GIS based site suitability assessment for wind and solar farms in Songkhla, Thailand

Accepted Manuscript GIS based site suitability assessment for wind and solar farms in Songkhla, Thailand

Shahid Ali, Juntakan Taweekun, Kuaanan Techato, Jompob Waewsak, Saroj Gyawali PII:

S0960-1481(18)31098-X

DOI:

10.1016/j.renene.2018.09.035

Reference:

RENE 10575

To appear in:

Renewable Energy

Received Date:

09 April 2018

Accepted Date:

12 September 2018

Please cite this article as: Shahid Ali, Juntakan Taweekun, Kuaanan Techato, Jompob Waewsak, Saroj Gyawali, GIS based site suitability assessment for wind and solar farms in Songkhla, Thailand, Renewable Energy (2018), doi: 10.1016/j.renene.2018.09.035

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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GIS based site suitability assessment for wind and solar farms in Songkhla, Thailand. Shahid Alia, Juntakan Taweekunb*, Kuaanan Techatoc, Jompob Waewsakd, Saroj Gyawalie a Program

of Sustainable Energy Management (SEM), Faculty of Environmental Management (FEM), Prince of Songkla University (PSU), Kor-Hong, Had Yai, Songkhla 90112, Thailand. b Department

of Mechanical Engineering, Faculty of Engineering, Prince of Songkla University, Kor-Hong, Hat Yai, Songkla 90112, Thailand. c Environmental

Assessment and Technology for Hazardous Waste Management, Faculty of Environmental Management, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand d Solar

and Wind Energy Research Laboratory (SWERL), Research Center in Energy and Environment, Thaksin University, Phatthalung campus, Thailand. e Research

and Development Centre (RDC), GPO Box 9804, Kathmandu, Nepal.

Email Addresses: [email protected]; [email protected] ; [email protected] ; [email protected] ; [email protected]

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GIS based site suitability assessment for wind and solar farms in Songkhla, Thailand.

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Abstract

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The objective of this study was to identify ideal sites to locate utility-scale wind and solar farms in Songkhla, a province in southern Thailand. Geographic Information System (GIS) and analytical hierarchy process (AHP) were used to assess various physiographic, environmental and economic siting criteria. The data used in this work were primarily obtained from governmental organizations. Additionally, a Global Horizontal Irradiation (GHI) solar map with a spatial resolution of 1km/pixel for the years 2007-2015 was obtained from Solargis as well as a 200 m resolution wind resource map of 100 m above ground level obtained from previous research conducted in the study area. The results of the study indicate that Songkhla has potential land areas of up to 66.113 km2 and 844.93 km2 available for wind and solar farms respectively, though only areas of 38.749 km2 and 69.509 km2 respectively were judged as being “highly suitable”. Most of these highly suitable areas were located in the Ranot District. The results of this study provide an important starting point for stakeholders interested in investing in renewable energy in Southern Thailand. Knowledge of the suitability of sites will provide a greater level of confidence and therefore likely expedite the renewable energy investment process.

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Keywords: GIS, multi-criteria decision making, analytic hierarchy process, wind farms, solar farms, Thailand

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1. Introduction

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Energy plays a leading role in the economic and industrial growth of the entire world [1,2]. Fossil fuels have been the primary source of energy traditionally used by all countries on which the, International Energy Agency (IEA) reported in 2015 [3], noting that fossil fuels fulfill 80% of energy demand worldwide and are responsible for 90% of energy related emissions in the form of CO2. Rising environmental concerns, depleting reserves and high energy prices have driven the search for renewable energy options [4,5] of which wind and solar energy are the bestknown, with mature technologies, the use of which is rapidly expanding around the world [2,6–8].

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Thailand’s energy scenario is not as such different to that in other countries with demand increasing yearly. In 2012 the electricity generated in Thailand was 32600 MW [7,9] which had increased to 42163 MW by October 2017 (EPPO, 2017), with, over 67% being produced from natural gas [10]. Thailand’s Ministry of Energy power development plan 2010 (PDP 2010) predicted that the net installed capacity of electricity generation in Thailand will reach 70,868 MW by 2030 [11]. However, the Alternative Energy Development Plan (AEDP) for Thailand, aims to replace fossil fuels up to 30% by 2036 under PDP 2015-2036 [12]. This objective defines the renewable energy road map in Thailand, to which all relevant energy departments are committed and which they are actively pursuing [10,12–14].

