On the effect of geographical, topographic and climatic conditions on feed-in tariff optimization for solar photovoltaic electricity generation: A case study in Iran

Journal Pre-proof On the effect of geographical, topographic and climatic conditions on feed-in tariff optimization for solar photovoltaic electricity generation: A case study in Iran Hamzeh Karimi Firozjaei, Mohammad Karimi Firozjaei, Omid Nematollahi, Majid Kiavarz, Seyed Kazem Alavipanah PII:

S0960-1481(20)30149-X

DOI:

https://doi.org/10.1016/j.renene.2020.01.127

Reference:

RENE 12990

To appear in:

Renewable Energy

Received Date: 7 May 2019 Revised Date:

17 October 2019

Accepted Date: 26 January 2020

Please cite this article as: Firozjaei HK, Firozjaei MK, Nematollahi O, Kiavarz M, Alavipanah SK, On the effect of geographical, topographic and climatic conditions on feed-in tariff optimization for solar photovoltaic electricity generation: A case study in Iran, Renewable Energy (2020), doi: https:// doi.org/10.1016/j.renene.2020.01.127. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

On the Effect of Geographical, Topographic and Climatic Conditions on Feed-in Tariff Optimization for Solar Photovoltaic Electricity Generation: A Case Study in Iran Hamzeh Karimi Firozjaei1, Mohammad Karimi Firozjaei2, Omid Nematollahi3, Majid Kiavarz*2, Seyed Kazem Alavipanah2 1

Department of Economics, University of Mazandaran, Mazandaran, Iran

2

Department of Remote Sensing and GIS, University of Tehran, Tehran, Iran

3

Mechanical Engineering Department, Isfahan University of Technology

(*Corresponding Author: [email protected] )

On the Effect of Geographical, Topographic and Climatic Conditions on Feed-in Tariff Optimization for Solar Photovoltaic Electricity Generation: A Case Study in Iran

1 2 3 4

Abstract

5

One of the important factors of sustainable development is renewable energies

6

penetration in the energy systems. The present study evaluates the optimum feed-in

7

tariff of photovoltaic electricity production based on the available downward solar

8

radiation potential of each province of Iran while this potential calculated

9

considering geographical, topographic and climatic conditions. For downward solar

10

radiation modeling a set of mathematical, geometric and spatial models were used.

11

Also, the net present value model was applied for evaluates the optimum feed-in

12

tariff of photovoltaic electricity production. The results showed that downward solar

13

radiation potential varies from 380 to 578 W m

-2

across the Iran country.

14

Furthermore, the optimal photovoltaic electricity generated feed-in tariff varies from

15

0.0835 to 0.1272 United States dollar for a different region in the country. Based on

16

government approved feed-in tariff and the current study the provinces of Ardebil

17

and Kohkiluyeh and Boyer-Ahmad are the riskiest and the securest regions for

18

investors in case of photovoltaic project, respectively.

19

Keywords: solar radiation, feed-in tariff, net present value, photovoltaic, Iran.

20 21

1.

Introduction

22

Energy consumption is considered as one of the most important factors of

23

development condition. Energy sources are categorized into the three groups of

24

fossil fuels, non-renewable energy, and renewable one (solar, wind, geothermal

25

resources, etc.). Over the past century, fossil fuels provided the largest share of

26

energy consumption in the world [1]. The greatest negative impacts of using fossil

27

resources include increased CO2 emissions, increased air temperature and,

28

consequently, global climate change [2-5].

29

1

Renewable energy penetration is one of the important features in the sustainable

30

development planning in different countries [6-8]. One of the outcomes of this

31

planning is the replacing fossil power plants with the renewable energies power

32

plants [9-11]. As one of the usage of solar energy the expansion of the use of solar

33

photovoltaic (PV) power plants has some advantages including the economic

34

development,

35

technology

development,

reducing

fossil

fuel

consumption,

greenhouse gas emission reduction, and climate change effects reduction [12, 13].

36

Nowadays, the development of renewable energy is limited by Research and

37

development (R&D) and investment cost. In addition there has been some problem

38

related to investment risk and unreliable return and long term design [5, 14]. Thus,

39

the policy and decision makers should prepare the flexible pattern to adjust the

40

suitable policies for renewable energies [15, 16]. To solve the investor inaction, the

41

government must prepare some rules to encourage the investors in the renewable

42

energy field by Feed-In Tariff (FIT) for electricity which generated by renewable

43

energies [17-19]. During such a policy, an evaluation should be done to assess the

44

benefit of applied policies to help the government for developing the friendly

45

policies in renewable energy field [20].

