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This code is in support of my research paper 'Design and Analysis of 2kW Residential PEM Fuel Cell Inverter'. This code uses data uploaded on IEEE DataPort, titled 'Data:2kW Hydrogen Residential Inverter', and on Mendeley titled 'Data:2KW PEM Fuel Cell Residential Inverter'.For more details,pls read ReadMe.Txt.

The files contained within this dataset describe a simulation tool that predicts the energy consumption of a battery electric vehicle. The tool is written in MATLAB-code and is connected to various API's to make use of up-to-date route information (Overpass OpenStreetMap API), height information (SRTM elevation map), and weather information (OpenWeatherMap API). The prediction method relies on a physics-based interpretation of the energy consumption of the vehicle. Both the velocity profile prediction algorithm and the subsequent energy consumption model are based on data obtained from dedicated vehicle tests. In the supplied version of this tool, the parameters represent the Voltia eVan, which is a fully electric delivery van with a swappable traction battery. The tool was developed within the Dynamics & Control research-group at Eindhoven University of Technology. This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No. 713771 (EVERLASTING).Version: 0.1.3 Date: 2021-08-23Change Log Version 0.1.3------------------------- Updated 'readhght' from Fran��ois Beauducel, which again omits the use of the NASA/EarthDATA account that was introduced in the previous version. SRTM elevation data can now again be obtained without account.Change Log Version 0.1.2------------------------- Changed the inputs to 'readhght' to use the NASA/EarthDATA server, because the original server hosting the SRTM DEM appears to be off-line. This new server requires a NASA/EarthDATA account. If road slope is to be included, 'settings.includeSlope' (l. 76, TUe_MECPRO.m) should be set to 'true' and NASA/EarthDATA account credentials should be entered in 'NASA_Earthdata_Login.txt'.- Corrected an error in velocityProfilePredicion.m where the prescribed maximum vehicle velocity was wrongly considered to be [m/s] instead of [km/h].- Corrected an error in processOSMmap.m that resulted in the incorrect registration of the maximum legislated velocity, in case it is prescribed as 'none' (German Highways). Now, 200km/h is assumed in case the maximum legislated velocity is 'none'.

Increasing levels of anthropogenic underwater noise have caused concern over their potential impacts on marine life. Offshore renewable energy developments and seismic exploration can produce impulsive noise which is especially hazardous for marine mammals because it can induce auditory damage at shorter distances and behavioural disturbance at longer distances. However, far-field effects of impulsive noise remain poorly understood, causing a high level of uncertainty when predicting the impacts of offshore energy developments on marine mammal populations. Here we used a 10-year dataset on the occurrence of coastal bottlenose dolphins over the period 2009-2019 to investigate far-field effects of impulsive noise from offshore activities undertaken in three different years. Activities included a 2D seismic survey and the pile installation at two offshore wind farms, 20-75 km from coastal waters known to be frequented by dolphins. We collected passive acoustic data in key coastal areas and used a Before-After Control-Impact design to investigate variation in dolphin detections in areas exposed to different levels of impulsive noise from these offshore activities. We compared dolphin detections at two temporal scales, comparing years and days with and without impulsive noise. Passive acoustic data confirmed that dolphins continued to use the impact area throughout each offshore activity period, but also provided evidence of short-term behavioural responses in this area. Unexpectedly, and only at the smallest temporal scale, a consistent increase in dolphin detections was observed at the impact sites during activities generating impulsive noise. We suggest that this increase in dolphin detections could be explained by changes in vocalization behaviour. Marine mammal protection policies focus on the near-field effects of impulsive noise; however, our results emphasize the importance of investigating the far-field effects of anthropogenic disturbances to better understand the impacts of human activities on marine mammal populations. Echolocation detectors (CPODs; Chelonia Ltd) were deployed between 2009 and 2019 to investigate the variation in dolphin detections in relation to the impulsive noise from three energy developments: a seismic survey for oil and gas exploration and the installation of foundation piles for two offshore wind farms (Beatrice Offshore Wind Farm and Moray East Offshore Wind Farm). Data on the timing of the seismic survey and piling operations were provided by the developers (Oil and Gas UK Ltd., COWRIE, Beatrice Offshore Wind Ltd. and Moray Offshore Wind Farm East). Data consist of 7 files and include the datasets and R code required to repeat all the analyses. A full description of the files provided in the Readme.txt file: OFB_FarField_DPH.csv OFB_FarField_BOWL.csv OFB_FarField_MEOW.csv OFB_FarField_BACI_Obtain_DPH_Dataset.R OFB_FarField_DPH_for_BACI.csv OFB_FarField_BACI_DPH_Models.R OFB_FarField_Readme.txt

