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Feasibility analysis of a new tree-shaped wind turbine for urban application: A case study

Ali Mostafaeipour1 , Mostafa Rezaei1 , Mehdi Jahangiri2 and Mojtaba Qolipour1

Abstract

In this study, feasibility of a new wind power generation system for urban application in

Hormozgan Province of Iran is investigated. The wind turbine system in this study is a novel,

aesthetically pleasing, noiseless, pollution-free, potentially cost-effective, and high efficiency design

called tree-shaped wind turbine (TSWT). Techno-economic evaluation is performed on eight

urban areas in the province using the software HOMER. Multi-criteria decision making

approaches are used to prioritize the areas in terms of the best location for installing such a

new system. The results of techno-economic analysis examining a wind power system consisting

of 25 TSWTs show that the most electricity production would occur for Jask city which is

529,450 kWh/yr. Also, the least amount of electricity which is 339,275 kWh/yr belongs to

Bandar Abbas. Considering the most important criteria including electricity production, levelized

cost of electricity, population, land price, environmental impact, and frequency of natural disas-

ters, data envelopment analysis, and the fuzzy technique for order of preference by similarity to

ideal solution are employed to rank the cities. The results are validated by two different methods.

Finally, it is suggested that Sirik is the best location for using the aforementioned wind turbine.

Keywords

Wind energy, feasibility study, economic evaluation, tree-shaped wind turbine, data envelopment

analysis, HOMER software

1Industrial Engineering Department, Yazd University, Yazd, Iran 2Department of Mechanical Engineering, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran

Corresponding author:

Ali Mostafaeipour, Industrial Engineering Department, Yazd University, Yazd, Iran.

Email: [email protected]

Energy & Environment

2020, Vol. 31(7) 1230–1256

! The Author(s) 2019

Article reuse guidelines:

sagepub.com/journals-permissions

DOI: 10.1177/0958305X19888878

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Introduction

Renewable energies like wind, solar, geothermal, tidal, and biomass have turned into eco- nomically feasible sources of clean energy for many countries in the world.1 Today, eco- nomic feasibility of wind energy compared to other renewable sources encourages continued research in this field.2 There are ample feasible potentials in Iran for the utilization of wind turbines, and the existing research and investments in this field give a bright outlook on the future development and application of this technology and its role in the country’s energy sector.3,4 Like other renewable energy sources, wind energy is a geographically pervasive yet scattered and decentralized phenomenon with almost ubiquitous availability. Furthermore, “renewability” and “reliability” of wind energy are significant enough to make it even more attractive.5 The Hormozgan Province is suitable for implementing wind and solar energy, but solar panels do not provide pleasing aesthetic view for the cities. But, TSWTs are able to provide energy with low wind speed too. There have been many solar panels in different cities of Iran, but the purpose of this study was to introduce a new pleasing system to provide electricity. By comparing cities of northern parts of Iran, we could see that most of the cities in Hormozgan lack suitable trees in streets to be aesthetically attractive. But, the cities in north of Iran are very attractive in case of green attractiveness.

On the other hand, the growing energy demand, limited nature of fossil resources, and negative environmental effects of these resources, i.e. greenhouse gas effects, global warm- ing, and acid rains, and CO2 emissions highlight the importance of conserving fossil fuels and focus on the use of renewable energy sources in the future.6 Among renewable energy sources, wind energy is one of the most economic means of unlimited electricity generation without environmental pollution. According to existing statistics, each kWh of energy pro- duced from wind instead of fossil fuel prevents the emission of about 0.6 kg of CO2. Replacing fossil fuel power plants with wind turbines can play a significant role in reducing the greenhouse gas emissions.7 Encouraging the use of wind turbines to meet the power demand reduces the reliance on the power produced by other power plants and therefore the reliance on fossil fuels, which entails obvious environmental benefits.8 Also, the aesthetic aspect of wind turbine in the frame of natural landscape can serve as a symbol of clean energy.9 Another advantage of wind power systems is that 99% of the surface dedicated to a wind farm can be used simultaneously for farming and animal husbandry. Evidence suggests that domestic and wild animals around the wind farms suffer no adverse effect from wind power systems.10 In view of the above merits, wind power is a justified solution for miti- gating the environmental downsides of utilizing other non-renewable resources of energy.11

Wind power sector also stimulates the economic growth and contributes to emergence of new approaches for development of industrial and production activities.12 Figure 1 illus- trates the global cumulative wind energy capacity13 which shows an increasing trend up to 2018.

The wind power potential in different areas can be evaluated by simulation of wind farms and related wind power systems using dedicated wind equations and software.14 Accurate evaluation of wind energy potential needs accurate examination of statistical wind profile and the available turbine designs that can be considered as options.15

Tiwari and Babu16 believed that renewable energy solutions, including wind power, are becoming more attractive because of steady increase in electricity demand and effects of carbon pollution, which resulted in growing demand for wind energy as a high-quality power source.

Mostafaeipour et al. 1231

Jiang et al.17 stated that in some countries like China, development and increasing use of

wind energy systems led to significant economic development. Their findings showed that

state-supported investment and development of technologies used for harvesting

wind energy in areas with low wind speed could stimulate this industry and result in eco-

nomic growth. Al-Sharafi et al.18 conducted a technical and economic analysis on the potential of solar-

wind hybrid systems for producing hydrogen and power in five regions of Saudi Arabia.

Simulations were conducted by HOMER software and findings showed that using a hybrid

system consisting of 2 kW panel, three turbines, 2 kW converter, and seven batteries could

produce 43.1 kg of hydrogen in addition to electricity. Karthikeya et al.19 evaluated the urban wind generation in Singapore-based rooftop wind

mast by the use of mobile light detection and ranging (LiDAR) profiler, wind rose diagram,

Weibull distribution function, and other statistical analyses. Their findings showed that the

studied areas were economically and technically feasible for supplying a part of Singapore’s

electricity. Hooper et al.20 evaluated the possibility of using energy systems for ecosystem services

based on offshore wind power systems. To extract the suitable evaluation criteria, they

reviewed 78 previous studies under the Common International Classification for

Ecosystem Services (CICES) framework. Their results showed that offshore wind farms

had several impacts on ecosystem services, e.g. they had a positive effect on fish trade,

but a negative impact on sea view. It was also found that in the long run, population

would have an impact on the status of wind farms and coastal environment. With the rapid growth of wind energy industry, construction of new wind farms based on

new technologies is becoming a global trend.21 Fast progress of technologies and methods

contributing to better exploitation of wind potential has also reinforced this trend.9

Figure 1. Cumulative installed wind capacity in the world from 2005 up to 2018.13

1232 Energy & Environment 31(7)

The notable features among these developments are the new types of rotor turbines, Invelox

turbines, and tree-shaped turbine. While persistent efforts to develop new wind energy

technology are paramount, the techno-economic evaluation of these alternative solutions

and exploring the prospect of further use of such technologies are necessary as well.22,23

There have been numerous research works related to wind energy in Iran. Rezaei et al.24

conducted a sensitivity analysis of localizing wind energy in middle parts of Iran. Socio-

economic feasibility of wind power stations to generate hydrogen in Iran was evaluated by