46

To encourage the investment a FIT police defined that means a fixed price for

47

purchasing renewable energy based power plants. An acceptable price of FIT cause

48

sufficient investor benefit. This policy is known as one of the effective

49

encouragement method for investment in renewable energies power generation [21-

50

24]. The quantity of FIT is dependent of a number of parameters. A proper FIT

51

should be attractive to investors and in other hand should not create an excessive

52

load for government and customers. Table 1 provides a summary of relevant studies.

53 54

Table. 1. A brief summary of relevant works.

Country/Region

Methods

Research purposes

China

Net Present Value (NPV) method

Calculation of the precise level of FIT [19]

Taiwan

Real options technique

Assesses the special effects of FIT on overall strategy [20]

2

55

European countries

Cost benefit study

Inspects the subsidy level required to create renewable energies reach fee equality [25]

European countries

Cost benefit and scenario study

Examines the FIT for numerous energy storage mechanisms [26]

-

Real options technique

Offers a quantifiable model which can estimate and optimize an FIT [27]

Ireland

Electricity request and output

Propose an FIT for domestic PV network connected [17]

United Kingdom

Cost–bene FIT analysis

Investigates the result of the new FIT on the customers [28]

-

NPV method

Evaluates the investors’ attitudes effects on the risk problems of performance of the FIT methods [29]

Austria

NPV and optimization technique

Evaluates the effects of two FIT patterns (fixed FIT and premium FIT) on wind turbine locations [22]

Real options method

Estimates FIT effects on the save path rate, decision value, and the presence of an equilibrium interest point [15]

China

56

Iran as Middle Eastern country is located in the high radiation area and has a special

57

solar energy advantage [5, 14, 30, 31]. The first PV power plant of Iran has been

58

built in Shiraz in 2008, with the capacity of 250 KW. The total capacity of the PV

59

power plants built in Iran until 2015 was less than five megawatts. Through adopting

60

the new supportive policies by the government of Iran in 2017 to guarantee solar

61

power production, good conditions have been provided for the investment in the

62

construction of PV power plants for electricity generation. During the last three

63

years, through adopting new supportive policies, the capacity of less than five

64

megawatts in 2015 has been increased to more than 45 MW in 2018. A total of 30

65

MW of this capacity have been exploited in 2017, indicating Iran's welcome to the

66

construction of PV systems [32, 33]. However, so far the installed capacity has not

67

reached its ideal level, and there are some plans to increase the amount of electricity

68

generated from PV systems. At the present time, the Iranian government's policy to

69

support investment in PV systems is to purchase electricity generated from PV

70

power plants at a guaranteed uniform FIT for all Iran's provinces.

71

3

In 2017, the Iranian government has determined the same FIT for all provinces with

72

different environmental conditions. However, based on the results of some previous

73

studies in this regard, FITs for purchasing electricity generated from PV power

74

plants should be different for different regions, depending on the environmental and

75

climate conditions. The FITs should be in a way that, in addition to encouraging

76

investors to construct PV projects, the lowest financial burden is imposed on the

77

government (a win-win strategy).

78

Following different environmental conditions in each location, an optimization of a

79

FIT for each place is highly necessary, particularly for investors. This price can

80

affect the distribution of renewable energy developments in each country. To the

81

best of authors' knowledge literature doesn’t include the effect of such parameters in

82

optimum FIT even in other countries. Therefore, regarding the geographical and

83

environmental conditions of Iran as a case study, the potential of different provinces

84

for receiving solar radiation is different. Therefore, the purpose of the present study

85

was to investigate the solar radiation potential for different provinces of Iran based

86

on the geographical, topographic and climatic conditions and, ultimately, determine

87

the optimal FITs for electricity purchase based on the radiation potential of each

88

province. However, this works has been done for a special country, the method and

89

other outcomes can be useful for other researchers in different countries.

90

2.

91

Case Study

Iran is placed in western regions on Asia between 25° to 40° north, and 44° to 60°.

92

The country is restricted by Afghanistan, Pakistan, Iraq, Turkey, Azerbaijan,

93

Armenia, Turkmenistan, Persian Gulf, Caspian Sea and Gulf of Oman (Figure 1).

94

Iran has a wide area so climate are very different from southern to northern part.

95

Based on previous studies Iran is located in a suitable location regarding solar

96

energy potentials. Furthermore a yearly average of 280 days with sun is reported for

97

more than 90% of country [30, 31].

98

4

Figure 1. Location of the Iran, current solar power plants and weather stations. 99

3.

Data and Methodology

100

In the current study, a set of mathematical, geometric and spatial models were used to

101

seasonal and annual solar radiation modeling. Then, the province average seasonal and

102

annual solar radiation was calculated. Thereafter, these values were extracted for current PV

103

power plants. Furthermore, using the NPV economic model, the optimal FIT for purchasing

104

the electricity generated from PV is calculated for each provinces and current solar power

105

plants with respect to the certain downward solar radiation potential of each region. Finally,

106

the optimal FIT for electricity purchase is compared with the FIT determined by the Iranian

107

government. The general trend of this study is represented in Figure 2.

