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PSU-UNS International Conference on Engineering and Environment - ICEE-2007, Phuket May 10-11, 2007
Prince of Songkla University, Faculty of Engineering Hat Yai, Songkhla, Thailand 90112
Abstract: The objective of this paper is to apply geographic information system (GIS) integrated with multi criteria decision making (MCDM) for effective site selection for large wind turbine in Thailand. GIS has been designed to be as flexible as possible, allowing the user to specify which criteria will be used for the site selection, and if included what buffer distances to use around each excluded feature. The criteria include various parameters and exclusion factors such as: wind speed information, elevation, slope, highways and railways, built up area, forest zone and scenic area. It was found that eastern coasts of Thailand from Nakhon Si Thammarat Province to Narathiwas Province are the feasible areas for installation of wind turbines. Key Words: Site Selection/ Geographic Information System/Wind Turbine/Multi Criteria Decision Making 1. INTRODUCTION
The total energy consumption in Thailand is exponentially increasing. The wind energy project is one of the most possible ways for sustainable energy development project. Since the cost of large wind turbine project is rather high, the project feasibility should be done before construction of the large wind turbine project. Selecting the site for wind turbine positions is a complex process involving not only technical requirement, but also physical, economical, social, environmental and political requirements that may result in conflicting objectives. Such complexities necessitate the simultaneous use of several decision support tools such as high spatial resolution remotely sensed data, Geographical Information System (GIS) and Multi Criteria Decision Making (MCDM).
The report on Thailand’s Wind Energy Potential was studied by the Energy Conservation Promotion Fund. The wind resources map evaluated by using surveyed data, Digital Elevation Model (DEM), surface roughness, and statistical analysis of wind data. It revealed that coastal areas along the Gulf of Thailand in southern Thailand between Nakhon Si Thammarat
Province and Narathiwas Province have high wind energy potentials appropriate for setting up electricity generating wind turbines.
The study area of this research covers 5 provinces adjacent to Gulf of Thailand, as shown in Figure 1. This paper aims to describe the site selection for Large Wind Turbine by using overlaying technique in GIS integrated MCDM techniques.
THAILAND
(̂ Bangkok
Nakhon Si ThammaratPattalung Songkhla
PattaniNarathiwas
MALAYSIA
MYENMAR
LAO
KOMBODIA
VIETNAM
Figure 1. Study Area
2. SITE SELECTION OF WIND TURBINE 2.1 Site Selection Process Site selection for large wind turbine requires consideration of a comprehensive set of factors and balancing of multiple objectives in determining the suitability of a particular area for a defined land use. The selection of suitable project areas involves a complex array of critical factors drawing from physical, demographical, economic, policies, and environmental
SITE SELECTION FOR LARGE WIND TURBINE USING GIS
Adul Bennui1, Payom Rattanamanee2, Udomphon Puetpaiboon2 Pornchai Phukpattaranont2 and Kanadit Chetpattananondh2
1 Prince of Songkla University, Faculty of Environmental Management, Thailand 2 Prince of Songkla University, Faculty of Engineering, Thailand
*Authors to correspondence should be addressed via email: [email protected]
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disciplines. The current spatial decision making could benefit from more systematic methods for handling multi-criteria problems while considering the physical suitability conditions. Selection criteria must also satisfy the optimistic criteria. 2.2 Site Selection Tools
Geographic information systems (GIS) and Multi-criteria decision making (MCDM) techniques have been used in solving site selection problems. A brief description of the strength and weakness of each tool with regard to sitting problems is provided below.
2.3 Geographic Information Systems (GIS)
Geographic information systems (GIS) have emerged as useful computer-based tools for spatial description and manipulation. Although often described as a decision support system, there have been some supporting modules for site selection based on various area conditions, and conflicting objectives.
2.4 Multi Criteria Decision Making (MCDM) The techniques adopted in the various approaches of decision analysis are called multi criteria decision methods (MCDM). These methods incorporate explicit statements of preferences of decision-makers. Such preferences are represented by various quantities, weighting scheme, constraints, goal, utilities, and other parameters. They analyze and support decision through formal analysis of alternative options, their attribute, evaluation criteria, goals or objectives, and constraints. MCDM used to solve various site selection problems. MCDA results can be mapped in order to display the spatial extent of the best areas or index of land suitability. 2.5 The Analytical Hierarchy Process (AHP)
The most important factor in MCDM is how to establish “weights” for a set of criteria according to importance. Location decisions such as the ranking of alternative communities are representative multi-criteria decisions that require prioritizing multiple criteria. The analytic hierarchy process (AHP) is a comprehensive, logical and structural framework, which allows analyzer to improve the understanding of complex decisions by decomposing the problem in a hierarchical structure. The incorporation of all relevant decision criteria, and their pairwise comparison allows the decision maker to determine the trade-offs among objectives. Such multi-criteria decision problems are typical for housing sites selection. The AHP allows decision-makers to model a complex problem in a hierarchical structure showing the relationship of the goal, objectives, criteria, and alternatives.