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The whole of Thailand has high exposure to sun light because of its position near the equator, and it leads all the other ASEAN countries combined in solar energy exploitation [8]. However, its geographical positioning does not greatly favor the exploitation of wind as a source of energy. Nevertheless, the southern provinces reportedly have class 3 (above 6 m/s) winds on average [7,15,16]. Songkhla, a province in southern Thailand, has shown a rising trend in electricity demand in recent years as reported by the Electricity Generating Authority of Thailand (EGAT) in 2016 [9] as it is one of the nearest tourist areas for people entering the country from Malaysia. Therefore, government organizations and other energy stakeholders are continuously seeking suitable locations for the exploitation of sources of renewable energy. Although a feasibility study on wind energy for some of the provinces of southern Thailand has already been conducted this is somewhat out of date because changing demographics and growing industrialization have by now changed the land availability [17,18].

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Combining Solar PV and wind facilities has been favored in commercial utilities since this allows them to back each other up and thus mitigate the drawbacks of intermittency associated with renewable energy exploitation [19,20]. However, despite their substantial economic and environmental benefits, wind and solar technologies have been a matter of public debate [21,22]. The issues of most concern for wind technology are visual intrusion and noise nuisance, while those of utility-scale solar facilities are land degradation and habitat loss [22,23]. Nevertheless,

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careful assessment of locations for the development of wind and solar facilities can minimize the impacts of these issues [24].

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Geographic information systems (GIS) have recently become popular due to their ability to combine apparently distinct data to accomplish defined objectives like solar and wind resource assessments [4,25–27]. Moreover, GIS data can be combined with the views of energy stakeholders, public perceptions and experts’ opinion as inputs to with multi-criteria decision making (MCDM) models [25,28,29]. MCDM is a proven method of resolving complex problems, especially when multiple factors affect a single objective [30,31]. Previous studies [25,28,29,32,33] have highly recommended the use of the GIS-MCDM approach in conducting suitability studies of wind and solar energy applications, either separately or in combination in a single area. Further, the analytic hierarchy process (AHP) technique in MCDM provides the opportunity to blend the opinions of experts and decision makers in pairwise comparisons of numerous factors, which can be used in a GIS environment to achieve specific goals[34].

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The aim of this study was to explore suitable areas for siting wind and solar farms, using multi-criteria GIS modeling techniques. To date, this is the first detailed study to explore Songkhla Province with a view to identifying potential locations for siting wind and solar farms simultaneously using AHP and GIS. One previous study by Adul Bennui [18] considered wind turbine site selection in the southern Thailand. This previous study used the MCDM technqiue, and had the limitation of using arbitrary scores for their AHP calculations. Therefore, in this current study we are considering the views of both regional experts and public opinion to provide some basis for our AHP calculations. As this study covers both wind and solar farm siting, it is expected to provide important insight for both stand-alone and hybrid versions of wind and solar energy applications.

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2. Materials and methodology

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2.1. Methodology overview

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Fig. 1 presents the various steps in the proposed methodology in this research. As described in section 2.2, the study area was initially narrowed after preliminary consideration of the geographical position, renewable resources availability and indications of the growing demand for electricity in the region. Section 2.4 describes the criteria for the siting of both wind and solar energy applications. Section 2.5 concludes the methodology section by describing MCDM using AHP in a GIS environment.

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Fig. 1. Flow chart of the proposed methodology of this study (adapted source:[27]) [use color]

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2.2. Study Area

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Songkhla is the 26th largest Province in Thailand, located in the south and bordering Malaysia. Songkhla province is located 7° 12' 13.20" N latitude and 100° 35' 28.79" E longitude. The lowest elevation is 11 m (36 ft.) and the highest elevation is 295 m (968 ft.) and the difference in elevation is thus 284 m (932 ft.) [35]. This low elevation difference signifies a mostly smooth surface, which affects its appropriateness for the development of wind and solar projects. In terms of climate, Songkhla has a tropical monsoon climate and on average the temperatures are high. In 2016, in the South of Thailand the lowest monthly mean temperature on the east coast (26.5 oC) was recorded in December and the highest mean temperature (30.1 oC) was recorded in April while on the west coast, the lowest monthly mean temperature (27.1 oC) was recorded in December and the highest mean temperature (30.5 oC) was recorded in April with the annual mean temperatures being 28.2 oC on the east coast and 28.3 oC on the west coast [36]. The generally high temperature justifies investigation of this region for energy purposes.

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In April 2016, EGAT [9] announced a rapid increase in electricity demand in the southern provinces of Thailand of 5% against previous years by 125 MW to 2600 MW consequent on the hot weather and the growing use of air conditioners [9]. Songkhla is one of the highest power-consuming provinces in Thailand being a gateway on the Malaysian border to enter Thailand, and it therefore, hosts many tourists throughout the year. This underscores the need to develop new energy resources with the least environmental impacts, if possible using renewable resources.