108

5

Figure 2. The methodology of the research 109

3.1.

Data

110

In this study, the topographic parameters of elevation, slope and aspect were

111

extracted from Digital Elevation Model (DEM) with the spatial resolution of 30 m,

112

and these parameters were used for modeling of downward solar radiation on the

113

tilted surfaces [34].

114

Also, the data recorded in Iran's radiation monitoring stations were used to estimate

115

the accurateness of the results of the radiation model and modeling the atmospheric

116

transparency coefficient. There have been sixty-three places that homogeneously

117

distributed through the country which collecting data for every 10 minutes intervals.

118

The quantity of data and the covered area is large enough to produce reliable and

119

robust evaluation of radiation. all The data for the current study were gathered from

120

Iran’s renewable Energies Organization [33]. The location of each station is shown

121

in the Figure 1.

122

6

123

3.2.

Methodology

124

3.2.1. Downward solar radiation modelling

125

For modeling of downward solar radiation at the surface, the parameters such as the

126

declination of the earth, the hour angle of the sun, the zenith angle, the local

127

incidence angle of sunlight, and the atmospheric transmission coefficient are

128

necessary [35, 36].

129

The angle between the rotation axis of the earth and the line perpendicular to its

130

orbital plane is called the declination angle. The concept of the hour angle describes

131

the rotation of the Earth around its polar axis, which equates to +15 ° per hour in the

132

morning and -15 ° per hour in the afternoon. The declination angle and the hour

133

angle are calculated according to the relations presented in [36-39]. Declination

134

angle is defined by Eq. (1):

135

 

δ = 23.45Sin  360

284 + n   365 

(1)

Where n is the number of the day in year.

136

The angle between the direction of sunlight and the normal direction on the

137

horizontal surface is called the zenith angle. The zenith angle is dependent on the

138

parameters of the geographic position, the time position and the earth's declination

139

angle; and the incidence angle, besides the above parameters, is dependent on the

140

topographic conditions of the region that means it's related to the slope and aspect

141

[35-39]. The zenith angle and the incidence angle of sunlight are calculated using the

142

Eq. (2) and (3), respectively.

143

cos (α ) = cos (δ ) cos (ϕ ) cos (ω ) + sin (δ ) sin (ϕ ) cos

= sin

sin

+ cos

+ cos

+ cos

cos

cos

sin

sin

− sin cos

sin

sin

7

cos

cos

cos

sin

(2)

sin

cos

cos (3)

Where ϕ is the latitude,

is slope angle,

is the surface aspect angle, and ω is hour

144

angle.

145

The downward solar radiation at the top of the atmosphere is a function of the solar

146

constant parameters and the relative distance of the sun from the earth, which is

147

calculated using the Eq. (4) [37-39].

148

  360n   G Bn = G sc 1 + 0.033cos    365   

(4)

Downward solar radiation on the horizontal and tilted surfaces is shown in Figure 3.

149

150

Figure 3. Solar radiation on the horizontal and tilted surfaces [37].

151

According to Figure 3, downward solar radiation on a horizontal surface and on a

152

tilted surface in the clear atmospheric conditions is calculated using the Eq. (5) and

153

(6), respectively.

154

G B = G Bn cos (α )

(5)

G Bt = G Bn cos (θ )

(6)

In the Eq. (5) and (6), G Bt is the downward solar radiation on a tilted surface in the

155

clear atmospheric conditions; G B is the downward solar radiation on a horizontal

156

surface in the clear atmospheric conditions; G Bn is the total downward solar

157

radiation in the clear atmospheric conditions; θ is the local incidence angle; and α

158

is the solar zenith angle [37-39].

159

According to the above Equations, the tilt factor for beam radiation ( R b ) is

160

calculated by using the Eq. (7) and the downward solar radiation on each surface

161

with the desired topographic conditions and in the clear atmospheric conditions is

162

calculated using the Eq. (8) [37-39].

163

8

=

=

=

7 8

Using the Equations (1) to (8), the instantaneous downward solar radiation is firstly

164

calculated in a clear atmosphere condition on the tilted surface of each pixel and

165

then the average seasonal and annual downward solar radiation is calculated in the

166

completely clear atmospheric conditions. Since the atmospheric conditions are not

167

completely clear, it is necessary to consider the atmospheric transmission coefficient

168

to accurately calculate the amount of downward solar radiation to the surface.

169

Therefore, the downward solar radiation to each surface with the desired topographic

170

and atmospheric conditions is calculated by Eq. (9).

