GIS Assignment 7
Xiandeng Yi
Assessing Hainan Island’s (China) wind and solar energy farm suitability
I. Project Question
Hainan Island is the smallest province but the largest island under the jurisdiction China. Dubbed as
China’s Hawaii, Hainan strives to build itself into an ‘international tourism island’ by 2020 1 . Though hosting no heavy industries, Hainan is highly dependent on external energy supply including oil, coal, refined products. 75% of electricity is generated by coal, and only 25% by wind and solar.
2 80% of hydro resources have been developed. A large portion of electricity is transmitted from the mainland. As a result, tension exists between the increasing dependency and scarce energy productivity as well as between the emission of coal-based power generation and Hainan’s tourism-centered development plan. Renewables like wind and solar thus can help address both the concerns: energy self-sufficiency and emission reduction.
Presumably, Hainan is ideal for developing its wind and solar energy. It is in tropical regions with year around sunshine, and it is considered as one of three most wind-dense regions in China with its western coastline worth particular attention. However, within the same island, wind energy is severely unevenly distributed and the less populated west coast is the most resourceful. This is why I decided to analysis the solar potential as well. There seems a complimentary relation between the two renewables. My project aims to assess the Hainan Island’s renewable potential including wind and solar, so as to contribute to reducing Hainan’s independence on external energy supply and the share of clean energy in energy consumption. So my question is:
What areas on the Island are best suitable for wind farms, what areas are best suitable for solar farms, presumably what areas are good for both in one place, especially considering the necessity of the reconciliation with its demographic distribution, regional energy demands and ‘tourism first’ industrial development goal and its strict land use regulation?
II. Dataset Checklist
Data Layer
Wind Density 50m above the ground
Solar Density (monthly and annual average direct normal (DNI)GIS data at
40km resolution for China from)
Elevation
Data Source
NREL http://en.openei.org/datasets/node/609
NREL http://en.openei.org/datasets/node/550
CGIAR SRTM (3 seconds resolution)
Minimum Accuracy
1 kilometer
40 kilometers
30 seconds
1 Office, Haikou Foreign and Overseas Chinese Affairs. "Unswerving Devotion to the Development of an
International Tourism Island."http://en.haikoutour.gov.cn/view.asp?ArticleID=162
2 Province, The People's Government of Hainan. "A Major Marine Province's Energy Needs." http://www.hainan.gov.cn/hn/zt/jsfzl/zltzcyjg/hyjj/201204/t20120404_658554.html.

Land cover
Inland water
Gridded population
Cities
Cities area
Airports
Power grid
Roads
Historical sites http://www.diva-gis.org/gdata
GLC2000
Digital Chart of the World http://www.diva-gis.org/gdata
The LandScan 2005
NREL China solar tool kit
GFK M: drive
Ibid
GFK M:drive
NREL China solar tool kit
GFK M:drive
30 seconds
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30 seconds
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-
-
-
-
-
III. Methodology
The basic methodology is derived from the Beijing Wind Farm exercise, that is, reclassify all indicators and set the weight of each indicator then calculate the aggregate. Criteria of suitability of each indicator will be selected based on research results of existing academic literature.
I will map by GIS three scenarios: wind, solar, and wind and solar both considered. Each scenario would be mainly based on the following considerations:
For wind
Wind density
Distance to Main Grid
Distance to protected lands (agriculture, historical sites, tourism zones)
Distance to population center
Distance to major roads (easy installation)
Distance to lakes
For solar
Solar radiation
Distance to main grid
Distance to protected lands
Distance to population centers
Dataset Analysis Roadmap (by layer)
Layer
Wind density
Action
Solar Density
Elevation
Land cover
Reclassify according to NREL standards http://rredc.nrel.gov/wind/pubs/atlas/tables/1-1T.html
Reclassify Ranking standards are being found and may be arbitrary.
Use GIS slope tool to convert it into a raster file and reclassify.
Identify the reserves and other off-limits land with the help of Google map.
Use Euclidean Distance to create a distance grid and reclassify it. Names like sparse bush or evergreen forests should be further sorted out.

