Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 614514, 9 pages
http://dx.doi.org/10.1155/2015/614514

Research Article

Economic Analysis of Wind Turbine Installation in Taiwan

Jeeng-Min Ling and Kunkerati Lublertlop

Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan

Received 30 September 2014; Accepted 2 December 2014

Academic Editor: Mo Li

Copyright © 2015 Jeeng-Min Ling and Kunkerati Lublertlop. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

The wind speed characteristics are analyzed statistically based on a long-term hourly data record to evaluate the proper wind energy potential. The annual average wind speed and wind power density are investigated and compared by some significant indices, wind energy output and capacity factor, to show the variations of proper wind turbine specifications of installation in different locations of Taiwan. The minimum cost of wind energy is used to assess the economical feasibility for turbine installation in Taiwan. Great variations occur in the simulation results in both of the cost of energy and capacity factor. The detailed statistical analysis should be conducted to ensure the successful operation after wind turbine installations.

1. Introduction

The demand for energy and particularly for electricity is growing rapidly in the country of rapid economic growth. Taiwan has no nature reserves, but electricity mainly relied on conventional fossil fuel. The development of electricity capacity from renewable energy in Taiwan is vital. In this aspect, wind power plays a major role in the enhancement of renewable energy before a sharp increasing in the photovoltaic energy after year 2020 [1]. With abundant wind resources along the west coast of Taiwan and some offshore islands, the Asian monsoon, tropical cyclones during the summer season and the northeast trade winds during the winter season, induces high winds speed in many regions. Taiwan has superior advantages to develop wind energy geographically.

Many studies related to the study of wind characteristics and wind power potentials have been conducted worldwide recently [2–6]. Belu and Koracin studied the wind characteristic in western Nevada, USA, in which the wind speed at different tower heights is estimated using the standard power extrapolation equation and consequently the power law exponent values are analyzed for different time periods and locations [7]. The empirical and graphical methods were used to analyze the wind power density at the heights of 10, 30, and 60 m, respectively [8]. Two statistical methods, meteorological and Weibull, were presented to evaluate the wind speed characteristic and the wind power potential at an open area of 17 synoptic sites distributed throughout the territory of Tunisia [9].

Many researchers have proposed different economic methods to assess installation of wind turbine. Fingersh et al. presented a simple payback period method [10]. The simple payback period is the number of years which will be taken to recover the initial capital cost for installation of a new wind turbine generator (WTG). An important issue of this model is assumed by the fact that WTG will produce the same amount of electricity each year and attain constant revenue stream. However, the discount rate and the lifetime of the project are not considered. The cost model of wind energy is defined as the unit cost to produce energy from the WTG system. The numbers of lifetime of project and discount rate are included when annual cost is evaluated. Schmidt constructed expressions for computing component costs of wind turbine [11].

This paper proposes a procedure based on the detailed economical model including wind turbine components cost, the annual operation, and maintenance cost of wind turbine to get the minimum cost of wind energy. The minimum cost of wind energy was used to assess the feasibility of installed wind turbine at 24 locations in Taiwan. The wind energy output and capacity factor of twenty commercial wind turbines in terms of different designed hub height were also investigated.

2. The Mathematical Models for Wind Energy

Using estimation of regional wind resources, one can estimate the electrical producing potential of wind energy. This wind energy resource atlas identifies the wind characteristics and distribution of the wind resource. An important parameter in the characterization of the wind resource is the variation of horizontal wind speed which is expected to be zero at the earth’s surface and to increase with height in the atmospheric boundary layer. Wind speed is the most important aspect of the wind resource; in fact the year variation of long-term mean wind speed provides an understanding of the long-tern pattern of wind speed and also gives confidence to an investor in the availability of wind power in coming years [12].

The wind speed measured at weather station differs from the height of WTG hub. If these heights do not match the hub height of a WTG, it is necessary to extrapolate the wind speeds to the hub height of the WTG. This variation of wind speed with elevation is called the vertical profile of the wind speed or vertical wind shear, and it can be implemented by the following [13]:

where is wind speed at the hub height of WTG; is wind speed at the weather station; is hub height of WTG; is sea level height of the weather station; is wind speed power law coefficient.

The actual wind power output of WTG is determined by the turbine performance curve, which is well described by (2). The coefficients of the power curve can be described by the specification of wind turbine manufacturer. The power curve with third-order equation is easily digitized into any discrete points dependent on the simulation accuracy. Consider

where , , , and are coefficients of the power curve of WTG; is cut-in speed of WTG; is rate speed of WTG; is cut-off speed of WTG; is rated power of WTG (kW); is the electrical power output of WTG (kW).