171 9

G Btτ = G Bt τ

In equation (9), G Bt is the downward solar radiation at the completely clear

172

conditions and τ is the atmospheric transmission coefficient.

173 174

3.2.2.The coefficient of atmospheric transparency

175

To calculate the coefficient of atmospheric transparency, with an approximation

176

clearness index from Alamdari et al. [30]. This assumption will be evaluated in the

177

next part. In their study, the average monthly clearness index has been proposed.

178

Therefore, in the present study, the seasonal and annual means of these values were

179

calculated and used. But these data are from distinct points. Thus for having the

180

downward solar radiation in other location the interpolation methods were used.

181

Interpolation methods are based on the spatial dependence that consider dependence

182

or independence between near and far objects.

183

The Inverse Distance Weighted (IDW) interpolation method are utilized for

184

generating the maps to find the information in all the place through the country. The

185

IDW basically stand on two rules: (1) the unknown cells value in one place is more

186

affected by near point than by those far points. (2) The degree of effeteness or

187

weight of cells on each other is right related to the inverse of the distance between

188

the cells. In this technique, the interpolation operator is as follows [40, 41]:

189

9

∑ wz z (x ) = ∑ w n

i =1 n

i

i =1

i

(10)

i

w i = d i−u

(11)

Where, Z ( x ) is the unknown value, whereas z i is known value. n is the total

190

number of known cells which used in the interpolation, and di is the distance among

191

the i th point and the unknown point, w i is the weight allocated to point i th . Higher

192

values of w goes to the closer point to unknown point. With increasing the distance

193

the weight decrease [42].

194

Using equations (1) to (11), and considering the climatic and geographical

195

conditions, all the seasonal and annual maps for downward radiation are prepared.

196

Then the average of available downward solar radiation for each province are

197

calculated.

198

3.2.3. NPV for evaluates the optimum feed-in tariff of PV electricity

199

production

200

For a solar PV electricity generation system is financed in year t with a lifetime of L.

201

suppose that the project creation can be completed at the same time [18]. This plan is

202

founded by annually reduced cash flow through the lifespan and preliminary asset

203

cost. Since the net present amount is dependent on many parameter it is better to

204

express this value using its expectation E [ . ]. Therefore:

205

 t +l Y CF  i V t = E ∑ − I  t i −t  i =t (1 + r ) 

0 ≤ t ≤ tv

(12)

Here r is discount rate, Y CF represents the Annual cash flow, tv means the final

206

stage of the investment validation period, and I characterizes the cost of

207

investment.

208

I t = UI × IC

(13)

Where I t is the initial investment cost, UI investment cost of one unit and IC is

209

the capacity of installation.

210

10

Figure 4 shows the annual cash flow for a solar PV project in Iran. Y CFt frequently

211

includes the returns from marketing electricity ERt , and operation and maintenance

212

costs OMC t , we obtain

213 214

Same

Same

(t) (t+1

t+L)

Lifetime (L)

Figure 4. PV electricity generated project cash flow chart

Y CFt = ER t − OMC t

3.2.3.1.

(14)

Income from electricity sale

215

The project revenue during a period depends on the amount of produced electricity

216

and its sales price. The climatic and geographical conditions are among the most

217

important factors affecting the amount of electricity generated from PV power

218

plants. In this study, taking into account the annual average of the incident solar

219

radiation to the Earth's surface, the annual production level was calculated.

220

Due to the natural aging of equipment and the dust accumulation on the surface of

221

PV panels, the percentage of the efficiency of these panels to receive the solar

222

radiation decreases annually [24]; this reduction in efficiency would reduce the

223

amount of electricity generated by these panels; therefore, this parameter should be

224

considered in calculating the amount of the produced electricity in the coming years.

225

The amount of proceeds from the sales of solar electricity is calculated using the

226

equations (15) and (16) [24].

227

11

(

)

ER t = G Btτ × Ge t × IC × 1 − R ZV × FIT t

(15)

Get +1 = Get × (1 − Rr )

(16)

Where Get is system generation efficiency, IC is capacity of installation, G Btτ is

228

yearly average of solar radiation, R ZV is station service power consumption rate,

229

Rr is the yearly reduction of generating efficiency and finally FIT t is the FIT.

