Mathematical Problems in Engineering

Mathematical Problems in Engineering / 2015 / Article
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Macroscopic/Mesoscopic Computational Materials Science Modeling and Engineering

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Research Article | Open Access

Volume 2015 |Article ID 614514 | 9 pages | https://doi.org/10.1155/2015/614514

Economic Analysis of Wind Turbine Installation in Taiwan

Academic Editor: Mo Li
Received30 Sep 2014
Accepted02 Dec 2014
Published12 Oct 2015

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 [26]. 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. Considerwhere , , , 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].

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.


NumberSitesLongitude
(deg)
Latitude
(deg)
Altitude
(m)

1Pengchiayu122°04′ E 25°37′ N12.5
2Anpu121°31′ E 25°11′ N7.31
3Chutzehu121°32′ E 25°09′ N11.03
4Tanshui121°26′ E 25°09′ N12.2
5Keelung121°43′ E 25°08′ N34.6
6Taipei121°30′ E 25°02′ N34.9
7Hsinchu120°58′ E 24°48′ N15.6
8 Ilan121°44′ E 24°45′ N26
9Taichung120°40′ E24°08′ N17.2
10Wuchi120°30′ E 24°15′ N32.2
11Hualien121°36′ E 23°58′ N12
12Sun Moon Lake120°53′ E 23°52′ N8
13Penghu119°33′ E 23°34′ N14.6
14Alishan120°48′ E 23°30′ N15.1
15Chiayi120°25′ E 23°29′ N14.5
16Yushan120°57′ E 23°29′ N9.2
17Tungchitao119°39′ E 23°15′ N9.1
18Cheng Kung121°21′ E 23°05′ N12.8
19Tainan120°11′ E 22°59′ N37.6
20Taitung121°08 E 22°45′ N11.4
21Kaohsiung120°18′ E 22°34′ N14
22Tawu120°53′ E 22°21′ N12.7
23Lanyu121°33′ E 22°02′ N12.5
24Hengchun120°44′ E 22°00′ N14.3

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.


NumberWTG typeRated power (kW)Cut-in speed (m/s)Rated wind speed (m/s)Cut-off speed (m/s)Rotor diameter (m)Hub height (m)

1E3333031728–3433.436–50
2E4490021728–344445–55
3E4880021428–354850–76
4E5380021328–3652.960/73
5E71230021528–377164–113
6E82200021228–388278–138
7G52850416255245/55/65
8G58850316215845/55/65
9G802000417258044–71
10G872000416258767–100
11G902000316219067–100
12GE15xle15003.512.52082.580
13GE15sle15003.514257765/80
14GE2525003.512.52510075/85/100
15V52850416255244/49/55/65/74
16V802000415258060/67/78/100
17V8216503.513208270/78/80
18V90_1.818003.512259080/95/105
19V90_2.020002.51325/219080/95/106
20V90_3.03000415259080/105


Sites Extreme statusWeibull Gamma Lognormal Rayleigh
Type WTGHub heightCapacity factorType WTGHub heightCapacity factorType WTGHub heightCapacity factorType WTGHub heightCapacity factor

PengchiayuHighestV90_18001050.5772V90_18001050.5612V90_18001050.5245V90_18001050.5245
LowestE44450.3765E444503599E44450.3455E44450.3455

AnpuHighestV90_2M1050.1461V90_2M1050.1477V90_2M1050.1650V90_2M1050.1433
LowestE44450.0734E44450.0772G52550.1027E44450.0661

ChutzehuHighestE821380.1421E821380.1414E821380.1496E821380.1365
LowestG80440.0506G80440.0519V52440.0708G80440.0345

TanshuiHighestV90_2M1050.0541E821380.0607E821380.0878V90_2M1050.0529
LowestG80440.0112G80440.0160V52440.0392G80440.0062

KeelungHighestE821380.0451E821380.0512E821380.0812V90_2M1050.0438
LowestG80440.0038G80440.0070V52440.0290G80440.0012

TaipeiHighestE821380.1037E821380.1137E821380.1418E821380.0905
LowestG80440.0257G80440.0336V52440.0627G80440.0163

HsinchuHighestE821380.0868E82780.0936E82780.1192V90_2M1050.0772
LowestG80440.0246G80440.0298V52440.0538G80440.0162

IlanHighestE821380.0570E821380.0598E821380.0860V90-2M1050.0547
LowestG80440.0151G80440.0171V52440.0406G80440.0071

