Renewable Energy sources monitoring: Part 8
This is the continuation of the previous article. For better understanding it is recommended to look through the earlier blog posts.
Russia’s Renewable Energy Sources
According to the description provided on the “Renewable Energy Sources” GIS (RRES GIS) official website the project is accomplished in cooperation with following scientific research centers:
Moscow State University’s Faculty of Geography
Joint Institute for High Temperatures of the Russian Academy of Sciences, (research institute in the fields of high energy densities physics, shock wave physics, thermodynamics databases, numerical simulations and cluster computing, dusty plasma, applied electrodynamics, combustion, green power (JIHT))
Renewable Energy Sources and Energy Conservation Laboratory
Data provided on the website is based on the Global Atlas for Solar energy in Russia and climate databases (special datasets applied in renewable energy monitoring in Russia). Data provided by the GIS may also be used in constructing mathematical models, including dynamic ones, of power stations working on renewable energy.
The Global Atlas for Solar energy in Russia is a set of maps of total solar radiation on the whole surface of Russia. Mean values of solar radiation are calculated from radiation sums of different periods of time. It should be noticed that the data format is defined by heliotechnical equipment requirements . Data for the Global Atlas is provided by Russian weather stations (multiyear solar radiation measurements), Meteonorm RetScreen и NASA SSE(NASA Surface meteorology and Solar Energy) databases. Data taken from NASA SSE, is satellites measurements values used to calculate the Earth's Radiation Energy Balance. Using these values solar radiation distribution models are developed. In these models the following weather parameters are considered:
Earth's surface albedo
Humidity in air
Atmospheric aerosols concentration
Together all these parameters allow determining mean solar radiation streams with a little inaccuracy. It still should be mentioned that these parameters have been calculated in accordance with USA surface specifications.
On the basis of NASA SSE datasets wind annual average velocities distribution maps have been constructed (50 and 10 m heights), which are also included in the Global Atlas (Figure 1). These maps are essential in calculating wind velocities in different regions of Russia. Data taken from the maps is later used in defining power stations’ potential efficiency (including those ones that are integrated into combined solar and wind power stations). Nevertheless, due to the dependency of actual wind velocity and direction from the region’s geographic conditions, it is obligatory to verify all the data, i.e. wind parameters are measured directly at the site of the planned power station. Multiyear data of the surrounding weather station may also be taken into consideration.
Using this statistical data direct (beam), diffuse and reflected solar radiation indices have been calculated for multidirectional horizon degrees and different time periods (including the daily distribution indices of the total amount of solar radiation striking the horizontal surface on the earth).
All these indices may be used afterwards to evaluate the efficiency of solar power stations working on luminescent solar concentrators.
Thus, data used in ‘Russia’s Renewable Energy Sources’ GIS is a special dataset that includes statistical information and results of processing this data.
In general sense, this system integrates stores, edits, analyzes, shares, and displays geographic information. Its applications are tools that allow site vistors to create interactive queries (user-created searches), analyze spatial information, edit data in maps, and present the results of all these operations.
Besides providing spatial data in a map form, the system can relate unrelated information by using location as the key index variable. In this case, object locations or extents in the Earth space–time are recorded as dates/times of occurrence, and x, y, and z coordinates representing, longitude, latitude, and elevation, respectively. All Earth-based spatial–temporal location and extent references should, ideally, be relatable to one another and ultimately to a "real" physical location or extent.
The Release 6.0 Surface meteorology and Solar Energy (SSE) data set contains parameters formulated for assessing and designing renewable energy systems. This latest release contains new parameters based on recommendations by the renewable energy industry and it is more accurate than previous releases. Global, regional and site specific radiation and meteorological data allow quick evaluation of potential renewable energy projects for any region of the world. The SSE data set is formulated from NASA satellite- and reanalysis-derived insolation and meteorological data for the 22-year period July 1983 through June 2005. Results are provided for 1° latitude by 1° longitude grid cells over the globe.
Statistical data is obtained from weather stations all over the country and from the NASA SSE weather database, created on the basis of long-term surface observations with Earth satellites.
Simplified data structure allows implementing the following features:
directly store each layer as a single table represent each layer as a separate table and store it in the database (in this case layers look like spredsheets)
there is no need of heavy host-based DBMS because many raster GIS already have them integrated
The Atmospheric Science Data Center (ASDC) at NASA Langley Research Center is responsible for the processing, archival, and distribution of NASA Earth science data.
The upper left of the grid is commonly defined as its origin, but there are certain features to be mentioned:
State Plane and Universal Transverse Mercator UTM have their orign at the lower left
Latitude/longitude and Cartesian coordinates are defined to have an origin at the center
Every cell is associated with exactly one value (in common case a single value is assigned 8 bits there are consequently 256 possible values (from 0 to 255)).
It is a common case that there are objects that do not cover the entire cell. In these cases there are certain rules to be applied to assign the value to the cell.
in case if continuous coverage feature the most suitable solution is to select the value calculated from the majority of the area
to take the value from the center of the cell
in case of linear feature (e.g. a road) it is convenient to take a ‘touches’ cell
in case of rare features that are represented it is necessary to apply weighting in order to ensure the calculations
There is also a common strategy frequently applied while creating the raster model. Firstly, it is necessary to identify the minimum mapping unit or ressel (resolution element). The ressel is defined as the smallest feature to map. Then the 1/2 the length of ressel (or 1/4 the area) is calculated. The obtained value is the generally known as a raster cell size.
Later the raster orientation is calculated. The raster orientation is defined as the angle between true north and direction defined by raster columns. There are certain notations that are commonly applied while constructing the raster model, as class, zone and neighborhood:
class is a combination of cells with the similar value (values may be defined by the object type, as for example the cells defining sandy soil, i.e. having the same type)
unlike the class a zone is a combination of contiguous cells h aving the similar value
neighborhood is a combination of cells adjacent to a target cell(cells may be adjacent in a certain systematic manner)
All the maps provided by the system may be divided into several categories. Maps are also divided with the respect to the energy sources they are representing. Thus, all the maps are divided into solar, wind, hydro and bio energy. The division is provided explicitly and is highly understandable.
From the point of view of representation concepts the majority of the maps are raster with a certain number of vector maps. It should be mentioned that this division is not unequivocal (i.e. there is no separation evident to the user). Yet, it is possible to find these categories as subdivisions. Thus, there are raster, resource and object maps
To be continued.