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2.3. Data description and sources The objective of this research was to find feasible sites for wind and solar resources and it was reliant on the availability of data. Thus, necessary data were collected and assessed prior to embarking on its analysis in a GIS environment. Portals of various governmental organization were accessed to obtain the latest available data. However, a wind resource map at 200 m resolution and 100 m above ground level was obtained from a previous study, which was conducted by the Solar and Wind Research Unit of Thaksin University who constructed a high resolution wind atlas of Nakhon Si Thammarat and Songkhla provinces (see Fig. 2) [37]. Long term daily average, global horizontal irradiance (GHI) data was obtained from the Solargis on-line resource at 1km/spatial resolution covering the period from 2007 to 2015, and is available in raster (gridded) data in two formats: GeoTIFF and AAIGRID (Esri ASCII Grid) (see Fig. 3) [38]. Table 1 describes all the data sources for this study.

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Table 1

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List of data layers, their types and sources. Data Layer

Type

Wind

Wind speed

Solar Slope

GHI Topographic Digital Map Topographic Digital Map Land Use Map LandSat

1000 m 100 m

Topographic Digital Map Transmission line Map

__

Elevation Land use Airport Road

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Transmission line

Spatial resolution 200 m

100 m __ 30 m

__

Source

Edit Source

Wind and solar research unit, Thaksin University [37] Solargis [38] Royal Thai Survey Department, 1999 [40] Royal Thai Survey Department, 1999 [40] Land Development Department, 2012 Southern Regional Center of Geo-Informatics and Space Technology, PSU (2015) [39]. Royal Thai Survey Department, 1999 [40] Electricity Generating Authority of Thailand, 2015 [9]

Edited by Southern Regional Center of Geo-Informatics and Space Technology, PSU [39]. Convert from contour by Southern Regional Center of Geo-Informatics and Space Technology, PSU (2017) [39]. Edited from LandSat by Southern Regional Center of Geo-Informatics and Space Technology, PSU (2015) [39]. Edited by Southern Regional Center of Geo-Informatics and Space Technology, PSU (2015) [39].

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Fig. 2. Wind resource map of Songkhla at 100 m agl (source [37] ) [use color]

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Fig. 3. Average daily GHI map for Songkhla (adapted source [38]) [use color]

2.4. Scrutiny of criteria for wind and solar resource siting

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The selection criteria for this study were finalized after an extensive literature review and were in compliance with national and international guidelines [25,34,41,42]. They were additionally endorsed by seven experts’ opinions with a view to minimizing conflicts of interest and personal bias [25,43]. The experts chosen were all based in Thailand and were professional engineers, university professors and researchers with strong knowledge of wind and solar energy applications as well as being familiar with the ground conditions of the study area.

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The appropriateness of wind speed (m/s) and solar irradiance (W/m²) are not the only aspects to be considered when choosing wind or solar farm locations [27,42], rather, economic and environmental aspects are equally significant in solar and wind farm placement decisions [27,41,44]. Therefore, based on the literature and interviews with experts, physiographic, environmental and economic aspects were categorized as the main criteria for this study, with sub and sub-sub criteria in a hierarchy to achieve the optimum outcome, as shown in Fig. 4. The physiographic layer encapsulates the factors related to the physical conditions and processes of the Earth, most importantly, climate (wind and GHI), topography (slope and elevation) and land use types. The environmental layer encompasses the factors which have a direct and indirect connection to the environment in terms of solar or wind resource viability such as, buffers to residential areas (rural and urban) and protection buffers to necessary installations (e.g., airports) and scenery (e.g., wetlands and forests). Distance to market including both main roads and transmission lines and the area required for siting wind or solar farms mainly influences the capital and operating costs and hence, were assigned to the economic layer.

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Fig. 4. Categorization of parameters and their overlaying to achieve the study objectives. [use color]

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2.4.1. Physiographic aspects

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2.4.1.1 Climate

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Despite the fact that wind availability is not the only reason for siting wind farms, it is still the criterion which is given most weight in previous research [4,44–46]. Although the minimum useable wind speed observed in the literature [1] was 5 m/s, in this study, 4 m/s was considered as the threshold wind speed based on the opinions of regional experts, since Thailand has relatively poor wind resources because of its position near the equator. However previous studies [7,15,16] have indicated that the class 3 wind (above 6m/s) in southern Thailand is sufficient to operate wind-based generating facilities. Moreover, the average operational cut in speed for the Siemens (SWT-2.3-101) horizontal axis wind turbine, which has previously been used in a wind farm in Korat (Thailand), is 3-4 m/s. In a previous study, areas with a wind speed of more than 6 m/s on land heights over 100 m agl have been proposed as being highly suitable [7].