After comparing the actual WTG energy output and the energy output with rated capacity, the capacity factor (CF) can be conducted. Consider

The wind energy output from a wind turbine evaluated by the Weibull, Rayleigh, Lognormal, and Gamma probability models are denoted by the following:

where is the actual wind energy output of the WTG for the period (kW/h), is the wind energy output operated with the full capacity for the period (kW/h), is the probability density function (Weibull, Rayleigh, Lognormal, and Gamma), and is the mean winds speed (m/s).

3. Economic Analysis Methods

The unit cost of wind electricity power can be determined by knowing capital investment and operating costs. It is important for estimating the investment cost of each WTG type in each location before installation. However, the value of the wind electricity power is somewhat difficult to determine, but it must be evaluated before making investment decision. The significant cost of wind energy will be included and discussed in this study.

The cost of energy (COE, $/kWh) can be defined by (5), where is the total annual cost ($) and is the annual electrical energy output of WTG (kWh). Consider

The total annual cost of a WTG is the sum of its operation, maintenance expenses, and annual repayments on its capital. It can be determined by the following:

where is the discount factor; is the discount rate (%) and often takes about 10%; is the lifetime of project, often taken to be 20 years; is the initial capital cost of building WTG ($).

The operation and maintenance cost yearly may be expressed as a proportion of the initial capital cost about 2.5% [14]. In this study, the initial capital cost of WTG is set based on references [10]. Using the model, the total annual cost () of the project can be estimated. The annual COE is a popular index to estimate the different amount of electricity for each WTG at each year. The component cost models needed to calculate the initial capital cost of wind turbine can be summarized in the following list of the components cost and in Figure 1 [11].

Figure 1: Initial capital cost model.

List of the Components Cost

 , , , , , , , , , , , , , , , , , , , , , , , , , .

In the above list, represents rotor radius (m), is power nominal of wind turbine (kW), is rotor swept area (m²), and Hub is height of tower (m).

4. Simulation Results and Discussions

In this study, the wind speed data were measured hourly by Central Weather Bureau of Taiwan in these six years at the 24 locations. All measurements in the wind observation station are recorded using the cup anemometer at a height of 10 m above the ground level. The geographical and meteorological information of the 24 stations is given in Figure 2 [1] and Table 1, respectively.

Table 1: Details of the geographical information for the tested weather stations.

Number
Sites
Longitude
(deg)
Latitude
(deg)
Altitude
(m)

1
Pengchiayu
122°04′ E
25°37′ N
12.5

2
Anpu
121°31′ E
25°11′ N
7.31

3
Chutzehu
121°32′ E
25°09′ N
11.03

4
Tanshui
121°26′ E
25°09′ N
12.2

5
Keelung
121°43′ E
25°08′ N
34.6

6
Taipei
121°30′ E
25°02′ N
34.9

7
Hsinchu
120°58′ E
24°48′ N
15.6

8
Ilan
121°44′ E
24°45′ N
26

9
Taichung
120°40′ E
24°08′ N
17.2

10
Wuchi
120°30′ E
24°15′ N
32.2

11
Hualien
121°36′ E
23°58′ N
12

12
Sun Moon Lake
120°53′ E
23°52′ N
8

13
Penghu
119°33′ E
23°34′ N
14.6

14
Alishan
120°48′ E
23°30′ N
15.1

15
Chiayi
120°25′ E
23°29′ N
14.5

16
Yushan
120°57′ E
23°29′ N
9.2

17
Tungchitao
119°39′ E
23°15′ N
9.1

18
Cheng Kung
121°21′ E
23°05′ N
12.8

19
Tainan
120°11′ E
22°59′ N
37.6

20
Taitung
121°08 E
22°45′ N
11.4

21
Kaohsiung
120°18′ E
22°34′ N
14

22
Tawu
120°53′ E
22°21′ N
12.7

23
Lanyu
121°33′ E
22°02′ N
12.5

24
Hengchun
120°44′ E
22°00′ N
14.3

Figure 2: Geographical location of the meteorological stations used in the study.

4.1. Simulation Results of Capacity Factor

The average power output and capacity factor of a WTG are very important parameters to show the performance of WTG. The related specifications of 20 popular commercial WTGs for testing are shown in Table 2 [13]. In general, the capacity factor decreases when the cut-in wind speed of a WTG reduces and its cut-off wind speed increases; another significant factor is the hub height of a WTG. The best and worst capacity factors at 24 tested sites evaluated by four different PDFs are listed in Table 3.

Table 2: Specifications of twenty different wind turbines.