Rezaei et al.25 To scrutinize electricity production in a wind farm, a mathematical model was

proposed by Qolipour et al.26 Rezaei-Shouroki et al.27 examined and prioritized different

cities of Fars province for the purpose of hydrogen production by wind energy. FTOPSIS

approach for ranking some central regions of Iran due to harnessing wind and solar energy

was conducted by Rezaei et al.28 Pishgar and Akram29 studied the wind energy potential for

installing different wind turbine models in the city of Zabol. Economic evaluation of using

small wind turbines for electricity production purpose in residential sectors was done by

Hosseinalizadeh et al.30

The research seeks to scrutinize the utilization of TSWTs in Iran, which can be regarded

as one of the most distinctive wind energy technologies. This technology is based on a

practical design developed by Jerome Michaud-Lariviere in the French company “New

Wind”. The system is designed in the form of a tree, where the leaves operate as small

wind turbines and the branches carry the wires transferring the generated electricity to the

outlet at the base of the tree shown in Figure 2. In this tree, geometric design of the leaves

allows them to capture the relatively slow winds. The energy produced from the leaves is

then transferred toward the root, where it can be stored or forwarded toward the grid. The

aggregation of power in the trunk and roots is enhanced by a synergic mechanism, which

allows the electricity to be stored at a given time to exceed the total electricity generated by

all leaves. Topology of the tree branches and leaves is based on Pascal’s triangle.31,32

Figure 2. Structure and installation of tree-shaped wind turbine.31

Mostafaeipour et al. 1233

Each tree has 72 artificial leaves, each being a small conical turbine standing vertically for an emphasis on beauty. The leaves can have 360� rotation, so they can be rotated by any wind regardless of direction. The leaves are very light (15 kg for each leaf) and can produce electricity by winds as slow as 2 m per second.

Reason for using the TSWT is because it is made of a light weight material and is able to generate power with low wind speed as low as 4.4mph which is durable to withstand wind speed of up to 129mph. Wind Tree is silent and its noise is very low. Wind tree works silently with least noise about 5 dB and therefore can be installed in different parts of the cities. The initial total investment of each tree in 2019 is about $35,000, but the test results are expected to show that the technology can pay for itself in a few years. The white metal structure looks like the sculptures. But it is another creative and innovative step toward sustainability.32

Due to the existence of suitable wind energy potential and according to the necessity of constructing renewable-based systems for lightening purpose in different parts of Hormozgan province, feasibility of installing TSWTs is examined in the current study. MCDM methods are also used to rank the under study regions using the most important criteria including electricity production, LCOE, population, land price, environmental impact, and frequency of natural disasters. TSWTs could be installed in all locations and cities, but it is preferred to be used in cities which are situated in areas with limited varieties of trees. There are few limitations regarding the use of these turbines like damage to these trees by very high speed winds. According to the data, there is no chance of blowing high wind speed in the province.

This research consists of five sections: introduction, geographical specifications of the studied urban areas, methodology, analysis of results, and conclusion.

Geographical specifications

Hormozgan, with an area of 70,697 km2 (27,296 sq. mi) is one of the most strategically situated southern provinces of Iran. The province has a hot and humid climate with temper- atures that reach up to 49�C (120 �F) in summers and scarce precipitation throughout the year. Aerology data suggest that there are eight regions with adequate wind power potential in this province.33,34 The map of Iran including Hormozgan province and eight nominated cities is shown in Figure 3.

Methodology

Electricity production using TSWT

Each small vertical axis wind turbine has a power curve with a maximum power of 163W and the TSWT has 72 wind turbines.35 In other words, the nominal power of each tree is 11,736 W. The total power curve of a TSWT is illustrated in Figure 4 as following36,37:

These turbines cover an area of 8m2 at the ground surface with weight of up to 4 Tons. The turbines installed on the tree are completely noiseless and the energy produced by the tree can be used for urban lighting and residential purposes. Another advantage of TSWT is its ability to generate power by the slight air flow produced in places like highways. The price of this tree is currently $35,000.31,38

1234 Energy & Environment 31(7)

Advantages of TSWT are as followings31,39:

1. It does not need gearbox. 2. It creates very low noise. 3. Since it is located near the consumer premises transmission, grid cost will be eliminated. 4. It will be operational with low wind speed. 5. It can produce electricity irrespective of the wind direction. 6. It is aesthetically pleasing for cities. 7. High maneuverability in terms of location and numbers. 8. Lower operating and investment costs.

0 5 10 15 20 0

3

6

9

12

Po w

er (k

W )

Wind Speed (m/s)

Figure 4. Power curve of a TSWT.36,37

Figure 3. Map of Iran including Hormozgan province and nominated cities.

Mostafaeipour et al. 1235

Disadvantages of TSWT are as followings39:

1. Since it is a kind of vertical axis turbine, it is not able to produce more electricity from a

given amount of wind. 2. It is not widely used because horizontal axis turbine dominates most of the wind turbine

industries.

According to the information released by the manufacturer of this type of wind turbines,

cut-in speed, rated speed and cut-out speed of the turbines are respectively 2, 12 and 18m/s.

Furthermore, the price of a wind tree is about $35,000.35–37

In order to calculate the amount of expected electricity production in a year, firstly

capacity factor of the turbines, shape (c) and scale (k) parameters should be calculated

using equations (1) to (3), respectively.40

Capacity factor ¼ e�ðvi=cÞk � e�ðvr=cÞk

ðvr=cÞk � ðvi=cÞk � e�ðvo=kÞk (1)

k ¼ rv �v

� ��1:086

(2)

c ¼ �v

U� ð1þ 1 kÞ

(3)

where vi, vr and vo are cut-in speed, rated speed and cut-out speed. �V is the mean wind

speed during the period, rv denotes the standard deviation of wind speed data, and

U is the gamma function. Average yearly electricity production is computed using

equation (4).40

Electricity production ðkWh=yrÞ ¼ Capacity factor� nominal capacity� 8760 (4)

Meanwhile, given that carbon dioxide (CO2) is the major pollutant produced during the

process of generating electricity using fossil fuels, 632 g of this emission can be prevented per

kWh of renewable electricity production.41

MCDM approaches

To avoid any further losses when deciding to choose a location for constructing the

TSWT system, crucial criteria should be taken into account and then all alternatives

must be prioritized. In this regard, MCDM techniques are of high importance since

they compare all alternatives considering different criteria. DEA and FTOPSIS are the

most common and reliable methods which were used in this study to prioritize the nomi-

nated locations.

DEA. This method simply evaluates the ratio of outputs to inputs of decision-

making units (DMUs) as their efficiency score, and then this ratio is applied

1236 Energy & Environment 31(7)

to rank DMUs. Meanwhile, this technique deems several constraints as shown in equation (5)27

Zp ¼ Max XS r¼1

uryrp

Subject to : XK i¼1

ViXip ¼ 1

XS r¼1

uryrj � XK i¼1

ViXij � 0

j ¼ 1; 2; . . . ; n ur; vi � e r ¼ 1; 2; . . . ; S i ¼ 1; 2; . . . ;K

(5)

where Zp denotes relative ranking of pth DMU, i and r are used for input and output, respectively. n, K, and S, respectively, show the number of alternatives, inputs, and outputs. One of the benefits of DEA is that it can compute weights pertaining to inputs and outputs, which are shown by the variables ur and vi, respectively. yrj means the rth output (r¼ 1 to s) of the jth DMU (j¼ 1 to n). Also, xij shows the ith input (i¼ 1 to k) of the jth DMU (j¼ 1 to n).