This repository will contain the code for the paper:Veviurko, G.; Böhmer, W.; Mackay L.; de Weerdt, M. Surrogate DC Microgrid Models for Optimization of Charging Electric Vehicles under Partial Observability, Energies 2022.

A model of the European power system built using Calliope. This repository contains the workflow routines that automatically build the model from source data. Alternatively to building models yourself, you can use pre-built models that run out-of-the-box. See README.md for further information. If you use euro-calliope in an academic publication, please cite the following article: Tr��ndle, T., Lilliestam, J., Marelli, S., Pfenninger, S., 2020. Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe. Joule.

This software aims to simulate the energy yield of single-junction and multi-junction solar cells. In contrast to the power conversion efficiency (PCE), the energy yield (EY) accounts for environmental conditions, such as constantly changing irradiation conditions or the ambient temperature. This software allows a rapid simulation of complex architectures and was developed with the aim to handle textured perovskite-based multi-junction devices. However, it is possible to simulate any combination of thin-film architecture with incoherent photovoltaic materials (e.g., crystalline silicon). By making use of pre-simulated textures (e.g., inverted pyramids, regular upright pyramids, random pyramids) by geometrical ray tracing, any incoherent interface within the architecture can also be textured. The software is available as source code and as a simple to use graphical user interface (GUI), which requires either a MATLAB (>R2017a and >R2020b, respectively) installation or the MATLAB runtime. Basic Features The basic features of the EYcalc are: Spectral and angular-resolved realistic irradiance data (from 1020 locations in the USA) is used A simple cloud model is used to adjust the diffuse irradiation Fast optical simulations, by combining the transfer matrix method and geometric ray tracing Optics can handle arbitrary combinations of thin (coherent) and thick (incoherent) layers, which also can be textured Single- and multi-junction solar cells can be simulated No limitation on the number of absorbers Energy yield is computed for different electrical interconnection schemes (e.g., 2T, 3T, 4T) Energy yield can be derived for constant tilt (and constant rotation) angle Energy yield can be derived for various tracking algorithms (e.g., 1-axis, 2-axis) Bifacial solar cells can be simulated Albedo can be considered by choosing one out of 3400 spectra of natural and man-made materials from the ECOSTRESS spectral library Modular framework The software is divided into individual modules, which handle the irradiation, optics, electrics and energy yield simulations. Those modules can also be operated independently (e.g., calculate the reflectance, transmittance, absorptance of a solar cell architecture). The Irradiation Module calculates the spectral and angular-resolved irradiance over the course of one year with a temporal resolution of one hour by applying SMARTS to typical meteorological year (TMY3) data of locations in various climatic zones. A simple model is employed to account for cloud coverage such that realistic direct and diffuse irradiance are derived. The Optics Module rapidly calculates the spectral and angular-resolved absorptance of the non-simplified architecture of multi-junction solar cells. It is able to handle multiple planar and textured interfaces with coherent and incoherent light propagation by combining transfer matrix method (TMM) and geometrical ray-tracing. The Electrical Module determines the temperature-dependent current density-voltage (J-V) characteristics accounting for series and shunt resistances for a given short-circuit current density (JSC) of the sub-cells forming the multi-junction in either a 2T-, 3T- or 4T-configuration. Furthermore, the maximum power point is determined to calculate the power output of the multi-junction solar module. The Energy Yield Core Module calculates the EY over the course of one year of the sub-cells depending on their orientation (rotation and/or tilt of the module) and location. The EY is computed by combining the spectral and angular resolved solar irradiation (with or without albedo), the absorptance of the multi-junction solar