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In the context of solar irradiation, the GHI is the essential criterion in solar power project developments [4,33,42]. In contrast to wind, the geographical positioning of Thailand is best suited to solar energy, since on average the country collects a solar irradiation of 5 kW/m2/day (18.0 MJ/m2/day) [8]. However in this study, areas receiving GHI of less than 3.5 kW/m2/day have been excluded in view of the findings of a previous study [27]. More details are included in Appendix Tables A.1 and B.1.

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2.4.1.2. Topography and land use

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Slope and elevation are the basics of topography and land with a steep slope and high elevation is not recommended for solar and wind projects by almost all the studies [27,41,45,47]. The threshold range of slope in the case of wind varies between 10 and 30% [27,29] and for solar varies between 3 [42] and 5% [27] in previous reports. The elevation above sea level differs from area to area, and previous studies [1,42] set an elevation of 2000 m as the cut-off in Iran and Turkey. Bennui et al. [18] excluded elevations above 200 m for wind projects in southern Thailand. As the maximum elevation in the study area is 295m, a slope of 15% for wind and 5% for solar and elevation above 200 m for both wind and solar were set as the cut-offs in this study. Fig. 6(a) shows the distribution of slope and elevation in the study area. More details are included in Appendix Tables A.1 and B.1.

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Land use requires careful assessment prior to any investment in energy projects. Studies [2,42] recommend barren land as being highly compatible with both wind and solar energy projects. Vegetation landscapes are dealt with in this category and shorter vegetation is preferred over taller vegetation as nearby tall vegetation can accelerate turbulence intensity and conversely decelerate the wind speed, which may harm the rotary equipment [2,48]. Further, shorter vegetation will not obstruct solar insolation [4]. The regional experts’ opinions also agreed with above the land use preference classification for this study (see Tables A.1 & B.1 in the Appendix).

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2.4.2 Environmental aspects

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2.4.2.1 Residential buffer

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Buffers to residential areas have been dealt with specifically in all similar studies for wind and solar power generation [4,33,46,49]. Such buffers are necessary to avoid inconvenience to human life. At minimum a 500 m residential buffer (urban-rural) has been recommended for solar and wind in [42] and [2] and to collect first-hand experience in this study a special visit was paid to the wind farm in Hua Sai District in Nakhon Si Thammarat Province (Thailand) (see Fig. 5), where people in the neighborhood have complained about noise nuisance and visual intrusion in daylight hours and moonlight reflections which disrupt shrimp farming during at nighttime hours, as the residential buffer at some of the places were under 500 m. Thus, in this study buffers of 500 m for rural areas and 1000 m for urban areas were adopted for wind farms, with 500 m for solar facilities. This was proposed based both on expert opinions as well as public opinion rather than relying on information from previous literature since human comfort is of paramount importance. Fig. 6(b) shows the distribution of urban and rural populations in the study area. More details are included in Appendix Tables A.1 and B.1.

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Fig. 5. Hua Sai wind farm Nakhon Si Thammarat, Thailand [source: author’s own] [use color]

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2.4.2.2 Protection buffer

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Distance to airports has been considered in all similar studies [18,41,49] relating to wind farms, as wind turbines disturb the airport surveillance radar signals which are vital for controlling air traffic. Previous studies [1,50]have proposed distances of 2500 m [1,48] and 3000 m [18] and this study adopted the latter distance as this was deemed to be safer. However consultation is indispensable for any wind farms within a range of 55 km [50] of an airport. In the case of solar farms, glint and glare [51,52] from panels can distract pilots’ vision and can also have adverse effects on radar systems if solar panels are placed closely together. Therefore, a 1000 m buffer between airports and solar farms was adopted in this study. More details are included in Appendix Tables A.1 and B.1.

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A buffer to wetlands and forest is necessary to avoid damage to the expensive equipment as well as preserving biodiversity and natural reserves [7,44]. There is no specific rule covering the buffer distance to wetland or forest in wind and solar energy applications but [50] used 100 m and [44] used 400 m for watercourses in wind energy applications. Moreover placing facilities much closer to forests may give rise to wind turbulence or obstruct solar insolation [2]. In a previous study, [53] a cordon of 250 m was allowed between forest and wind energy applications. However, this study mostly relied on the regional experts’ opinions on this aspect and their suggestion was to allow a minimum buffer of 400 m to wetland (solar and wind applications) due to previous flooding in the study area [54,55] and that the distance to forests should not be less than 1.5 km for wind energy applications and 1 km for solar energy applications. More details are included in Appendix Tables A.1 and B.1.

