site selection for large wind turbine using gis · selection for large wind turbine in thailand....

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


Table 3-3. Scores and classes for Layer 3 – Important Places


Table 3-4. Ranging Scores for GIS Layer 4 – Scene Areas


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


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


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

[1] Bimonthly Magzine - July/August 2003, EWEA. [2] Bragdon, C. 1971. Noise Pollution The Unquiet

Crisis. Philadelphia: University of Pennsylvania Press.

[3] Environmental Research Institute, Inc. 1996. ArcView GIS. Redlands California, USA.

[4] Environmental Research Institute, Inc. 1996. ArcView Image Analysis. Redlands California, USA.

[5] Gipe, P. 1995. Wind Energy Comes of Age. New York: Wiley.

[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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site selection for large wind turbine using gis · selection for large wind turbine in thailand....

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