Number
WTG type
Rated power (kW)
Cut-in speed (m/s)
Rated wind speed (m/s)
Cut-off speed (m/s)
Rotor diameter (m)
Hub height (m)

1
E33
330
3
17
28–34
33.4
36–50

2
E44
900
2
17
28–34
44
45–55

3
E48
800
2
14
28–35
48
50–76

4
E53
800
2
13
28–36
52.9
60/73

5
E71
2300
2
15
28–37
71
64–113

6
E82
2000
2
12
28–38
82
78–138

7
G52
850
4
16
25
52
45/55/65

8
G58
850
3
16
21
58
45/55/65

9
G80
2000
4
17
25
80
44–71

10
G87
2000
4
16
25
87
67–100

11
G90
2000
3
16
21
90
67–100

12
GE15xle
1500
3.5
12.5
20
82.5
80

13
GE15sle
1500
3.5
14
25
77
65/80

14
GE25
2500
3.5
12.5
25
100
75/85/100

15
V52
850
4
16
25
52
44/49/55/65/74

16
V80
2000
4
15
25
80
60/67/78/100

17
V82
1650
3.5
13
20
82
70/78/80

18
V90_1.8
1800
3.5
12
25
90
80/95/105

19
V90_2.0
2000
2.5
13
25/21
90
80/95/106

20
V90_3.0
3000
4
15
25
90
80/105

Table 3: Capacity factors of wind turbines extreme status at 24 sites during 6 years are computed from probability distributions.

Sites
Extreme status
Weibull
Gamma
Lognormal
Rayleigh

Type WTG
Hub height
Capacity factor
Type WTG
Hub height
Capacity factor
Type WTG
Hub height
Capacity factor
Type WTG
Hub height
Capacity factor

Pengchiayu
Highest
V90_1800
105
0.5772
V90_1800
105
0.5612
V90_1800
105
0.5245
V90_1800
105
0.5245

Lowest
E44
45
0.3765
E44
45
03599
E44
45
0.3455
E44
45
0.3455

Anpu
Highest
V90_2M
105
0.1461
V90_2M
105
0.1477
V90_2M
105
0.1650
V90_2M
105
0.1433

Lowest
E44
45
0.0734
E44
45
0.0772
G52
55
0.1027
E44
45
0.0661

Chutzehu
Highest
E82
138
0.1421
E82
138
0.1414
E82
138
0.1496
E82
138
0.1365

Lowest
G80
44
0.0506
G80
44
0.0519
V52
44
0.0708
G80
44
0.0345

Tanshui
Highest
V90_2M
105
0.0541
E82
138
0.0607
E82
138
0.0878
V90_2M
105
0.0529

Lowest
G80
44
0.0112
G80
44
0.0160
V52
44
0.0392
G80
44
0.0062

Keelung
Highest
E82
138
0.0451
E82
138
0.0512
E82
138
0.0812
V90_2M
105
0.0438

Lowest
G80
44
0.0038
G80
44
0.0070
V52
44
0.0290
G80
44
0.0012

Taipei
Highest
E82
138
0.1037
E82
138
0.1137
E82
138
0.1418
E82
138
0.0905

Lowest
G80
44
0.0257
G80
44
0.0336
V52
44
0.0627
G80
44
0.0163

Hsinchu
Highest
E82
138
0.0868
E82
78
0.0936
E82
78
0.1192
V90_2M
105
0.0772

Lowest
G80
44
0.0246
G80
44
0.0298
V52
44
0.0538
G80
44
0.0162

Ilan
Highest
E82
138
0.0570
E82
138
0.0598
E82
138
0.0860
V90-2M
105
0.0547

Lowest
G80
44
0.0151
G80
44
0.0171
V52
44
0.0406
G80
44
0.0071

Taichung
Highest
E82
138
0.0525
E82
138
0.0598
E82
138
0.0867
V90_2M
105
0.0500

Lowest
G80
44
0.0056
G80
44
0.0093
G80
44
0.0291
G80
44
0.0025

Wuchi
Highest
E82
138
0.2587
E82
138
0.2545
E82
138
0.2533
E82
138
0.2675

Lowest
E44
45
0.1336
E44
45
0.1395
E44
45
0.1605
E44
45
0.1287

Hualien
Highest
E82
138
0.1805
E82
138
0.1728
E82
138
0.1819
E82
138
0.1809

Lowest
E44
45
0.0737
E44
45
0.0737
V52
44
0.0881
E44
45
0.0705

Sun Moon Lake
Highest
E82
138
0.0335
E82
138
0.0352
E82
138
0.0514
E82
138
0.0368

