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authors' submission copy African Journal of Science, Technology, Innovation and Development ISSN: 2042-1338 (Print) 2042-1346 (Online) Journal homepage: http://www.tandfonline.com/loi/rajs20

A comparative study of selected multi-criteria decision-making methodologies for location selection of very large concentrated solar power plants in Nigeria Olayinka S. Ohunakin & Burak Omer Saracoglu

To cite this article: Olayinka S. Ohunakin & Burak Omer Saracoglu (2018): A comparative study of selected multi-criteria decision-making methodologies for location selection of very large concentrated solar power plants in Nigeria, African Journal of Science, Technology, Innovation and Development, DOI: 10.1080/20421338.2018.1495305 To link to this article: https://doi.org/10.1080/20421338.2018.1495305

https://www.tandfonline.com/doi/full/10.1080/20421338.2018.1495305

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1.

Introduction

34

At present, about 10% of rural households and 30% of the total population of Nigeria have

35

access to electricity (Roadmap for Power Sector Reform, 2013). This made the country the third

36

largest without access to electricity. Most of the power generating plants (running on fossil fuels)

37

are located in the region of the country where abundant natural resources needed for their operation

38

exist (Figure 1). The vast fossil based energy sources has failed the country (Ohunakin, 2010);

39

harnessing the vast deposit of renewable energy sources may be a way out of the impending energy

40

crises. Among the renewable energy resources in vast deposit in the country, is the solar energy

41

from the Sun. It has been enjoying a very high-level utilization by rural dwellers for agricultural

42

processing in the country for decades (being the world's most abundant and permanent energy

43

source) (Ohunakin et al., 2014). It is vastly deposited with an estimated 17,459,215.2 million

44

MJ/day of solar energy falling on the country's 923,768 km2 land area (approximate range of 12.6

45

MJ/m2/day in the coastal region to about 25.2 MJ/m2/day in the far north) (NEP, 2003; REMP,

46

2005). The solar radiation distribution in the country is shown in Figure 2; five solar radiation

47

zones (I, II, III, IV and V), are defined and the irradiation ranges (needed for a particular project

48

selection and sizing), as distributed among the 36 States of the federation are listed in Table 1.

49

Based on the irradiation ranges (Table 1), every part of the country is found suitable for a

50

particular type of solar application: stand-alone solar photovoltaic (PV) systems to large scale solar

51

PV or Concentrated Solar Power (CSP) systems. Detailed information concerning the availability,

52

quality, reliability and dynamics of solar radiation in a particular area, is thus needed prior to the

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siting of any of the solar energy systems (PV or CSP) for optimum performance, since the projects

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require huge investment that will span several years. With the country's location on the equator,

55

concentrated solar power (CSP) is very viable due to the irradiation level (especially the high

2

56

Direct Normal Irradiance (DNI) found in Zones I, II and III). According to Habib et al., (2012)

57

and Ogunmodimu and Marquard (2013), an area is considered eligible for solar CSP application

58

when it receives minimum direct normal irradiance of 4.1 kWh/m2/day, with a land slope having

59

a threshold that excludes areas greater than 3o. Zones I, I and III, all in the Northern region of

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Nigeria are endowed with DNI above 4.1 kWh/m2/day in addition to a relatively flat terrain; these

61

zones are thus considered suitable for CSP application. The potential capacity of CSP in states

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within Zones I, II and III is shown in Table 2. It can further be observed from Table 2 that the total

63

potential capacity of CSP within the states is estimated at 427,829 MW while the electricity

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potential is estimated at 26,841 TWh/yr (Habib et al., 2012; Nigeria Climate Change Assessment,

65

2011).

66

However, despite the abundant solar energy deposit in the country, solar applications and

67

utilization in Nigeria are majorly limited to small-scale and isolated applications. The existing

68

solar projects found in the country are listed in Ohunakin et al., (2014). This research study is thus

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conducted to select the most appropriate locations in Nigeria suitable for the deployment of very

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large concentrated solar power plants (1,000 MW ≤ installed power (Saracoglu, 2014)), that may

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not only serve the national power grid, but also the Supergrids and Global Grid (e.g. European

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Supergrid (The Friends of the Supergrid Working Group 2, 2016), African Supergrid, Global Grid

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(Chatzivasileiadis, 2013)) in the future, using the Multi-Criteria Decision Making (MCDM)

74

technique. The Five (5) MCDM methods including: Analytic Hierarchy Process (AHP),

75

Consistency-Driven Pairwise Comparisons (CDPC), Decision Expert for Education (DEXi),

76

Elimination and Choice Translating Reality/Elimination Et Choix Tradusiant la Realite

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(ELECTRE III, and ELECTRE IV) are concurrently applied with respect to concentrated solar

78

power plants (CSPP), renewable energy, national power grid of Nigeria and Supergirds/Global

3

79

Grid. This comparative research approach will hopefully help the country, and international

80

communities/organizations (e.g. United Nations, World Energy Council) to develop policies and

81

build a common framework through detailed research studies and coupled with investments in

82

these power plants. The findings of this research study are based on very large concentrated solar

83

power plant (VLCSPP).

84 85

2.

Literature Review & Methodology

86

A potential site for all CSP technologies (solar stirling engine, parabolic trough, parabolic

87

dish, tower, concentrated PV) needed for the Duqum Master Plan in Wilayat Duqum, Oman was

88

recommended in the work of Charabi and Gastli (2010). In Clifton and Boruff (2010), the CSP

89

potential in rural Australia was conducted. In the work of Azadeh et al., (2011), artificial neural

90

network (ANN) and fuzzy data envelopment analysis (FDEA) was adopted for the optimization of

91

solar plants' location. Several other works have been carried out on CSP using various techniques

92

(Noone et al., 2011; Dawson and Schlyter, 2012; Choudhary and Shankar, 2012; Merrouni et al.,

93

2014; Wu et al., 2014; Sanchez-Lozano et al., 2015). These works are further summarized in Table

94

3.

95

It was observed very clearly, that the adoption of MCDM methods, were not common in

96

most of the studies for the location selection of CSP plants (Table 3). This work thus contributed

97

to research field very clearly in the following areas: (1) global grid, (2) supergrid, (3) national grid,

98

(4) Africa, (5) Nigeria, (6) very large concentrated solar power plants (VLCSPP) (above 1000

99

MW), (7) preliminary screening project development stage, (8) Decision Expert for Education

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(DEXi), (9) Consistency-Driven Pairwise Comparisons (CDPC), (10) Elimination and Choice

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Translating Reality/Elimination Et Choix Tradusiant la Realite (ELECTRE) III, (11) ELECTRE

4

102

IV, and (12) comparative study of AHP, CDPC, DEXi, ELECTRE III, and ELECTRE IV

103

applications on a unique problem.

104

Although there are some comparative studies in literature using MCDM methodologies on

105

renewable energy projects, none dealt with comparative study using AHP, CDPC, DEXi,

106

ELECTRE III and ELECTRE IV methods for location selection problems of CSP plants. For

107

instance, in Sanchez-Lozano et. al., (2015), the best solar thermoelectric power plant locations was

108

determined using TOPSIS and ELECTRE-TRI on Geographic Information Systems. Furthermore,

109

in the work of Saracoglu (2014a; 2014b), the most preferable private small hydropower plant

110

investments in Turkey were investigated using AHP, ELECTRE III and ELECTRE IV methods.

111

Finally, as an example, Wood (2016) studied the solution of the supplier selection problem in the

112

petroleum industry using TOPSIS method. Hence, from all these studies, comparative studies

113

using the combination of these methodologies are not available in any research paper.

114 115

2.1

Analytic Hierarchy Process

116

Analytic Hierarchy Process (AHP) method was one of the most preferred MCDM

117

methodologies in literature (e.g. new shipbuilding yards’ location selection by Saracoglu (2013),

118

urban development planning by Minhas (2015)). Some of the important principles and terms in its

119

basic form were given in Tables 4-6 including number of judgments: n(n-1)/2 n matrix size,

120

"consistency", "inconsistency", "consistency index: CI or µ" (Equation 1), "consistency ratio: CR"

121

(Equation 2), "intransitive" as given by its developer and other theoretical contributors (Saaty,

122

1980; Saaty, 1987; Saaty 1990; Ishizaka and Nemery, 2013). This method was extensively

123

discussed in the work of Saaty (1990; 1977).

124 125

Consistency index (𝐶𝐼 𝑜𝑟 µ) was expressed in Saaty (1987; 1977) and shown in Equation (1) as: 5

𝜆𝑚𝑎𝑥 −𝑛

126

𝐶𝐼 𝑜𝑟 µ ≡

127

where n: n by n positive reciprocal matrix

128

Consistency ratio (𝐶𝑅) was as expressed in Saaty (1987; 1977) as:

129

𝐶𝑅 = 𝑅𝐼 and CR < 0.1 (see RI in Table 7)

(1)

𝑛−1

𝐶𝐼

(2)

130 131

2.2

Elimination and Choice Translating Reality (Elimination Et Choix Tradusiant la Realite)

132

Elimination and Choice Translating Reality (ELECTRE) III and ELECTRE IV methods

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are one of the most recognized outranking or French (speaking) school approaches (Ishizaka and

134

Nemery, 2013). One of the first applications of ELECTRE III was also in the energy sector (in

135

nuclear power plant siting) by Roy and Bouyssou (1986). These two methodologies were also

136

applied in a private small hydropower plant investment selection problems by Saracoglu (2014a;

137

2014b). ELECTRE III and ELECTRE IV were respectively dated back to 1978 and 1982 in the

138

studies of Roy (1978), and Roy and Hugonnard (1982). One of the very interesting and powerful

139

foundations of ELECTRE III and IV was its "fuzzy binary outranking relations" (Figure 3).

140

Decision makers could handle vagueness, uncertainty, and hesitation by these methods' own

141

approaches and ways. Its crispness and fuzziness ("embedded") was like a very small part of fuzzy

142

set theory and logic developed by Zadeh (1965). Some of the important principles and terms in the

143

basic form of ELECTRE III and IV were "action" (e.g. a and b in Figures 3 and 4), "binary

144

outranking relation S" (Figure 3), "concordance index" (Figure 4), "credibility index",

145

"discordance index" (Figure 3), "equal merit: ex aequo", "incomparable: R" (non-reflexive &

146

symmetric), "indifference: I" (reflexive & symmetric), "indifference threshold: qj", "non-

147

discordance", "preference threshold: pj", "pseudo-criterion" (F = {g1, g2,……, gj,……, gn} where

148

n ≥ 3, gj on Figures 3 and 4), "pseudo-dominance relation 𝑆𝑝", "quasi-dominance relation Sq", 6

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"sub-dominance relation 𝑆𝑠", "strict preference: P" (non-reflexive & asymmetric), "veto-

150

dominance relation 𝑆V", "veto threshold: vj", "weak preference: Q" (hesitation, non-reflexive &

151

asymmetric), "𝜆-cut level", "𝜆-strength", "𝜆-weakness" etc. (Saracoglu (2014a; 2014b), Roy and

152

Bouyssou, 1986; Roy, 1978; Roy and Hugonnard, 1982). These methods were extensively

153

discussed in the work of Thurstone, 1994 and Kulakowski, 2014.