230

With regard to the sales price of electricity generated by PV power plants, in

231

general, the investor faces two prices; the electricity market price and the guaranteed

232

FIT provided by the government. Most part of the electricity generated in Iran is

233

from the gas power plants and the combined cycle power plants; natural gas is the

234

fuel used in these plants. Iran has a lot of natural gas reserves; for this reason, the

235

fuel for these power plants is provided at a very low price, and the sale price of the

236

electricity generated in these power plants is not real [43]. This has led to the lack of

237

dynamism of the electricity market in Iran as the sale price of electricity for

238

household consumers is less than 0.025 United States dollar (1000 Rials) per kWh;

239

this price is less than a quarter of the guaranteed FIT administered by the

240

government for purchasing the solar electricity. In the current situation, it is not

241

expected that the electricity market price to be close to the FIT that fixed by the

242

government. This makes it possible for the investors of PV projects to sell their

243

generated electricity at a higher price, which is in fact the same as the FIT. The

244

current policy of the Ministry of Energy in Iran is to support the investment in PV

245

power plants with guaranteeing the power purchase agreement for a period of twenty

246

years at fixed basic rates. According to the Ministry of Energy act in 2016, the

247

guaranteed electricity purchase tariff for PV power plants would be 0.01011 United

248

States dollar (4004 Rials) per kilowatt hour [32, 33].

249

According to the Act of the Ministry of Energy, the considered FIT during the first

250

10 years of the contract period would be adjusted according to the Eq. (17) [32, 33].

251

12

Adjustment factor

5 Retail price index at the beginning of the payment year =+ 4 Retail price index at the beginning of the contract year

the average exchange rate Euro 8 < for a period of one year before the payment 7 ; × the average exchange rate Euro 7 ; before the time of the contract 7 ;

=>5

(17)

Also, the adjustment coefficient for the second 15 year (11st-25th) of the contract

252

period will be multiplied by 0.7. To calculate the adjustment rate, the data related to

253

the Retail Price Index (RPI) obtained from the Statistical Centre of Iran, and the

254

information on the Euro exchange rates from the Central Bank of Iran were used

255

[32, 33].

256

In this study, it is assumed that the amount of the adjustment factor for all years of

257

the contract period (1st-10th) is the same. The amount of this parameter for the first

258

10 years is considered to be equal to the amount of the adjustment coefficient for the

259

first year. Also, to calculate the amount of the adjustment factor for the subsequent

260

years (the 15 years), the adjustment factor for the first year is multiplied by 0.7.

261

The construction costs of a power plant depend on some factors including the

262

capacity and the structure of the power plant, the type of equipment used, the price

263

of the equipment, the cost of installing the panels, the cost of land, and other costs

264

involved in the construction of the power plant. The major part of the costs for the

265

PV systems includes the system costs and the installation costs. For the process

266

related to the system and installation costs, the reports from the National Renewable

267

Energy Laboratory [44] were used, which is represented in Figure 5.

268

13

5.0

2017 United States dollar/wdc

4.0 Other

3.0

Labor Bos

2.0

Inverter 1.0

Module

0.0 2010

2011

2012

2013

2014

2015

2016

2017

years

Figure 5. The process related to the system and installation costs the reports from

269 270

the National Renewable Energy Laboratory (NREL).

271

Major part of Operation and Maintenance Costs (OMCt) include labor costs,

272

insurance costs, and maintenance and repair costs that have been considered in the

273

analyses. The economic parameters used in Eq. (12) to (17) are shown in Table 2.

274

Table 2. the Economic and technical factors

275

Factors

Description

amount

System generating efficiency

?

0.8

Reduction rate of generating efficiency

A

Installed capacity last period validation of investment Solar PV lifetime

@

24

L

25

@

Discount Rate

FA

Unit investment cost (United States dollar/W)

14

10 MW

BC DE

Station service power consumption rate

0.02

0.03 0.08 1.592 276

Using the Eq. (3) to (8), the optimal price of the electricity generated from the PV

277

power plants were calculated for different provinces of Iran based on the average

278

annual solar radiation of each province. Finally, the modeled optimal price for each

279

province is compared with the FIT administered by the Iranian government.

280 281

4.

Results and Discussion

282

Using the data obtained from the 63 radiation monitoring stations in Iran and the

283

IDW interpolation model, the seasonal and annual mean of the clearness indices was

284

calculated, and used in the algorithm determining the downward solar radiation. The

285

map of the annual mean of the clearness indices on the province scale is represented

286

in Figure 6.

287 288

289

Figure 6. The map of the annual mean of clearness index on the province scale

290 291

According to Figure 6, the provinces located on the shores of the Caspian Sea have a

292

high cloud cover in most of the months of the year due to their moderate and very

293

humid climatic conditions, and therefore have a low level of the clearness index. The

294

provinces of Bushehr and Hormozgan, despite having hot weather conditions, have

295

high water vapor levels in most seasons of the year, especially in hot seasons, due to

296

15

their proximity to the Persian Gulf coast; that is why the clearness index for these

297

provinces is not at the high levels. The values of the clearness index for the central

298

provinces of Iran are higher due to their placement in the dry climatic conditions.