On the other side, one of the downsides of the model is that DMUs’ efficiency and ranking would not be efficient and reliable if either the following terms were not satisfied.27

1. Total number of DMUs should be equal or greater than three times of total number of inputs and outputs.

2. Total number of DMUs should be equal or greater than two times of the number of inputs multiplied by the number of outputs.

If neither of the aforementioned conditions complied with the number of DMUs, inputs and outputs, then the following model (equation (6)) called dual form of DEA could be used27

Min Zp ¼ h

Subject to : Xn j¼1

ljyrj � yrp

hxip � Xn j¼1

ljxij � 0

lj � 0 r ¼ 1; 2; . . . ; S i ¼ 1; 2; . . . ;K j ¼ 1; 2; . . . ; n

(6)

where parameter h is the objective variable that should be minimized. l refers to the weights of the inputs and outputs of DMUs.

Mostafaeipour et al. 1237

After calculating efficiency of all DMUs, if h for more than one DMU became 1, then

another model of DEA called Andersen-Petersen (AP) would be used to rank just the full-

efficient DMUs (those which had h ¼ 1). AP model allows these DMUs to gain efficiency

score more than 1 and is presented in equation (7)27

Min Zp ¼ h

Subject to : Xn

j¼1 j 6¼p

ljyrj � yrp

hxip � Xn

j¼1 j 6¼p

ljxij � 0

lj � 0 r ¼ 1; 2; . . . ; S i ¼ 1; 2; . . . ;K j ¼ 1; 2; . . . ; n

(7)

FTOPSIS. To cope with uncertainty in decision-making problems, fuzzy concept is utilized as

a powerful tool. Fuzzy logic and TOPSIS method was mixed to introduce a more suitable

technique for solving such problems. FTOPSIS divides selected criteria into two groups of

positive criteria and negative criteria, which the former means the more the better and the

latter means the less the better. To implement this method, firstly, a decision matrix should

be created as equation (8)28

~D ¼ ~x11 � � � ~x1n

..

. . . . ..

.

~xm1 � � � ~xmn

2 664

3 775 (8)

where m and n are, respectively, the number of alternatives and number of criteria. ~Xij is a fuzzy

triangular number referring to the value of nth criterion for mth alternative. Then, the matrix of

weights of all criteria should be formed as equation (9)28 in which numbers are triangular

~W ¼ ½~w1; ~w2; . . . ; ~wn� (9)

To dedicate suitable weights to criteria, several experts in the fields need to be asked to

give their own opinion. Therefore, the verbal values given to each criterion should be trans-

formed into quantitative values using Table 1. For instance, if an expert said that the value

of population criterion is medium, then its equivalent would be (3, 5, 7). After turning all the verbal values into quantitative forms, all experts’ opinions should be

mixed to acquire just one triangular fuzzy number for each criterion, which can be done

using equation (10)

~wn ¼ ðmin of all a; average of all b; maxof all cÞ (10)

where a, b and c, respectively, represent the first number, second number and third number

of a triangular fuzzy number like (a, b, c).

1238 Energy & Environment 31(7)

To simplify the process, all values must become scale-less. To this end, equations (11) and

(12) are respectively employed for positive criteria and negative criteria to form scale-less

decision matrix as shown by equation (13)28

~rij ¼ aij c j

; bij c j

; cij c j

! (11)

~rij ¼ a�j cij

; a�j bij

; a�j aij

! (12)

~R ¼ ~r11 � � � ~r1n

..

. . . . ..

.

~rm1 � � � ~rmn

2 664

3 775 (13)

where c j and a�j refer to the biggest number and smallest number among all third numbers

and all first numbers in triangular fuzzy numbers, respectively. Then weighted decision

matrix can be obtained by equation (14), and the ideal fuzzy solution and anti-ideal fuzzy

solution are computed using equations (15) and (16)28

~V¼ ~R � ~W ¼ ~r11 � � � ~r1n

..

. . . . ..

.

~rm1 � � � ~rmn

2 664

3 775� ½~w1; ~w2; . . . ~wn� (14)

A ¼ f ~v 1 ; ~v 2 ; ~v 3g (15)

A� ¼ f ~v�1 ; ~v � 2 ; ~v

� 3 g (16)

where ~v i and ~v�i are the best value and the worst value of criterion i among all alternatives.

Finally, the distance of every alternative from ideal solution and anti-ideal solution should

Table 1. Verbal values and their equivalent in the form of triangular fuzzy numbers.28

Verbal variables of criteria Fuzzy triangle number

Very low (0, 0, 1)

Low (0, 1, 3)

Almost low (1, 3, 5)

Medium (3, 5, 7)

Almost high (5, 7, 9)

High (7, 9, 10)

Very high (9, 10, 10)

Mostafaeipour et al. 1239

be projected to obtain closeness coefficient; their equations are respectively (17), (18)

and (19)28

s i ¼ Xn j¼1

dð ~vij ; ~v j Þ; i ¼ 1; 2; . . . :;m and j ¼ 1; 2; . . . :;m (17)

s�i ¼ Xn j¼1

dð ~vij ; ~v�j Þ; i ¼ 1; 2; . . . :;m and j ¼ 1; 2; . . . :;m (18)

ci ¼ s�i s i þ s�i

(19)

To rank the alternatives, the more the parameter ci, the higher the rank of the related

alternative.

Analysis

This section describes the techno-economic feasibility analysis of TSWTs for the eight stud-

ied urban areas, and then the ranking of these areas in terms of their suitability for the use of

TSWTs. Technical feasibility of TSWT systems for Hormozgan was evaluated based on the

local recordings pertaining to a 15-year period from 2000 to 2015. Table 2 shows the average

monthly wind speed (m/s) at the height of 10m for each of the eight studied areas.

Techno-economic analysis for TSWT

Most important technical aspect of analyzing a TSWT is the amount of electricity which can

be generated after its utilization in the areas. For this, capacity factor of the turbine is of

great value and should be computed which means parameters k and c are needed. The

results showed that using one set of the TSWT in Jask can produce 21,178 kWh of electricity

and consequently can prevent from emitting 13.384 ton of CO2 in a year. Mean yearly

electricity generation in Jask is greater than other areas because its mean wind speed is

higher than that of others. Table 3 contains computed values of mean wind speed, k, c,

capacity factor and yearly electricity production in the regions. Given that the present worth of money is considerably different from its future value, so

to set its value in the future, equation (20) can be used1

F ¼ P� 1þ ir

1þ fr

� �n

! P ¼ F� 1þ fr

1þ ir

� �n

(20)

where F and P refer to the future worth of money and the present worth of money, respec-

tively. ir is the interest rate and fr denotes inflation rate. To analyze economic aspects of applying TSWTs in the areas under study, the following

terms were postulated:

1. Interest rate and inflation rate are 18% and 20% in Iran.1

2. The number of TSWTs in each area that must be considered is 25 because this number

was determined by the software HOMER and if the number is below 25, the project

1240 Energy & Environment 31(7)

becomes technically unjustifiable, so the authors were not involved in the determination

of this number. 3. Capital cost of installing 25 TSWTs equals to 25� 35,000 ($); 4. Cost of inverter equals to 600 ($) and since the lifetime of the inverter is 10 years,41the

replacement cost occurs when n is 11 and it equals (1þ0:18 1þ0:2 Þ11 � 600¼ 499 ($);

5. Operation and maintenance cost when n is 1 equals to 25� 50 ($); 6. Salvage value when n is 20 equals to 25� 2000 ($) so present worth of it equals

( 1þ0:2 1þ0:18 Þ20 � 25� 2000¼ 69,977 ($);

7. Total life time of the turbines is 20 years; 8. According to Mostafaeipour et al.,1 the price of renewable-generated electricity is $0.12

in Iran.