TaichungHighestE821380.0525E821380.0598E821380.0867V90_2M1050.0500
LowestG80440.0056G80440.0093G80440.0291G80440.0025

WuchiHighestE821380.2587E821380.2545E821380.2533E821380.2675
LowestE44450.1336E44450.1395E44450.1605E44450.1287

HualienHighestE821380.1805E821380.1728E821380.1819E821380.1809
LowestE44450.0737E44450.0737V52440.0881E44450.0705

Sun Moon LakeHighestE821380.0335E821380.0352E821380.0514E821380.0368
LowestG80440.0030G80440.0043G80440.0180G80440.0007

PenghuHighestE821380.2505E821380.2515E821380.2532E821380.2533
LowestE44450.1186E44450.1310V52440.1566E44450.1130

AlishanHighestE821380.0243E821380.0231E821380.0264E821380.0284
LowestG80440.0002G80440.0005G80440.0049G80440.0000

ChiayiHighestE821380.0619E821380.0690E821380.1013V90-2M1050.0572
LowestG80440.0154G80440.0205V52440.0492G80440.0077

YushanHighestE821380.4824E821380.4493E821380.3858E821380.5329
LowestE44450.3111E44450.2950E44450.2648E44450.3323

TungchitaoHighestE821380.6192E821380.5805E821380.5069E821380.6546
LowestE44450.4598E44450.4299E44450.3779E44450.4823

Cheng KungHighestE821380.1867E821380.1853E821380.1991E821380.1859
LowestE44450.0841E44450.0885V52440.1109E44450.0798

TainanHighestE821380.1179E821380.1267E821380.1529E821380.1174
LowestG80440.0320G80440.0385G80440.0616G80440.0316

TaitungHighestV90_2M1050.0537E821380.0552E821380.0693V90_2M1050.0537
LowestG80440.0111G80440.0118G80440.0118G80440.0118

KaohsiungHighestV90_2M1050.0528V90_2M1050.0560E821380.0749V90-2M1050.0530
LowestG80440.0102G80440.0130G80440.0297G80440.0081

TawuHighestE821380.1369E821380.1408E821380.1588E821380.1335
LowestG80440.0378G80440.0378G80440.0427G80440.0344

LanyuHighestV90_18001050.5576V90_18001050.5457V90_18001050.5004V90_18001050.5870
LowestE44450.3898E44450.3710E44450.3442E44450.4051

HengchunHighestE821380.2534E821380.2502E821380.2400E821380.2706
LowestE44450.1176E44450.1245V52490.1427E44450.1072

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.


Type wind turbineHeight (m)  
($)