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2.4.3 Economic aspects Nearness to roads and transmission lines determines the economic viability of energy projects [1,4,7,29,42], as higher distances to roads and transmission lines may incur higher construction costs and power line losses. The Department of Alternative Energy and Development Efficiency, Thailand [14] guidelines suggest avoiding sites more than 10 km from roads and electricity substations, in energy applications [7]. Therefore, areas violating the 10 km requirement were not included in this study. Fig. 6(c) shows the existing transmission lines and main road network in the study area. The area required for either a solar or wind generation facility (farm required area) may govern the relative cost per kW of energy and the minimum area required for utility scale farms is not less than 4 km2 (1000 acres) for wind energy and 0.4 km2 (100 acres) for continuous solar energy applications, as indicated by [27]. Therefore, this study only considered areas above the minimum space availability for the construction of wind and solar farms (see Tables A.1 & B.1in the Appendix). (a) Slope & Elevation

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(b) Urban & Rural population

(c)Transmission lines & Roads network

Fig. 6. Thematic maps with added factors that influence the study area (Songkhla) (adapted source [39]). [use color]

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2.5 MCDM using an AHP approach in GIS environment Renewable energy assessment involves certain complexities, which necessitate an advanced decision process. MCDM provides a sophisticated method of handling decision related problems and previous studies have used MCDM in a GIS environment for energy application assessments [28,30,31]. Abu Taha [31] reported on the use of MCDM for multi-attribute decision making (MADM) and multi-objective decision making (MODM), while in 1977 Saaty [56] introduced AHP, which is a function-based MADM method, highly regarded for energy system problems [32]. According to Saaty, AHP is based on four axioms, reciprocity, homogeneity, synthesis and expectation. It allows the evaluation of criteria and sub-criteria in orderly patterns and allows weight to be assigned to the criteria involved to achieve a specified goal. Three important steps need to be conducted consecutively to apply AHP in any situation [2,31,32,34,57]. Initially, the goal must be defined then the hierarchy of criteria and subcriteria which will ultimately influence the goal. Thus, in this study the goal was to find suitable sites for wind and solar resources (as shown in Fig. 4). The next step in AHP is to conduct pairwise comparisons of the main criterion and sub-criteria, where the input for pairwise comparisons comes from the opinions of experts or decision makers, and to allot them scores on a fundamental scale as defined by Saaty (1990) as shown in Table 2.

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Table 2

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Preference score values as defined by Saaty (1990) [58]. Intensity of Importance

Definition

Explanation

1

Equal importance

Two criteria contribute equally to the objective

3 5

Moderate importance Strong importance

Experience and judgment slightly favor one activity over another Experience and judgment strongly favor one activity over another

7

Very strong importance

An activity is favored very strongly, and its dominance is demonstrated in practice

9

Extreme importance

The evidence favoring one activity over another is of the highest possible order of affirmation

2,4,6,8 Reciprocals

Intermediate values When compromise is needed If one activity, i has one of the above activities assigned to it when compared with activity j, then j has the reciprocal value when compared with i (i.e. 2 = ½ or 0.500)

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Based on [25] seven experts with relevant expertise were asked for their opinions in order to construct the matrices (𝑴𝒙) for pairwise comparisons in wind and solar site selections, in accordance with Saaty’s discrete 9value scale (Table 2) as shown below (1).

|

𝐶11 𝐶12 𝐶21 𝐶22 𝑴𝒙 = . . : : 𝐶𝑛1 𝐶𝑛2

|

... 𝐶1𝑛 ... 𝐶2𝑛 ... . ::: : ... 𝐶𝑛𝑛

(1)

𝑴𝒙 = |𝐶𝑖𝑗| ∀ 𝑖,𝑗 = 1,2,...,𝑛 for n criteria that influences the ultimate objective of the study, where, 𝐶𝑖𝑗 demonstrates the relative importance of the criteria 𝐶𝑖 over 𝐶𝑗 and the reciprocal will be 𝐶𝑗𝑖 or 1/𝐶𝑖𝑗 ∀ 𝑖 ≠ 𝑗 𝑎𝑛𝑑 𝐶𝑖𝑖 = 1 [58]. Therefore, all associated criteria and sub criteria were assessed using the matrix as in (1). Afterwards weights were determined by normalizing the individual eigenvectors associated with the maximum eigenvector of the reciprocal ratio matrix [29]. The final weights used for wind and solar resource assessment realized for this study using AHP pairwise comparisons based on experts’ opinions are presented in Table 3.