299

Through combining the values of the clearness index and the downward solar

300

radiation at top of the atmosphere, the seasonal mean of downward solar radiation

301

was calculated for the whole of Iran, which is shown in Figure 7.

302

Spring

Summer

Autumn

Winter

Figure 7. The seasonal mean of downward solar radiation in Iran (W m-2). 303

According to Figure 7, in general, the amount of downward solar radiation decreases

304

from the south to the north of Iran [5]. The main reason for this is the increase in the

305

latitude from the south to the north of Iran, and the high level of cloud cover in the

306

north compared to the south in all seasons of the year. In addition, eastern areas

307

receive more downward solar radiation than western areas due to having higher clear

308

sky in most of the seasons of the year. Seasonal changes in the Mediterranean winds

309

and sky cloud cover have caused more regions of Iran to receive less solar radiation

310

in the fall and winter than in the spring and summer. The Mediterranean winds

311

increase the cloud cover and rainfall in western areas of Iran, thus reducing the

312

16

amount of downward solar radiation in the cold seasons compared to the hot

313

seasons. The southern and eastern regions of Iran receive more downward solar

314

radiation than other areas. A part of Khuzestan province in southeastern Iran

315

receives a high level of downward solar radiation in most of the seasons compared to

316

many other regions of Iran; however, other parts of this province that are affected by

317

the dust and cloud phenomena in the summer and spring receive lower downward

318

solar radiation. The eastern parts of Iran, such as the South Khorasan province and

319

the south of Khorasan Razavi province, receive a high level of downward solar

320

radiation during the year due to the dryness of the air and the very low levels of

321

cloud cover throughout the year and, consequently, benefiting from more sunlight

322

hours. Sistan and Baluchistan province in southeastern Iran receives high downward

323

solar radiation in the cold seasons compared to other regions of Iran; however, in

324

warm seasons, many areas of this province do not receive a high level of solar

325

radiation due to the effect of 120-day wind of Sistan. 120-day wind of Sistan has a

326

great influence on the atmospheric conditions and increases the dust in the

327

atmosphere of Sistan and Baluchistan province. The reduction in the level of

328

incoming solar radiation directly relates to the number of transparent days and the

329

amount of dust in the air in this province. The solar radiation received in the coastal

330

areas of Iran is directly associated with the amount of water vapor in the air. Due to

331

the high level of water vapor in the air, the coastal regions of southern Iran receive

332

less downward solar radiation than other regions in the warm seasons compared to

333

the cold seasons. Taking into account the mean solar radiation for different seasons,

334

the average annual downward solar radiation map of Iran was calculated and shown

335

in Figure 8.

336

17

Figure 8. The average annual downward solar radiation in Iran (W m-2). According to Figure 8 and analysis of the location of the current solar PV power

337

plants, it is obvious that the power plants located in the southern half of Iran enjoy a

338

higher level of downward solar radiation potential than the solar power plants

339

located in the northern half of Iran. To evaluate the replacing coefficient of

340

atmospheric transparency with clearness index a comparison is made between the

341

recorded downward solar radiation by the monitoring stations in Iran, and modeled

342

radiation. This comparison is presented in Figure 9. The results indicated the high

343

accuracy of the downward solar radiation modelling for Iran.

344 345

18

R = 0.8774 Average irradiation (W m-2)_Observed

700

600

500

400

300

200 200

300

400

500

Average irradiation (W

600

700

m-2)_Modeled

Figure 9. Accuracy investigation of downward solar radiation modelling in Iran 346

The average annual downward solar radiation was calculated for the geographic

347

location of the current solar power plants and is shown in Table 3.

348 349

Table 3. The average annual downward solar radiation was calculated for the

350

geographic location of the current solar power plants

351

Site

Name

Province

Average irradiation (W m-2)

1

Makran

Kerman

584.26

2

Bam

Kerman

582.98

3

Birjand

South-Khorasan

600.28

4

Elahieh

Razavi-Khorasan

493.88

5

Sarkavir

Semnan

523.94

6

Damghan

Semnan

527.27

7

Malard

Tehran

443.12

8

Tabriz

East-Azerbaijan

443.35

9

Arak

Markazi

495.90

10

Persian gulf

Hamedan

520.97

19

11

Amirkabir

Hamedan

509.45

12

Yazd

Yazd

548.03

13

Jarqavieh

Isfahan

558.84

14

Shiraz

Fars

587.96 352

According to the results of Table (3), the PV power plants located in the provinces

353

of Markazi, East Azerbaijan, Khorasan Razavi, and Tehran receive an average

354

downward solar radiation of 495.9 W m-2, 443.35 W m-2, 493.83 W m-2, and 443.12

355

W m-2, respectively. Suitable regions for the construction of solar power plant

356

should receive at least 500 W/m2 solar radiation [7]. The average annual downward

357

solar radiation was calculated on the scale of Iranian provinces and the results are

358

shown in Figure 10.