Lowest
G80
44
0.0030
G80
44
0.0043
G80
44
0.0180
G80
44
0.0007

Penghu
Highest
E82
138
0.2505
E82
138
0.2515
E82
138
0.2532
E82
138
0.2533

Lowest
E44
45
0.1186
E44
45
0.1310
V52
44
0.1566
E44
45
0.1130

Alishan
Highest
E82
138
0.0243
E82
138
0.0231
E82
138
0.0264
E82
138
0.0284

Lowest
G80
44
0.0002
G80
44
0.0005
G80
44
0.0049
G80
44
0.0000

Chiayi
Highest
E82
138
0.0619
E82
138
0.0690
E82
138
0.1013
V90-2M
105
0.0572

Lowest
G80
44
0.0154
G80
44
0.0205
V52
44
0.0492
G80
44
0.0077

Yushan
Highest
E82
138
0.4824
E82
138
0.4493
E82
138
0.3858
E82
138
0.5329

Lowest
E44
45
0.3111
E44
45
0.2950
E44
45
0.2648
E44
45
0.3323

Tungchitao
Highest
E82
138
0.6192
E82
138
0.5805
E82
138
0.5069
E82
138
0.6546

Lowest
E44
45
0.4598
E44
45
0.4299
E44
45
0.3779
E44
45
0.4823

Cheng Kung
Highest
E82
138
0.1867
E82
138
0.1853
E82
138
0.1991
E82
138
0.1859

Lowest
E44
45
0.0841
E44
45
0.0885
V52
44
0.1109
E44
45
0.0798

Tainan
Highest
E82
138
0.1179
E82
138
0.1267
E82
138
0.1529
E82
138
0.1174

Lowest
G80
44
0.0320
G80
44
0.0385
G80
44
0.0616
G80
44
0.0316

Taitung
Highest
V90_2M
105
0.0537
E82
138
0.0552
E82
138
0.0693
V90_2M
105
0.0537

Lowest
G80
44
0.0111
G80
44
0.0118
G80
44
0.0118
G80
44
0.0118

Kaohsiung
Highest
V90_2M
105
0.0528
V90_2M
105
0.0560
E82
138
0.0749
V90-2M
105
0.0530

Lowest
G80
44
0.0102
G80
44
0.0130
G80
44
0.0297
G80
44
0.0081

Tawu
Highest
E82
138
0.1369
E82
138
0.1408
E82
138
0.1588
E82
138
0.1335

Lowest
G80
44
0.0378
G80
44
0.0378
G80
44
0.0427
G80
44
0.0344

Lanyu
Highest
V90_1800
105
0.5576
V90_1800
105
0.5457
V90_1800
105
0.5004
V90_1800
105
0.5870

Lowest
E44
45
0.3898
E44
45
0.3710
E44
45
0.3442
E44
45
0.4051

Hengchun
Highest
E82
138
0.2534
E82
138
0.2502
E82
138
0.2400
E82
138
0.2706

Lowest
E44
45
0.1176
E44
45
0.1245
V52
49
0.1427
E44
45
0.1072

The highest CFs were operated by V90_1800 WTG at the 105 m hub height and E82 WTG at the height of 138 m. The lowest CF was operated by the E44 WTG at the height of 44 m. From the results of Table 3, three groups with different CF can be set.

(i) The First Group with High CF Regions. The CF varies from 0.482 to 0.6192 (with highest extreme status) and from 0.3111 to 0.4598 (with lowest extreme status); it includes the sites of Tungchitao, Lanyu, Yushan, and Pengchiayu. The highest CF occurs at Tungchitao with E82 WTG. The perfect CF operates at the sites of Lanyu and Pengchiayu by the V90_1800 WTG.

(ii) The Second Group with Average CF Regions. The CF varies from 0.045 to 2.587 (with highest extreme status) and from 0.0038 to 0.1336 (with lowest extreme status); it includes the sites of Wuchi, Hengchun, Penghu, Cheng Kung, Anpu, Keelung, Taipei, and Tainan. The average wind speed of this group varies in a range of 3 m/s~5 m/s, and the best CF in this group operated by E82 WTG with 138 m height occurs at Wuchi site. The worst CF calculated from whole probability density operated by E44 WTG with 45 m height occurs at Anpu site.

(iii) The Third Group of Small CF Regions. The remaining sites are Chutzehu, Tanshui, Hsinchu, Ilan, Taichung, Hualien, and so forth. The CF of WTG in this group is very low, while the lowest CF (with highest extreme status) occurs at Sun Moon Lake (0.0335) and Alishan (0.0243). The best capacity factor is operated by E82 WTG and the worst is operated by G80 WTG.

In general, the CF evaluated by the Weibull probability density in most of the sites is higher than that evaluated by other probability distributions, while the CF calculation by the lognormal model derived the lowest value.