cell and the electrical properties. Credits This software project was initiated by Ulrich W. Paetzold. The code development was driven by: Raphael Schmager (energy yield core, irradiance module, optics module, electrics module, GUI) Malte Langenhorst (optics module, irradiance module) Jonathan Lehr (electrics module, albedo) Fabrizio Gota (numerical modelling on 3T interconnection, optics module) The financial support by the following projects and grants is gratefully acknowledged: PERCISTAND (funding code: 850937), European Union's Horizon 2020 research and innovation programme Helmholtz Young Investigator Group of U. W. Paetzold (funding code: VH-NG-1148), Helmholtz Association PEROSEED (funding code: ZT-0024), Helmholtz Association CAPITANO (funding code: 03EE1038B), Federal Ministry for Economic Affairs and Energy 27Plus6 (funding code: 03EE1056B), Federal Ministry for Economic Affairs and Energy This software uses codes and data from other programmers and resources: Parts of the transfer matrix code is taken from Steven Byrnes Matlab implementation of the NREL solar position algorithm by Vincent Roy Logarithmic Lambert W function from Michael The SMARTS from Dr. Christian A. Gueymard see also The TMY3 data from the National Solar Radiation Database Reference Air Mass 1.5 Spectra ECOSTRESS spectral library for albedo Getting started To use all features of the EYcalc software, you need to download and add some external files, like the SMARTS code and the TMY3 data. Please see our setup guide for help in setting up the required external files! On our wiki page you can also find a detailed description for each of the modules as well as a quick start guide. Contributing If you want to contribute to this project and make it better, your help is very welcome! Contact For any questions regarding the software, please contact Ulrich W. Paetzold. Citing If you use our software or parts of it in the current or a modified version, you are obliged to provide proper attribution. This can be to our paper describing the software: R. Schmager and M. Langenhorst et al., Methodology of energy yield modelling of perovskite-based multi-junction photovoltaics, Opt. Express. (2019). doi:10.1364/oe.27.00a507. or to this code directly: EYcalc - Energy yield calculator for multi-junction solar modules with realistic irradiance data and textured interfaces. (2021). doi.org/10.5281/zenodo.4696257. License This software is licensed under the GPLv3 license. © 2021 EYcalc - Ulrich W. Paetzold, Raphael Schmager, Malte Langenhorst, Jonathan Lehr, Fabrizio Gota Interested in a sublicense agreement to use EYcalc in a non-free/restrictive environment? Contact Ulrich W. Paetzold! Further reading This energy yield software has been used in the following publications: M. De Bastiani et al., Efficient bifacial monolithic perovskite/silicon tandem solar cells via bandgap engineering, Nature Energy. (2021). doi.org/10.1038/s41560-020-00756-8. J. Lehr et al., Numerical study on the angular light trapping of the energy yield of organic solar cells with an optical cavity, Opt. Express. (2020) doi.org/10.1364/OE.404969. F. Gota et al., Energy Yield Advantages of Three-Terminal Perovskite-Silicon Tandem Photovoltaics, Joule, (2020). doi.org/10.1016/j.joule.2020.08.021. J. Lehr et al., Energy yield of bifacial textured perovskite/silicon tandem photovoltaic modules, Sol. Energy Mater. Sol. Cells. (2020). doi:10.1016/j.solmat.2019.110367. R. Schmager et al., Methodology of energy yield modelling of perovskite-based multi-junction photovoltaics, Opt. Express. (2019). doi:10.1364/oe.27.00a507. M. Langenhorst et al., Energy yield of all thin-film perovskite/CIGS tandem solar modules, Prog. Photovoltaics Res. Appl. (2019). doi:10.1002/pip.3091. J. Lehr et al., Energy yield modelling of perovskite/silicon two-terminal tandem PV modules with flat and textured interfaces, Sustain. Energy Fuels. (2018). doi:10.1039/c8se00465j. Grants: - PERCISTAND (funding code: 850937), European Union's Horizon 2020 research and innovation programme - Helmholtz Young Investigator Group of U. Paetzold (funding code: VH-NG-1148), Helmholtz Association - PEROSEED (funding code: ZT-0024), Helmholtz Association - CAPITANO (funding code: 03EE1038B), Federal Ministry for Economic Affairs and Energy - 27Plus6 (funding code: 03EE1056B), Federal Ministry for Economic Affairs and Energy