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Table 3

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Criteria divisions and the final weights for this study. Main criteria

Sub criterion

Sub-sub criterion

Physiographic

Climate Topography

Resources (wind/solar) Slope Elevation Land Use

Wind 0.3940 * 0.0297 0.0594 0.0462

Solar 0.3578 * 0.0532 0.0076 0.1163

Dis. to urban area Dis. to rural area Dis. to wetland Dis. to forests Dis. to airports

0.0539 * 0.0269 0.0058 0.0274 0.0476

0.0201 0.0100 0.0423 * 0.0060 0.0423 *

Proximity to main roads 0.0257 Proximity to transmission line 0.0772 * Available potential area Farm required area 0.2058 * * indicates the highest weighting factors Sum = 1.000 (criteria adapted from sources: [1–5,7–10,12–14,16–22])

0.0286 0.0859 * 0.2293 * 1.000

Land type Environmental

Residential buffers Protection buffers

Economical

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Proximity to market

The involvement of experts’ judgments could lead to inconsistencies owing to human error and therefore Saaty [58] also developed a method to check the level of inconsistency, known as the consistency ratio (CR). The formula for CR (3) has been used in various similar studies dealing with energy applications [25,29,32,42] and first requires the computation of the consistency index (CI) as shown below (2),

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Final Weight

𝐶𝐼 =

‒𝑛 (𝜆𝑚𝑎𝑥 𝑛‒1 )

(2)

CI is a deviation of consistency where, ‘𝝀𝒎𝒂𝒙’ is the maximum eigenvalue and n is the matrix size (n x n) in a pairwise comparison. This allows the determination of the CR, which is calculated by dividing the CI by the random consistency index (RI). RI values for different matrix size are shown in Table 4 [58]. 𝐶𝐼

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(3)

𝐶𝑅 = 𝑅𝐼

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Table 4

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Random index values for different matrix sizes in pairwise comparisons. n

1

2

3

4

5

6

7

8

9

10

RI

0.00

0.00

0.58

0.90

1.12

1.24

1.32

1.41

1.45

1.49

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CR values below 0.10 or less than 10% are acceptable, however, a CR above 0.10 indicates a major inconsistency in the judgment of the experts, which requires immediate reassessment [25]. This process was conducted in this study, and all the CR values were found to be less than 0.10 for all relevant criteria, which means that the weights assigned are appropriate.

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The computed weight was then assessed using MCDM in the GIS environment. The ArcGIS 10.3.0 [59] tool was used to produce maps for both the wind and solar cases and those maps were multiplied by the assigned overall weight to produce a score on a 4-point suitability scale of 0 for unsuitable, 1 for low suitability, 2 for moderately suitable and 3 for highly suitable as presented in Table C.1 in the Appendix. Each dataset was resampled to a common spatial resolution of 100 m using an averaging filter [4]. They were all incorporated inside the 'attribute

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table' using the add field function within GIS toolbox. The maps with the respective weights were then overlaid using a GIS overlaying tool under the spatial analysis tool section to syndicate the factors and constraints involved for both the wind and solar cases. The total suitability score was calculated by summing up the weights of the factors following the formulae (4):

𝑖=𝑁

S = ∑𝑖 = 1 𝑊𝑖𝑃𝑖

(4)

𝑊𝑖 = ith criterion weight, Pi = criterion score of ith factor, N = no of factor and 𝑆 = the classifying suitability value in output map. This allowed to merge the distinct data layers to accomplish the stated objective. Where the results are presented in next section.

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3. Results and discussion

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This was the first ever site suitability study for wind and solar farms in Songkhla, Thailand, using GISMCDM. Twelve criteria under the psychographic, economic and environmental aspects were chosen based on previous studies and the attributes of the study area. The map for each criterion was prepared using ArcMap 10.3.0 [59]. The weights of the criteria were obtained from AHP analysis as shown in Table 3, based on expert’s opinions derived from interviews.

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Climate under the psychographic aspect was the dominant criterion for both wind and solar having weights of 0.3940 (39.4%) and 0.3578 (35.78%) respectively, which agrees with the findings of previous studies [4,45]. The next most important criterion was the farm required area under the economic aspect which was assigned weights of 0.2058 (20.58%) and 0.22.93 (22.93%) for wind and solar respectively. This reflects the importance of having sufficient space for utility scale energy projects since otherwise they will not be economically feasible. Distance to Transmission line was the third important criterion for both wind and solar having weights of 0.0772 (7.72%) and 0.0859 (8.59%) respectively. For wind farms, a buffer to urban areas was yet another important criterion having a weight of 0.0539 (5.39%) while for solar farms, buffers for wetlands and airports had equal weights of 0.0423 (4.23%) as the important criteria. This reflects the fact that solar farms receive less public opposition than wind farms.

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The farm required area criterion was implemented in the final stage after assessing all the other layers using the overlaying technique in the GIS environment, which initially indicated the potentially available area for wind farms as 66.113 km2 and for solar farms as 844.93 km2, representing 0.89% and 11.42%, respectively, of the total area of Songkhla Province of 7,394 km2. The final maps for both wind and solar farms were grouped into four suitability classes as shown in Appendix C as “highly suitable”, “moderately suitable”, “low suitability” and “not suitable”, where, the “restricted areas” represent the areas which were masked out before applying the farm required area criterion. The final maps from this study indicate that the northernmost district of Songkhla, known as Ranot has most of the “highly suitable” areas for wind and solar farms. Fig. 7 presents the final suitability map for utility scale wind farms and that of solar farms is presented in Fig. 8.