154 155

2.3

Decision Expert for Education

156

Decision Expert for Education (DEXi) method was one of the interesting and easy MCDM

157

methods. It was grouped under both the full aggregation approaches and the outranking approaches

158

according to the decision-making model cases (see Bohanec et al., 2013). It was the latest

159

generation of DEX (Decision EXpert) method (expressed chronologically as DMP: Decision

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Making Process, DECMAK: DECision MAKing, DEX, DEXi) (Bohanec et al., 2013; Efstathiou

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and Rajkovic, 1979; Bohanec and Rajkovic, 1987). The foundation of this method was developed

162

in 1979 by Efstathiou and Rajkovic (1979) (also see (Bohanec et al., 2013)). This method was

163

directly influenced by, and based on the fuzzy set theory and logic developed by Zadeh (1965)

164

(see also (Bohanec et al., 2013)). It was also related to the probability theory (Bohanec et al., 1983).

165

DECMAK was first presented in 1983 in the work of Rajkovic et al., (1988). The methodology of

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DEX in 1987 was based on multi-attribute decision making and expert systems (Bohanec and

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Rajkovic, 1987; Bohanec and Rajkovic, 1990). DEX that run on disk operating system (DOS), was

168

presented in 1990 in the work of Bohanec and Rajkovic (1990). Finally, DEXi was implemented

169

in 2013 in Bohanec et al., (2013). DEXi method had already been applied in the renewable energy

170

sector for a private small hydropower plant investments selection problem in the work of Saracoglu

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(2015). Some of the important principles and terms in the basic form of DEXi were "attribute

7

172

(parameter, criteria, variable)", "basic attribute (basic variable: leave)", "linguistic variable"

173

(Efstathiou and Rajkovic, 1979), "interval", "scale", "elementary decision rules", "tree of

174

attributes", "knowledge base" etc. (Bohanec et al., 2013; Efstathiou and Rajkovic, 1979; Bohanec

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and Rajkovic, 1987). Extensive discussion of this method was given in the work of Bohanec and

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Rajkovic, (1990) and Bohanec et al., (1983, 1987, 2013).

177 178

2.4

Consistency Driven Pairwise Comparisons

179

Consistency Driven Pairwise Comparisons (CDPC) method was proposed by Koczkodaj

180

in 1997 (Koczkodaj and Mackasey, 1997). It was fully based on pairwise comparisons (Koczkodaj

181

and Mackasey, 1997). It was grouped under goal, aspiration or reference level approaches (see

182

Ishizaka and Nemery (2013), on classification by Waldemar W. Koczkodaj). Several studies

183

clearly indicated that pairwise comparison approach was the oldest MCDM approach (Koczkodaj

184

and Mackasey, 1997; Kulakowski, 2014). Afterwards, the method referred to as ‘earliest pair−wise

185

comparison methodological form’ was introduced in its organized manner. This method became

186

known and well organized by Thurstone (1994). The definition of inconsistency was improved by

187

considering the triads of comparisons matrix elements in the work of Koczkodaj (1993). This is

188

indicated in Table 8.

189

The inconsistency measure for single triad (n=3) was defined as "the relative distance to

190

the nearest consistent basic reciprocal matrix represented by one of these three vectors for a given

191

metric" (Equation 3 and 4) (Koczkodaj, 1993; Koczkodaj and Szybowski, 2015).

192

𝐶𝑀 = 𝑚𝑖𝑛 (𝑎 |𝑎 − 𝑐 | , 𝑏 |𝑏 − 𝑎𝑐|, 𝑐 |𝑐 − 𝑎|)

193

𝐶𝑀 = 𝑚𝑎𝑥 {𝑚𝑖𝑛 (|1 − 𝑎𝑐| , |1 −

194

where CM was the consistency measure in Euclidean distance/metrics.

1

𝑏

1

1

𝑏

𝑎𝑐 𝑏

𝑏

(3)

|)}

(4)

8

195

The inconsistency measure for 𝑛 × 𝑛 (𝑛 > 2) reciprocal matrix was defined for each triad as

196

expressed in (Koczkodaj and Szybowski, 2015). The CDPC method was applied in solving

197

construction, mining and health problems (Koczkodaj and Trochymiak, 1996). Some important

198

principles and terms in the basic form of CDPC (different from common AHP terms) were "basic

199

reciprocal matrix", "triad-based inconsistency index", "inconsistent triads" (Koczkodaj and

200

Mackasey, 1997; Kulakowski, 2014; Koczkodaj, 1993; Koczkodaj and Szybowski, 2015). Further

201

discussion on this method was given in the work of Koczkodaj et al., (2014).

202

This research study was based on comparative analysis approach using AHP, CDPC,

203

DEXi, ELECTRE III, & ELECTRE IV as MCDM methods, because the VLCSPP investment

204

decisions on the Super-grid and the Global Grid perspective analysis could be very costly and

205

expensive during and after the decision analysis studies. The authors thought that this decision

206

analysis had to be a method-free analysis. Hence, the current problem had to be solved with as

207

many MCDM methods as possible in literature, in order to select the most suitable locations that

208

will be recommended for VLCSPP citing in Nigeria that will support connection with the Super-

209

grid and Global grid. It is believed that this comparative analysis will further help the Federal

210

government, and other international consortiums/organizations to make unbiased judgments and

211

evaluations, considering the many technical and social factors and alternatives. This research

212

approach will also play a key role in multinational and international conflict precaution, to manage

213

facts based on realistic mid- to long-term perspective, so as not to face any conflict related to these

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large-scale and cost intensive investments.

215 216

3.

Models, Results and Discussion

9

217

The comparative research model (Figure 5) is built on the theoretical and practical

218

principles of the adopted methods (AHP, CDPC, DEXi, ELECTRE III, and ELECTRE IV), and

219

their cognitive and psychological principles and limitations (e.g. long and short-term memory

220

(Miller, 1956; Shiffrin and Nosofsky, 1994). The short descriptions of the framework of the study,

221

are as follows:

222 223

Level I (Goal): Location selection of very large concentrated solar power plants in Nigeria in the preliminary screening project development/investment stage.

224

Level II (Aggregate Main Criteria): There are three main factors for clustering purposes.

225

These include: C1: Technological, C2: Environmental, C3: Legal, Political, and Social. They are

226

not so important or influential as the factors (Level III), in the solution and the findings of this

227

problem, but they are very important and influential for structuring purposes (e.g. AHP, CDPC

228

and DEXi).

229

Level III (Basic Criteria): There are nine major factors in this problem. These are: C11: Direct

230

Normal Irradiance (DNI), C12: Grid Infrastructure (HVDC etc.), C13: Climatic conditions, C14:

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Water availability conditions under C1, C21: Natural disaster/hazard conditions, C22: Topographical

232

conditions, C23: Geological conditions under C2, and C31: Land use, allocation and availability,

233

C32: War, terror & security conditions under C3 cluster. These basic criteria are based on some

234

previous works (Saracoglu 2014a; Saracoglu 2014b), and common views by the authors. These

235

basic criteria are appropriate for the VLCSPP preliminary screening of project investment stages,

236

and mainly for the Super-grid and the Global grid concepts. However, they can also be used for

237

the national grid and other grid applications, and for other project development stages with some

238

linguistic, semantic, syntax and scope changes. These factors considered, are as follows:

10

239

C11: Direct Normal Irradiance (DNI) (direct beam radiation/beam radiation) (objective

240

criterion, more is better ↑ ↑): This technology has only one source. The DNI in any location is

241

approximated by some equations (Habib et al., 2012). The accepted threshold DNI value for a

242

commercial CSP application varies between 1900 and 2100 kWh/m²/year (Habib et al., 2012).

243

Below this range, CSP developers suggest the use of solar photovoltaic systems as a better

244

technology, because of its economic implications (Habib et al., 2012). Hence, all the alternatives

245

in this study are in Zones I and II of Figure 1.

246

C12 (i.e. High-Voltage Alternating Current (HVAC) and High-Voltage Direct Current

247

(HVDC) electrification/power grid infrastructure (subjective criterion, more is better ↑ ↑)): The

248

current HVAC, HVDC and other high capacity power transmission infrastructures and their

249

expansion possibilities are very important in these kind of models and designs (FOSG 2014; Nabe

250

2011). All items related to this subject, such as losses, capacities, lengths and costs are taken into

251

consideration. The current transmission networks in Nigeria exist as 330 kV (4,889.2 km), and 132

252

kV (6,319.33 km). It also has 66 kV (62.5 km) and sub-stations that consist of 21No. 330/132 kV

253

with total capacity of about 6,098 MVA, 99 No. 132/33/11 kV with total capacity of about 8,107.5

254

MVA. From these, the transmission network is thus a weak link in the country's power sector. The

255

future possibilities of power grid extension are mainly evaluated in this study.

256

C13 (i.e. Climatic conditions (subjective criterion, more is better ↑↑)): All the factors related to

257

climatic conditions such as temperature, wind, and humidity are evaluated at once in this study.

258

The appropriate locations in Nigeria for siting a VLCSPP is the northern region (north-central,

259

north-east and north-west). This region is characterized by the Sahel climate or Tropical dry

260

climate. Annual total rainfalls are lower compared to the southern and central part of the country.

11

261

Rainy season in the northern part of Nigeria last for about three to four months (June–September).

262

The rest of the year is hot and dry, with temperatures climbing as high as 44 °C.

263

C14 (Water availability conditions (subjective criterion, more is better ↑↑)): This criterion is

264

very important for the cooling systems and the cleaning methods of mirrors in CSP technology;

265

the total investment and operational costs are closely related to this factor. Main water resources

266

are rivers, ground water aquifers, lakes, seas and oceans. The investigated alternative locations for

267

the VLCSPP in this study are endowed with large rivers (Niger, Benue, Shiroro, Hadejia, Sokoto,

268

Kaduna, and all other small rivers). These large rivers provide abundant water resources for the

269

cooling and cleaning purposes of VLCSPPs. In locations that are far from these watersheds, dry

270

cooling could be an option; however, this may reduce the efficiency of the plant thereby raising

271

the capital cost of the plant (Habib et al., 2012).

272

C21 (Natural disaster/hazard conditions (subjective criterion, more is better ↑ ↑)): The natural

273

disasters and hazardous conditions such as earthquakes, floods, landslides, sea-level rise, and

274

sandstorms are evaluated under this factor. If some indexes (e.g. Natural Disasters Index 2010)

275

can be found, then this criterion can be revised as an objective factor. Other than mild sandstorm

276

that seldom occur, the alternative locations in this study did not have any major history of natural

277

disasters.

278

C22 (Topographical conditions (subjective criterion, more is better ↑ ↑)): The surface land

279

shapes, elevation differences, and field/land slopes are very important for this technology due to

280

the electricity generation and the total cost of power plants. Many researchers and engineers in

281

practical applications, accept the maximum threshold value as 5% for this technology (maximum

282

ground slope of 5 %) (see Clifton and Boruff, 2010). The topography of the northern region in

283

Nigeria is generally flat, with slopes that is less than 3%.

12

284

C23 (Geological conditions (subjective criterion, more is better ↑ ↑)): The geological conditions

285

are mainly important for the power blocks, and the thermal storage area in this technology. The

286

geological conditions of the power blocks, the solar fields and the thermal storages affect the total

287

cost.

