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Geospatial unleashing renewable power

Anand Kashyap
Sub-editor, Geospatial World
[email protected]

For most of our day-to-day activities, we depend on energy sources like crude oil, coal and natural gas. However, these energy sources are exhaustible and will get depleted in a few decades. In addition, they are also associated with environmental issues like global warming. Experts are therefore paying close attention to alternative, renewable energy sources like wind, solar, geothermal and biomass that can be used repeatedly. They also meet the requirements of smarter and cleaner energy production and transmission.

Geospatial technology has an important role to play in this process. As a tool, the technology is positioned to analyse and monitor these processes and ensure that renewable power generation sites are optimally sited and that the power generated is delivered efficiently.

According to a market report by Datamonitor, Renewable Energy: Global Industry Guide, the global renewable energy market grew by 6.8 percent in 2010 to reach a value of USD 322.5 billion. In 2015, the global renewable energy market is forecast to have a value of USD 479.9 billion, an increase of 48.8 percent since 2010. Undoubtedly, geospatial technology will have a bigger role to play because it is at the forefront of intelligent decision making. It helps users organise geographic data to facilitate selection of data necessary to carry out a specific project or task. As an essential ingredient of GIS, a thematic map contains a table of contents that allows readers to add layers of information to a basemap of real-world locations.

“The growth of the renewable energy space is going to open doors for Smart Grid vendors and manufacturers who can provide niche systems, software and equipment that can integrate renewable energy sources in the Intelligent Utility ecosystem” explained Jason S. Rodriguez, CEO and Director of Research of Zpryme, a research and consulting firm.

Wind energy
The National Renewable Energy Laboratory (NREL) works to advance many renewable resources, including solar, hydrogen and fuel cells, biomass and geothermal, but wind is currently the most developed renewable energy. Windmills appeared on the US landscape in the early 20th century and evolved into wind turbines that increasingly capture more energy and become more cost-effective. In May 2008, the US Department of Energy (DOE) released a report, 20 Percent Wind Energy by 2030: Increasing Wind Energy’s Contribution to U.S. Electricity Supply. The report provided a roadmap to reaching this important goal, including identifying steps and challenges. In the same year, the US surpassed Germany as the world’s biggest generator by volume of wind energy.

As part of the research on having 20 percent of all energy demand to be met by wind energy by 2030, NREL team members were tasked with updating wind resource maps. The updated maps were a critical component of the wind deployment model used to achieve the 20 percent scenario, according to Esri, which played a lead role in implementing GIS at NREL. Using GIS, the NREL team determined the most favourable locations for wind farms based on the cost of transmission, locations of load centres and wind resources, and the layout of the electrical grid. GIS-based modelling enabled analysis of terrain, which significantly impacts the quality of wind at a particular site.

The life of a wind farm project starts by observing the potential plots of land. Most developers require land within a prescribed distance of a transmission line to tie in power to the grid. If the wind is strong and steady, developers may decide to build their own transmission lines. By loading utility data into the GIS, researchers can quickly see the existing transmission routes and estimate the benefits of accessing existing electric lines.

Another important consideration for developers is land ownership. State and county land-use data in GIS identifies areas under development restrictions from the US Bureau of Land Management and those requiring right-of-way grants. If land is privately owned, developers need to obtain consent from individual landowners.

“We use GIS for policy analysis and implementation analysis,” said Marguerite Kelly, senior project manager at NREL to a US-based Journal. “We use it to help decision makers at all levels understand what their resource is.”


These Indiana high-resolution wind resource maps show how wind resources increase with height
Solar energy
In Germany, about 20 percent of the country’s rooftops are suitable for solar power production, according to recent results from the SUN-AREA Research Project. The project aims to determine how solar energy resources can be optimised by placing photovoltaic panels on rooftops around the country. Preliminary findings of the SUN-AREA project estimate that, at full potential, solar power could meet the entire energy needs of homes in Germany. SUN-AREA researchers collected rooftop data using aerial laser scanners. Using ArcGIS Desktop tools, including ArcGIS Spatial Analyst, they identified all necessary rooftop data, such as outer form, inclination, orientation, and clouding. The team used an algorithm sequence, created with the ArcGIS Desktop ModelBuilder application, to determine the solar potential of all roof areas. The SUN-AREA project also calculated solar suitability, potential power output, CO2 reduction and investment volume for each subarea of a roof.