280 281 282 283 284 285

Around 38.749 km2 or 4.94% out of the total area of 783.8 km2 of Ranot is designated as “highly suitable” for utility scale wind farms, however, “moderately suitable" was not detected for the case of wind in Ranot upon considering the required farm area to be used for utility scale purpose, and for solar power generation, 56.592 km2 or 7.22% of the area of Ranot has the potential to host utility scale farms. Table 5 shows the statistical distribution of the remaining area’s suitability and Fig. 9 compares the results of the assessment for wind and solar power in this study.

286 287 288 289

Although the results of studies such as this are highly influenced by the criteria chosen to be included, the criteria selected in this study were based not only on relevant literature but also on the opinions of experts with detailed knowledge of the study area conditions. One of the main achievements of this study is to identify the Ranot District in Songkhla Province as being suitable for the siting of both wind and solar farms, which would allow their

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290 291

combined use for commercial purposes by overcoming the intermittency challenge in the generation of renewable energy.

292

Table 5

293 294

Statistical information of feasible sites for the utility scale wind and solar farms in all districts of Songkhla, Thailand. District (Amphoe)

1

Not Suitable1 (km2)

Low Suitability (km2)

Moderately Suitable (km2)

Highly Suitable (km2)

Total (km2)

Wind

Solar

Wind

Solar

Wind

Solar

Wind

Solar

Wind

Solar

Bang Klam

0.113

4.447

---

6.47

---

4.611

---

---

0.113

15.528

Chana

2.508

15.895

---

10.708

---

34.34

---

1.144

2.508

62.087

Hat Yai

1.843

15.874

---

11.813

---

72.023

---

---

1.843

99.71

Khlong Hoi Khog Khuan Niang Muang Songkhla Na Mom

---

23.202

---

2.567

---

40.161

---

---

---

65.93

1.325

3.84

---

5.673

---

10.204

---

---

1.325

19.717

0.038

1.171

---

0.665

---

---

---

0.038

1.836

---

8.196

---

1.374

---

---

---

---

14.925

Na Thawi

---

17.443

---

6.136

---

---

---

Ranot

11.709

3.015

4.687

7.016

---

2.848

38.749

56.529

55.145

69.408

Rattaphum

0.62

20.8

---

11.283

---

47.218

---

---

0.62

79.301

Sabayoi

---

23.816

---

5.507

---

0.031

---

---

---

29.354

Sadao

---

78.247

---

18.99

---

162.448

---

---

---

259.68

Thepha

4.521

41.229

---

19.827

---

30.445

---

11.836

4.521

103.33

Total(km2)

22.677

257.175

4.687

108.029

---

409.684

38.749

69.509

66.113

844.39

5.355

23.579

Not suitable in a view of the minimum area required to be used for utility scale wind and solar farms.

11

Fig. 7. Sites suitability map for utility scale wind farms in Songkhla, Thailand. [use color]

Fig. 8. Sites suitability map for utility scale solar farms in Songkhla, Thailand. [use color]

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297 66.113 844.39

900 800

60

700

50 38.749

40

500

30

409.684

22.677

400 300

257.175

20

200

4.687108.029

10

0

69.509

0

299 300 301

100 0

Total Potential Area

Not Suitable

Low Suitablity Wind

298

600

Moderately Suitable

Area (Sq. km) for Solar farms

Area(Sq. km) for wind farms

70

Highly Suitable

Solar

Fig. 9. Graphical interpretation of the results of the study in various suitability classes (both wind and solar). [use color] 4. Conclusions

302 303 304 305 306 307 308 309 310

This study presents a GIS based approach to identify suitable locations for utility scale wind and solar farms in Songkhla Province, Thailand. Assessing the most suitable sites for wind and solar farms is a preliminary step before the utilization of these resources for energy-generation purposes. The MCDM approach in a GIS environment allows the solution of complex problems and facilitates the decision-making process. In this study physiographic, environmental and economic parameter were investigated in the light of previous studies and regional experts’ and public opinions. Considering public opinion will significantly reduce the soft costs, associated with solar and specifically wind energy projects development. AHP was used to establish the importance and weight of the criteria selected. Based on the AHP-calculated weights, twelve maps were produced for both the wind and solar cases using GIS and were then overlaid to obtain the final suitability maps.

311 312 313 314 315 316

This study concludes that Ranot District has the highest suitability for both wind and solar utility-scale farms compared with other districts in Songkhla Province, therefore this district might be preserved for the construction of renewable energy projects. Districts adjacent to Ranot, namely Karasae-Sin, Sathing-Phra and Singha-Nakhon,would have also had high GIS model scores, however, they face constraints related to access to transmisision lines. Dispersed informal settlements were observed in some locations. Further invesigation may be required to understand how these settlements might interface with future energy developments.