288

C31 (Land use, allocation and availability (subjective criterion, more is better ↑ ↑)): The

289

VLCSPPs need vast land. Previous work on land/area requirements of the CSP technology have

290

shown different values of approximately 20,000–30,000 m2/MW, 40,000 m2/MW and 50,000

291

m2/MW (see Clifton and Boruff, 2010; Dawson and Schlyter, 2012). Fortunately, land area in the

292

alternatives locations is appreciably large in all the regions. An approximate land area of 720,801

293

km2 exists in the North-Central, North-West and North-East geopolitical zones (that constitute the

294

northern region of Nigeria), and with a very high sunshine insolation.

295

C32 (Political, war, terror and security conditions (subjective criterion, more is better ↑ ↑)):

296

The most controversial factor may be war, terror, security conditions and political conflict issues

297

in this subject. Power plant equipment and components may be rehabilitated and replaced

298

considering the useful life of the systems, but the sites will be used by the power plants for

299

electricity generation for a life time. Hence, this factor should thus be studied very carefully in the

300

mid- to long-term perspectives. Some alternative locations that are very suitable for the

301

development of solar energy system in Nigeria (especially the North-East region) had played host

302

to insurgency in the past. Insecurity has affected power plant constructions and other

303

infrastructures through kidnaps and killing of workers in various parts of the country (Ohunakin,

304

2010). General insecurity of solar infrastructures, especially in the northern region where there is

305

abundant solar insolation can be a potential threat that will stall future investment in large scale

306

grid-connected solar infrastructure (Ohunakin, 2010).

13

307

The data and information related to these factors are gathered from several sources including

308

website directories (internet links could be supplied upon request), and more importantly, from

309

decision makers/experts (deep local knowledge).

310

The alternatives are defined very carefully on Google Earth Pro 7.1.5.1557 (GE). This is

311

sufficient and very helpful as a geographical information system (GIS) software, needed for

312

screening at the preliminary project development/investment stage. The GIS data and files for each

313

factor are found and opened layer by layer on each embedded layer on the GE (e.g. Borders and

314

Labels, Roads on the GE layers, and Protected Areas by the International Union for Conservation

315

of Nature and Natural Resources (IUCN), and the United Nations Environment Programme

316

(UNEP) on the ProtectedPlanet.net as WDPA_Feb2016_NGA-kml.kml file; see Figure 6). All the

317

layers are studied one after the other, and very carefully overlayed in multiple on the GE. Some

318

additional maps and tools were also used on few cases, to incorporate other additional data and

319

information. Finally, thirty-five (35) candidate VLCSPP locations are defined with their borders

320

and areas around the center coordinates of the sites. These alternatives are planned to be

321

investigated and evaluated at the same time, within one alternative cluster/group.

322

The current AHP, CDPC, DEXi, ELECTRE III, and ELECTRE IV models are constructed on

323

their separate software and tools. The outcome of these models does not aim at helping the decision

324

maker to discover a good approximation of a decision that would objectively be one of the best by

325

taking into account their own value system, but rather to provide the decision maker with a set of

326

recommendations derived from the reasoning modes and working hypotheses (Figueira et al.,

327

2013).

328

The current models are built and the findings were gathered by using the Super Decisions

329

Software Version 2.4.0 on Windows 10 (AHP) (AHP, 2016), the ELECTRE 34 version 3x on

14

330

Windows XP (ELECTRE III and IV) (ELECTRE, 2016), the DEXi Version 5.00 on Windows 10

331

(DEXi, 2016), and the JConcluder Version 33301 on Windows 10 (Java) (CDPC) [96]. All were

332

done according to the limitations of the tools (see Figure 7).

333

The criteria in the AHP model are compared based on the cognitive, linguistic, or verbal

334

evaluation statements related to the fundamental scale and Likert type scale (Saaty, 1980; Saaty,

335

2008). The scale (1 to 9 point scale) is applied and evaluated on the Microsoft Office Excel and

336

Apache OpenOffice Calc (Apache, 2016) (Figure 8). The evaluations of alternatives were made

337

by the general pairwise comparison approach at first, and then converted into the ratings scale

338

using the Microsoft Office Excel or the Apache OpenOffice Calc., with the AHP consistency check

339

and the normalized weight priorities. The ratings scale must be used in this case because of the

340

limitations of the software/tool (e.g. maximum 7 to 8 child nodes possible from parent node). The

341

original evaluations are not so consistent as the improved evaluations, so that the latest improved

342

evaluations were considered.

343

The CDPC evaluations are very similar to the AHP evaluations. The factors and the

344

alternatives are evaluated according to their relative importance/value with each other (how much

345

'a' is important to 'b'? 'a' is 4 times more important than 'b'). The CDPC model and calculations

346

were done according to the available software/tool limitations (e.g. minimum 3 child nodes

347

necessary, maximum 7 child nodes possible from parent node). It cannot work beyond its

348

limitations. For instance, if there are more than 8 nodes (9, 10, 11, ... 35, etc.), then the model

349

should be split into parts. If there are 2 nodes, then the model does not work, but it should be

350

remembered that 2 nodes are always fully consistent. The goal with C1, C2, C3 reaches its most

351

consistent (TRIAD) evaluations in four steps (inconsistency1: 0.4099999964237213;

352

inconsistency2: 0.28999999165534973; inconsistency3: 0.17000000178813934; inconsistency4:

15

353

0.09000000357627869) (Figure 9). The evaluations with respect to C1 have two steps i.e.

354

inconsistency1: 0.5; inconsistency2: 0.12999999523162842. Finally, the evaluations with respect

355

to C2 have only one step i.e. inconsistency1: 0.11999999731779099. In this study, the evaluations

356

of alternatives are not made in a similar manner in 8 by 8 alternative groups, instead the previous

357

rankings were used for direct evaluations; only factors were analyzed with CDPC in the current

358

study. The Super Decisions was thereafter used for ranking, without any recourse to Super

359

Decision's consistency check.

360

The AHP and the CDPC evaluations are shortly compared in an organized manner (Table

361

9). The natural disaster/hazard condition and the geological condition factors take the minimum

362

weight (0.01) in the AHP evaluations. The geological condition factor takes the minimum weight

363

(0.0115) in the CDPC evaluations. The most important factor in the AHP evaluations is the Direct

364

Normal Irradiance (DNI), and the war, terror and security condition factors (0.13). The maximum

365

weight in the CDPC evaluations is 0.1655 for the war, terror and security condition factors. The

366

important order of the factors in the AHP evaluations and the CDPC evaluations are C32, C11, C12,

367

C13, C31, C14, C22, C21, C23 and C32, C11, C12, C31, C13, C14, C22, C21, C23 respectively. These orders

368

are almost the same, but the criteria weights are different for the AHP and the CDPC.

369

The criteria structure, scale and rules in the DEXi model are based on the cognitive, linguistic,

370

or verbal evaluation limitations of the method. Generally, two to four descendants for each

371

aggregate node is good for a DEXi model. Moreover, the basic attributes should have the least

372

number of distinguishable values in scale. The scale should gradually be increased on each

373

aggregate attribute on DEXi models. Three (3) point scale (poor, fair, good) is applied and

374

evaluated in the basic attributes (Level III). The Five (5) point scale (very poor, poor, fair, good,

375

very good) is applied and evaluated in the aggregate main attributes (Level II). The Seven (7) point

16

376

scale (extremely poor, very poor, poor, fair, good, very good, extremely good) is applied and

377

evaluated with the goal (Level I). The evaluations of options are made on Microsoft Office Excel

378

(Apache OpenOffice Calc) using the AHP and CDPC evaluations. All the rules are defined directly

379

on the software/tool after which the utility functions are gathered by the tool (Figure 10). The

380

options are selected and made on the "Evaluation" tab. Finally, the findings are gathered and

381

studied in this work.

382

The current ELECTRE III & IV model structures are based on the limitations of the tool (i.e.

383

AHP, CDPC, DEXi). In this study, the ELECTRE III & IV models work only with the basic

384

attributes (Level III). The criteria weights are directly defined by the EDMs accordingly with the

385

AHP and CDPC normalized weights (Table 9), because this approach (direct weight and decided

386

weights) is very easy in this case. The alternatives for the subjective criteria are evaluated by the

387

9 type Likert scale (like AHP, CDPC); by similar words in the AHP, CDPC, DEXi, evaluations

388

were done due to cognitive and linguistics reasons including: (1) fair, (2) little moderately good,

389

(3) moderately good, (4) little strongly good, (5) strongly good, (6) quite a lot strongly good, (7)

390

very strongly good, (8) little absolutely good, and (9) absolutely good. The objective criteria are

391

directly evaluated by using its own values. The threshold values are directly defined by the EDMs,

392

based on principles adopted in previous studies (Saracoglu, 2014a; 2014b). (Table 10). The ranks

393

are presented with all other methods as shown in Table 11.

394

The findings show that alternative ranks are very different for the AHP, CDPC, DEXi,

395

ELECTRE III and ELECTRE IV methods (Table 11). Under this condition, the ranks from 1 to 5

396

are directly selected in the AHP, CDPC, and ELECTRE IV methods for further investigation. The

397

ranks from 30 to 35 are directly eliminated in the AHP and CDPC methods. In the ELECTRE IV

398

method, the ranks from 14 to 19 are directly eliminated in this study. In the DEXi method, rank 1

17

399

is selected for further investigation, while rank 7 is eliminated directly in the current case. In the

400

ELECTRE III method, rank 1 is selected while rank 3 is eliminated for detailed investigations. It

401

is observed that the AHP and CDPC are the most discriminating methods (i.e. 35 ranks) in the

402

current case. The ELECTRE IV follows these methods in discriminative power, followed by DEXi

403

method. The poorest method according to the discriminating ability is the ELECTRE III. Property

404

of this discriminating power cannot be generalized, and hence defined only for this case.

405

Accordingly, the selected alternatives for the AHP, CDPC, DEXi, ELECTRE III and ELECTRE

406

IV methods, are Alternatives 13, 6, 18, 12, 1; Alternatives 13, 6, 18, 12, 1; Alternatives 1;

407

Alternatives 1, 17-35 and Alternatives 22-31, 33, 35 respectively.

408

In this work, when the preliminary screening project development stage is taken into

409

consideration, the selection rate should be increased and the elimination rate decreased. Hence, the

410

Alternatives with ranks 6 to 10 in the AHP and CDPC, rank 4 in the DEXi are also clustered in

411

one grouped for other research studies. The most contradictory findings are observed with the

412

selection and elimination issues and their corresponding sets of methods (AHP, CDPC, DEXi,

413

ELECTRE III and ELECTRE IV). Although, the AHP and CDPC methods give similar findings,

414

the others (i.e. DEXi, ELECTRE III and ELECTRE IV) are not supportive of these two methods.

415

A good examples is Alternative 6. It falls in the second rank in the AHP and CDPC, thus making

416

it to be in the selected set. However, it is in the seventh rank in DEXi, third rank in ELECTRE III

417

and nineteenth in ELECTRE IV. Practically, it is assumed that the findings should be very similar

418

with these methods, but in this case, the findings are very different even though all the methods,

419

their models, scales, evaluations and rules are satisfactory for the decision makers.

18

420

In view of these, the following procedural rules are thus defined in this study. The alternatives

421

in the first rank of the methods are placed in the pre-selection set and the most satisfactory ones

422

for the EDMs are selected for further investigations. Hence, the sets are:

423

Pre-selection = {A1, A6, A12, A13, A18} ⋃ {A1, A6, A12, A13, A18} ⋃ {A31} ⋃ {A1, A17,

424

A18, A19, A20, A21, A22, A23, A24, A25, A26, A27, A28, A29, A30, A31, A32, A33, A34,

425

A35} ⋃ {A1, A22, A23, A24, A25, A26, A27, A28, A29, A30, A31, A33, A35}.