2.6 Pairwise comparisons method
The Pairwise comparisons method was developed by Saaty (1980) in the context of the Analytical Hierarchy Process (AHP). This method involves pairwise comparisons to create a ratio matrix. As input, it takes the pairwise comparisons of the parameters and produces their relative weights as output.
3. METHODOLOGY Construction Site of large wind turbine depends upon numerous factors. These include physical, socio-economic and environmental quality and amenities. The criteria must be identified and include factors and constraints. In this study criteria were selected based on “Best Practice Guideline for Wind Energy Development” and “Thai Government Regulations”. 3.1 GIS Data
To find out the suitable sites for large wind turbine, there are ten spatial data layers of input for overlaying in ArcGIS9.1 with GIS extension modules; Image Analysis, Spatial Analyst and 3D Analyst. Some details of input data are shown in Table 1.
Table 1. Detail of GIS input data
GIS data Description Data Source
Layer 1 Urban Areas Layer 2 Community Zones
LANDSAT-5 image data BAND 3, 4, 5
Layer 3 Important Places Topographic map 1:50,000 scaleRoyal Thai Survey Department
Layer 4 Scenic Areas Department of Environmental and Quality Promotion
Layer 5 Airport Areas LANDSAT-5 image data Layer 6 Highway Department of Highways
Layer 7 Wind Energy Potential
Layer 8 Surface Roughness
Department of Alternative Energy and Efficiency
(Figure 2) Layer 9 Elevation
Layer 10 River/Canal Topographic map 1:50,000 scaleRoyal Thai Survey Department
Figure 2. Wind Energy Potential Chart
3.2 Exclusion Zones
Topography factors affect the land use planning and the important factors associated with topography include aspect, elevation and steep slopes. From the master plan policies, considered that the sites on or near cliffs is not suitable for wind turbine development also we have to
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avoid the high elevation area because cost of investment is high.
Exclusion zone is restrictedly unsuitable zone for wind turbine installation. It is excluded for protecting effects on environment, communities, visualization, eco-conservation, and engineering frontier. In the last stage of data analysis, inappropriate zones will be excluded. These zones are as follows:
1) Reservation areas in 1st class watershed, 2) Areas elevation higher than 200 m above msl, 3) Hilling areas steeper than 15% slope, 4) Buffer zones within 2.5 km from urban, 5) Buffer zones within 1.0 km from rural
communities, 6) Buffer areas within 2 km from important places, 7) Safety areas 1.0 km around tourist places, 8) Safety areas 3.0 km from airports, 9) Safety trips 0.5 km offset from highways, 10) Nature safety zones within of 200 m from water
bodies and main rivers
3.3 Weighing Weighing scores for each criterion is derived from analytic hierarchy process (AHP), by directly comparing importance of one criterion to another criterion. Rules for defining the score are; the score equal to “1” when criteria in columns are less significant than those in row, the score to be “2” when criteria in columns are as same significant as those in row, and the score up to “3” when criteria in columns are more significant than those in row. When criteria in columns are same as those in row, the score are equal to “0”. Summary of weighing scores for each criterion are shown in Table. 2. Table 2. Weighing scores for each criterion.
Decision Parameters(Criterion)
1. U
rban
2. V
illag
e,C
omm
unity
3. I
mpo
rtan
t Pla
ces
4. S
ceni
c A
rea
5. A
irpo
rt A
rea
6. H
ighw
ay
7. W
ind
Cla
ss
8. S
urfa
ce R
ough
ness
9. E
leva
tion
10.