Table 4 shows the results of economic evaluation of applying 25 sets of TSWTs. Capital cost means the summation of purchasing and installation of 25 TSWTs and an inverter at the beginning of the project (when n¼ 0). Since the project lifetime was presumed to be

20 years and the useful lifespan of the studied wind turbines is 20 years, therefore there is no

Table 2. Average monthly wind speed for nominated cities (m/s).

Months Abumusa Bandar Abbas Bandar Lengeh Jask Kish Lavan Qeshm Sirik

January 4.19 3.19 3.55 4.3 3.6 3.99 3.12 5.05

February 4.82 3.38 3.93 5.18 4.26 4.52 4.3 5.33

March 4.72 3.76 4.05 4.42 4.5 4.86 4.9 5.35

April 4.4 3.78 4.16 4.85 4.45 4.63 5.02 5.2

May 4.16 3.79 4.05 4.72 4.4 5.15 5.18 5.09

July 3.72 3.85 3.68 5.56 3.72 4.19 4.8 4.23

June 3.56 3.9 4.05 6.19 3.49 3.93 4.12 4.1

August 3.65 3.83 4.3 6.1 3.53 3.92 4.66 4.76

September 3.33 3.42 3.42 4.88 3.18 3.88 4.00 3.98

October 3.27 3.13 3.35 4.12 3.09 3.78 3.5 4.02

November 3.5 3.25 3.4 3.76 3.09 3.63 3.05 4.68

December 4.08 3.2 3.39 4.09 3.16 3.3 2.98 4.88

Table 3. Calculated values of k, c, capacity factor, yearly electricity production and CO2 emission reduction.

Cities

Mean wind

speed (m/s) k c

Capacity

factor (%)

Yearly electricity

production (kWh/yr)

CO2 emission

reduction (ton/yr)

Abumusa 3.95 1.65 4.42 15.4 15,832 10.005

Bandar Abbas 3.54 1.56 3.94 13.2 13,571 8.577

Bandar Lengeh 3.78 1.61 4.22 14.5 14,907 9.421

Jask 4.85 1.83 5.46 20.6 21,178 13.384

Kish 3.71 1.59 4.14 14.2 14,599 9.227

Lavan 4.15 1.69 4.65 16.5 16,963 10.721

Qeshm 4.14 1.68 4.64 16.6 17,066 10.786

Sirik 4.72 1.80 5.31 19.9 20,459 12.930

Mostafaeipour et al. 1241

T a b le

4 . Fi n an ci al fa ct s o f e co n o m ic an al ys is o f u ti liz in g 2 5 se ts

o f T SW

T s.

C it ie s

C ap it al

co st

($ )

R e p la ce m e n t

co st

($ )

ju st

o cc u rs

w h e n n ¼ 1 1

O p e ra ti o n an d

m ai n te n an ce

co st

($ /y r)

w h e n n is 1

P re se n t w o rt h

o f o p e ra ti o n

an d m ai n te n an ce

co st

($ )

N e t p re se n t

w o rt h o f al l

co st s ($ )

Sa lv ag e

va lu e ($ )

w h e n n

is 2 0

P re se n t

w o rt h

o f sa lv ag e

va lu e ($ )

E le ct ri ci ty

p ro d u ct io n

u si n g 2 5

tu rb in e s

(k W

/y r)

P re se n t w o rt h

o f in co m e s b y

se lli n g p ro d u ce d

e le ct ri ci ty

d u ri n g

2 0 ye ar s ($ )

L C O E

($ /k W

h )

A b u m u sa

8 7 5 ,6 0 0

4 9 9

1 2 5 0

2 5 ,4 2 4

9 0 1 ,6 2 4

5 0 ,0 0 0

6 9 ,9 7 7

3 9 5 ,8 0 0

1 ,1 3 8 ,5 8 8

0 .1 1 3

B an d ar

A b b as

8 7 5 ,6 0 0

4 9 9

1 2 5 0

2 5 ,4 2 4

9 0 1 ,6 2 4

5 0 ,0 0 0

6 9 ,9 7 7

3 3 9 ,2 7 5

9 7 5 ,9 8 4

0 .1 3 2

B an d ar

L e n ge h

8 7 5 ,6 0 0

4 9 9

1 2 5 0

2 5 ,4 2 4

9 0 1 ,6 2 4

5 0 ,0 0 0

6 9 ,9 7 7

3 7 2 ,6 7 5

1 ,0 7 2 ,0 6 5

0 .1 2 0

Ja sk

8 7 5 ,6 0 0

4 9 9

1 2 5 0

2 5 ,4 2 4

9 0 1 ,6 2 4

5 0 ,0 0 0

6 9 ,9 7 7

5 2 9 ,4 5 0

1 ,5 2 3 ,0 5 5

0 .0 8 5

K is h

8 7 5 ,6 0 0

4 9 9

1 2 5 0

2 5 ,4 2 4

9 0 1 ,6 2 4

5 0 ,0 0 0

6 9 ,9 7 7

3 6 4 ,9 7 5

1 ,0 4 9 ,9 1 4

0 .1 2 3

L av an

8 7 5 ,6 0 0

4 9 9

1 2 5 0

2 5 ,4 2 4

9 0 1 ,6 2 4

5 0 ,0 0 0

6 9 ,9 7 7

4 2 4 ,0 7 5

1 ,2 1 9 ,9 2 6

0 .1 0 6

Q e sh m

8 7 5 ,6 0 0

4 9 9

1 2 5 0

2 5 ,4 2 4

9 0 1 ,6 2 4

5 0 ,0 0 0

6 9 ,9 7 7

4 2 6 ,6 5 0

1 ,2 2 7 ,3 3 3

0 .1 0 5

Si ri k

8 7 5 ,6 0 0

4 9 9

1 2 5 0

2 5 ,4 2 4

9 0 1 ,6 2 4

5 0 ,0 0 0

6 9 ,9 7 7

5 1 1 ,4 7 5

1 ,4 7 1 ,3 4 7

0 .0 8 8

1242 Energy & Environment 31(7)

need to replace them during these years. On the other hand, the inverter lasts only for 10 years which means it should be replaced at the beginning of year 11.

Net present worth of all costs during 20 years of project lifespan includes present worth of replacement cost and operation and maintenance costs plus capital cost (875,600þ 25,424þ 600¼ 901,624). As to incomes, there are two more incomes of salvage value and renewable electricity revenues. Levelized cost of electricity (LCOE) refers to the cost that will be imposed for producing each kWh of electric power over project lifetime. For instance, to generate 1 kWh of electricity in Jask using 25 sets of examined TSWTs, $0.085 is needed.