426

Selection = {A1, A6, A12, A13, A18} ⋃ {A1, A6, A12, A13, A18} ⋃ {A31} ⋃ {A1, A17, A18,

427

A19, A20, A21, A22, A23, A24, A25, A26, A27, A28, A29, A30, A31, A32, A33, A34, A35} ⋃

428

{A1, A22, A23, A24, A25, A26, A27, A28, A29, A30, A31, A33, A35}.

429 430

4.

Conclusions and Recommendation

431

This work will thus be very helpful in selecting viable locations for siting very large power

432

plants with due consideration to environment, health (human, animal, plant), and climate change.

433

Moreover, this study would corroborate other previous work in wealth re-allocation and

434

sustainable lifestyle (sustainable world, materials and models) under climate change (see e.g.

435

Fenichel et al., (2016).

436

The methodologies adopted in this study would open doors to present some critical issues

437

to existing knowledge that may be adopted in solving other renewable energy problems and real-

438

life issues. For instance, detailed investigations of locations useful for agriculture such as in crop

439

cultivation, livestock production, wildlife and wild places priority regions etc.

440

The AHP and CDPC rankings are very close to each other in this study. The DEXi,

441

ELECTRE III and IV rankings spread out amongst the methods. Hence, a small procedural rule is

442

defined for the selection of candidate locations for detailed investigations. Several candidate

443

VLCSPP locations exist as alternatives with approximate local coordinates 13°38'55.37"N, 19

444

13°20'41.41"E and 13° 6'58.83"N, 13°26'53.63"E in Nigeria. These locations should be further

445

investigated at the investment stage.

446 447

5.

448

Favorable policies are fundamental to long-term sustainability of solar energy development

449

(Ohunakin et al., 2014). Ensuring that laws are stable and enforced is very vital as potential

450

investors will need reasonable certainty that key legislative provisions put in place for solar

451

activities will remain stable, unambiguous and enforced, thus allowing the continuity of

452

investment into the future (Ohunakin et al., 2014). The findings in this work will spur the

453

development of specific policies needed to maximize renewable power share on the grid, and to

454

develop and operate %100 renewable power grid system is very necessary. Other than supporting

455

domestic investors, the policies will also encourage foreign investment in renewable energy

456

deployment across regions. The findings will also ensure that key legislative provisions put in

457

place will encourage new investment models that will give opportunity to people, project

458

developers, private companies and so on, to invest in the renewable power plants. Furthermore,

459

through these policies, necessary financial assistance will be readily available to support renewable

460

energy development and deployment to the grid.

Policy Implications of Findings

461

To ensure the development of efficient policies, a computer-based collaborative system for

462

investments via the Anatolian Honeybees' Investment Decision Support System, with several

463

modules and having each module for a region will be needed for efficient data/ideas gathering,

464

evaluation, and execution in a collaborative and widespread way using computers.

465

Relevant stakeholders for the policies should include: government agencies/parastatals,

466

energy entities/organizations, civil society organizations, technical advisory committees on power,

20

467

domestic and foreign investors, and household renewable energy consumers. For effective

468

collaboration among stakeholders, the Anatolian Honeybees' Investment Decision Support System

469

should be encouraged. By this investment decision support system, policy, location and investment

470

alternatives and selection methods/factors, including stakeholders’ profiles, alternatives and

471

selection methods will be quickly processed.

472 473

Authors' contributions

474

One of the authors performs mainly the analyst role, the other author performs mainly the expert

475

decision maker role. They work together in an interactive way for exploring, arguing and building

476

the whole models. They are both able to respond the contextual questions of the ingredients of

477

these models.

478 479

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1 2 3

A Comparative Study of Selected Multi-Criteria Decision-Making Methodologies for Location Selection of Very Large Concentrated Solar Power Plants in Nigeria

4 5 6 7

Olayinka S. Ohunakin1, Burak Omer Saracoglu 2

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

1

The Energy and Environment Research Group (TEERG), Mechanical Engineering Department, Covenant University, P.M.B 1023, Ota, Ogun State, Nigeria.

2

Orhantepe Mahallesi, Tekel Caddesi, Geziyolu Sokak, 34865 Dragos, Kartal, Istanbul, Turkey

Abstract This work studies the location selection of very large concentrated solar power plants (VLCSPPs) in Nigeria using five Multi-Criteria Decision Making (MCDM) methodologies including: Analytic Hierarchy Process (AHP), Consistency-Driven Pairwise Comparisons (CDPC), Decision Expert for Education (DEXi), Elimination and Choice Translating Reality (ELECTRE) III and IV. A comparative investigation is performed on only one unique model that is structured in four levels. This model has nine basic factors (Direct Normal Irradiance, grid infrastructure, climatic conditions, water availability conditions, natural disaster/hazard conditions, topographical conditions, geological conditions, land use, allocation and availability, war, terror & security conditions) taken from previous factors selection studies. There are thirty-five alternatives for the VLCSPP locations in Nigeria for the pre-development investment stage and are presented on Google Earth file (GE). The Super Decisions, JConcluder, DEXi and ELECTRE III-IV software are mainly used in this study. The findings show that the AHP and CDPC rankings are very close to each other. On the other hand, the DEXi, ELECTRE III and IV rankings spread very much amongst the methods. Hence, a small procedural rule is defined for the selection of candidate locations for detailed investigations. Several candidates VLCSPP locations were found to exist as alternatives, with approximate local central coordinates of 13°38'55.37"N, 13°20'41.41"E and 13° 6'58.83"N, 13°26'53.63"E in Nigeria. These should be further investigated in the following investment stages.

29 30 31

Keyword: Very Large Concentrated Solar Power Plants, Multi-Criteria Decision Making, AHP, CDPC, DEXi, ELECTRE III and IV, Nigeria.

32 33 34 35 36 37

Corresponding author: E-mail address: [email protected] (OS Ohunakin); [email protected] (BO Saracoglu) 1

38

1.

Introduction

39

At present, about 10% of rural households and 30% of the total population of Nigeria have

40

access to electricity (Roadmap for Power Sector Reform, 2013). This made the country the third

41

largest without access to electricity. Most of the power generating plants (running on fossil fuels)

42

are located in the region of the country where abundant natural resources needed for their operation

43

exist (Figure 1). The vast fossil based energy sources has failed the country (Ohunakin, 2010);

44

harnessing the vast deposit of renewable energy sources may be a way out of the impending energy

45

crises. Among the renewable energy resources in vast deposit in the country, is the solar energy

46

from the Sun. It has been enjoying a very high-level utilization by rural dwellers for agricultural

47

processing in the country for decades (being the world's most abundant and permanent energy

48

source) (Ohunakin et al., 2014). It is vastly deposited with an estimated 17,459,215.2 million

49

MJ/day of solar energy falling on the country's 923,768 km2 land area (approximate range of 12.6

50

MJ/m2/day in the coastal region to about 25.2 MJ/m2/day in the far north) (NEP, 2003; REMP,

51

2005). The solar radiation distribution in the country is shown in Figure 2; five solar radiation

52

zones (I, II, III, IV and V), are defined and the irradiation ranges (needed for a particular project

53

selection and sizing), as distributed among the 36 States of the federation are listed in Table 1.

54

Based on the irradiation ranges (Table 1), every part of the country is found suitable for a

55

particular type of solar application: stand-alone solar photovoltaic (PV) systems to large scale solar

56

PV or Concentrated Solar Power (CSP) systems. Detailed information concerning the availability,

57

quality, reliability and dynamics of solar radiation in a particular area, is thus needed prior to the

58

siting of any of the solar energy systems (PV or CSP) for optimum performance, since the projects

59

require huge investment that will span several years. With the country's location on the equator,

60

concentrated solar power (CSP) is very viable due to the irradiation level (especially the high

2

61

Direct Normal Irradiance (DNI) found in Zones I, II and III). According to Habib et al., (2012)

62

and Ogunmodimu and Marquard (2013), an area is considered eligible for solar CSP application

63

when it receives minimum direct normal irradiance of 4.1 kWh/m2/day, with a land slope having

64

a threshold that excludes areas greater than 3o. Zones I, I and III, all in the Northern region of

65

Nigeria are endowed with DNI above 4.1 kWh/m2/day in addition to a relatively flat terrain; these

66

zones are thus considered suitable for CSP application. The potential capacity of CSP in states

67

within Zones I, II and III is shown in Table 2. It can further be observed from Table 2 that the total

68

potential capacity of CSP within the states is estimated at 427,829 MW while the electricity

69

potential is estimated at 26,841 TWh/yr (Habib et al., 2012; Nigeria Climate Change Assessment,

70

2011).

71

However, despite the abundant solar energy deposit in the country, solar applications and

72

utilization in Nigeria are majorly limited to small-scale and isolated applications. The existing

73

solar projects found in the country are listed in Ohunakin et al., (2014). This research study is thus

74

conducted to select the most appropriate locations in Nigeria suitable for the deployment of very

75

large concentrated solar power plants (1,000 MW ≤ installed power (Saracoglu, 2014)), that may

76

not only serve the national power grid, but also the Supergrids and Global Grid (e.g. European

77

Supergrid (The Friends of the Supergrid Working Group 2, 2016), African Supergrid, Global Grid

78

(Chatzivasileiadis, 2013)) in the future, using the Multi-Criteria Decision Making (MCDM)

79

technique. The Five (5) MCDM methods including: Analytic Hierarchy Process (AHP),

80

Consistency-Driven Pairwise Comparisons (CDPC), Decision Expert for Education (DEXi),

81

Elimination and Choice Translating Reality/Elimination Et Choix Tradusiant la Realite

82

(ELECTRE III, and ELECTRE IV) are concurrently applied with respect to concentrated solar

83

power plants (CSPP), renewable energy, national power grid of Nigeria and Supergirds/Global

3

84

Grid. This comparative research approach will hopefully help the country, and international

85

communities/organizations (e.g. United Nations, World Energy Council) to develop policies and

86

build a common framework through detailed research studies and coupled with investments in

87

these power plants. The findings of this research study are based on very large concentrated solar

88

power plant (VLCSPP).

89 90

2.

Literature Review & Methodology

91

A potential site for all CSP technologies (solar stirling engine, parabolic trough, parabolic

92

dish, tower, concentrated PV) needed for the Duqum Master Plan in Wilayat Duqum, Oman was

93

recommended in the work of Charabi and Gastli (2010). In Clifton and Boruff (2010), the CSP

94

potential in rural Australia was conducted. In the work of Azadeh et al., (2011), artificial neural

95

network (ANN) and fuzzy data envelopment analysis (FDEA) was adopted for the optimization of

96

solar plants' location. Several other works have been carried out on CSP using various techniques

97

(Noone et al., 2011; Dawson and Schlyter, 2012; Choudhary and Shankar, 2012; Merrouni et al.,

98

2014; Wu et al., 2014; Sanchez-Lozano et al., 2015). These works are further summarized in Table

99

3.