Riv
er/W
ater
Bod
y
Tot
al S
core
Wei
gh S
core
1. Urban 0 3 1 1 3 3 1 3 1 3 19 0.106
2. Village, Community 1 0 1 1 3 3 1 3 1 3 17 0.0943. Important Places 3 3 0 2 3 3 1 3 1 3 22 0.1224. Scenic Area 3 3 2 0 3 3 1 3 1 3 22 0.1225. Airport Area 1 1 1 1 0 3 1 3 1 3 15 0.0836. Highway 1 1 1 1 1 0 1 1 1 3 11 0.0617. Wind Class 3 3 3 3 3 3 0 3 2 3 26 0.1448. Surface Roughness 1 1 1 1 1 3 1 0 1 3 13 0.0729. Elevation 3 3 3 3 3 3 2 3 0 3 26 0.14410. River/Water Body 1 1 1 1 1 1 1 1 1 0 9 0.050
Total 180 1.000 3.4 Suitability Function
In this study perform a GIS Spatial analysis and 3D analysis using ArcView3.3 which represented as sets of spatial processes, such as buffer, classification, and overlay techniques. Each of the input criteria is assigned a weight influence based on its importance, then the result successively multiplying the results by each of the constraints. This process is often used in site suitability studies where several factors affect the suitability of a site. Then the GIS overlay process can be used to combine the factors and constraints in the form of a weighting overlaying process. The result is then summed
up producing a suitability function ( F ) as described by the formula;
F ( )∑=
=×=
Ni
iii MW
0
1021 050.0...094.0106.0 MMM +++=
3.5 Score Score for each GIS data layer depends upon its
importance and suitability. Score for specific buffer/offset zones are ranged in Tables 3-1 to 3-10.
Table 3-1. Ranging Scores for GIS Layer 1 - Urban area
Category Offset (km) Score Class 1 0.0-2.5 0 Exclusion Zone 2 2.5-3.5 1 Less suitable 3 3.5-4.5 2 Suitable 4 4.5-5.5 3 Moderate suitable 5 5.5-6.5 4 High suitable 6 > 6.5 5 Extremely suitable
Table 3-2. Ranging Scores for GIS Layer 2 – Communities
Category Offset (km) Score Class 1 0.0-1.0 0 Exclusion Zone 2 1.0-2.0 1 Less suitable 3 2.0-3.0 2 Suitable 4 3.0-4.0 3 Moderate suitable 5 4.0-5.0 4 High suitable 6 > 5.0 5 Extremely suitable
Table 3-3. Scores and classes for Layer 3 – Important Places
Category Offset (km) Score Class 1 0.0-2.0 0 Exclusion Zone 2 2.0-2.5 1 Less suitable 3 2.5-3.0 2 Suitable 4 3.0-3.5 3 Moderate suitable 5 3.5-4.0 4 High suitable 6 > 4.0 5 Extremely suitable
Table 3-4. Ranging Scores for GIS Layer 4 – Scene Areas
Category Offset (km) Score Class 1 0.0-1.0 0 Exclusion Zone 2 1.0-2.0 1 Less suitable 3 2.0-3.0 2 Suitable 4 3.0-4.0 3 Moderate suitable 5 4.0-5.0 4 High suitable 6 > 5.0 5 Extremely suitable
Table 3-5. Ranging Scores for GIS Layer 5 – Airport
Category Offset (km) Score Class 1 0.0-3.0 0 Exclusion Zone 2 3.0-6.0 1 Less suitable 3 6.0-9.0 2 suitable 4 9.0-12.0 3 Moderate suitable 5 12.0-15.0 4 High suitable 6 > 15.0 5 Extremely suitable
Table 3-6. Ranging Scores for GIS Layer 6 – Highways
Category Offset (km) Score Class 1 0.0-0.5 0 Exclusion Zone 2 0.5-1.0 1 Less suitable 3 1.0-1.5 2 suitable 4 1.5-2.0 3 Moderate suitable 5 2.0-2.5 4 High suitable 6 > 2.5 5 Extremely suitable
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Table 3-7. Ranging Scores for GIS Layer 7 – Wind Classes Category Power (W/m2) Score Class
1 < 100 1 Less suitable 2 100-150 2 suitable 3 150 -200 3 Moderate suitable 4 200-250 4 High suitable 5 > 250 5 Extremely suitable
Table 3-8. Ranging Scores for GIS Layer 8 – Roughness
Category Roughness (mm) Score Class 1 400 1 Less suitable 2 100 2 Suitable 3 50 3 Moderate suitable 4 10 4 High suitable 5 0.1 5 Extremely suitable
Table 3-9. Ranging Scores for GIS Layer 9 – Elevations
Category Elevation (m) Score Class 1 0-40 5 Extremely suitable 2 40-80 4 High suitable 3 80-120 3 Moderate suitable 4 120-150 2 suitable 5 150-200 1 Less suitable 6 >200 0 Exclusion Zone
