• Alibek Jakupov

Renewable Energy sources monitoring: Part 9



This is the continuation of the previous article. For better understanding it is recommended to look through the earlier blog posts.

National Renewable Energy Laboratory, NREL



Data


The National Renewable Energy Laboratory (NREL) is the main USA scientific laboratory center implementing researches on renewable energy, efficient energy use (or energy efficiency) and sustainable development. The Laboratory is under the state financial support (is being financed by the US energy department) and is considered as a governmental organization (thus any commercial operation and third party agreement is done on the contact basis). NREL collaborates with the National Center for Photovoltaics in order to implement common researches on solar energy.

The Laboratory was created in 1974 but started fulfilling its activity in 1977 as the Solar Energy Research Institute. The NREL Colorado campus is aimed in implementing research on solar and bio energy. The Science and Technology Faculty made experiments on solar batteries, thin lenses and nanostructure researches. At the center scientist have an opportunity to make either indoor or outdoor measurements.. The outdoor measurements are necessary for full solar stations evaluation in real-life conditions. The measurements results are later used in developing the standards of solar stations evaluation. Among the the solar laboratory’s main activities are: semi-conductor materials researches, solar batteries prototype development, and efficiency evaluation. The laboratory roof is covered with ten solar panels that allows evaluating the commercial potential of solar stations.

NREL actively cooperates with several private companies and enterprises to make a free transfer of the results of researches on renewable energy possible, to share the technologies of the efficient energy use and, finally to integrate their products into real systems.

A great deal of NREL’s introduced technologies allow decreasing the consummation level of petrol industry products in USA, to decrease the carbon dioxide atmospheric emission, but still saving the companies’ of the energy sector marketability.

All the data obtained during the researches is freely available on www.nrel.gov. The data has been visualized, but especially for the power station engineers, it is also available in a table format. All the information presented on the official website has been obtained from one of three research centers:

  • The National Center for Photovoltaics

  • The National Bioenergy Center

  • The National Wind Technology Center - NWTC

All the data obtained during field and indoor measurement is published at Renewable Resource Data Center. Solar radiation dataset is an array of indices obtained during multiyear solar activity measurements (starting from 1984). Maps available on the NREL official website are classified by energy sources and by indices accuracy. For instance, 10 km data maps show hourly solar activity data obtained from field weather stations, daily data on snow coverage, monthly air humidity data, data on greenhouse gases, and a total amount of aerosols in atmosphere in order to let calculate daily surface insolation level. The same maps, but with 40 km data provide information on cloud coverage, atmosphere higher levels humidity, greenhouse gases concentration and a total amount of aerosols in atmosphere in order to allow calculation of the total insolation level. It still should be mentioned that satellite data is to be verified by field measurements whenever it is possible. For example, a cloud map is an height year histogram of satellite data taken in a 40 x 40 km resolution. Thus, the accuracy and the space map resolution are defined by the database itself.


Climate indices presented in the map form on the NREL official website are available as datasets in the following sources:

  • Renewable Resource Data Center -RReDC that provides data on biomass, geothermal, solar and wind energy (Figure 2.20).

  • Measurement and Instrumentation Data Center providing weather data from all the USA weather stations


Visualization tools


Besides providing raster maps the NREL represents some data by means of vector model. To create its raster model NREL commonly applies square or rectangular grid. Naturally, that square grid (creating each cell with equal length sides) is conceptually much simpler than any other. In this case, it is possible to divide cells recursively into squares (cells) of same shape.


The location is defined by neighborhood, i.e. left/right, above/below directions. This is commonly referred as rook’s case. As all the cells (at least neighborhood ones) are equidistant, the calculation may be simplified. It is also possible to apply a queen’s case, meaning that neighborhood includes 8 connections (including diagonals), But in this case all the cells may not be equidistant. Diagonal cells are 1.41 points away from the cell center (square root of 2).

For example NREL wind data (50 meter height above ground/surface) is obtained from original raster data that varied in resolution from 200-meter to 1000-meter cell sizes. Then, this data have been modified to produce geographic shapefiles. Thus, these shapefiles have been applied to create 50 meter wind maps. There are two distinct groups of data provided in this section: data produced by NREL and data produced by AWS Truepower but validated by NREL.


USA bioenergy data visualization sample

NREL provides datasets that may be later modified by other tools. For example solar data of Colorado is provided as a shape file that is later possible to be used in such tools as QGIS. In QGIS this shape file may be added as vector layer. These shapefiles are geometry records and attributes corresponding to each record. The information about records/columns and their explication are provided in the metadata. In order to visualize dataset provided by NREL it is necessary to use data based on attributes.

As it has been mentioned above in order to divide the square sides it is commonly applied a recursion with the respect to:

  • length (the length is decreased twice with each iteration)

  • number of areas (it is increased fourfold with each iteration)

  • area (it is decreased by one fourth with each iteration)

Every four cell values are resampled by combining, i.e. average of four values is calculated (in this case the storage is increased as it is necessary to save all samples, but it is possible to reduce processing costs as there are certain operations that does not require high resolution).

It is also possible to economize the required storage by applying maximum block representation in case of nominal or binary data. Thus: if blocks have the same value and are situated on the same level in tree they may be considered as a single value.


Unlike the raster model the representing the data the vector model requires much more calculation power. But still from the sight of view of certain task high resolution data visualization is extremely important. In this case the key terms that should always be considered are polygons, lines and points. It should be also mentioned that points are commonly referenced as nodes (and arcs as lines). To sum up, it is possible to define key features common to different representation tools.