100

It was observed very clearly, that the adoption of MCDM methods, were not common in

101

most of the studies for the location selection of CSP plants (Table 3). This work thus contributed

102

to research field very clearly in the following areas: (1) global grid, (2) supergrid, (3) national grid,

103

(4) Africa, (5) Nigeria, (6) very large concentrated solar power plants (VLCSPP) (above 1000

104

MW), (7) preliminary screening project development stage, (8) Decision Expert for Education

105

(DEXi), (9) Consistency-Driven Pairwise Comparisons (CDPC), (10) Elimination and Choice

106

Translating Reality/Elimination Et Choix Tradusiant la Realite (ELECTRE) III, (11) ELECTRE

4

107

IV, and (12) comparative study of AHP, CDPC, DEXi, ELECTRE III, and ELECTRE IV

108

applications on a unique problem.

109

Although there are some comparative studies in literature using MCDM methodologies on

110

renewable energy projects, none dealt with comparative study using AHP, CDPC, DEXi,

111

ELECTRE III and ELECTRE IV methods for location selection problems of CSP plants. For

112

instance, in Sanchez-Lozano et. al., (2015), the best solar thermoelectric power plant locations was

113

determined using TOPSIS and ELECTRE-TRI on Geographic Information Systems. Furthermore,

114

in the work of Saracoglu (2014a; 2014b), the most preferable private small hydropower plant

115

investments in Turkey were investigated using AHP, ELECTRE III and ELECTRE IV methods.

116

Finally, as an example, Wood (2016) studied the solution of the supplier selection problem in the

117

petroleum industry using TOPSIS method. Hence, from all these studies, comparative studies

118

using the combination of these methodologies are not available in any research paper.

119 120

2.1

Analytic Hierarchy Process

121

Analytic Hierarchy Process (AHP) method was one of the most preferred MCDM

122

methodologies in literature (e.g. new shipbuilding yards’ location selection by Saracoglu (2013),

123

urban development planning by Minhas (2015)). Some of the important principles and terms in its

124

basic form were given in Tables 4-6 including number of judgments: n(n-1)/2 n matrix size,

125

"consistency", "inconsistency", "consistency index: CI or µ" (Equation 1), "consistency ratio: CR"

126

(Equation 2), "intransitive" as given by its developer and other theoretical contributors (Saaty,

127

1980; Saaty, 1987; Saaty 1990; Ishizaka and Nemery, 2013). This method was extensively

128

discussed in the work of Saaty (1990; 1977).

129 130

Consistency index (𝐶𝐼 𝑜𝑟 µ) was expressed in Saaty (1987; 1977) and shown in Equation (1) as: 5

𝜆𝑚𝑎𝑥 −𝑛

131

𝐶𝐼 𝑜𝑟 µ ≡

132

where n: n by n positive reciprocal matrix

133

Consistency ratio (𝐶𝑅) was as expressed in Saaty (1987; 1977) as:

134

𝐶𝑅 = 𝑅𝐼 and CR < 0.1 (see RI in Table 7)

(1)

𝑛−1

𝐶𝐼

(2)

135 136

2.2

Elimination and Choice Translating Reality (Elimination Et Choix Tradusiant la Realite)

137

Elimination and Choice Translating Reality (ELECTRE) III and ELECTRE IV methods

138

are one of the most recognized outranking or French (speaking) school approaches (Ishizaka and

139

Nemery, 2013). One of the first applications of ELECTRE III was also in the energy sector (in

140

nuclear power plant siting) by Roy and Bouyssou (1986). These two methodologies were also

141

applied in a private small hydropower plant investment selection problems by Saracoglu (2014a;

142

2014b). ELECTRE III and ELECTRE IV were respectively dated back to 1978 and 1982 in the

143

studies of Roy (1978), and Roy and Hugonnard (1982). One of the very interesting and powerful

144

foundations of ELECTRE III and IV was its "fuzzy binary outranking relations" (Figure 3).

145

Decision makers could handle vagueness, uncertainty, and hesitation by these methods' own

146

approaches and ways. Its crispness and fuzziness ("embedded") was like a very small part of fuzzy

147

set theory and logic developed by Zadeh (1965). Some of the important principles and terms in the

148

basic form of ELECTRE III and IV were "action" (e.g. a and b in Figures 3 and 4), "binary

149

outranking relation S" (Figure 3), "concordance index" (Figure 4), "credibility index",

150

"discordance index" (Figure 3), "equal merit: ex aequo", "incomparable: R" (non-reflexive &

151

symmetric), "indifference: I" (reflexive & symmetric), "indifference threshold: qj", "non-

152

discordance", "preference threshold: pj", "pseudo-criterion" (F = {g1, g2,……, gj,……, gn} where

153

n ≥ 3, gj on Figures 3 and 4), "pseudo-dominance relation 𝑆𝑝", "quasi-dominance relation Sq", 6

154

"sub-dominance relation 𝑆𝑠", "strict preference: P" (non-reflexive & asymmetric), "veto-

155

dominance relation 𝑆V", "veto threshold: vj", "weak preference: Q" (hesitation, non-reflexive &

156

asymmetric), "𝜆-cut level", "𝜆-strength", "𝜆-weakness" etc. (Saracoglu (2014a; 2014b), Roy and

157

Bouyssou, 1986; Roy, 1978; Roy and Hugonnard, 1982). These methods were extensively

158

discussed in the work of Thurstone, 1994 and Kulakowski, 2014.

159 160

2.3

Decision Expert for Education

161

Decision Expert for Education (DEXi) method was one of the interesting and easy MCDM

162

methods. It was grouped under both the full aggregation approaches and the outranking approaches

163

according to the decision-making model cases (see Bohanec et al., 2013). It was the latest

164

generation of DEX (Decision EXpert) method (expressed chronologically as DMP: Decision

165

Making Process, DECMAK: DECision MAKing, DEX, DEXi) (Bohanec et al., 2013; Efstathiou

166

and Rajkovic, 1979; Bohanec and Rajkovic, 1987). The foundation of this method was developed

167

in 1979 by Efstathiou and Rajkovic (1979) (also see (Bohanec et al., 2013)). This method was

168

directly influenced by, and based on the fuzzy set theory and logic developed by Zadeh (1965)

169

(see also (Bohanec et al., 2013)). It was also related to the probability theory (Bohanec et al., 1983).

170

DECMAK was first presented in 1983 in the work of Rajkovic et al., (1988). The methodology of

171

DEX in 1987 was based on multi-attribute decision making and expert systems (Bohanec and

172

Rajkovic, 1987; Bohanec and Rajkovic, 1990). DEX that run on disk operating system (DOS), was

173

presented in 1990 in the work of Bohanec and Rajkovic (1990). Finally, DEXi was implemented

174

in 2013 in Bohanec et al., (2013). DEXi method had already been applied in the renewable energy

175

sector for a private small hydropower plant investments selection problem in the work of Saracoglu

176

(2015). Some of the important principles and terms in the basic form of DEXi were "attribute

7

177

(parameter, criteria, variable)", "basic attribute (basic variable: leave)", "linguistic variable"

178

(Efstathiou and Rajkovic, 1979), "interval", "scale", "elementary decision rules", "tree of

179

attributes", "knowledge base" etc. (Bohanec et al., 2013; Efstathiou and Rajkovic, 1979; Bohanec

180

and Rajkovic, 1987). Extensive discussion of this method was given in the work of Bohanec and

181

Rajkovic, (1990) and Bohanec et al., (1983, 1987, 2013).

182 183

2.4

Consistency Driven Pairwise Comparisons

184

Consistency Driven Pairwise Comparisons (CDPC) method was proposed by Koczkodaj

185

in 1997 (Koczkodaj and Mackasey, 1997). It was fully based on pairwise comparisons (Koczkodaj

186

and Mackasey, 1997). It was grouped under goal, aspiration or reference level approaches (see

187

Ishizaka and Nemery (2013), on classification by Waldemar W. Koczkodaj). Several studies

188

clearly indicated that pairwise comparison approach was the oldest MCDM approach (Koczkodaj

189

and Mackasey, 1997; Kulakowski, 2014). Afterwards, the method referred to as ‘earliest pair−wise

190

comparison methodological form’ was introduced in its organized manner. This method became

191

known and well organized by Thurstone (1994). The definition of inconsistency was improved by

192

considering the triads of comparisons matrix elements in the work of Koczkodaj (1993). This is

193

indicated in Table 8.

194

The inconsistency measure for single triad (n=3) was defined as "the relative distance to

195

the nearest consistent basic reciprocal matrix represented by one of these three vectors for a given

196

metric" (Equation 3 and 4) (Koczkodaj, 1993; Koczkodaj and Szybowski, 2015).

197

𝐶𝑀 = 𝑚𝑖𝑛 (𝑎 |𝑎 − 𝑐 | , 𝑏 |𝑏 − 𝑎𝑐|, 𝑐 |𝑐 − 𝑎|)

198

𝐶𝑀 = 𝑚𝑎𝑥 {𝑚𝑖𝑛 (|1 − 𝑎𝑐| , |1 −

199

where CM was the consistency measure in Euclidean distance/metrics.

1

𝑏

1

1

𝑏

𝑎𝑐 𝑏

𝑏

(3)

|)}

(4)

8

200

The inconsistency measure for 𝑛 × 𝑛 (𝑛 > 2) reciprocal matrix was defined for each triad as

201

expressed in (Koczkodaj and Szybowski, 2015). The CDPC method was applied in solving

202

construction, mining and health problems (Koczkodaj and Trochymiak, 1996). Some important

203

principles and terms in the basic form of CDPC (different from common AHP terms) were "basic

204

reciprocal matrix", "triad-based inconsistency index", "inconsistent triads" (Koczkodaj and

205

Mackasey, 1997; Kulakowski, 2014; Koczkodaj, 1993; Koczkodaj and Szybowski, 2015). Further

206

discussion on this method was given in the work of Koczkodaj et al., (2014).

207

This research study was based on comparative analysis approach using AHP, CDPC,

208

DEXi, ELECTRE III, & ELECTRE IV as MCDM methods, because the VLCSPP investment

209

decisions on the Super-grid and the Global Grid perspective analysis could be very costly and

210

expensive during and after the decision analysis studies. The authors thought that this decision

211

analysis had to be a method-free analysis. Hence, the current problem had to be solved with as

212

many MCDM methods as possible in literature, in order to select the most suitable locations that

213

will be recommended for VLCSPP citing in Nigeria that will support connection with the Super-

214

grid and Global grid. It is believed that this comparative analysis will further help the Federal

215

government, and other international consortiums/organizations to make unbiased judgments and

216

evaluations, considering the many technical and social factors and alternatives. This research

217

approach will also play a key role in multinational and international conflict precaution, to manage

218

facts based on realistic mid- to long-term perspective, so as not to face any conflict related to these

219

large-scale and cost intensive investments.

220 221

3.

Models, Results and Discussion

9

222

The comparative research model (Figure 5) is built on the theoretical and practical

223

principles of the adopted methods (AHP, CDPC, DEXi, ELECTRE III, and ELECTRE IV), and

224

their cognitive and psychological principles and limitations (e.g. long and short-term memory

225

(Miller, 1956; Shiffrin and Nosofsky, 1994). The short descriptions of the framework of the study,

226

are as follows:

227 228

Level I (Goal): Location selection of very large concentrated solar power plants in Nigeria in the preliminary screening project development/investment stage.

229

Level II (Aggregate Main Criteria): There are three main factors for clustering purposes.