In another part of the globe, Masdar in UAE is working on an ambitious plan to reduce carbon footprint. It is building Masdar City, located 30 kilometres from Abu Dhabi city. Masdar is committed to meet the goals of zero waste, sustainable living and ultimately carbon neutrality. Hence, the company is extensively using GIS to design this future city. Most of the electricity will be generated through a photovoltaic power plant, while the city’s cooling will be provided via concentrated solar power, according to CH2M HILL which is providing GIS tech support to Masdar. It claims that the zero-waste target of Masdar City will be achieved through a combination of recycling, reuse and some breakthrough waste-to-energy technologies. Through this design, residents in Masdar City are poised to consume far less energy. Peak demand at Masdar City is currently projected to be only 200 megawatts instead of the 800 megawatts normally required by a conventional city of the same size and climate zone.

For the city to meet its challenging goals, CH2M HILL considered the geography of the area: sun angles, wind patterns, street widths and building density and height. The orientation of buildings on a diagonal grid to provide maximum natural shading was modelled in ArcGIS. To understand all the variables and communicate effectively during the project, the company used a geodatabase that enforces use of a single, shared coordinate system across the project. A common basemap was created to support planning, design and construction of the city. The city is also planned to be maintained and operated using the same data.

Biomass energy
According to a research report published in Trends in Ecology and Evolution, abandoned croplands and pasturelands globally amount to approximately 1.5 million square miles. Realistically, energy crops raised on this land could be expected to yield about 27 exajoules of energy each year. This is a huge amount of energy—an exajoule is a billion billion joules, equivalent to 172 million barrels of oil. Yet, the biomass yield can still satisfy only about 5% of global primary energy consumption by humans, which in 2005 was 483 exajoules.

Researchers used a combination of historical data, satellite imagery and productivity models to determine best-case estimates of potential yields and of how much biomass could sustainably contribute to the world’s energy needs while also mitigating global warming.

The role of geospatial technology in the biomass energy production is evident from one of the case studies of Food and Agriculture Organisation of the UN. The FAO used GIS-based modelling for biomass estimation. According to FAO:
Potential biomass density index (PBI) = Climatic index + precipitation + soil (texture, depth, slope) + topography.
Geothermal energy
According to report on geothermal market by ABS Energy Research, this renewable energy market will grow by 78 percent from 10,711 MW at the end of 2009 to 19,016 MW in 2015. In terms of new capacity, growth markets will be the three biggest geothermal countries: the US, the Philippines and Indonesia. The number of countries generating geothermal electricity is expected to rise from 24 at the end of 2009 to 36 in 2015.

According to J. Mungania and L. Shako from Kenya-based Geothermal Development Co., vast amounts of data/information from multiple sources are used in geothermal resources exploration and development. In all phases of geothermal resources development, exploration, resource appraisal, drilling, exploitation and management of steam/hot water fields, most of the resource data/information is location based (or geographic data). GIS therefore emerges as the best option for handling the information.

Mungania and Shako explained in their paper, GIS in geothermal resources development, that GIS has wide applications in geosciences – from basic mapping to more complex activities like modelling of geological and geographic processes. It has been used in geological mapping, especially integration of remote sense data with ground collected data, hydrology, agriculture, urban planning, environmental planning and monitoring etc.

They added that the ability to create spatial databases (geo-databases) that represent information in terms of GIS data models is a very important aspect for geological data management since most of geo-scientific data/information is comprised of Earth features and events. Another important benefit of GIS is geoprocessing, which is the use of information transition tools (functions in the software programs) to derive new datasets from existing datasets. This includes use of analytical functions like statistical analyses. Geo-visualisation is another very important functionality of GIS in which different maps views of underlying geographic information are constructed into sets of intelligent map that show various feature relationships. It is akin to creating a window to look into the database for querying and analysing the data.

Conclusion
There are plenty of renewable energy options. The performance of these various options depends on location-based variables, implying the need for geospatial analysis to find the best fit for each segment. Geospatial technology is proving to be an essential component of decision making process in renewable energy. The ongoing and increasingly enhanced observations about these energy sources will continue to improve the quality of the analysis and hence the performance of the power plants. The move to renewable power is definitely on.

References

  • https://www.fao.org/docrep/w4095e/w4095e09.htm
  • https://www.os.is/gogn/unu-gtp-sc/UNU-GTP-SC-11-17.pdf
  • https://www.esri.com/library/bestpractices/renewable-energy.pdf
  • https://www.esri.com/news/arcnews/spring10articles/gis-for-renewable.html
  • https://www.marketresearch.com/Datamonitor-v72/Renewable-Energy-Global-Guide-6385266/