4.2. Simulation Results of Wind Energy Cost

The capital cost of wind turbine depends on the size of wind turbine and its hub height. This study analyzes the sensitivity of minimizing cost of energy (COE) and specific rotor rating on various parameters and wind resource characteristics. The important decision is the choices of COE. As mentioned above, the wind energy resources of CF in Taiwan can be classified into three groups. The study of COE is focused on the first group with the higher wind energy potentials. Table 4 shows the evaluation of Pengchiayu site. In general, the wind speed at the first group, which includes Pengchiayu, Yushan, Tungchitao, and Lanyu, is very high; the wind speed at the weather stations’ height is over 5.9 m/s. Therefore, the average capacity factors of wind turbines in this group are high. The maximum wind energy can be found in V90-3M wind turbine with 105 m hub height and the minimum can be found in E33 wind turbine with 36 m hub height.

Table 4: The average capacity factor of WTG and the average cost of wind energy at “Pengchiayu” site evaluated from four different probability distributions.

Type wind turbine
Height (m)

($)

($)
 (m/s)
  (m/s)
Weibull
Gamma
Lognormal
Rayleigh

CF

(kWh)
COE  
($)
CF

(kWh)
COE  
($)
CF

(kWh)
COE  
($)
CF

(kWh)
COE  
($)

E33
36
351683.21
66735.922
8.17
3.91
0.4656407
1346074
0.051
0.448
1295884
0.053
0.427
1235276
0.055
0.449
1298153
0.053

E33
50
367008.95
69644.158
8.47
4.06
0.4902
1417073
0.0503
0.4731
1367527
0.0520
0.4493
1298957
0.0547
0.4720
1364516
0.0522

E44
45
758045.53
143847.84
8.38
4.01
0.3765
2968249
0.0496
0.3599
2837622
0.0518
0.3455
2724304
0.0539
0.3659
2884902
0.0510

E44
55
774782.46
147023.86
8.56
4.10
0.3912
3084305
0.0488
0.3742
2950208
0.0510
0.3579
2821934
0.0532
0.3796
2993059
0.0503

E48
50
751905.43
142682.68
8.47
4.06
0.4601
3224316
0.0454
0.4416
3094976
0.0472
0.4194
2938985
0.0497
0.4441
3112572
0.0470

E48
76
801628.7
152118.24
8.87
4.25
0.4913
3443048
0.0453
0.4731
3315335
0.0470
0.4472
3133946
0.0496
0.4732
3316498
0.0470

E53
60
829915.6
157486
8.65
4.14
0.5148
3607771
0.0448
0.4971
3483821
0.0463
0.4702
3295026
0.0489
0.4953
3471394
0.0465

E53
73
859053.45
163015.25
8.83
4.23
0.5292
3708292
0.0451
0.5119
3587412
0.0465
0.4835
3388252
0.0492
0.5088
3565517
0.0468

E70
64
2008539.6
381143.44
8.71
4.17
0.4112
8283960
0.0471
0.3927
7911310
0.0493
0.3734
7522395
0.0518
0.3981
8020871
0.0487

E70
113
2188995.1
415386.94
9.27
4.44
0.4537
9140657
0.0466
0.4346
8756834
0.0486
0.4099
8257840
0.0514
0.4376
8816383
0.0482

E82
78
2121719.1
402620.53
8.90
4.26
0.5291
9270084
0.0445
0.5111
8954247
0.0461
0.4815
8436746
0.0488
0.5084
8907754
0.0463

E82
138
2405766.6
456521.81
9.48
4.54
0.5701
9987912
0.0468
0.5539
9703543
0.0481
0.5203
9116219
0.0512
0.5468
9580310
0.0488

G52
55
834916.73
158435.03
8.56
4.10
0.4568
3400969
0.0477
0.4384
3264454
0.0497
0.4128
3074068
0.0527
0.4397
3274152
0.0495

G52
44
810736
153846.46
8.36
4.00
0.4400
3276224
0.0481
0.4219
3141445
0.0501
0.3985
2967149
0.0530
0.4242
3158578
0.0498

G58
44
939752.92
178328.9
8.72
4.18
0.5151
3835784
0.0476
0.4939
3677435
0.0496
0.4556
3392727
0.0537
0.4912
3657608
0.0499

G80
44
1916854.2
363745.08
8.36
4.00
0.4303
7539464
0.0494
0.4125
7226231
0.0515
0.3896
6825318
0.0544
0.4150
7271575
0.0512

G80
71
2039716.6
387059.62
8.81
4.22
0.4661
8166670
0.0485
0.4478
7844775
0.0505
0.4203
7363750
0.0537
0.4482
7852279
0.0504