230

These include: C1: Technological, C2: Environmental, C3: Legal, Political, and Social. They are

231

not so important or influential as the factors (Level III), in the solution and the findings of this

232

problem, but they are very important and influential for structuring purposes (e.g. AHP, CDPC

233

and DEXi).

234

Level III (Basic Criteria): There are nine major factors in this problem. These are: C11: Direct

235

Normal Irradiance (DNI), C12: Grid Infrastructure (HVDC etc.), C13: Climatic conditions, C14:

236

Water availability conditions under C1, C21: Natural disaster/hazard conditions, C22: Topographical

237

conditions, C23: Geological conditions under C2, and C31: Land use, allocation and availability,

238

C32: War, terror & security conditions under C3 cluster. These basic criteria are based on some

239

previous works (Saracoglu 2014a; Saracoglu 2014b), and common views by the authors. These

240

basic criteria are appropriate for the VLCSPP preliminary screening of project investment stages,

241

and mainly for the Super-grid and the Global grid concepts. However, they can also be used for

242

the national grid and other grid applications, and for other project development stages with some

243

linguistic, semantic, syntax and scope changes. These factors considered, are as follows:

10

244

C11: Direct Normal Irradiance (DNI) (direct beam radiation/beam radiation) (objective

245

criterion, more is better ↑ ↑): This technology has only one source. The DNI in any location is

246

approximated by some equations (Habib et al., 2012). The accepted threshold DNI value for a

247

commercial CSP application varies between 1900 and 2100 kWh/m²/year (Habib et al., 2012).

248

Below this range, CSP developers suggest the use of solar photovoltaic systems as a better

249

technology, because of its economic implications (Habib et al., 2012). Hence, all the alternatives

250

in this study are in Zones I and II of Figure 1.

251

C12 (i.e. High-Voltage Alternating Current (HVAC) and High-Voltage Direct Current

252

(HVDC) electrification/power grid infrastructure (subjective criterion, more is better ↑ ↑)): The

253

current HVAC, HVDC and other high capacity power transmission infrastructures and their

254

expansion possibilities are very important in these kind of models and designs (FOSG 2014; Nabe

255

2011). All items related to this subject, such as losses, capacities, lengths and costs are taken into

256

consideration. The current transmission networks in Nigeria exist as 330 kV (4,889.2 km), and 132

257

kV (6,319.33 km). It also has 66 kV (62.5 km) and sub-stations that consist of 21No. 330/132 kV

258

with total capacity of about 6,098 MVA, 99 No. 132/33/11 kV with total capacity of about 8,107.5

259

MVA. From these, the transmission network is thus a weak link in the country's power sector. The

260

future possibilities of power grid extension are mainly evaluated in this study.

261

C13 (i.e. Climatic conditions (subjective criterion, more is better ↑↑)): All the factors related to

262

climatic conditions such as temperature, wind, and humidity are evaluated at once in this study.

263

The appropriate locations in Nigeria for siting a VLCSPP is the northern region (north-central,

264

north-east and north-west). This region is characterized by the Sahel climate or Tropical dry

265

climate. Annual total rainfalls are lower compared to the southern and central part of the country.

11

266

Rainy season in the northern part of Nigeria last for about three to four months (June–September).

267

The rest of the year is hot and dry, with temperatures climbing as high as 44 °C.

268

C14 (Water availability conditions (subjective criterion, more is better ↑↑)): This criterion is

269

very important for the cooling systems and the cleaning methods of mirrors in CSP technology;

270

the total investment and operational costs are closely related to this factor. Main water resources

271

are rivers, ground water aquifers, lakes, seas and oceans. The investigated alternative locations for

272

the VLCSPP in this study are endowed with large rivers (Niger, Benue, Shiroro, Hadejia, Sokoto,

273

Kaduna, and all other small rivers). These large rivers provide abundant water resources for the

274

cooling and cleaning purposes of VLCSPPs. In locations that are far from these watersheds, dry

275

cooling could be an option; however, this may reduce the efficiency of the plant thereby raising

276

the capital cost of the plant (Habib et al., 2012).

277

C21 (Natural disaster/hazard conditions (subjective criterion, more is better ↑ ↑)): The natural

278

disasters and hazardous conditions such as earthquakes, floods, landslides, sea-level rise, and

279

sandstorms are evaluated under this factor. If some indexes (e.g. Natural Disasters Index 2010)

280

can be found, then this criterion can be revised as an objective factor. Other than mild sandstorm

281

that seldom occur, the alternative locations in this study did not have any major history of natural

282

disasters.

283

C22 (Topographical conditions (subjective criterion, more is better ↑ ↑)): The surface land

284

shapes, elevation differences, and field/land slopes are very important for this technology due to

285

the electricity generation and the total cost of power plants. Many researchers and engineers in

286

practical applications, accept the maximum threshold value as 5% for this technology (maximum

287

ground slope of 5 %) (see Clifton and Boruff, 2010). The topography of the northern region in

288

Nigeria is generally flat, with slopes that is less than 3%.

12

289

C23 (Geological conditions (subjective criterion, more is better ↑ ↑)): The geological conditions

290

are mainly important for the power blocks, and the thermal storage area in this technology. The

291

geological conditions of the power blocks, the solar fields and the thermal storages affect the total

292

cost.

293

C31 (Land use, allocation and availability (subjective criterion, more is better ↑ ↑)): The

294

VLCSPPs need vast land. Previous work on land/area requirements of the CSP technology have

295

shown different values of approximately 20,000–30,000 m2/MW, 40,000 m2/MW and 50,000

296

m2/MW (see Clifton and Boruff, 2010; Dawson and Schlyter, 2012). Fortunately, land area in the

297

alternatives locations is appreciably large in all the regions. An approximate land area of 720,801

298

km2 exists in the North-Central, North-West and North-East geopolitical zones (that constitute the

299

northern region of Nigeria), and with a very high sunshine insolation.

300

C32 (Political, war, terror and security conditions (subjective criterion, more is better ↑ ↑)):

301

The most controversial factor may be war, terror, security conditions and political conflict issues

302

in this subject. Power plant equipment and components may be rehabilitated and replaced

303

considering the useful life of the systems, but the sites will be used by the power plants for

304

electricity generation for a life time. Hence, this factor should thus be studied very carefully in the

305

mid- to long-term perspectives. Some alternative locations that are very suitable for the

306

development of solar energy system in Nigeria (especially the North-East region) had played host

307

to insurgency in the past. Insecurity has affected power plant constructions and other

308

infrastructures through kidnaps and killing of workers in various parts of the country (Ohunakin,

309

2010). General insecurity of solar infrastructures, especially in the northern region where there is

310

abundant solar insolation can be a potential threat that will stall future investment in large scale

311

grid-connected solar infrastructure (Ohunakin, 2010).

13

312

The data and information related to these factors are gathered from several sources including

313

website directories (internet links could be supplied upon request), and more importantly, from

314

decision makers/experts (deep local knowledge).

315

The alternatives are defined very carefully on Google Earth Pro 7.1.5.1557 (GE). This is

316

sufficient and very helpful as a geographical information system (GIS) software, needed for

317

screening at the preliminary project development/investment stage. The GIS data and files for each

318

factor are found and opened layer by layer on each embedded layer on the GE (e.g. Borders and

319

Labels, Roads on the GE layers, and Protected Areas by the International Union for Conservation

320

of Nature and Natural Resources (IUCN), and the United Nations Environment Programme

321

(UNEP) on the ProtectedPlanet.net as WDPA_Feb2016_NGA-kml.kml file; see Figure 6). All the

322

layers are studied one after the other, and very carefully overlayed in multiple on the GE. Some

323

additional maps and tools were also used on few cases, to incorporate other additional data and

324

information. Finally, thirty-five (35) candidate VLCSPP locations are defined with their borders

325

and areas around the center coordinates of the sites. These alternatives are planned to be

326

investigated and evaluated at the same time, within one alternative cluster/group.

327

The current AHP, CDPC, DEXi, ELECTRE III, and ELECTRE IV models are constructed on

328

their separate software and tools. The outcome of these models does not aim at helping the decision

329

maker to discover a good approximation of a decision that would objectively be one of the best by

330

taking into account their own value system, but rather to provide the decision maker with a set of

331

recommendations derived from the reasoning modes and working hypotheses (Figueira et al.,

332

2013).

333

The current models are built and the findings were gathered by using the Super Decisions

334

Software Version 2.4.0 on Windows 10 (AHP) (AHP, 2016), the ELECTRE 34 version 3x on

14

335

Windows XP (ELECTRE III and IV) (ELECTRE, 2016), the DEXi Version 5.00 on Windows 10

336

(DEXi, 2016), and the JConcluder Version 33301 on Windows 10 (Java) (CDPC) [96]. All were

337

done according to the limitations of the tools (see Figure 7).

338

The criteria in the AHP model are compared based on the cognitive, linguistic, or verbal

339

evaluation statements related to the fundamental scale and Likert type scale (Saaty, 1980; Saaty,

340

2008). The scale (1 to 9 point scale) is applied and evaluated on the Microsoft Office Excel and

341

Apache OpenOffice Calc (Apache, 2016) (Figure 8). The evaluations of alternatives were made

342

by the general pairwise comparison approach at first, and then converted into the ratings scale

343

using the Microsoft Office Excel or the Apache OpenOffice Calc., with the AHP consistency check

344

and the normalized weight priorities. The ratings scale must be used in this case because of the

345

limitations of the software/tool (e.g. maximum 7 to 8 child nodes possible from parent node). The

346

original evaluations are not so consistent as the improved evaluations, so that the latest improved

347

evaluations were considered.

348

The CDPC evaluations are very similar to the AHP evaluations. The factors and the

349

alternatives are evaluated according to their relative importance/value with each other (how much

350

'a' is important to 'b'? 'a' is 4 times more important than 'b'). The CDPC model and calculations

351

were done according to the available software/tool limitations (e.g. minimum 3 child nodes

352

necessary, maximum 7 child nodes possible from parent node). It cannot work beyond its

353

limitations. For instance, if there are more than 8 nodes (9, 10, 11, ... 35, etc.), then the model

354

should be split into parts. If there are 2 nodes, then the model does not work, but it should be

355

remembered that 2 nodes are always fully consistent. The goal with C1, C2, C3 reaches its most

356

consistent (TRIAD) evaluations in four steps (inconsistency1: 0.4099999964237213;

357

inconsistency2: 0.28999999165534973; inconsistency3: 0.17000000178813934; inconsistency4:

15

358

0.09000000357627869) (Figure 9). The evaluations with respect to C1 have two steps i.e.

359

inconsistency1: 0.5; inconsistency2: 0.12999999523162842. Finally, the evaluations with respect

360

to C2 have only one step i.e. inconsistency1: 0.11999999731779099. In this study, the evaluations

361

of alternatives are not made in a similar manner in 8 by 8 alternative groups, instead the previous

362

rankings were used for direct evaluations; only factors were analyzed with CDPC in the current

363

study. The Super Decisions was thereafter used for ranking, without any recourse to Super

364

Decision's consistency check.