359

Figure 10. Iran’s Provinces annual average of total seasonal solar radiation (W m-2). According to Figure 10, the top rank of receiving downward solar radiation is

360

Kohkiluyeh and Buyer Ahmad Province with the yearly average of total of about

361

578.6 W m-2. In contrast Ardabil Province with 380.1 W m-2 is the lowest rank.

362

Furthermore, it is specified that the Iranian provinces have different solar radiation

363

potential, which is directly associated with the geographical, environmental, and

364

climatic conditions of each province. The heterogeneous downward solar radiation

365

potential of different provinces of Iran leads to a difference in the amount of

366

20

electricity generated from the PV power plants with the same power and cost

367

structure in different provinces. Given the current Iranian government's policy of

368

securing guaranteed electricity purchase in all provinces at the same FIT, the income

369

level of solar PV power plants located in different provinces will be different. The

370

difference in the income level of PV power plants makes investors uninterested to

371

invest in the provinces with lower average of solar radiation; for this reason, it is

372

anticipated that the implementation of solar PV projects be focused in certain

373

regions of Iran; this issue is in contradiction with the goals of implementing the

374

Iranian government's policy of exploiting and utilizing the renewable energy power

375

plants in all regions of Iran. Furthermore, due to the high downward solar radiation

376

potential of some regions in Iran, and given the guaranteed FIT, we will witness an

377

overpayment made by the government to the investors benefit. In other words, the

378

government can guarantee the cost-effectiveness of these projects by setting lower

379

FITs for these regions (with high downward solar radiation potential) in addition to

380

preventing the overpayment. Considering the Iranian government's policy of the

381

pervasiveness of production and use of renewable energy including the solar energy

382

in all regions, as well as preventing the government's overpayment, it is necessary to

383

determine the feed-in electricity tariffs in accordance with the certain geographical

384

and climatic conditions of each region.

385

Given the different downward solar radiation potential for each province, the

386

optimal FIT for purchasing PV electricity was calculated using the NPV model and

387

is shown in Figures 11 and 12.

388

21

389

Figure 11. Optimal FIT for PV Power Purchase (United States dollar (Rials)).

390

Yazd

South Khorasan

Alborz

Tehran

Ardabil

Bushehr

East Azarbaijan

Chahar Mahaal and Bakhtiari

Fars

Isfahan

Gilan

Lorestan

Golestan

Hormozgan

Ilam

FIT set by government in 2016

islands

Kerman

Kermanshah

North Khorasan

Khuzestan

Razavi Khorasan

Kurdistan

Kohkiluyeh and Buyer Ahmad

Markazi

Hamedan

Mazandaran

Qom

Qazvin

Semnan

Sistan and Baluchistan

Zanjan

West Azarbaijan

Value (United States dollar)

391

FIT in this study

Figure 12. Comparison of the FIT fixed by the government and the optimal price of electricity purchasing with regard to the solar radiation potential (United States dollar)

392 393 394 395 396

According to Figures 11 and 12, for the provinces of Zanjan, North Khorasan,

397

Semnan, Qazvin, Markazi, Kurdistan, Ilam, and Bushehr, the modeled optimal price

398

of electricity purchasing from PV power plants is equal to the FIT determined by

399

22

the government. For the provinces of East and West Azerbaijan, Qom, Mazandaran,

400

Golestan, Gilan, Ardabil, Alborz, and Tehran, the modeled optimal price is higher

401

than the tariff set by the government. This result suggests that for these provinces,

402

considering the government's FIT, the solar projects are uneconomic and investors

403

have less motivation for the investment in these provinces. These provinces are

404

located in the northern half of Iran, and in terms of the climatic conditions, they have

405

relatively few sunny days because of the high degree of cloud cover; they also have

406

a relatively low downward solar radiation potential due to their high latitude

407

location. However, in other provinces of Iran including Sistan and Baluchistan,

408

Khorasan

Khuzestan,

409

Chaharmahal and Bakhtiari, Fars, Lorestan, Hormozgan, and Hamedan, the modeled

410

optimal price is lower than the tariff set by the government. This result indicates that

411

for these provinces, the government faces overpayments for purchasing electricity

412

from the solar PV power plants. In general, with respect to the FIT fixed by the

413

government and the results of Figure 12, the provinces of Ardebil and Kohkiluyeh

414

and Buyer Ahmad are the worst and the best provinces for the investor to construct a

415

solar project, respectively. The average optimal price for all provinces of Iran is

416

0.1011 United States dollar, which is close to the FIT stated by the government for

417

purchasing electricity from the solar PV power plants.