G87
67
2197598.8
417019.58
8.75
4.19
0.5049
8845380
0.0483
0.4872
8536126
0.0500
0.4570
8007278
0.0532
0.4843
8485555
0.0503

G87
100
2370945.5
449914.11
9.15
4.38
0.5335
9347258
0.0492
0.5164
9047727
0.0508
0.4830
8461965
0.0543
0.5109
8950975
0.0514

G90
67
2279840.4
432625.86
8.75
4.19
0.5208
9123928
0.0485
0.5004
8766327
0.0505
0.4622
8097666
0.0545
0.4966
8699848
0.0508

G90
100
2463887.3
467550.87
9.15
4.38
0.5459
9564877
0.0500
0.5262
9219648
0.0518
0.4851
8498163
0.0561
0.5191
9095502
0.0525

GE15 xle
80
1848137.4
350705.28
8.92
4.27
0.4930
6477982
0.0553
0.4695
6168810
0.0580
0.4285
5630400
0.0634
0.4678
6147450
0.0582

GE15 sle
65
1648152.6
312755.86
8.72
4.18
0.4497
5909668
0.0541
0.4289
5635277
0.0566
0.4019
5281593
0.0603
0.4328
5687517
0.0561

GE15 sle
80
1711806.8
324834.97
8.92
4.27
0.4655
6116669
0.0542
0.4444
5839736
0.0567
0.4153
5457118
0.0606
0.4473
5877890
0.0564

GE25
75
2970201.4
563629.78
8.86
4.24
0.5048
11054583
0.0521
0.4856
10634667
0.0541
0.4541
9944176
0.0578
0.4840
10600468
0.0543

GE25
85
3037456.1
576392.13
8.98
4.30
0.5140
11256219
0.0523
0.4949
10839270
0.0543
0.4623
10124534
0.0580
0.4926
10786941
0.0546

GE25
100
3138342.2
595536.43
9.15
4.38
0.5258
11515020
0.0528
0.5070
11102857
0.0547
0.4729
10357531
0.0586
0.5035
11026052
0.0551

V52
44
810736
153846.46
8.36
4.00
0.3995
2974314
0.0529
0.3843
2861735
0.0549
0.3660
2725029
0.0576
0.3867
2879177
0.0546

V52
49
821752.85
155937.03
8.46
4.05
0.4071
3031211
0.0526
0.3917
2916877
0.0546
0.3724
2772684
0.0574
0.3938
2932120
0.0543

V52
55
834916.73
158435.03
8.56
4.10
0.4153
3092333
0.0524
0.3997
2976187
0.0544
0.3793
2823922
0.0572
0.4014
2988921
0.0541

V52
65
856758.16
162579.69
8.72
4.18
0.4272
3180733
0.0522
0.4112
3062106
0.0542
0.3892
2898128
0.0572
0.4124
3070931
0.0541

V52
74
876343.56
166296.24
8.85
4.24
0.4364
3249266
0.0523
0.4202
3128840
0.0543
0.3970
2955760
0.0574
0.4209
3134389
0.0542

V80
60
1989770.7
377581.82
8.65
4.14
0.4229
7409170
0.0521
0.4062
7116137
0.0542
0.3843
6733133
0.0572
0.4084
7155137
0.0539

V80
67
2021565.1
383615.17
8.75
4.19
0.4309
7549126
0.0519
0.4139
7252161
0.0540
0.3910
6850262
0.0571
0.4158
7284622
0.0538

V80
78
2071461.6
393083.6
8.90
4.26
0.4419
7741548
0.0519
0.4246
7439563
0.0540
0.4002
7011673
0.0572
0.4259
7462357
0.0538

V80
100
2171159.6
412002.44
9.15
4.38
0.4597
8054318
0.0523
0.4421
7745219
0.0543
0.4152
7275163
0.0577
0.4424
7750490
0.0543

V82
78
1911790.3
362784.13
8.90
4.26
0.5314
7680907
0.0483
0.5114
7392389
0.0502
0.4702
6795652
0.0545
0.5047
7294478
0.0508

V82
70
1873908.6
355595.66
8.79
4.21
0.5249
7586920
0.0480
0.5047
7294960
0.0498
0.4642
6708857
0.0541
0.4989
7210721
0.0504

V82
80
1921256.9
364580.53
8.92
4.27
0.5329
7702552
0.0484
0.5130
7414938
0.0503
0.4716
6815803
0.0546
0.5060
7313749
0.0509