Analysis of TSWT performance for Jask using HOMER software

HOMER software was developed in 1993 by the National Renewable Energy Laboratory (NREL) in Golden, Colorado of the United States. The software application is used for designing and evaluating technical and financial options for on-grid and off-grid power systems for different remote, stand-alone, and distributed generation applications. It can be used for different renewable energies, also a tool for hybrid renewable electric generation systems in the world. HOMER is a model of optimization for small power systems, and is also able to evaluate many different structures. The software is an amazing powerful tool for optimal design, sizing and planning of hybrid renewable energy systems. It also simulates configuration systems by balancing energy for each hour and takes the electric or thermal loads per hour that a system can supply.42,43

Daily performance of using TSWT in Jask was estimated by HOMER. By entering monthly mean wind speed data into HOMER software based on weather conditions, geo- graphic location and altitude, it is able to use the synthesis algorithm to track the range of wind speed changes for anticipating frequency of changes in electricity production. Figure 5 shows the frequency of changes in power output or performance of the TSWT during the first 4 h of turbine operation in Jask. It is clear from the charts that in the 1st and 2nd h, the dominant power generation was 0.5 kW with a frequency of between 35 and 40%. In the 3rd and 4th hours, the dominant power generation reaches below 35%. This indicates higher power output due to higher wind speeds. Figure 6 shows the frequency of changes in TSWT performance during the 5th to 8th h of turbine operation. The dominant power decreases over time and instead reaches 0.25 kW at a frequency of about 10% at the 8th h. Figure 7 shows the frequency variations of the TSWT for the 9th to 12th h. The process of change from the 1st h to the 8th is also true here. And over time, the range of power generation has increased so that power of 0.375 kW reaches a frequency of more than 5% at 12th h.

Figure 8 shows the frequency of TSWT performance changes for the 13th to 16th h, the power growth of up to 0.6 kW was increased, and its frequency for 16th h reached almost 4%. Figure 9 shows the frequency of TSWT performance changes for the 17th to 20th h. Compared to the first to fourth operating hours, the maximum frequency reduced to almost 75% during this time, due to the fact that higher generation capacities were produced that reduced the dominant power share by about 0.5 kW. Figure 10 shows the frequency of TSWT performance changes for 21 to 24 h. Similar to the behavior of other time intervals, the frequency of the larger powers begins to grow.

For example, at 24 h, the power frequency of 1 kW is more than 0.5KW which is almost 10% more. Also, for a 24-h time, the generated power frequency is 3 kW and reaches about 4%. Overall, it can be stated that the TSWT function is normal and follows the Gaussian

Mostafaeipour et al. 1243

-15 -10 -5 0 5 10 15 0

5

10

15

20

25

30 Frequency of Changes in TSWT (Aeroleaf) over 6 hours

Change in TSWT (Aeroleaf) (kW) -15 -10 -5 0 5 10 15 0

5

10

15

20

25

30

35 Frequency of Changes in TSWT (Aeroleaf) over 5 hours

Change in TSWT (Aeroleaf) (kW)

-15 -10 -5 0 5 10 15 0

5

10

15

20

25 Frequency of Changes in TSWT (Aeroleaf) over 8 hours

Change in TSWT (Aeroleaf) (kW) -15 -10 -5 0 5 10 15 0

5

10

15

20

25

30 Frequency of Changes in TSWT (Aeroleaf) over 7 hours

Change in TSWT (Aeroleaf) (kW)

Figure 6. Frequency of change in TSWT performance for Jask over 5–8 h.

21606-21- 0

10

20

30

40 Frequency of Changes in TSWT (Aeroleaf) over 2 hours

Change in TSWT (Aeroleaf) (kW) 404-8- 8

0

10

20

30

40 Frequency of Changes in TSWT (Aeroleaf) over 1 hour

Change in TSWT (Aeroleaf) (kW)

-15 -10 -5 0 5 10 0

5

10

15

20

25

30

35 Frequency of Changes in TSWT (Aeroleaf) over 4 hours

Change in TSWT (Aeroleaf) (kW) 21606-21-

0

10

20

30

40 Frequency of Changes in TSWT (Aeroleaf) over 3 hours

Change in TSWT (Aeroleaf) (kW)

Figure 5. Frequency of changes in TSWT performance for Jask over 1–4 h.

1244 Energy & Environment 31(7)

-15 -10 -5 0 5 10 15 0

5

10

15

20 Frequency of Changes in TSWT (Aeroleaf) over 10 hours

Change in TSWT (Aeroleaf) (kW) -15 -10 -5 0 5 10 15 0

5

10

15

20

25 Frequency of Changes in TSWT (Aeroleaf) over 9 hours

Change in TSWT (Aeroleaf) (kW)

-15 -10 -5 0 5 10 15 0

5

10

15

20 Frequency of Changes in TSWT (Aeroleaf) over 12 hours

Change in TSWT (Aeroleaf) (kW) -15 -10 -5 0 5 10 15 0

5

10

15

20 Frequency of Changes in TSWT (Aeroleaf) over 11 hours

Change in TSWT (Aeroleaf) (kW)

Figure 7. Frequency of change in TSWT performance for Jask over 9–12 h.

-15 -10 -5 0 5 10 15 0

4

8

12

16 Frequency of Changes in TSWT (Aeroleaf) over 14 hours

Change in TSWT (Aeroleaf) (kW) -15 -10 -5 0 5 10 15 0

4

8

12

16 Frequency of Changes in TSWT (Aeroleaf) over 13 hours

Change in TSWT (Aeroleaf) (kW)

-15 -10 -5 0 5 10 15 0

3

6

9

12 Frequency of Changes in TSWT (Aeroleaf) over 16 hours

Change in TSWT (Aeroleaf) (kW) -15 -10 -5 0 5 10 15 0

2

4

6

8

10

12

14 Frequency of Changes in TSWT (Aeroleaf) over 15 hours

Change in TSWT (Aeroleaf) (kW)

Figure 8. Frequency of change in TSWT performance for Jask over 13–16 h.

Mostafaeipour et al. 1245

-15 -10 -5 0 5 10 15 0

3

6

9

12 Frequency of Changes in TSWT (Aeroleaf) over 18 hours

Change in TSWT (Aeroleaf) (kW) -15 -10 -5 0 5 10 15 0

3

6

9

12 Frequency of Changes in TSWT (Aeroleaf) over 17 hours

Change in TSWT (Aeroleaf) (kW)

-15 -10 -5 0 5 10 15 0

3

6

9

12 Frequency of Changes in TSWT (Aeroleaf) over 20 hours

Change in TSWT (Aeroleaf) (kW) -15 -10 -5 0 5 10 15 0

3

6

9

12 Frequency of Changes in TSWT (Aeroleaf) over 19 hours

Change in TSWT (Aeroleaf) (kW)

Figure 9. Frequency of change in TSWT performance for Jask over 17–20 h.

-15 -10 -5 0 5 10 15 0

3

6

9

12 Frequency of Changes in TSWT (Aeroleaf) over 22 hours

Change in TSWT (Aeroleaf) (kW) -15 -10 -5 0 5 10 15 0

3

6

9

12 Frequency of Changes in TSWT (Aeroleaf) over 21 hours

Change in TSWT (Aeroleaf) (kW)

-15 -10 -5 0 5 10 15 0

3

6

9

12 Frequency of Changes in TSWT (Aeroleaf) over 24 hours

Change in TSWT (Aeroleaf) (kW) -15 -10 -5 0 5 10 15 0

3

6

9

12 Frequency of Changes in TSWT (Aeroleaf) over 23 hours

Change in TSWT (Aeroleaf) (kW)

Figure 10. Frequency of change in TSWT performance for Jask over 21–24 h.

