Building on the geo-database created in the “Siemens Global Energy Demand” project at the client Siemens, the follow-up project “Siemens Global Energy Generation” dealt with the processing of weather data from the “Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2)” model  of NASA’s Global Modeling and Assimilation Office in order to generate global generation profiles for photovoltaic and wind power plants. The MERRA-2 project began in 1970 and had been continuously collecting comprehensive data on various parameters of the Earth’s atmosphere, oceans, landmasses, and aerosols.
MERRA-2 provides a wide range of weather parameters, among other things in hourly resolution. The data are available on a geographical grid of 0.625° longitude and 0.5° latitude, corresponding to about 48 x 57 km in southern Germany. However, not all parameters needed to calculate the generation paths are directly available – missing intermediate results have to be derived first. For the calculation of the wind generation paths, for example, wind speeds at 10 and 50 m height, as well as air pressure, can be used. Therefore, in order to calculate the wind speed at 60, 80, 100, 120, and 140 m, respectively, the atmospheric stability factor Z0 is first calculated from the inverse of the logarithmic wind profile for each hour. Then, the remaining heights can be calculated using this profile.
In the case of photovoltaics, on the other hand, the direct and diffuse components of solar radiation must first be calculated. These can be derived via the “Erbs model”  from the total solar radiation on the ground as well as the extraterrestrial solar radiation. Furthermore, parameters necessary for photovoltaics such as the sun position at each hour, the solar radiation incidence angles, reflected radiation, as well as low light behavior of the PV modules, have to be determined. Figure 1 shows an exemplary analemma (daily reached solar maximum) of the location Greenwich, United Kingdom, in 2012.
For photovoltaics, the generation curves are created for roof pitches in 10° steps from 10° to 40° and additionally 45° and orientations in 22.5° steps. Thus, the optimal or any other combination of inclination and orientation can be selected for each grid cell and the corresponding generation profiles or full load hours can be rread out
On the other hand, wind power generation profiles of selected onshore and offshore turbines and hub heights are generated.
The following animation shows the global PV full load hours for the most effective combination of orientation and roof pitch and the wind power full load hours for the Siemens SWT-3.6-107 wind turbine. For clarity, all data are aggregated to 9 MERRA cells each and represent the weather year 2015.
The animation can be switched between PV and wind using the Toggle button. The Start and Stop buttons start and stop the animation. The globe can be enlarged or reduced using the mouse wheel and moved using drag&drop. The reset button returns the globe to its original position. The full load hours of a cell are displayed in the tooltip when the mouse is moved over it.
Furthermore, an expandable global database of exclusion areas for onshore wind power was created, which provides variable distances to settlement areas from OpenStreetMap in addition to nature conservation areas and slope gradients.
Based on this extensive database structure, regional analyses of electricity generation by wind power and photovoltaics can now be performed globally, taking into account exclusion areas that can be added. In combination with the small-scale electricity consumption data of private households and the sectors of trade, commerce, services, and industry from the first project, an initial overview of the energy situation at any location on earth can be obtained.
|Global Modeling and Assimilation Office (GMAO) (2015)
|Albrecht, Peter: Weiterentwicklung eines Tools zur Analyse der potentiellen Solar- und Windenergieausbeute weltweit. Masterarbeit. Herausgegeben durch die Technische Universität München: München, 2016.