Table 3-10. Ranging Scores for GIS Layer 10 – Main River
Category Buffer (km) Score Class 1 0.0-0.2 0 Exclusion Zone 1 0.2-0.4 1 Less suitable 2 0.4-0.6 2 suitable 3 0.6-0.8 3 Moderate suitable 4 0.8-1.0 4 High suitable 5 > 1.0 5 Extremely suitable
4. RESULTS OF STUDY
The results from the study, the total scores in terms of “Suitability function” can be ranged into 5 classed as defined in Table. 4. Suitability areas in 5 provinces are summarized in Tables 5. Suitability areas are depicted in Figures 3-1 to 3-5. Table 4. Ranges and Classes of Suitability Function
Category Range of Suitability function Class
1 0.00-1.00 Unsuitable 2 1.01-2.00 Low suitable 3 2.01-3.00 Moderate suitable 4 3.01-4.00 High suitable 5 4.01-5.00 Extremely suitable
4.1 Extremely Suitable Areas
The extremely suitable areas, class 5, are totally 143.842 hectare. These areas are mostly found in Narathiwas province (86.0 hectare), Nakorn Sri Thamarat province (44.2 hectare), and Phatthalung province (13.7 hectare). 4.2 High Suitable Areas
The high suitable areas, class 4, are totally 198,763 hectare. These areas cover all study areas, mostly predominating in Nakhon Si Thammarat province (84,428.8 hectare), Songkhla province (54,030.0 hectare), Phatthalung province (39,477.9 hectare),
Narathiwas province (18,214.1 hectare), and Pattani province (2,611.7 hectare).
4.3 Moderate Suitable Areas
The moderate suitable areas for large wind turbine, class 3, are totally 284,806.3 hectare. These area mainly found in Songkhla province (98,560.2 hectare), Nakhon Si Thammarat province (92,866.7 hectare), Phatthalung province (44,135.8 hectare), Narathiwas province (36,900.465 hectare), and Pattani province (12,343.2 hectare). 4.4 Low Suitable Areas
The low suitable areas for large wind turbine are with totally 7,675.3 hectare. These areas are mostly found in Phatthalung province (2,132.1 hectare), Narathiwas province (1,769.0 hectare), Songkhla province (1,672.5 hectare), Nakhon Si Thammarat province (1,424.9 hectare), and Pattani province (676.8 hectare), respectively. 4.5 Unsuitable Areas
Unsuitable areas are mostly defined as “Exclusion Zones”, those not include in these calculation. So, based on computed results, there are no polygons with the total score ranged at 0.00 – 1.00. Table 5. Summary of Suitability Areas
Province Class 1 Class 2 Class 3 Class 4 Class 5 SUM
Nakhon Si Thammarat - 1,424.9 92,866.7 84,428.8 44.2 178,765
Patthalung - 2,132.1 44,135.8 39,477.9 13.7 85,759
Songkhla - 1,672.5 98,560.2 54,030.0 - 154,263
Pattani - 676.8 12,343.2 2,611.7 - 15,632
Narathiwas - 1,769.0 36,900.5 18,214.1 86.0 56,970
SUM - 7,675 284,806 198,763 144 491,388
Thung Song
Cha Uat
Si Chon
Thung Yai
Phipun
Nop Phi Tam
Bang Khan
Pak Phanang
Chawang
Tha Sala
Ron Phibun
Khanom
Chian Yai
Muang Phrom Khiri
Tham Phanra
Chang KlangLan Saka
Phra Phrom
ChulaphonCharoem Phakiat
Hua Sai
560000
560000
600000
600000
640000
640000
8800
00
880000
9200
00
920000
9600
00
960000
1000
000 1000000
SUITABLE LEVELLOWMODERATEHIGH
AMPHOELEGEND
VERY HIGH0 10 20 30 Kilometers
S
N
EW
NAKHON SI THAMMARAT
Figure 3-1 Suitable areas in Nakhon Si Thammarat Province
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Pa Bon
Khuan Khanun
Ta Mot
Kong Ra
Muang
Pa Phayom
Si Ban Phot
Khao Chai Son
Si Nakharin
Pak Phayun
Bang Kaeo
600000
600000
620000
620000
640000
640000
8000
00
800000
8200
00
820000
8400
00
840000
8600
00
860000
VERY HIGH
LEGENDAMPHOE
HIGHMODERATELOW
SUITABLE LEVEL
0 10 20 30 KM.