365

The AHP and the CDPC evaluations are shortly compared in an organized manner (Table

366

9). The natural disaster/hazard condition and the geological condition factors take the minimum

367

weight (0.01) in the AHP evaluations. The geological condition factor takes the minimum weight

368

(0.0115) in the CDPC evaluations. The most important factor in the AHP evaluations is the Direct

369

Normal Irradiance (DNI), and the war, terror and security condition factors (0.13). The maximum

370

weight in the CDPC evaluations is 0.1655 for the war, terror and security condition factors. The

371

important order of the factors in the AHP evaluations and the CDPC evaluations are C32, C11, C12,

372

C13, C31, C14, C22, C21, C23 and C32, C11, C12, C31, C13, C14, C22, C21, C23 respectively. These orders

373

are almost the same, but the criteria weights are different for the AHP and the CDPC.

374

The criteria structure, scale and rules in the DEXi model are based on the cognitive, linguistic,

375

or verbal evaluation limitations of the method. Generally, two to four descendants for each

376

aggregate node is good for a DEXi model. Moreover, the basic attributes should have the least

377

number of distinguishable values in scale. The scale should gradually be increased on each

378

aggregate attribute on DEXi models. Three (3) point scale (poor, fair, good) is applied and

379

evaluated in the basic attributes (Level III). The Five (5) point scale (very poor, poor, fair, good,

380

very good) is applied and evaluated in the aggregate main attributes (Level II). The Seven (7) point

16

381

scale (extremely poor, very poor, poor, fair, good, very good, extremely good) is applied and

382

evaluated with the goal (Level I). The evaluations of options are made on Microsoft Office Excel

383

(Apache OpenOffice Calc) using the AHP and CDPC evaluations. All the rules are defined directly

384

on the software/tool after which the utility functions are gathered by the tool (Figure 10). The

385

options are selected and made on the "Evaluation" tab. Finally, the findings are gathered and

386

studied in this work.

387

The current ELECTRE III & IV model structures are based on the limitations of the tool (i.e.

388

AHP, CDPC, DEXi). In this study, the ELECTRE III & IV models work only with the basic

389

attributes (Level III). The criteria weights are directly defined by the EDMs accordingly with the

390

AHP and CDPC normalized weights (Table 9), because this approach (direct weight and decided

391

weights) is very easy in this case. The alternatives for the subjective criteria are evaluated by the

392

9 type Likert scale (like AHP, CDPC); by similar words in the AHP, CDPC, DEXi, evaluations

393

were done due to cognitive and linguistics reasons including: (1) fair, (2) little moderately good,

394

(3) moderately good, (4) little strongly good, (5) strongly good, (6) quite a lot strongly good, (7)

395

very strongly good, (8) little absolutely good, and (9) absolutely good. The objective criteria are

396

directly evaluated by using its own values. The threshold values are directly defined by the EDMs,

397

based on principles adopted in previous studies (Saracoglu, 2014a; 2014b). (Table 10). The ranks

398

are presented with all other methods as shown in Table 11.

399

The findings show that alternative ranks are very different for the AHP, CDPC, DEXi,

400

ELECTRE III and ELECTRE IV methods (Table 11). Under this condition, the ranks from 1 to 5

401

are directly selected in the AHP, CDPC, and ELECTRE IV methods for further investigation. The

402

ranks from 30 to 35 are directly eliminated in the AHP and CDPC methods. In the ELECTRE IV

403

method, the ranks from 14 to 19 are directly eliminated in this study. In the DEXi method, rank 1

17

404

is selected for further investigation, while rank 7 is eliminated directly in the current case. In the

405

ELECTRE III method, rank 1 is selected while rank 3 is eliminated for detailed investigations. It

406

is observed that the AHP and CDPC are the most discriminating methods (i.e. 35 ranks) in the

407

current case. The ELECTRE IV follows these methods in discriminative power, followed by DEXi

408

method. The poorest method according to the discriminating ability is the ELECTRE III. Property

409

of this discriminating power cannot be generalized, and hence defined only for this case.

410

Accordingly, the selected alternatives for the AHP, CDPC, DEXi, ELECTRE III and ELECTRE

411

IV methods, are Alternatives 13, 6, 18, 12, 1; Alternatives 13, 6, 18, 12, 1; Alternatives 1;

412

Alternatives 1, 17-35 and Alternatives 22-31, 33, 35 respectively.

413

In this work, when the preliminary screening project development stage is taken into

414

consideration, the selection rate should be increased and the elimination rate decreased. Hence, the

415

Alternatives with ranks 6 to 10 in the AHP and CDPC, rank 4 in the DEXi are also clustered in

416

one grouped for other research studies. The most contradictory findings are observed with the

417

selection and elimination issues and their corresponding sets of methods (AHP, CDPC, DEXi,

418

ELECTRE III and ELECTRE IV). Although, the AHP and CDPC methods give similar findings,

419

the others (i.e. DEXi, ELECTRE III and ELECTRE IV) are not supportive of these two methods.

420

A good examples is Alternative 6. It falls in the second rank in the AHP and CDPC, thus making

421

it to be in the selected set. However, it is in the seventh rank in DEXi, third rank in ELECTRE III

422

and nineteenth in ELECTRE IV. Practically, it is assumed that the findings should be very similar

423

with these methods, but in this case, the findings are very different even though all the methods,

424

their models, scales, evaluations and rules are satisfactory for the decision makers.

18

425

In view of these, the following procedural rules are thus defined in this study. The alternatives

426

in the first rank of the methods are placed in the pre-selection set and the most satisfactory ones

427

for the EDMs are selected for further investigations. Hence, the sets are:

428

Pre-selection = {A1, A6, A12, A13, A18} ⋃ {A1, A6, A12, A13, A18} ⋃ {A31} ⋃ {A1, A17,

429

A18, A19, A20, A21, A22, A23, A24, A25, A26, A27, A28, A29, A30, A31, A32, A33, A34,

430

A35} ⋃ {A1, A22, A23, A24, A25, A26, A27, A28, A29, A30, A31, A33, A35}.

431

Selection = {A1, A6, A12, A13, A18} ⋃ {A1, A6, A12, A13, A18} ⋃ {A31} ⋃ {A1, A17, A18,

432

A19, A20, A21, A22, A23, A24, A25, A26, A27, A28, A29, A30, A31, A32, A33, A34, A35} ⋃

433

{A1, A22, A23, A24, A25, A26, A27, A28, A29, A30, A31, A33, A35}.

434 435

4.

Conclusions and Recommendation

436

This work will thus be very helpful in selecting viable locations for siting very large power

437

plants with due consideration to environment, health (human, animal, plant), and climate change.

438

Moreover, this study would corroborate other previous work in wealth re-allocation and

439

sustainable lifestyle (sustainable world, materials and models) under climate change (see e.g.

440

Fenichel et al., (2016).

441

The methodologies adopted in this study would open doors to present some critical issues

442

to existing knowledge that may be adopted in solving other renewable energy problems and real-

443

life issues. For instance, detailed investigations of locations useful for agriculture such as in crop

444

cultivation, livestock production, wildlife and wild places priority regions etc.

445

The AHP and CDPC rankings are very close to each other in this study. The DEXi,

446

ELECTRE III and IV rankings spread out amongst the methods. Hence, a small procedural rule is

447

defined for the selection of candidate locations for detailed investigations. Several candidate

448

VLCSPP locations exist as alternatives with approximate local coordinates 13°38'55.37"N, 19

449

13°20'41.41"E and 13° 6'58.83"N, 13°26'53.63"E in Nigeria. These locations should be further

450

investigated at the investment stage.

451 452

5.

453

Favorable policies are fundamental to long-term sustainability of solar energy development

454

(Ohunakin et al., 2014). Ensuring that laws are stable and enforced is very vital as potential

455

investors will need reasonable certainty that key legislative provisions put in place for solar

456

activities will remain stable, unambiguous and enforced, thus allowing the continuity of

457

investment into the future (Ohunakin et al., 2014). The findings in this work will spur the

458

development of specific policies needed to maximize renewable power share on the grid, and to

459

develop and operate %100 renewable power grid system is very necessary. Other than supporting

460

domestic investors, the policies will also encourage foreign investment in renewable energy

461

deployment across regions. The findings will also ensure that key legislative provisions put in

462

place will encourage new investment models that will give opportunity to people, project

463

developers, private companies and so on, to invest in the renewable power plants. Furthermore,

464

through these policies, necessary financial assistance will be readily available to support renewable

465

energy development and deployment to the grid.

Policy Implications of Findings

466

To ensure the development of efficient policies, a computer-based collaborative system for

467

investments via the Anatolian Honeybees' Investment Decision Support System, with several

468

modules and having each module for a region will be needed for efficient data/ideas gathering,

469

evaluation, and execution in a collaborative and widespread way using computers.

470

Relevant stakeholders for the policies should include: government agencies/parastatals,

471

energy entities/organizations, civil society organizations, technical advisory committees on power,

20

472

domestic and foreign investors, and household renewable energy consumers. For effective

473

collaboration among stakeholders, the Anatolian Honeybees' Investment Decision Support System

474

should be encouraged. By this investment decision support system, policy, location and investment

475

alternatives and selection methods/factors, including stakeholders’ profiles, alternatives and

476

selection methods will be quickly processed.

477 478 479 480

Acknowledgements The authors sincerely express their deepest appreciation to Dr. Marko Bohanec, Dr. Waldemar W

481

Koczkodaj, Dr. Bernard Roy and Mrs. Rozann W. Saaty for guidance and support.

482 483

Authors' contributions

484

One of the authors performs mainly the analyst role, the other author performs mainly the expert

485

decision maker role. They work together in an interactive way for exploring, arguing and building

486

the whole models. They are both able to respond the contextual questions of the ingredients of

487

these models.

488 489

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Table

Click here to download Table Tables.docx

Tables

Table 1: Solar radiation zones (global horizontal irradiation) Zones kWh/m² h/d kWh/m²/yr States 6.5-7.5 8.5 2186 Sokoto, Borno, Yobe, Jigawa, Kano, Kaduna, Bauchi, Gombe, Zone I and Katsina 5.7-6.5 7.0 2006 Zamfara, Kebbi, Niger, Plateau, Adamawa, Nasarawa, Taraba, Zone II and Katsina 5.0-5.7 6.5 1822 Kwara, Abuja, some section of Abuja, Niger and Plateau Zone III 4.6-5.0 5.0 1700 Oyo, Osun, Ekiti, Kogi, Benue Zone IV ˂4.1 3.5˂5.0 1500 Rivers, Bayelsa, Cross Rivers Zone V

Table 2: Estimated Solar CSP potential of selected 10 states in Nigeria. Selected States DNI Area DNI Area with Eligible (Km2) Slope < 3% (Km2)

Potential Potential CSP Electricity Capacity (MW) Production TWh/Year 50% 9,489 474 857 14,233 Adamawa 100% 14,459 722 1,306 21,688 Bauchi 100% 65,490 3,274 6,991 98,235 Borno 95% 13,245 662 1,196 19,867 Gombe 60% 11,239 561 1,015 16,858 Jigawa 50% 11,054 552 998 16,581 Kaduna 100% 16,311 815 1,473 24,466 Kano 90% 17,151 857 1,549 25,727 Katsina 60% 17,433 871 1,574 26,150 Kebbi 50% 25,222 1,261 2,278 37,834 Niger 80% 16,984 849 1,534 25,477 Plateau 50% 11,251 562 1,016 16,876 Sokoto 80% 32,313 1,615 2,919 48,470 Yobe 100% 23,566 1,178 2,128 35,350 Zamfara 26,841 427,820 TOTAL Source: Habib et al., (2012); Nigeria Climate Assessment, preliminary report (World Bank/Lumina Decision) (2011). NOTE: (1) Potential is 5% of eligible area and, (2) Capacity estimated at 50MW/km2