418

Finally, the optimal price of electricity purchasing was calculated for the current PV

419

power plants in Iran (Table 3) considering the solar radiation potential and compared

420

with the FIT set by the government. The results are presented in Figure 13.

421

Razavi,

South

Khorasan,

23

Yazd,

Isfahan,

Kerman,

422

Figure 13. Comparison between optimal and government FIT for current PV power

423

plant location (United States dollar).

424

The results of Figure 13 show that the optimal price modeled for the power plants of

425

Khorasan Razavi, Semnan, Semnan, Markazi, Hamedan, and Hamedan is exactly the

426

same as the FIT set by the government. With regard to the obtained values for the

427

optimal prices, the power plants of Kerman, Kerman, Isfahan, Yazd, Fars, and South

428

Khorasan are the most profitable solar power plants for the investors; this means that

429

the government should overpay for purchasing electricity from these PV power

430

plants. Among the 14 current power plants in Iran, only Tehran and East Azerbaijan

431

power plants are not economically cost-effective considering the FIT set by the

432

government.

433 434

5.

Conclusion

435

Over the past decades, fossil fuels have provided the largest share of energy in the

436

world. The most negative effects of using fossil resources include increased CO2

437

emissions, increased air temperature and, consequently, the change in the global

438

24

climate. The expansion of the use of solar power plants has some advantages

439

consisting of the economic development, technology development, reducing fossil

440

fuel consumption, greenhouse gas emissions reduction, and climate change impacts

441

reductions. The results of this study indicated that:

442

•

The potential incoming solar radiation is different for the different provinces

443

of Iran and it varies from 380-578 (W m-2) considering the geographical,

444

topographic, and climatic conditions.

445

•

Using the NPV model, the optimal price of electricity purchasing (from the

446

PV power plants) varies between 0.0835 and 0.1272 United States dollar for

447

different provinces.

448

•

The provinces of Ardebil and Kohkiluyeh and Boyer Ahmad are the riskiest

449

and the securest provinces for the investors to construct a solar PV project,

450

respectively.

451

•

Among the 14 current power plants in Iran, only Tehran and East Azerbaijan

452

power plants are not economically cost-effective regarding the tariff set by the

453

government.

454

•

The power plants of Kerman, Kerman, Isfahan, Yazd, Fars, and South

455

Khorasan are the most profitable solar power plants for the investors, in which the

456

government should overpay for purchasing electricity from these PV power plants.

457

•

Moreover, the optimal price modeled for the power plants of Khorasan

458

Razavi, Sarkavir, Damghan, Markazi, Hamedan, and Hamedan is exactly the same

459

as the FIT determined by the government.

460

The results of the present study indicated that in order to establish a win-win balance

461

between the government and investors of solar PV projects, the Iranian government

462

should revise the same FIT policy for the different provinces considering the

463

geographical, topographic and climatic conditions. It is recommended for the future

464

studies to consider other parameters (besides the solar radiation potential) in

465

determining the optimal electricity purchasing price. Furthermore, in this study, the

466

amount of the adjustment factor was considered the same for all years (1-10). The

467

amount of this parameter for the first 10 years is considered to be equal to the

468

amount of the adjustment coefficient for the first year. Also, to calculate the amount

469

of the adjustment factor for the subsequent years (the 15 years), the adjustment

470

factor for the first year is multiplied by 0.7. An improvement in this considered

471

25

assumption would be very effective in improving the research results. Moreover,

472

continuous modeling of the atmospheric transparency index for the whole of Iran by

473

using the satellite imagery can increase the accuracy of the modeled solar radiation.

474

For last not least, following different environmental conditions in each

475

location, an optimization of a FIT for each place is highly necessary, particularly for

476

investors. This price can affect the distribution of renewable energy developments

477

in each country. Therefore, this work can be a roadmap for other countries to

478

see the effects of environmental parameters on the FIT. However, this study

479

focused on a special location, but this study can be good literature on the effect of

480

environmental parameters on the FIT price for other researchers across the world.

481

6.

482

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28

Highlights: • The optimum feed-in tariff of photovoltaic is evaluated. • Different parameter such as geographical, topographic and climatic condition are considered. • Net Present Value (NPV) model is utilized for economical evaluation. • Iran is chose as a case study to evaluate the optimum feed-in tariff. • The results show that the optimum feed-in tariff is varied for each province.

Conflicts of Interest Statement

Manuscript title: On the Effect of Geographical, Topographic and Climatic Conditions on Feedin Tariff Optimization for Solar Photovoltaic Electricity Generation: A Case Study in Iran

The authors whose names are listed immediately below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patentlicensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript. Author names: Hamzeh Karimi Firozjaei, Mohammad Karimi Firozjaei, Omid Nematollahi, Majid Kiavarz, Seyed Kazem Alavipanah