V90 1800
80
2230658.5
423293.03
8.92
4.27
0.5582
8802234
0.0492
0.5414
8536623
0.0507
0.5066
7987870
0.0541
0.5343
8424996
0.0514

V90 1800
95
2314298.1
439164.6
9.09
4.35
0.5703
8992593
0.0499
0.5540
8734997
0.0514
0.5180
8167046
0.0549
0.5455
8602205
0.0522

V90 1800
105
2370064.8
449746.98
9.19
4.40
0.5772
9101759
0.0505
0.5612
8849141
0.0519
0.5245
8270478
0.0555
0.5520
8703766
0.0528

V90 2M
80
2352366.3
446388.48
8.92
4.27
0.5456
9559137
0.0478
0.5266
9225238
0.0495
0.4926
8629990
0.0528
0.5235
9172330
0.0498

V90 2M
95
2436005.8
462260.05
9.09
4.35
0.5578
9772264
0.0484
0.5391
9444581
0.0500
0.5037
8824293
0.0535
0.5348
9369516
0.0504

V90 2M
105
2491772.6
472842.43
9.19
4.40
0.5648
9894664
0.0489
0.5463
9571053
0.0505
0.5101
8936730
0.0540
0.5412
9482678
0.0510

V90 3M
80
3067317
582058.58
8.92
4.27
0.4418
11609528
0.0513
0.4239
11141236
0.0534
0.3992
10489692
0.0566
0.4258
11188777
0.0531

V90 3M
105
3206723.3
608512.52
9.19
4.40
0.4614
12126387
0.0513
0.4432
11646236
0.0534
0.4157
10923987
0.0568
0.4438
11664188
0.0533

The average wind speeds shown in Table 4 in six years vary from 8.17 m/s to 9.48 m/s. The minimum average wind speed can be found in E33 with a hub height of 36 m, while the maximum can be found in E82 with a hub height of 138 m. As a result, the CF of WTG and wind energy output is different when evaluated by different probability distributions. The maximum capacity factors computing from all probability functions can be found in V90-1800 at hub height of 105 m, but the maximum wind energy output results from the V90-3M WTG at the same hub height. The minimum cost of wind energy cannot be found neither in V90-1800 nor in V90-3M but can be found in E82 WTG at hub height of 78 m. From the simulation results, it can be concluded that the E82 WTG with 78 m hub height is the best option for installation at Pengchiayu site. It is noted that the average cost per kWh produced by all tested WTGs at Pengchiayu site during six years is below 0.06 $/kWh.

Of course, the designer can choose a proper PDF at each site to evaluate the wind energy of WTG and its minimum COE. However, the general comment is similar after analysis of the results of all 24 tested sites. The minimum COE at 24 sites within these six years is shown in Table 5. The Enercon WTG type E82 with lower cut‐in wind speed and higher cut-off wind speed can be achieved by the best operational performance among all tested WTG. Lower COE result achieves smaller installation investment under the same wind energy output. The minimum COE (0.0477 $/kWh) at Tungchitao site has the best performance, while Alishan site has the maximum COE (1.0970 $/kWh).

Table 5: The minimum COE at 24 sites within these six years.

Sites

WTG
Hub height (m)

($)

($)
 (m/s)
CF

(kWh)
COE  
($/kWh)