($)
 (m/s)  (m/s)WeibullGammaLognormalRayleigh
CF  
(kWh)
COE  
($)
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 0.05030.4731 1367527 0.05200.4493 1298957 0.05470.4720 1364516 0.0522
E44 45 758045.53 143847.84 8.38 4.01 0.3765 2968249 0.04960.3599 2837622 0.05180.3455 2724304 0.05390.3659 2884902 0.0510
E44 55 774782.46 147023.86 8.56 4.10 0.3912 3084305 0.04880.3742 2950208 0.05100.3579 2821934 0.05320.3796 2993059 0.0503
E48 50 751905.43 142682.68 8.47 4.06 0.4601 3224316 0.04540.4416 3094976 0.04720.4194 2938985 0.04970.4441 3112572 0.0470
E48 76 801628.7 152118.24 8.87 4.25 0.4913 3443048 0.04530.4731 3315335 0.04700.4472 3133946 0.04960.4732 3316498 0.0470
E53 60 829915.6 157486 8.65 4.14 0.5148 3607771 0.04480.4971 3483821 0.04630.4702 3295026 0.04890.4953 3471394 0.0465
E53 73 859053.45 163015.25 8.83 4.23 0.5292 3708292 0.04510.5119 3587412 0.04650.4835 3388252 0.04920.5088 3565517 0.0468
E70 64 2008539.6 381143.44 8.71 4.17 0.4112 8283960 0.04710.3927 7911310 0.04930.3734 7522395 0.05180.3981 8020871 0.0487
E70 113 2188995.1 415386.94 9.27 4.44 0.4537 9140657 0.04660.4346 8756834 0.04860.4099 8257840 0.05140.4376 8816383 0.0482
E82 78 2121719.1 402620.53 8.90 4.26 0.5291 9270084 0.04450.5111 8954247 0.04610.4815 8436746 0.04880.5084 8907754 0.0463
E82 138 2405766.6 456521.81 9.48 4.54 0.5701 9987912 0.04680.5539 9703543 0.04810.5203 9116219 0.05120.5468 9580310 0.0488
G52 55 834916.73 158435.03 8.56 4.10 0.4568 3400969 0.04770.4384 3264454 0.04970.4128 3074068 0.05270.4397 3274152 0.0495
G52 44 810736 153846.46 8.36 4.00 0.4400 3276224 0.04810.4219 3141445 0.05010.3985 2967149 0.05300.4242 3158578 0.0498
G58 44 939752.92 178328.9 8.72 4.18 0.5151 3835784 0.04760.4939 3677435 0.04960.4556 3392727 0.05370.4912 3657608 0.0499
G80 44 1916854.2 363745.08 8.36 4.00 0.4303 7539464 0.04940.4125 7226231 0.05150.3896 6825318 0.05440.4150 7271575 0.0512
G80 71 2039716.6 387059.62 8.81 4.22 0.4661 8166670 0.04850.4478 7844775 0.05050.4203 7363750 0.05370.4482 7852279 0.0504
G87 67 2197598.8 417019.58 8.75 4.19 0.5049 8845380 0.04830.4872 8536126 0.05000.4570 8007278 0.05320.4843 8485555 0.0503
G87 100 2370945.5 449914.11 9.15 4.38 0.5335 9347258 0.04920.5164 9047727 0.05080.4830 8461965 0.05430.5109 8950975 0.0514
G90 67 2279840.4 432625.86 8.75 4.19 0.5208 9123928 0.04850.5004 8766327 0.05050.4622 8097666 0.05450.4966 8699848 0.0508
G90 100 2463887.3 467550.87 9.15 4.38 0.5459 9564877 0.05000.5262 9219648 0.05180.4851 8498163 0.05610.5191 9095502 0.0525
GE15 xle 80 1848137.4 350705.28 8.92 4.27 0.4930 6477982 0.05530.4695 6168810 0.05800.4285 5630400 0.06340.4678 6147450 0.0582
GE15 sle 65 1648152.6 312755.86 8.72 4.18 0.4497 5909668 0.05410.4289 5635277 0.05660.4019 5281593 0.06030.4328 5687517 0.0561
GE15 sle 80 1711806.8 324834.97 8.92 4.27 0.4655 6116669 0.05420.4444 5839736 0.05670.4153 5457118 0.06060.4473 5877890 0.0564
GE25 75 2970201.4 563629.78 8.86 4.24 0.5048 11054583 0.05210.4856 10634667 0.05410.4541 9944176 0.05780.4840 10600468 0.0543
GE25 85 3037456.1 576392.13 8.98 4.30 0.5140 11256219 0.05230.4949 10839270 0.05430.4623 10124534 0.05800.4926 10786941 0.0546
GE25 1003138342.2 595536.43 9.15 4.38 0.5258 11515020 0.05280.5070 11102857 0.05470.4729 10357531 0.05860.5035 11026052 0.0551
V52 44 810736 153846.46 8.36 4.00 0.3995 2974314 0.05290.3843 2861735 0.05490.3660 2725029 0.05760.3867 2879177 0.0546
V52 49 821752.85 155937.03 8.46 4.05 0.4071 3031211 0.05260.3917 2916877 0.05460.3724 2772684 0.05740.3938 2932120 0.0543
V52 55 834916.73 158435.03 8.56 4.10 0.4153 3092333 0.05240.3997 2976187 0.05440.3793 2823922 0.05720.4014 2988921 0.0541
V52 65 856758.16 162579.69 8.72 4.18 0.4272 3180733 0.05220.4112 3062106 0.05420.3892 2898128 0.05720.4124 3070931 0.0541
V52 74 876343.56 166296.24 8.85 4.24 0.4364 3249266 0.05230.4202 3128840 0.05430.3970 2955760 0.05740.4209 3134389 0.0542
V80 60 1989770.7 377581.82 8.65 4.14 0.4229 7409170 0.05210.4062 7116137 0.05420.3843 6733133 0.05720.4084 7155137 0.0539
V80 67 2021565.1 383615.17 8.75 4.19 0.4309 7549126 0.05190.4139 7252161 0.05400.3910 6850262 0.05710.4158 7284622 0.0538
V80 78 2071461.6 393083.6 8.90 4.26 0.4419 7741548 0.05190.4246 7439563 0.05400.4002 7011673 0.05720.4259 7462357 0.0538
V80 100 2171159.6 412002.44 9.15 4.38 0.4597 8054318 0.05230.4421 7745219 0.05430.4152 7275163 0.05770.4424 7750490 0.0543
V82 78 1911790.3 362784.13 8.90 4.26 0.5314 7680907 0.04830.5114 7392389 0.05020.4702 6795652 0.05450.5047 7294478 0.0508
V82 70 1873908.6 355595.66 8.79 4.21 0.5249 7586920 0.04800.5047 7294960 0.04980.4642 6708857 0.05410.4989 7210721 0.0504
V82 80 1921256.9 364580.53 8.92 4.27 0.5329 7702552 0.04840.5130 7414938 0.05030.4716 6815803 0.05460.5060 7313749 0.0509
V90 1800 80 2230658.5 423293.03 8.92 4.27 0.5582 8802234 0.04920.5414 8536623 0.05070.5066 7987870 0.05410.5343 8424996 0.0514
V90 1800 95 2314298.1 439164.6 9.09 4.35 0.5703 8992593 0.04990.5540 8734997 0.05140.5180 8167046 0.05490.5455 8602205 0.0522
V90 1800 1052370064.8 449746.98 9.19 4.40 0.5772 9101759 0.05050.5612 8849141 0.05190.5245 8270478 0.05550.5520 8703766 0.0528
V90 2M 80 2352366.3 446388.48 8.92 4.27 0.5456 9559137 0.04780.5266 9225238 0.04950.4926 8629990 0.05280.5235 9172330 0.0498
V90 2M 95 2436005.8 462260.05 9.09 4.35 0.5578 9772264 0.04840.5391 9444581 0.05000.5037 8824293 0.05350.5348 9369516 0.0504
V90 2M 1052491772.6 472842.43 9.19 4.40 0.5648 9894664 0.04890.5463 9571053 0.05050.5101 8936730 0.05400.5412 9482678 0.0510
V90 3M 80 3067317 582058.58 8.92 4.27 0.4418 11609528 0.05130.4239 11141236 0.05340.3992 10489692 0.05660.4258 11188777 0.0531
V90 3M 1053206723.3 608512.52 9.19 4.40 0.4614 121263870.05130.4432 116462360.0534 0.4157109239870.05680.4438116641880.0533