PATTALUNG
S
N
EW
Figure 3-2 Suitable areas in Pattalung Province
Sadao
Sabayoi
Hat Yai
Na Thawi
ChanaThepha
Rattaphum
Ranot
Khlong Hoi Khog
Khuan N iang
Na Mom
Bang Klam
Singha Nakhon
Muang
Sathing Phra
Krasaesin
630000
630000
660000
660000
690000
690000
720000
720000
7200
00
720000
7500
00
750000
7800
00
780000
8100
00
810000
8400
00
840000
8700
00
870000
VERY HIGH
LEGENDAM PHO E
HIGHMO DERATELO W
SUITABLE LEVEL
SONGKHLA
S
N
EW
0 10 20 30 Kilometers
Figure 3-3 Suitable areas in Songkhla Province
Khok Pho
Yaring
MayoYarang
Saiburi
Nong Chik Panare
Ka Pho
Mueang
Mae Lan Thung Yang DaengMai Kaen
740000
740000
760000
760000
780000
780000
800000
800000
7200
00
720000
7400
00
740000
7600
00
760000
7800
00
780000
SUITABLE LEVELLOWMODERATEHIGH
AMPHOELEGEND
VERY HIGH
0 10 20 30 KilometersS
N
EW
PATTANI
Figure 3-4 Suitable areas in Pattani Province
Ruso
Bacho
Yi NgoMuang
Sisakhon
Rangae
Chanae
Tak baiChoe Airong
Sungai Padi
SukhirinWaeng
Sungai Kolok
780000
780000
810000
810000
840000
840000
6300
00
630000
6600
00
660000
6900
00
690000
7200
00
720000
SUITABLE LEVELLOWMODERATEHIGH
AMPHOELEGEND
VERY HIGH
0 10 20 30 KilometersS
N
EW
NARATHIWAS
Figure 3-5 Suitable areas in Narathiwas Province.
5. CONCLUSIONS AND DISCUSSION
However, analyzing data for appropriate zones for
the wind turbine installation in this study had higher weights of the data on the effectiveness of wind power and on elevation than other factors. This shows that the extremely suitable, class5, and the high suitable, class 4, areas were found in mountainous zones. This is corresponding to the wind power map of Thailand. Moreover, for the engineering and construction possibilities in this study area, the highlands at more than 200 meter above mean sea level and steeper than 15%, areas are considered as ‘exclusion zones’. Furthermore, when plains and coast areas with ranged in moderate suitable areas, class 3, are embedded with other factors, they become ‘exclusion zones’. This is because plains and coast areas are the location of urban and rural communities, tourist and main places, and main roads. These areas are considered as ‘exclusion zones’ at 0 – 2.5 km. This results in the high scarcity of appropriate zones. Therefore, if these results are used, the intersection of the plains and coast areas should be adjusted at the lower level.
6. ACKNOWLEDGEMENTS
This research was financially supported by
Department of Alternative Energy Development and Efficiency, Ministry of Energy, Thailand. The Geo-informatics Research Center for Natural Resource and Environment, Faculty of Environment Management, Prince of Songkla University are gratefully acknowledged for other supports.
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7. REFERENCES
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[6] Goodchild, M.F. and Kemp, K.K. 1990. Application Issues in GIS. National Center for Geographic Information and Analysis University of California Santa Barbara, USA.
[7] Kidner, D.B. 1996. Site selection and visibility analysis for a wind farm development: A problem for GIS? In: Proceedings of the 1st International Conference on GIS in Urban, Regional and Environmental Planning, Samos, Greece, April 19th-21st, 1996, pp. 220-237.
[9] Kidner, D.B. and Dorey, M.I. 1995. Visual landscape assessment of wind farms using a geographical information system. In: Wind Energy Conversion 1995, Halliday, J. (Ed), MEP, London, pp. 182-189.
[10] Kidner, D.B., Dorey, M.I. and Sparkes, A.J. 1996. GIS and visual impact assessment for landscape planning. In: Proceedings of GIS Research in the UK (GISRUK'96), University of Kent, pp. 89-95.
[11] King Mongkut’s University of Technology Thonburi and Thai Meteorological Department. 1984. Win Energy Potential Map of Thailand.
[12] Lindley, D. and Swift-Hook, D.T. 1989. The technical and economic status of wind energy. In: Wind Energy and the Environment, Swift-Hook, D.T. (Ed), Peter Peregrinus, London, pp. 1-5.
[13] Manning, P.T. 1983. The environmental impact of the use of large wind turbines. Wind Engineering, 7 (1): 1-11.
[14] Maquire, D.J., Goodchild, M.F. and Rhind, D.W. 1991. Geographic Information System (Volume 2: Application). New York: John Wiley & Sons, Inc.
[15] Masser, I. and Blackmore, M. 1991. Handling Geographical Information: Methodology and Potential Application. New York: John Wiley & Sons, Inc.
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