When we are talking about a representation via points it is actually a 0-dimension representation.

  • every point is defined by a single x/y coordinate pair

  • every point is supposed to have no area (a material point)

  • commonly applied to represent tree, oil well, label location etc

When it concerns a line/arc representation the 1-dimension notation is generally referenced

  • every line is defined by a set of connected (but in most cases two) points (x/y pair)

  • applied in representing roads, streams etc.

A polygon is the most complicated figure applied in vector model and suggests a 2-dimensional notation

  • every polygon is presented as a set of four (or even more) connected and ordered points (the order is very important in polygon construction)

  • the origin and the end correspond (thus the start x/y pair is the same as the finish one)

  • polygons are generally applied to enclose an area

  • useful in representing census tracts, whole counties, lakes etc.

The whole polygon is commonly referenced as a boundary structure. In this case, the polygons are described as ordered coordinates. Thus, the coordinates of edges are listed in order (like walking around the outside boundary of the area). For polygon structure it it is useful to store all the information in a separate file.

It is possible to store the attribute data for the polygon in the same file, but is is quite inefficient. In case there are adjacent polygons the borders (coordinates) are stored twice, but is still possible that values may not be. Thus, there is a risk of slivers (gaps) or overlaps.

The following methods are applied to verify that both adjacent polygons are updated:

  • one should always remember that all the boundaries (lines) are double (it is not true for the ones on the outside periphery)

  • there is no topological data about polygons

Generally, the most complicated issue is to define which polygons are adjacent and have common boundary. It is also necessary to have clear understanding of how relations between different geographical parameters (e.g. tracts and corresponding zip codes). The first computer mapping program, SYMAP created in late 60s, applied the vector model. The vector model is adopted by SAS/GRAPH and other mapping programs used in business.

As it has been mentioned above the polygon edges are described by listing ID numbers like walking around the outside boundary of the area. There is also another file that contains information of all the points and their respective coordinates (an array of IDs). The second file’s efficiency is proved by the following reasons:

  • there is a problem with the duplicate coordinates and double borders; using the second file helps to solve this problem

  • as there is a list of IDs in the second file lines can be manipulated directly as polygons, but still there is a proble with networks representation

  • even if the second data file gives sufficient information there is still lack of topological data

  • second generation mapping package, from the Laboratory for Computer Graphics and Spatial Analysis at Harvard, called CALFORM was the first to apply second data file in their vector model.



Maps


Unlike ‘Russia’s Renewable Energy Sources’ GIS maps provided by NREL are explicitly divided into several categories. sThe team responsible for map development is NREL's Geospatial Data Science Team that creates tools for various renewable energy sources and for certain specific tasks (e.g. certain projects). As a benefit to the public, a majority of static maps are offered and Google Map (KML/KMZ) files on a tool called MapSearch To be beneficial for the public a majority of static maps and Google Map files (KML/KMZ) are available on a special tool created by NREL and called MapSearch. The main categories of maps in a public access are:

Biomass Maps

The biomass resources in the United States data provided in the map form shows the information throughout the county. Among the feedstock categories there are: crop residues, forest residues, primary and secondary mill residues, urban wood waste, and methane emissions from manure management, landfills, and domestic wastewater treatment.


Federal Energy Management Program

The main goal of the Federal Energy Management Program (FEMP) in cooperation with with Geospatial Analysis staff at NREL was to update the analysis for the development project. Working together they have finally composed an interactive FEMP Screening Map application. Nevertheless, the earlier map versions have been saved and archived. Thus all of them are still available and may be accessed by contacting the NREL’s Webmaster.

Geothermal maps

There are available maps showing not only favorable resources for enhanced geothermal systems, but also identified hydrothermal sites. Besides providing data on currently developed projects there are also sites on the map showing the planned geothermal power plant projects.


Hydrogen Maps

A tool known as GIS modeling is applied in analyzing and visualizing the spatial relationship between supply and demand. For instance, supply in the form of resources (renewable and traditional), hydrogen production facilities, and transportation infrastructure, from one side and hydrogen demand centers from another. This tool is a powerful facility allowing processing large and complex datasets, siting hydrogen production facilities and refueling stations on the map, and analyzing diversity of resources for hydrogen production throughout the United States.

International Maps

NREL provide powerful analysis Geospatial Toolkits and maps created in collaboration with with various countries. The maps are available through the MapSearch tool and as a separate category on the official web site (international maps).


Marine and Hydrokinetic Maps

There are several viable sites of marine and hydrokinetic resources in the United States. Maps of marine and hydrokinetic (MHK) resources are available on the NREL’s website. Some of them are: Nonpowered Dams Assessment, Wave Resource Assessment, Tidal Streams Resource Assessment, Wave Energy Resource Atlas, and Tidal Streams Resource Maps.


Solar Maps

Data for solar maps (solar radiation resources) has been obtained via several photovoltaic collector orientations in the United States. Among the available maps are: PV Solar Radiation Maps, Direct Normal Solar Radiation Maps, Map of U.S. Solar Measurement Station Locations, and the United States Solar Atlas.


Wind Maps

The wind power density is the main factor determining the wind resource potential. Thus, wind maps are based on these calculations. The wind energy resources are estimated for the maps and are represented as maps. The data is available only for the United States and its territories. It should be mentioned that these maps indicate general areas where a high wind resource may exist.

To be continued.

 
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