1246 Energy & Environment 31(7)

distribution function. According to Figure 10, the highest power output is 11.5 kW which

has a frequency of about 0.3%.

Ranking analysis

DEA. As the numbers of DMUs are 8 and total numbers of inputs and outputs are 5, neither

of the two conditions was satisfied, so dual DEA technique was used in order to calculate

the efficiency score for each city and rank them for installing TSWTs. Since in DEA model

the values of inputs are sought to be maximized, the electricity production, CO2 emission

reduction, and population are considered as inputs, whereas the amounts of outputs are

needed to be minimized; therefore, land price, LCOE, and frequency of natural disasters are

deemed as outputs of the model. Among these, LCOE and electricity production were

projected and shown in Tables 3 and 4. Land price, frequency of natural disasters and

population were imported to the model in accordance with Mostafaeipour et al.44

Frequency of natural disasters indicates the possibility of at least one occurrence of powerful

earthquakes, floods, tsunamis and dust storms during the 20 years lifetime of the project. Table 5 illustrates the values of inputs and outputs for each DMU. After running DEA

model by Lingo software, the findings showed that Bandar Abbas, Jask and Sirik had the

highest value of 1, which meant they could not be prioritized. To solve this issue, AP model

was conducted to rank all full-efficient DMUs. The results of AP model indicated that

Bandar Abbas is the best area for using TSWTs followed by Sirik and Jask. In spite of

the fact that utilization of TSWT in Jask can produce more electricity than other seven

areas, it was ranked by AP model as the third best place for using TSWT because this

ranking approach takes several criteria into account not just electricity production. Final

ranking of cities is shown in Table 6.

FTOPSIS. Despite DEA method, FTOPSIS needs weights for each criterion. In this regard,

five experts of the field were asked to dedicate their own opinion as weights of criteria, and

then these verbal values were turned into triangular fuzzy quantitative values using Table 1

and equation (10). Table 7 contains the weights of criteria.

Table 5. The values of inputs and outputs for each DMU imported to dual DEA model.

DMUs City

Inputs of DEA or positive

criteria for FTOPSIS

Outputs of DEA or negative

criteria for FTOPSIS

Electricity

production

CO2 emission

reduction Population42 Land

price42 LCOE

frequency

of natural

disasters42

DMU1 Abumusa 15,832 10.005 4232 270,000 0.113 0.56

DMU2 Bandar Abbas 13,571 8.577 460,812 650,000 0.132 0.50

DMU3 Bandar Lengeh 14,907 9.421 29,654 160,000 0.120 0.61

DMU4 Jask 21,178 13.384 30,134 145,000 0.085 0.46

DMU5 Kish 14,599 9.227 32,846 310,000 0.123 0.56

DMU6 Lavan 16,963 10.721 3968 90,000 0.106 0.50

DMU7 Qeshm 17,066 10.786 31,257 240,000 0.105 0.54

DMU8 Sirik 20,459 12.930 6248 50,000 0.088 0.51

Mostafaeipour et al. 1247

Table 8 illustrates the fuzzy scale-less decision matrix. In triangular fuzzy numbers ded-

icated to the criteria for each area, the first, second and third numbers are the same, i.e. the

amount of criterion electricity production for Abumusa which its triangular fuzzy amount is

0.748, 0.748, 0.748. After gaining the fuzzy scale-less decision matrix, weighted form of that matrix was

obtained as illustrated in Table 9. Fuzzy ideal solution and anti-ideal solution are shown in Table 10.Then, distances from

these solutions were evaluated and presented in Table 11. As indicated in Table 11, Sirik was

selected as the best area for installing and using TSWTs followed by Jask as the second best

location. Since this method is fuzzy and uses uncertainty approaches, the ranking obtained

by FTOPSIS is slightly different from that of DEA model.

Validation. To validate the results obtained by DEA and FTOPSIS, two other reliable

MCDM techniques, MAPPAC and WSA,45 were conducted to rank the cities. As shown

in Table 12, the results of these methods for the first and second best areas support the

results of FTOPSIS.

Discussion

Decision makers including private of government do not limit themselves to only one

method which may achieve different results using different methods.46 Implementing differ-

ent methods is necessary for final decision.47

Table 6. Efficiency scores obtained from dual DEA and AP methods for each city.

Cities

Efficiency score

by dual DEA

Ranking

by DEA

Efficiency score

by dual AP

Ranking

by AP

Abumusa 0.6140739 6 – –

Bandar Abbas 1.0 ? 14.06873 1

Bandar Lengeh 0.5784138 7 – –

Jask 1.0 ? 1.165877 3

Kish 0.5763167 8 – –

Lavan 0.8011089 4 – –

Qeshm 0.6924984 5 – –

Sirik 1.0 ? 2.207908 2

Table 7. Triangular fuzzy weights of criteria.

Criteria Weight

Population (0.3, 0.62, 1)

CO2 emission reduction (0.7, 0.94, 1)

Electricity production (0.7, 0.96, 1)

LCOE (0.3, 0.76, 1)

Land price (0.3, 0.74, 1)

Frequency of natural disasters (0.3, 0.66, 0.9)

1248 Energy & Environment 31(7)

T a b le

8 . Fu zz y sc al e -l e ss

d e ci si o n m at ri x .

C it ie s

E le ct ri ci ty

p ro d u ct io n

C O

2 e m is si o n

re d u ct io n

P o p u la ti o n

L an d p ri ce

L C O E

Fr e q u e n cy

o f

n at u ra l d is as te rs

A b u m u sa

(0 .7 4 8 , 0 .7 4 8 , 0 .7 4 8 )

(0 .7 4 8 , 0 .7 4 8 , 0 .7 4 8 )

(0 .0 0 9 , 0 .0 0 9 , 0 .0 0 9 )

(0 .1 8 5 , 0 .1 8 5 , 0 .1 8 5 )

(0 .7 5 2 , 0 .7 5 2 , 0 .7 5 2 )

(0 .8 2 1 , 0 .8 2 1 , 0 .8 2 1 )

B an d ar

A b b as

(0 .6 4 1 , 0 .6 4 1 , 0 .6 4 1 )

(0 .6 4 1 , 0 .6 4 1 , 0 .6 4 1 )

(1 , 1 , 1 )

(0 .0 7 7 , 0 .0 7 7 , 0 .0 7 7 )

(0 .6 4 4 , 0 .6 4 4 , 0 .6 4 4 )

(0 .9 2 , 0 .9 2 , 0 .9 2 )

B an d ar

L e n ge h

(0 .7 0 4 , 0 .7 0 4 , 0 .7 0 4 )

(0 .7 0 4 , 0 .7 0 4 , 0 .7 0 4 )

(0 .0 6 4 , 0 .0 6 4 , 0 .0 6 4 )

(0 .3 1 3 , 0 .3 1 3 , 0 .3 1 3 )

(0 .7 0 8 , 0 .7 0 8 , 0 .7 0 8 )

(0 .7 5 4 , 0 .7 5 4 , 0 .7 5 4 )

Ja sk

(1 , 1 , 1 )