This is the software and data set named "Supplementary Software 1" for the research paper "Offshore wind competitiveness in mature markets without subsidy". Please refer to the README file in the ZIP file for instructions. The paper is currently under review and access is for peer-review purposes only. Bundesministerium für Bildung und Forschung under project reference FKZ 01LA1821A. BTU Cottbus-Senftenberg for a postgraduate scholarship (GradV).

This map shows all emissions of CO and CO2 in Europe in selected industrial categories that contain processes with Carbon4PUR compatible emissions according to the E-PRTR. The information contained in this document has been prepared solely for the purpose of providing information about the Carbon4PUR consortium and its project. The document reflects only the Carbon4PUR consortium’s view and the European Commission is not responsible for any use that may be made of the information it contains. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 768919. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 768919 {"references": ["https://carbonnext-eu.github.io/"]}

The Open-Source PEMFC Simulation Tool (Opem) is an open-source mathematical simulation package for polymer electrolyte fuel cells. It contains a database of physical phenomena equations, and kinetics mathematical models in order to perform static/dynamic analysis of PEMFC. The goal of the software is to prepare a platform for collaborative development of PEMFC mathematical models. OPEM Version 0.1 : Amphlett Static Model Nernst Voltage PEMFC losses model Power of PEMFC Efficiency of PEMFC Flat Output Simulation Result CSV File

{"references": ["Racsko P, Szeidl L & Semenov MA (1991) A serial approach to local stochastic weather models. Ecological Modelling, 57:27-41, https://doi.org/10.1016/0304-3800(91)90053-4 2.\tSemenov MA & Barrow EM (1997) Use of a stochastic weather generator in the development of climate change scenarios Climatic Change, 35:397-414 https://doi.org/10.1023/A:1005342632279", "Semenov MA & Barrow EM (1997) Use of a stochastic weather generator in the development of climate change scenarios Climatic Change, 35:397-414 https://doi.org/10.1023/A:1005342632279", "Semenov MA & Brooks RJ (1999) Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain. Climate Research 11:137-148 https://doi.org/10.3354/cr011137", "Semenov MA (2008) Simulation of weather extreme events by a stochastic weather generator, Climate Research 35:203-212 https://doi.org/10.3354/cr00731", "Semenov MA, Donatelli M, Stratonovitch P, Chatzidaki E, and Baruth B. (2010) ELPIS: a dataset of local-scale daily climate scenarios for Europe. Climate Research 44:3-15 https://doi.org/10.3354/cr00865", "Semenov MA & Stratonovitch P (2010) The use of multi-model ensembles from global climate models for impact assessments of climate change. Climate Research 41:1-14 https://doi.org/10.3354/cr00836", "Semenov MA, Stratonovitch P (2015) Adapting wheat ideotypes for climate change: accounting for uncertainties in CMIP5 climate projections. Clim Res 65: 123\u2013139 https://doi.org/10.3354/cr01297"]} LARS-WG 6.0, a stochastic weather generator, is a computationally inexpensive downscaling tool to generate local scale climate scenarios based on global or regional climate models for impact assessments of climate change. LARS-WG has been used in more than 75 countries for research and education. The current version of LARS-WG incorporates climate projections from the CMIP5 ensemble used in the IPCC Fifth Assessment Report. LARS-WG has been well validated in diverse climates around the world.