Table 3: Summary of previous studies (N/A: not applicable or not applied) Project CSP technology Plant Size/ MCDM Stage Capacity All CSP & CPV 100 MW N/A Town Master Plan N/A

All

N/A

N/A

N/A

All

N/A

(1) artificial neural network (ANN) (2) fuzzy data envelopment analysis (FDEA)

R&D

Power Tower (CSPonD Concept)

4 MWe

N/A

N/A

All

N/A

(1) revised procedure

N/A

All

total 1000 MW capacity at each location

(1) Social, Technical, Economical, Environmental, And Political (STEEP) (2) fuzzy Analytic Hierarchy Process (AHP) (3) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)

Simos’

Criteria/Factor/Attribute (1) yearly solar radiation (2) topologies of the land areas (2.1) slope (%) (3) proximity to the sea (1) monthly average DNI (2) land suitability (3) land capability (1) population and human labor (2) distance of power distribution networks (3) land cost (4) solar global radiation (5) ıntensity of natural disasters occurrence (6) quantity of proper geological area (7) quantity of available water (8) quantity of proper topographical areas (1) average annual cosine efficiency (2) shading (3) blocking (4) average annual normal insolation (1) DNI (2) ground slope (3) water supply (4) land availability (5) access to network infrastructure (6) availability of auxiliary fuel supply (7) load (1) Cost (1.1) Land acquisition cost (1.2) Resettlement and rehabilitation cost (1.3) Infrastructure cost (2) Availability of resources (2.1) Land availability (2.2) Water availability (2.3) Fuel/Coal availability (2.4) Skilled manpower availability (3) Accessibility (3.1) Transmission grid accessibility (3.2) Electricity consumption point (3.3) Road/Rail/Airport accessibility (3.4) Urban area accessibility (4) Biological environment (4.1) Land cover and land use (4.2) Water bodies (4.3) Population centre

N/A

All

N/A

N/A

N/A

All

10 MW

(1) linguistic integral

Choquet

(5) Physical environment (5.1) Topography (5.2) Geology and soil type (5.3) Climate (6) Socio-economic development (6.1) Effect on agriculture, employment and tourism (6.2) Effect on economic progress of surrounding region (6.3) Possibility of capacity expansion in future (1) annual DNI (2) elevations (3) land exclusion (3.1) infrastructure (3.1.1) roads (3.1.2) power grid (3.1.3) railway (3.1.4) cities (3.2) vegetation (3.2.1) forests (3.2.2) protected areas (3.2.3) reforestations (3.3) hydrology (3.3.1) dams (3.3.2) waterways permanents (3.3.3) waterways non permanents (3.4) terrain (3.4.1) slopes (1) energy factor (1.1) sunshine time (1.2) DNI (2) ınfrastructure factor (2.1) water supply (2.2) transportation conditions (2.3) grid connected distance (3) land factor (3.1) land cost (3.2) soil structure and the geology (4) environmental factor (4.1) ecological environment influence (4.2) energy-saving benefit: standard coal (4.3) pollutant emission reduction benefits (5) social factor (5.1) impact on the local economy (5.2) local government support (5.3) public support

Table 4: Fundamental scale (Source: Saaty, 1980; Saaty, 1987; Saaty, 1990) Intensity of Definition Explanation importance on an absolute scale Equal importance Two activities contribute equally to the 1 objective Moderate importance of one over another Experience and judgment strongly favor one 3 activity over another Essential or strong importance Experience and judgment strongly favor one 5 activity over another Very strong importance An activity is strongly favored and its 7 dominance demonstrated in practice Extreme importance The evidence favoring one activity over 9 another is of the highest possible order of affirmation Intermediate values between the two When compromise is needed 2,4,6,8 adjacent judgments If activity i has one of the above numbers Reciprocals assigned to it when compared with activity j, then j has the reciprocal value when compared with i Ratios arising from the scale If consistency were to be forced by obtaining Rationals n numerical values to span the matrix

....

....

....

....

....

Table 5: Pair-wise comparisons matrix (Source: Saaty, 1990; Ishizaka and Nemery, 2013) A1 A2 ........... An A1 w1/w1 w1/w2 ........... w1/wn A2 w2/w1 w2/w2 ........... w2/wn A = An wn/w1 wn/w2 ........... wn/wn where A1, A2, .....An: objects, their respective weights: w1, w2,.......,wn

1/ α1n

1/ α2n

...........

αij

...........

1/αin

....... ...........

αnj

...........

1

.......

1/ α2j

.......

1/ α1j

.......

=

.......

[αij]

.......

=

.......

A

.......

Table 6: Positive and reciprocal matrices comparison matrix (Source: Alonso and Lamata, 2006) 1 α12 ........... α1j ........... α1n 1/ α12 1 ........... α2j ........... α2n

where αij=wi/wj

Table 7: Random consistency index (RI) (Source: Saaty, 1980) n 1 2 3 4 5 6 7 8 0 0.58 0.90 1.12 1.24 1.32 1.41 RI 0

9 1.45

10 1.49

11 1.52

12 1.54

13 1.56

14 1.58

15 1.59

Table 8: Reciprocal pairwise comparison matrix (similar to Table 5) (source: Koczkodaj, 1993)

R3(a,b,c)

=

1

a

b

1/a

1

c

1/b

1/c

1

where R3: basic reciprocal matrix (3×3 dimensional, n=3), R1: trivial case (1×1 dimensional), R2: always consistent (2×2 dimensional).

Table 9: Comparison of AHP vs CDPC findings Weight (Normalized) Consistency* Criteria AHP CDPC AHP CDPC (Triad) C1: Mainly Technological Essential 0,28 0,2345 Goal 0,05156 0,09000000357627869 C11: Direct Normal Irradiance (DNI) 0,13 0,1170 C1 0,08815 0,12999999523162842 C12: Grid Infrastructure (HVDC etc.) 0,07 0,0590 C2 0,05156 0,11999999731779099 C13: Climatic conditions 0,05 0,0360 C3 0,00000 0,00000 C14: Water availability conditions 0,03 0,0225 C2: Mainly Environmental Essential 0,04 0,0455 C21: Natural disaster/hazard conditions 0,01 0,0145 C22: Topographical conditions 0,02 0,0190 C23: Geological conditions 0,01 0,0115 C3: Mainly Legal, Political, & Social 0,18 0,2200 Essential C31: Land use, allocation and 0,04 0,0545 availability C32: War, terror & security conditions 0,13 0,1655 * AHP consistency is preferred to be less than 0,10; CDPC consistency is preferred to be minimum

Table 10: The threshold values Criteria C11: Direct Normal Irradiance (DNI) C12: Grid Infrastructure (HVDC etc.) C13: Climatic conditions C14: Water availability conditions C21: Natural disaster/hazard conditions C22: Topographical conditions C23: Geological conditions C31: Land use, allocation and availability C32: War, terror & security conditions Note: ″ same for all

Preference direction Max Max

Weight

0,09 0,09 0,02

Indifference 𝑞𝑗 121 moderately good ″ ″ ″

Preference 𝑝𝑗 122 strongly good ″ ″ ″

Veto v𝑗 123 very strongly good ″ ″ ″

Max Max Max Max Max Max

0,06 0,02 0,09

″ ″ ″

″ ″ ″

″ ″ ″

Max

0,23

″

″

″

0,26 0,14

Table 11: Comparison of AHP, CDPC, DEXi, ELECTRE III & ELECTRE IV findings Rankings In Each Method Alternatives (A) AHP CDPC DEXi ELECTRE III ELECTRE IV Alternative 1 5 5 Fair (4) 1 10 Alternative 2 6 6 Extremely Poor (7) 3 19 Alternative 3 33 33 Extremely Poor (7) 3 19 Alternative 4 24 23 Very Poor (6) 3 18 Alternative 5 28 28 Very Poor (6) 3 17 Alternative 6 2 2 Extremely Poor (7) 3 19 Alternative 7 23 27 Very Poor (6) 3 18 Alternative 8 8 7 Extremely Poor (7) 3 19 Alternative 9 13 13 Extremely Poor (7) 3 16 Alternative 10 20 20 Very Poor (6) 2 14 Alternative 11 26 25 Poor (5) 2 13 Alternative 12 4 4 Poor (5) 2 12 Alternative 13 1 1 Poor (5) 2 15 Alternative 14 7 8 Very Poor (6) 2 14 Alternative 15 35 35 Very Poor (6) 2 14 Alternative 16 10 10 Poor (5) 2 11 Alternative 17 16 16 Fair (4) 1 8 Alternative 18 3 3 Fair (4) 1 7 Alternative 19 15 15 Fair (4) 1 6 Alternative 20 34 34 Fair (4) 1 6 Alternative 21 21 21 Fair (4) 1 6 Alternative 22 25 24 Fair (4) 1 2 Alternative 23 29 29 Fair (4) 1 2 Alternative 24 30 30 Fair (4) 1 4 Alternative 25 19 19 Fair (4) 1 4 Alternative 26 27 26 Fair (4) 1 4 Alternative 27 31 31 Fair (4) 1 3 Alternative 28 32 32 Fair (4) 1 3 Alternative 29 12 12 Fair (4) 1 4 Alternative 30 22 22 Fair (4) 1 3 Alternative 31 11 11 Extremely Good (1) 1 1 Alternative 32 18 18 Poor (5) 1 9 Alternative 33 14 14 Fair (4) 1 5 Alternative 34 17 17 Poor (5) 1 9 Alternative 35 9 9 Fair (4) 1 5 Note: Greenish: Selected for further investigation, Yellowish: Preferable to added to further investigation, Reddish: Eliminated in this stage, DEXi no good and very good results hence fair in yellowish

Figure

Click here to download Figure Figures.docx

Figures

Figure 1: Map of the existing power generating units connected to the grid (REMP, 2005)

Zone I Zone II

Zone III

Zone IV

Zone V

Figure 2: Map showing the Direct Normal Irradiance for Nigeria [Solargis, 2018]

Figure 3: Pseudo-criterion preference situations and partial binary outranking relations (drawn and generated based on [Figueira et al., 2013; Saracoglu, 2013; Saracoglu, 2014])

Figure 4: Concordance index and (drawn and generated based on [Saracoglu, 2015)

Figure 5: Research framework (note: ELECTRE III & IV fully works with only Level III, DEXi & AHP fully works with all levels, CDPC partially works in current study)

Figure 6: Protected Areas (left), Alternatives (right) (e.g. A01: around Latitude 13°38'55.37" N & Longitude 13°20'41.41"E; A02: around Latitude 13°35'13.33"N & Longitude 13°19'58.59"E; open electronic supplementary material Saracoglu & Ohunakin 2016.kmz or kml)

(a)

(b)

(c) (d) Figure 7: Developed models from (a) AHP, (b) ELECTRE III & ELECTRE IV, (c) DEXi and (d) CDPC softwares

Figure 8: Final evaluation (1st evaluation on 02/04/2016, 2nd/final evaluation on 03/04/2016 for consistency improvements)

Figure 9: Evaluations for CDPC

Figure 10: DEXi parametric point by point graphical representation of the GOAL utility function (C 1−C2 on the left, C1−C3 at the middle, C2−C3 on the right)