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 cutin 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).


SitesWTGHub height (m)
($)

($)
 (m/s)CF
(kWh)
COE  
($/kWh)

PengchiayuWeibullE82782121719.1402620.58.8980.52992700840.0434
AnpuWeibullE821382405766.6456521.84.2580.14525539460.1788
ChutzehuWeibullE821382405766.6456521.83.9370.14224897820.1834
TanshuiWeibullE821382405766.6456521.82.7960.0539400630.4856
KeelungWeibullE821382405766.6456521.82.5250.0457899920.5779
TaipeiReileighE821382405766.6456521.83.6730.09015853780.2880
HsinchuWeibullE821382405766.6456521.83.3900.08615210360.3001
IlanGammaE821382405766.6456521.82.6660.05910471520.4360
TaichungWeibullE821382405766.6456521.82.7800.0529204580.4960
WuchiReileighE821382405766.6456521.85.7260.2674686970.9740
HualienGammaE821382405766.6456521.84.8680.17230269500.1508
Sun Moon LakeGammaE821382405766.6456521.82.0670.0356162060.7409
PenghuReileighE821382405766.6456521.85.6900.25344383470.1029
AlishanGammaE82782121719.0402620.51.4750.0213670331.0970
ChiayiWeibullE821382121719.0456521.82.8710.06210839300.4212
YushanReileighE82782121719.0402620.58.2180.48284430330.0477
TungchitaoWeibullE4850751905.40142682.79.8770.53237286490.0383
Cheng KungWeibullE821382121719.0456521.84.9360.18732701990.1396
TainanGammaE821382121719.0456521.84.2700.12722194980.2057
TaitungWeibullE821382121719.0456521.82.7600.0539339720.4888
KaohsiungGammaV90802352366.3446388.52.6770.0508787650.5080
TawuWeibullE821382405766.6456521.84.3750.13723976600.1904
LanyuReileighE82782121719.1402620.59.0880.54795799980.0420
HengchunWeibullE821382405766.6456521.85.5930.25344396440.1028

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.

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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.


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