(1 , 1 , 1 )

(0 .0 6 5 , 0 .0 6 5 , 0 .0 6 5 )

(0 .3 4 5 , 0 .3 4 5 , 0 .3 4 5 )

(1 , 1 , 1 )

(1 , 1 , 1 )

K is h

(0 .0 7 1 , 0 .0 7 1 , 0 .0 7 1 )

(0 .6 8 9 , 0 .6 8 9 , 0 .6 8 9 )

(0 .0 7 1 , 0 .0 7 1 , 0 .0 7 1 )

(0 .1 6 1 , 0 .1 6 1 , 0 .1 6 1 )

(0 .6 9 1 , 0 .6 9 1 , 0 .6 9 1 )

(0 .8 2 1 , 0 .8 2 1 , 0 .8 2 1 )

L av an

(0 .8 0 1 , 0 .8 0 1 , 0 .8 0 1 )

(0 .8 0 1 , 0 .8 0 1 , 0 .8 0 1 )

(0 .0 0 9 , 0 .0 0 9 , 0 .0 0 9 )

(0 .5 5 6 , 0 .5 5 6 , 0 .5 5 6 )

(0 .8 0 2 , 0 .8 0 2 , 0 .8 0 2 )

(0 .9 2 , 0 .9 2 , 0 .9 2 )

Q e sh m

(0 .8 0 6 , 0 .8 0 6 , 0 .8 0 6 )

(0 .8 0 6 , 0 .8 0 6 , 0 .8 0 6 )

(0 .0 6 8 , 0 .0 6 8 , 0 .0 6 8 )

(0 .2 0 8 , 0 .2 0 8 , 0 .2 0 8 )

(0 .8 1 , 0 .8 1 , 0 .8 1 )

(0 .8 5 2 , 0 .8 5 2 , 0 .8 5 2 )

Si ri k

(0 .9 6 6 , 0 .9 6 6 , 0 .9 6 6 )

(0 .9 6 6 , 0 .9 6 6 , 0 .9 6 6 )

(0 .0 1 4 , 0 .0 1 4 , 0 .0 1 4 )

(1 , 1 , 1 )

(0 .9 6 6 , 0 .9 6 6 , 0 .9 6 6 )

(0 .9 0 2 , 0 .9 0 2 , 0 .9 0 2 )

Mostafaeipour et al. 1249

T a b le

9 . W e ig h te d fu zz y sc al e -l e ss

d e ci si o n m at ri x .

C it ie s

E le ct ri ci ty

p ro d u ct io n

C O

2 e m is si o n

re d u ct io n

P o p u la ti o n

L an d p ri ce

L C O E

Fr e q u e n cy

o f

n at u ra l d is as te rs

A b u m u sa

(0 .5 2 3 , 0 .7 1 8 , 0 .7 4 8 )

(0 .5 2 3 , 0 .7 0 3 , 0 .7 4 8 )

(0 .0 0 3 , 0 .0 0 6 , 0 .0 0 9 )

(0 .0 5 6 , 0 .1 3 7 , 0 .1 8 5 )

(0 .2 2 6 , 0 .5 7 2 , 0 .7 5 2 )

(0 .2 4 6 , 0 .5 4 2 , 0 .7 3 9 )

B an d ar

A b b as

(0 .4 4 9 , 0 .6 1 5 , 0 .6 4 1 )

(0 .4 4 9 , 0 .6 0 2 , 0 .6 4 1 )

(0 .3 , 0 .6 2 , 1 )

(0 .0 2 3 , 0 .0 5 7 , 0 .0 7 7 )

(0 .1 9 3 , 0 .4 8 9 , 0 .6 4 4 )

(0 .2 7 6 , 0 .6 0 7 , 0 .8 2 8 )

B an d ar

L e n ge h

(0 .4 9 3 , 0 .6 7 6 , 0 .7 0 4 )

(0 .4 9 3 , 0 .6 6 2 , 0 .7 0 4 )

(0 .0 1 9 , 0 .0 4 , 0 .0 6 4 )

(0 .0 9 4 , 0 .2 3 1 , 0 .3 1 3 )

(0 .2 1 3 , 0 .5 3 8 , 0 .7 0 8 )

(0 .2 2 6 , 0 .4 9 8 , 0 .6 7 9 )

Ja sk

(0 .7 , 0 .9 6 , 1 )

(0 .7 , 0 .9 4 , 1 )

(0 .0 2 , 0 .0 4 1 , 0 .0 6 5 )

(0 .1 0 3 , 0 .2 2 5 , 0 .3 4 5 )

(0 .3 , 0 .7 6 , 1 )

(0 .3 , 0 .6 6 , 0 .9 )

K is h

(0 .4 8 3 , 0 .6 6 2 , 0 .6 8 9 )

(0 .4 8 3 , 0 .6 4 8 , 0 .6 8 9 )

(0 .0 2 1 , 0 .0 4 4 , 0 .0 7 1 )

(0 .0 4 8 , 0 .1 1 9 , 0 .1 6 1 )

(0 .2 0 7 , 0 .5 2 5 , 0 .6 9 1 )

(0 .2 4 6 , 0 .5 4 2 , 0 .7 3 9 )

L av an

(0 .5 6 1 , 0 .7 6 9 , 0 .8 0 1 )

(0 .5 6 1 , 0 .7 5 3 , 0 .8 0 1 )

(0 .0 0 3 , 0 .0 0 5 , 0 .0 0 9 )

(0 .1 6 7 , 0 .4 1 1 , 0 .5 5 6 )

(0 .2 4 1 , 0 .6 0 9 , 0 .8 0 2 )

(0 .2 7 6 , 0 .6 0 7 , 0 .8 2 8 )

Q e sh m

(0 .5 6 4 , 0 .7 7 4 , 0 .8 0 6 )

(0 .5 6 4 , 0 .7 5 8 , 0 .8 0 6 )

(0 .0 2 , 0 .0 4 2 , 0 .0 6 8 )

(0 .0 6 3 , 0 .1 5 4 , 0 .2 0 8 )

(0 .2 4 3 , 0 .6 1 5 , 0 .8 1 )

(0 .2 5 6 , 0 .5 6 2 , 0 .7 6 7 )

Si ri k

(0 .6 7 6 , 0 .9 2 7 , 0 .9 6 6 )

(0 .6 7 6 , 0 .9 0 8 , 0 .9 6 6 )

(0 .0 0 4 , 0 .0 0 8 , 0 .0 1 4 )

(0 .3 , 0 .7 4 , 1 )

(0 .2 9 , 0 .7 3 4 , 0 .9 6 6 )

(0 .2 7 1 , 0 .5 9 5 , 0 .8 1 2 )

1250 Energy & Environment 31(7)

T a b le

1 0 . Id e al an d an ti -i d e al fu zz y so lu ti o n .

