectories photo-chemical box-model calculations are performed. We linked a decline of −0.9% of mid-latitude ozone in this layer occurring in January and February 2000 to ozone destruction inside the vortex and successive transport of these air masses to mid-latitudes.
Further, the impact of denitrification, PSC-occurrence and anthropogenic chlorine loading on future stratospheric ozone is determined by applying various scenarios. Lower stratospheric temperatures and denitrification were found to play the most important role in the future evolution of polar ozone depletion.
Nitric acid partitioning in cirrus clouds: a synopsis based on field, laboratory and model studies
(2003)
From a synopsis of field, laboratory and model studies at T>205 K as well as from the field experiments POLSTAR at T<205 K we derive a general picture of the partitioning of nitric acid (HNO3) in cirrus clouds and a new hypothesis on the uptake of HNO3 on ice particles:
A substantial part of nitric acid remains in the gas phase under cirrus cloud conditions. The HNO3 removed from the gas phase is distributed between interstitial aerosol and ice particles in dependence on the temperature and ice surface, respectively. In cold cirrus clouds with small ice surface areas (T <205 K) the partitioning is strongly in favour of interstitial ternary solution particles while in warmer cirrus clouds with large ice surface areas the uptake on ice dominates. Consequently, denitrification via sedimenting ice particles may occur only in the -more frequently occurring- warm cirrus clouds
The HNO3 coverage on ice is found to be different for ice particles and ice films. On ice films the coverage can increase with decreasing temperature from about 0.1 to 0.8 monolayer, while that on ice particles is found to decrease with temperature and PHNO3 from 0.1 to 0.001 monolayer. An HNO3 uptake behaviour following dissociative Langmuir isotherms where the coverage decreases for descending temperatures may explain the observations for ice particles
From a comparison of the HNO3 measurements with model calculations it is found that (i) the global model of Lawrence and Crutzen (1998) overestimates the HNO3 partitioning in favour of the ice particles (ii) the Langmuir surface chemistry model of Tabazadeh et al. (1999) overestimates HNO3 coverages for temperatures ≤210 K More appropriate coverages are calculated when implementing in that model a temperature dependent function for the adsorption free energy (ΔGads (T)), which is empirically derived from the coverage measurements.
We examine the effect of nanometer-sized aircraft-induced aqueous sulfuric acid (H2SO4/H2O) particles on atmospheric ozone as a function of temperature. Our calculations are based on a previously derived parameterization for the regional-scale perturbations of the sulfate surface area density due to air traffic in the North Atlantic Flight Corridor (NAFC) and a chemical box model. We confirm large scale model results that at temperatures T>210 K additional ozone loss -- mainly caused by hydrolysis of BrONO2 and N2O5 -- scales in proportion with the aviation-produced increase of the background aerosol surface area. However, at lower temperatures (< 210 K) we isolate two effects which efficiently reduce the aircraft-induced perturbation: (1) background particles growth due to H2O and HNO3 uptake enhance scavenging losses of aviation-produced liquid particles and (2) the Kelvin effect efficiently limits chlorine activation on the small aircraft-induced droplets by reducing the solubility of chemically reacting species. These two effects lead to a substantial reduction of heterogeneous chemistry on aircraft-induced volatile aerosols under cold conditions. In contrast we find contrail ice particles to be potentially important for heterogeneous chlorine activation and reductions in ozone levels. These features have not been taken into consideration in previous global studies of the atmospheric impact of aviation. Therefore, to parameterize them in global chemistry and transport models, we propose the following parameterisation: scale the hydrolysis reactions by the aircraft-induced surface area increase, and neglect heterogeneous chlorine reactions on liquid plume particles but not on ice contrails and aircraft induced ice clouds.
This report has been prepared by the SETAC Europe Scientific Task Group on Global And RegionaL Impact Categories (SETAC-Europe/STG-GARLIC) that is installed by the 2nd SETAC Europe working group on life cycle impact assessment (WIA-2). This document is background to a chapter written by the same authors under the title “Climate change, stratospheric ozone depletion, photo-oxidant formation, acidification and eutrophication” in Udo de Haes et al. (2002). The chapter summarises the work of the STG-GARLIC and aims to give a state-of-the-art review of the best available practice(s) regarding category indicators and lists of concomitant characterisation factors for climate change, stratospheric ozone depletion, photo-oxidant formation, acidification, and aquatic and terrestrial eutrophication. Backgrounds on each of the specific impact categories are given in another background report from Klöpffer and Potting (2001).
This background report provides details on a selection of general issues relevant in relation to LCA and characterisation of impact in LCA. The document starts with a short introduction of the LCA methodology and impact assessment in LCA for non LCA-experts. LCA experts, on the other hand, will usually not be familiar in-depth with scientific and political backgrounds of the specific impact categories. A review of this is given. Also the discussion is provided about the issue of the position of the category indicator in the causality chain, and into the related issue of spatial differentiation. These two issues appeared to be one of the core items for SETAC-Europe/STG-GARLIC.
This thesis contributes to a better understanding of the effect of heterogeneous chemistry on ozone in the tropopause region. As part of the German research project ALTO, it especially focuses on the impact of aircraft emissions on heterogeneous ozone chemistry in this region. This is an important question as ozone is a strong greenhouse gas, whose radiative effect, is strongest near the tropopause.
In general, the treatment of heterogeneous processes on background and aviation-produced particles requires the consideration of processes ranging from nanometer to continental scale. For this reason the present modeling work includes a treatment of small scale processes as well as the development and subsequent application of parameterisations. Three numerical trajectory box models considering highly detailed microphysical and chemical processes have been developed: (a) an aircraft plume model including coagulation, chemistry and plume dilution, (b) a particle-size resolved microphysical box model and, (c) a comprehensive photo-chemical box model.
In this paper, a gas-to-power (GtoP) system for power outages is digitally modeled and experimentally developed. The design includes a solid-state hydrogen storage system composed of TiFeMn as a hydride forming alloy (6.7 kg of alloy in five tanks) and an air-cooled fuel cell (maximum power: 1.6 kW). The hydrogen storage system is charged under room temperature and 40 bar of hydrogen pressure, reaching about 110 g of hydrogen capacity. In an emergency use case of the system, hydrogen is supplied to the fuel cell, and the waste heat coming from the exhaust air of the fuel cell is used for the endothermic dehydrogenation reaction of the metal hydride. This GtoP system demonstrates fast, stable, and reliable responses, providing from 149 W to 596 W under different constant as well as dynamic conditions. A comprehensive and novel simulation approach based on a network model is also applied. The developed model is validated under static and dynamic power load scenarios, demonstrating excellent agreement with the experimental results.