Role of GIS in Renewable Energy Planning

Renewable energy sources, including wind, solar, and hydropower in particular, have become increasingly cheaper to operate, providing a boon for companies providing services to homes and power providers.

As renewable energy sources become more common, managing these resources becomes critical, particularly as the amount of energy produced can vary from these sources at different times. Opportunities and challenges arise as these sources increasingly become common, particularly determining what renewable sources to best exploit for given regions.

Renewable energy potential in Southeast Asia: A regional focus

In Southeast Asia (covering about 4,500,000 km2), the uptake of renewable energy sources is forecast to increase by 23% by 2025. However, one challenge that arises for Southeast Asia is determining what renewable energy type to install at given locations. In a new approach applied for this region, researchers combined information on solar, wind, and hydropower potential to create a suitability index model for different locations for these energy sources.

Mapping renewable energy potential: A multi-layered approach

Using satellite data, climate modeling data, and tabular data on potential, the researches were able to look at urban and rural regions and estimate their potential. Data, once integrated, were overlaid for their location, using 1 km resolution, and different energy infrastructure compared in given locations. Results were then compared to the World Resources Institute (WRI) Global Power Plant Database to know what potential energy could be generated.

Advanced spatial data for energy estimation

For water bodies’ energy production estimates, data from the United States Defense Mapping Agency’s (DMA) operational navigation chart (ONC) was used. Spatial data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) mission, Global Digital Elevation Model 2 (GDEM2), the United States Geological Survey (USGS) Global Multi-Resolution Terrain Elevation Data (GMTED), and the USGS National Elevation Dataset (NED) were all used.

Lighting conditions and weather estimates helped provide potential for different energy sources, particularly for wind and solar sources.

Climate models and energy source validation

Climate models help to produce prediction for given regions and data were validated using the Global Precipitation Climatology Center (GPCC) rain station datasets and surface temperature also compared to the Climate Forecast System (CFSv2) data. Demand from different regions as well as distribution were also similarly estimated from available data sources.

Optimal deployment of renewable energy in Southeast Asia

The model produces specific forecasts that allowed the researchers to compare which energy sources or combination of energy sources would be most suitable. Results indicate that 143,901,600 ha could deploy solar, wind was the second most common source (39,618,300 ha), followed then by a combination of solar and wind (37,302,500 ha), hydroelectric (7,665,200 ha), combined hydroelectric and solar (3,792,500 ha), and combined hydro and wind (582,700 ha) would be the optimal deployment in Southeast Asia.^[1]

Adapting renewable energy strategies to climate change

Researchers are continually attempting to improve estimates for determining renewable potential for different regions. In particular, as climate change is beginning to affect different regions, the potential of different sources may vary significantly from current conditions. Other attempts include using spatial regression modeling and GIS in order to evaluate the potential of solar energy, for instance, for different periods.^[2] 

GIS and system dynamics modeling: The future of energy prediction

One problem with these approaches is that potential for different energy sources is not static to a given region. One region may be optimal for a period for one energy source, but in other periods other energy sources might produce greater energy. Researchers have also developed a spatial and temporal assessment using geo-referenced information on renewable energy.

System dynamics modeling coupled with GIS can be used to evaluate evolving weather conditions in order to estimate which particular source at a given time might provide reliable energy. This could then be used by managers to use more from a given source and then switch sources at an appropriate time.

In a recent study, for instance, it was shown that onshore wind turbines could show long-term potential to provide a large energy surplus for Latvia, producing 5.5 GW of energy that exceeds daily demand. However, there might be a need to switch to alternative sources in cases when these wind sources do not reliably produce energy.^[3]

These studies help to produce some estimates power providers can use to determine what energy sources to install and perhaps how to dynamically switch to different sources. However, other research demonstrates we may be overlooking some other natural energy sources that are even more reliable.

The potential of geothermal energy in Africa

In Africa, a GIS approach was recently used to determine the geothermal capabilities of the continent. Using geological thematic layers (rock units and faults), geophysical layers that show heat flow from aeromagnetic data with seismic data, and geothermal layers that include hot springs and volcanoes, one can estimate potential for geothermal energy.  In this approach, 14 regions in Africa were shown to have good potential to use geothermal energy, which could be a low cost and low impact energy source that would also be highly reliable.^[4]

The critical role of GIS in renewable energy planning

With increasing need for renewable energy, GIS will play a critical role in bringing data and models together to estimate where certain renewable sources will be best utilized. The answers are not always straightforward, given the dynamic nature of how different energy sources, particularly in the future with climate change. However, we are now in a better position to estimate where we can combine different energy sources to better produce renewable energy in a cost-effective and efficient manner that could also help make these sources feasible in the coming years. 

References

[1]    For more on the use of different data sources to estimate the potential for different renewable energy sources in Southeast Asia, see:  Sakti, A.D., Rohayani, P., Izzah, N.A. et al. Spatial integration framework of solar, wind, and hydropower energy potential in Southeast Asia. Sci Rep 13, 340 (2023). https://doi.org/10.1038/s41598-022-25570-y.

[2]    For more on spatial regression modeling and GIS for solar energy potential estimates, see:  Raillani B, Mezrhab Abdelhamid, Amraqui S, et al. (2022) Regression-based spatial GIS analysis for an accurate assessment of renewable energy potential. Energy for Sustainable Development 69: 118–133. DOI: 10.1016/j.esd.2022.06.003.

[3]    For more on time and spatial estimate of energy generation using renewable sources, see:  Pakere I, Kacare M, Grāvelsiņš A, et al. (2022) Spatial analyses of smart energy system implementation through system dynamics and GIS modelling. Wind power case study in Latvia. Smart Energy 7: 100081. DOI: 10.1016/j.segy.2022.100081.

[4]    For more on geothermal sources in Africa and estimating them, see:  Elbarbary S, Abdel Zaher M, Saibi H, et al. (2022) Geothermal renewable energy prospects of the African continent using GIS. Geothermal Energy 10(1): 8. DOI: 10.1186/s40517-022-00219-1.

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Fonte : National Geographic