317 318 319 320 321

In conclusion, this study provides a potential solution to the complexity of decision making in the renewable energy sector in Songkhla Province. The process demonstrated in this study provides a scientific basis for selecting locations for utility-scale wind and solar farms. This knowledge should increase the confidence of stakeholders to invest in solar and wind energies in Songkhla Province, which will be integral in achieving Thailand’s attempts to reduce dependence on fossil fuel-based energy.

322 323 324 325 326 327 328

Acknowledgments The authors are highly appreciative of the help of the government organizations, researchers and experts who contributed to the accomplishment of this research work. This research was supported by Prince of Songkla University (PSU) Hat Yai, Songkhla 90112, Thailand, under a grant of a Thailand Education Hub Scholarship for ASEAN Countries (TEH-AC) 2/2016 to Student ID#5910920048, for the fulfillment of the requirements for a master’s degree. The authors would also like to give special thanks to Mr. Adul Bennui who is an officer in the GIS Department of PSU, for his advice and help in GIS related matters.

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329

Appendix A

330

See Table A.1

331

Wind farms location selection criteria. Factor

Highly Suitable

3 Wind Speed (m/s) >6 Slope (%) 0-7 Elevation (m) 0-50 m Land use Barren grassland Distance to urban area >3 km Distance to rural area >2 km Distance to wetland >1 km Distance to forest >3 km Distance to airport >4 km Proximity to main road >0.5-2 Proximity to transmission line 0-2 km Farm required area >6.00 km2 Sources: [2,4,7,15,16,27,34,41,42,44–50,54]

Suitability Ranking Moderately Suitable Low suitability 2 5-6 7-12 50-100 m Agricultural land 2-3 km 1.00-1.99 0.5-1 km 2-3 km 3.5-4 km 2-5 km 2-5 km 5- 6 km2

1 4-5 12-15 100-200 m Short vegetation and shrubs 1-2 km 0.5-1 0.4-0.5 km 1.5-2 km 3-3.5 km 5-10 km 5-10 km 4-5 km2

Not suitable 0 <4 >15 >200 m Public settlements, airport, wetland etc. <1 km <0.5 km <0.4 km <1.5 km <3 km >10 km >10 km <4 km2

Appendix B See Table B.1 Solar farms location selection criteria Factor Global Horizontal Irradiance (GHI) (kW/m2/day) Slope (%) Elevation (m) Land use

Highly Suitable

Suitability Ranking Moderately Suitable Low suitability

Not suitable

3 >5

2 4.5-5

1 3.5-4.5

0 <3.50

0-1 0-50 m Barren grassland

1-3 50-100 m Agricultural land

3-5 100-200 m Short vegetation and shrubs

1-1.5 km 1-1.5 km 0.5-1 km 1.25-1.5 km 1.5-2 km 2-5 km 2-5 km 1- 1.5 km2

0.5-1 km 0.5-1 km 0.4-0.5 km 1-1.25 km 1-1.5 km 5-10 km 5-10 km 0.4-1 km2

>5 >200 m Public settlements, airport, wetland etc.. <0.5 km <0.5 km <0.4 km <1 km <1 km >10 km >10 km <0.4 km2

332

Distance to urban >1.5 km Distance to R. A >1.5 km Distance to W. L >1 km Distance to Forest >1.5 km Distance to airport >2 km Proximity to Road >0.5-2 Proximity to transmission line 0-2 km Farm required area >1.5 km2 Sources: [2,4,8,27,29,33,41,42,45,52–54,60]

333

Appendix C

334

See Table C.1

335

Suitability index scale used in this study.

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336

Suitability Scale 0.00-1.00 1.00-2.00 2.00-3.00 3.00-4.00 Source: [42]

Suitability Score 0 1 2 3

Definition Not suitable Low suitability Moderately suitable Highly suitable

Explanation Constrained or entirely unsuitable for siting Lowest suitability Suitable to a great extent Perfectly suitable in all aspects

337

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GIS based site suitability assessment for wind and solar farms in Songkhla, Thailand. Highlights    

This is the first detailed study to explore Songkhla Province, Thailand with a view to identifying potential locations for siting wind and solar farms. Geographic Information System (GIS) and analytical hierarchy process (AHP) were used to assess various physiographic, environmental and economic siting criteria. The selection criteria for this study were finalized after an extensive literature review. They were additionally endorsed by seven experts’ opinions. This study concludes that Ranot District has the highest suitability for both wind and solar utility-scale farms compared with other districts in Songkhla Province, Thailand.