Pengchiayu
Weibull
E82
78
2121719.1
402620.5
8.898
0.529
9270084
0.0434

Anpu
Weibull
E82
138
2405766.6
456521.8
4.258
0.145
2553946
0.1788

Chutzehu
Weibull
E82
138
2405766.6
456521.8
3.937
0.142
2489782
0.1834

Tanshui
Weibull
E82
138
2405766.6
456521.8
2.796
0.053
940063
0.4856

Keelung
Weibull
E82
138
2405766.6
456521.8
2.525
0.045
789992
0.5779

Taipei
Reileigh
E82
138
2405766.6
456521.8
3.673
0.090
1585378
0.2880

Hsinchu
Weibull
E82
138
2405766.6
456521.8
3.390
0.086
1521036
0.3001

Ilan
Gamma
E82
138
2405766.6
456521.8
2.666
0.059
1047152
0.4360

Taichung
Weibull
E82
138
2405766.6
456521.8
2.780
0.052
920458
0.4960

Wuchi
Reileigh
E82
138
2405766.6
456521.8
5.726
0.267
468697
0.9740

Hualien
Gamma
E82
138
2405766.6
456521.8
4.868
0.172
3026950
0.1508

Sun Moon Lake
Gamma
E82
138
2405766.6
456521.8
2.067
0.035
616206
0.7409

Penghu
Reileigh
E82
138
2405766.6
456521.8
5.690
0.253
4438347
0.1029

Alishan
Gamma
E82
78
2121719.0
402620.5
1.475
0.021
367033
1.0970

Chiayi
Weibull
E82
138
2121719.0
456521.8
2.871
0.062
1083930
0.4212

Yushan
Reileigh
E82
78
2121719.0
402620.5
8.218
0.482
8443033
0.0477

Tungchitao
Weibull
E48
50
751905.40
142682.7
9.877
0.532
3728649
0.0383

Cheng Kung
Weibull
E82
138
2121719.0
456521.8
4.936
0.187
3270199
0.1396

Tainan
Gamma
E82
138
2121719.0
456521.8
4.270
0.127
2219498
0.2057

Taitung
Weibull
E82
138
2121719.0
456521.8
2.760
0.053
933972
0.4888

Kaohsiung
Gamma
V90
80
2352366.3
446388.5
2.677
0.050
878765
0.5080

Tawu
Weibull
E82
138
2405766.6
456521.8
4.375
0.137
2397660
0.1904

Lanyu
Reileigh
E82
78
2121719.1
402620.5
9.088
0.547
9579998
0.0420

Hengchun
Weibull
E82
138
2405766.6
456521.8
5.593
0.253
4439644
0.1028

5. Conclusion

In this study, the wind energy potential of 24 locations in Taiwan of six years was calculated. Great variations occur in the simulation results in both of the cost of energy (0.03936~1.105 $/kWh) and capacity factor for different tested WTGs and 24 sites in Taiwan. Generally, capacity factor of the E82 GTW (at a height of 138 m) was evaluated to achieve the best choice of WTG in Taiwan, but at some sites, the capacity factor of V90_1800 (the hub height is 105 m) was second option. The capacity factor of WTG calculated from a Weibull probability distribution was higher than that calculated from other probability distributions, while the capacity factors of wind turbine computed by lognormal probability distribution was lower than those computed by other probability distributions. Any renewable energy system designer can benefit from the dedicated statistical analysis as investigated in this study to ensure the successful operation after WTG installations.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

T.-J. Chang, Y.-T. Wu, H.-Y. Hsu, C.-R. Chu, and C.-M. Liao, “Assessment of wind characteristics and wind turbine characteristics in Taiwan,” Renewable Energy, vol. 28, no. 6, pp. 851–871, 2003.

A. Ucar and F. Balo, “Evaluation of wind energy potential and electricity generation at six locations in Turkey,” Applied Energy, vol. 86, no. 10, pp. 1864–1872, 2009.

A. Mostafaeipour, “Economic evaluation of small wind turbine utilization in Kerman, Iran,” Energy Conversion and Management, vol. 73, pp. 214–225, 2013.

K. Mohammadi and A. Mostafaeipour, “Economic feasibility of developing wind turbines in Aligoodarz, Iran,” Energy Conversion and Management, vol. 76, pp. 645–653, 2013.

S. L. Ferrell and E. A. DeVuyst, “Decommissioning wind energy projects: an economic and political analysis,” Energy Policy, vol. 53, pp. 105–113, 2013.

S. M. Shaahid, L. M. Al-Hadhrami, and M. K. Rahman, “Economic feasibility of development of wind power plants in coastal locations of Saudi Arabia—a review,” Renewable and Sustainable Energy Reviews, vol. 19, pp. 589–597, 2013.

R. Belu and D. Koracin, “Wind characteristics and wind energy potential in western Nevada,” Renewable Energy, vol. 34, no. 10, pp. 2246–2251, 2009.

F. Jowder, “Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain,” Applied Energy, vol. 86, no. 4, pp. 538–545, 2009.

M. Elamouri and F. Ben Amar, “Wind energy potential in Tunisia,” Renewable Energy, vol. 33, no. 4, pp. 758–768, 2008.

L. Fingersh, M. Hand, and A. Laxson, “Wind turbine design cost and scaling model,” Tech. Rep. NREL/TP-500-40566, National Renewable Energy Laboratory, 2006.

M. Schmidt, The economic optimization of wind turbine design [Master thesis], Georgia Institute of Technology, 2007.

Y. Himri, S. Himri, and A. B. Stambouli, “Assessing the wind energy potential projects in Algeria,” Renewable and Sustainable Energy Reviews, vol. 13, no. 8, pp. 2187–2191, 2009.

I. Munteanu, A. I. Bratcu, and N. A. Cutululis, Optimal Control of Wind Energy Systems, Springer, 2008.

T. Diveux, P. Sebastian, and J. R. Puiggali, “Horizontal axis wind turbine system: optimization using genetic algorithms,” Wind Energy, vol. 4, pp. 151–171, 2001.