C ri te ri a

E le ct ri ci ty

p ro d u ct io n

C O

2 e m is si o n

re d u ct io n

P o p u la ti o n

L an d p ri ce

L C O E

Fr e q u e n cy

o f

n at u ra l d is as te rs

Id e al fu zz y so lu ti o n

(1 , 1 , 1 )

(1 , 1 , 1 )

(1 , 1 , 1 )

(1 , 1 , 1 )

(1 , 1 , 1 )

(0 .9 , 0 .9 , 0 .9 )

A n ti -i d e al fu zz y so lu ti o n

(0 .4 4 9 , 0 .4 4 9 ,

0 .4 4 9 )

(0 .4 4 9 , 0 .4 4 9 ,

0 .4 4 9 )

(0 .0 3 , 0 .0 3 ,

0 .0 3 )

(0 .0 2 3 , 0 .0 2 3 ,

0 .0 2 3 )

(0 .1 9 3 , 0 .1 9 3 ,

0 .1 9 3 )

(0 .2 2 6 , 0 .2 2 6 ,

0 .2 2 6 )

Mostafaeipour et al. 1251

A system of 25 TSWTs for each of the areas showed that such system would create

highest total electricity of 529,450 kWh/yr for Jask, but the lowest belonged to Bandar

Abbas with amount of 339,275 kWh/yr. DEA method does not require any weights for criteria, and just needs values of them to

rank DMUs. As a result, its results might be different from those of FTOPSIS in any

decision-making problem. In this study, DEA chose Bandar Abbas as the best alternative,

while FTOPSIS, MAPPAC, and WSA methods which consider opinions of experts as

weights of criteria, indicated that Sirik was the best location regarding TSWTs utilization. In spite of the fact that Jask had the largest amount of electricity production among other

areas, DEA ranked it in the third place and FTOPSIS, MAPPAC and WSA indicated that

the city was the second best area. This showed that electricity production could not be

deemed as the only one deciding factor to launch a project.

Conclusion

This study aimed to evaluate the feasibility of a new wind power generation system for

urban uses in eight areas in Hormozgan province of Iran. The wind power system assessed

in this study is a new, noiseless, pollution-free, potentially cost-effective, aesthetically pleas-

ing, and high-efficiency design called tree-shaped wind turbine (TSWT), with the capability

of operating under low wind speeds. For this purpose, first the average wind speed in each

Table 11. Distances from fuzzy ideal and anti-ideal solutions, closeness index and cities’ ranks.

Cities

Distances from

fuzzy ideal solution

Distances from fuzzy

anti-ideal solution Closeness index Ranking

Abumusa 3.5476 1.3252 0.271964207 6

Bandar Abbas 3.2803 1.7480 0.347636113 4

Bandar Lengeh 3.5544 1.3081 0.269013092 7

Jask 2.8820 2.2227 0.435423747 2

Kish 3.6515 1.2079 0.248573897 8

Lavan 3.1649 1.7910 0.36138808 3

Qeshm 3.3608 1.5457 0.315032578 5

Sirik 2.6555 2.5211 0.487016623 1

Table 12. Efficiency scores obtained by MAPPAC and WSA methods.

Cities MAPPAC WSA

Abumusa 5 6

Bandar Abbas 7 8

Bandar Lengeh 4 5

Jask 2 2

Kish 8 7

Lavan 3 4

Qeshm 6 3

Sirik 1 1

1252 Energy & Environment 31(7)

area was obtained using the data pertaining to a 15-year period from 2000 to 2015 collected from the website of Iran Meteorological organization. After data collection, techno- economic feasibility analysis of installing and using TSWTs was carried out in the studied

areas. FTOPSIS and DEA were used to rank the areas considering the most important criteria including electricity production, LCOE, population, land price, environmental impact and frequency of natural disasters. Findings of this study are as followings:

• The results of technical analysis of a wind power system consisting of 25 TSWTs for each of the areas showed that such system would create highest total electricity of 529,450 kWh/yr for Jask, but the lowest belonged to Bandar Abbas with amount of

339,275 kWh/yr. • Scrutinizing economic aspects indicated that launching this project for Jask would impose

the least LCOE which is favorable to investors, and on the other hand Bandar Abbas has the greatest LCOE with values of 0.085 and 0.132 ($/kWh), respectively.

• Since the more the electricity generation by a means of renewable resources, the less the emission of CO2, and therefore Jask showed the most amount of CO2 emission reduction among others.

• DEA method does not require any weights for criteria, and just needs values of them to rank DMUs. As a result, its results might be different from those of FTOPSIS in any

decision-making problem. In this study, DEA chose Bandar Abbas as the best alterna- tive, while FTOPSIS which considers opinions of experts into account as weights of criteria, indicated that Sirik was the best area regarding TSWTs utilization.

• To solve this contrast, two more MCDM models named MAPPAC and WSA were applied. Their results subscribed to the results of FTOPSIS.

• Since FTOPSIS, MAPPAC and WSA methods suggested that Sirik was the best location for utilizing TSWTs, so it is recommended that these types of wind turbines should be

installed and used in Sirik rather than other areas examined in this study. • In spite of the fact that Jask had the largest amount of electricity production among other

areas, but DEA ranked it in the third place and FTOPSIS, MAPPAC and WSA indicated that the city was the second best area. This showed that electricity production could not be deemed as the only one deciding factor to launch a project.

Suggestion

• Low quality batteries in Iran are another problem which causes high cost of operating the wind turbines. A hybrid system of solar wind tree shaped turbine could be attractive to be

designed in the future, since the present system is operational by only wind power.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or

publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this

article.

Mostafaeipour et al. 1253

ORCID iDs

Ali Mostafaeipour https://orcid.org/0000-0002-2195-4511

Mostafa Rezaei https://orcid.org/0000-0002-9970-4635

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Ali Mostafaeipour is an Associate Professor of Industrial Engineering at Yazd University, Iran. He was selected as the top 1% scientist in the world for the year 2018 and 2019 by Thompson Reuther (ISI-ESI), and Clarivate Analytics. He has been teaching at Yazd University since 1989. He studied at Winona State University (University of Minnesota) in state of Minnesota, USA; University of Wisconsin at Platteville, Wisconsin, USA; Alabama A&M, Alabama, USA; and Iran University of Science and Technology, Tehran, Iran. He has undertaken and managed 18 research projects, and holds 3 patents. He has been editorial board of several professional journals. Finally, he has published 101 journal articles mostly at Elsevier, and he authored 5 books. He holds an award for excel- lence from Yazd University as the year 2013, and 2018 distinguished researcher, also holds distinguished author of “Wind Energy” book (INTech publisher, 2012, Croatia) with more than 5000 downloads in six months. He has received four awards at different IEOM inter- national conferences too. His research interest lies in renewable energies, wind energy, value engineering, economic evaluation, and feasibility study of project.

Mostafa Rezaei got his Industrial Engineering MS from Yazd University in 2014. He has published 16 journal papers mostly ISI. Also, he has published 4 international conference papers too.

Mehdi Jahangiri is an Assistant Professor of Mechanical Engineering in Shahrekord branch, Islamic Azad University, Shahrekord, Iran. He received his PhD degree in Mechanical Engineering from Isfahan University of Technology (IUT), Isfahan, Iran in 2016. His cur- rent research interest includes biomechanics and renewable energy. He is a referee in IEEE, Springer, Elsevier and other internationals journals. He is author of several publications on Mechanical Engineering, including about 60 papers published in scientific journals and conferences. Dr Mehdi Jahangiri is the winner of Shahrekord branch, Islamic Azad University “top researcher award” in 2014 and 2019.

Mojtaba Qolipour got his Industrial Engineering MS from Yazd University in 2015. He has published 29 journal papers mostly ISI. Also, he has published 12 international conference papers too.

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