Inland water
Gridded population
Cities
Cities area
Airports
Power grid
Roads
Historical sites
IV. Annotated References
No wind farms planned on lakes. S
In my project, population means different to wind and solar energy. While wind farming is better located from high population density, PV solar is the reverse. Reclassify.
Use Euclidean Distance to create a distance grid and reclassify it.
Same as above. Note: I am deciding which city layer to use or use both. City area layer is polygon file and less in number while cities layer is point layer with covering more cities and towns. Using both will result in double counting.
Same as above.
Same as above.
Same as above.
Same as above.
1) Yue, C., & Wang, S. (2006). GIS-based evaluation of multifarious local renewable energy sources: A
case study of the Chigu area of southwestern Taiwan. Energy Policy, 34, 730-742.
This article gave me the most inspiration. First, it is an evaluation of multifarious local renewable energy sources including solar and wind. Second, clear and transferable guidelines are given as to siting criteria on wind farms and photovoltaics installation.
After reviewing the variability of wind and solar criteria, it confirmed my assumed feasibility of complementarity of wind and solar facilities. What is more, the scenario analysis (in his case, there were two ‘renewable prioritized and wildlife prioritized) is enlightening to me. As a result, I plan to do a comparison between wind prioritized, solar prioritized and both wind and solar prioritized.
2) Baban, S., & Parry, T. (2001). Developing and applying a GIS-assisted approach to locating wind farms in the UK. Renewable Energy, 24, 59-71.
This article contains a wealth of ready-to-use wind farm siting criteria. I found its weighting standards very useful. Also, the paper classifies the standards into categories such as ‘first grade factors, second grade factors’ which simplifies the designing and prioritization process.
The author elaborates on the criteria of justification and I will do the same in my project. As Hainan is heavily dependent on tourism, I will link more argument with this unique local context.
The article also addresses the weakness and possible inaccuracies. I think it is very necessary. In particular, It has an amazingly easy 101 explanation of what tools are used to treat the data.
3) Janke, J. R. (October 01, 2010). Multicriteria GIS modeling of wind and solar farms in
Colorado. Renewable Energy, 35, 10, 2228-2234.
This article guides the thinking of my project in various ways. First, it has nearly same datasets with similar resolution compared with the data that I accessed. Second, it deals with both solar and wind with a host of criteria, which is exactly the way I want to approach in my project. The methodology is very simple and straightforward. The sequence of presentation and explanation is easy to follow. The

difference is that while this paper deals separately with solar and wind, I deal with both separately in addition to a co-location scenario.
The paper also uses a pre-GIS-model and GIS model divide, it accentuates the importance of non-solar and non-wind variables such us distance from roads in determining the energy potential. It is noted that overlay tool is used in different ways which I will adopt as well. The paper does not go further into economic feasibility analysis. I may lightly touch a little on the economic feasibility based on the results
(for example, the most suitable area’s location and area and current costs of the mainstream technology and facilities).
4) Sun, Y., Hof, A., Wang, R., Liu, J., Lin, Y., & Yang, D. (March 01, 2013). GIS-based approach for potential analysis of solar PV generation at the regional scale: A case study of Fujian Province. Energy
Policy, 9.)
This article uniquely incorporates three potential: Gerograhpical, economic and technical. In the geographical potential, he divides areas into built-up and non-built-up with the first one more suitable for roof PV and the latter more suitable for centralized solar plant. I think it makes more sense. The solar
PV potential for the suitable area outside of the built-up areas area can be calculated by multiplying annual total amount of solar radiation in the grid cell i by area of unit grid cell. To quantify the rooftop solar PV potential in the built-up area, the author uses a self-crafted equation. It may be too challenging for me to do this break down.
The only concern is that my solar layer cell is less detailed that those of the author. So the result I obtain even using the same approach might not be obvious and telling. Visualized analysis of economic and technical analysis is interesting, but overambitious for me to adopt.
5) Charabi, Y., & Gastli, A. (September 01, 2011). PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation. Renewable Energy, 36, 9, 2554-2561.
Essentially, it is a solar-version of the Beijing wind farm tutorial. It has an easy and step-by-step layout.
To simplify the process, Charabi used ‘Merge Tool’ to put all ‘off-limits areas’ into one single layer. I am still deciding whether it is a better approach for me. Further guidance from professors is much needed regarding this.
VI. Envisioned Final Products
Map 1: Hainan Island’s location
Map 2: A cluster of small visual maps of each variable such as elevation etc.
Map 3: Hainan Island’s wind farm suitability (possibly one small constraint-area map)
Map 4: Hainan Island’s solar farm suitability (possibly one small constraint-area map)
Map 5: Hainan Island’s wind/solar co-location suitability
Explanation: Introduction; Question; Methodology; Conclusion; Weaknesses