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Issue
Sci. Tech. Energ. Transition
Volume 79, 2024
Article Number 10
Number of page(s) 15
DOI https://doi.org/10.2516/stet/2024003
Published online 20 February 2024

© The Author(s), published by EDP Sciences, 2024

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1 Introduction

The domestic wind energy sector in India is the most advanced and has made constant advancements. The growth of the wind sector has led to a robust ecosystem, project operating capabilities, and a base of manufacture of around 10 000 MW annually. With a total installed capacity of 38.62 GW (as of December 31, 2020) and 64.64 Billion Units produced from wind power during 2019–2020, the nation now has the fourth-greatest wind installed capacity in the world [1]. The country achieved wind power capacity of 44736.24 MW as on 31 December 2023. A wind atlas of India has been recently published by the author in [2] in which wind potential at 100 m and 120 m above ground level is given. Assessment of the wind resource is essential to determining whether a given location or area has the capacity for an economically successful wind farm. A variety of techniques that can be used to evaluate wind resources has been reported by various researchers worldwide. Her et al. [3] did a Wind Resource Assessment (WRA) for communities in the Arctic and suggested a 100 kW wind turbine with a Levelized Cost Of Energy (LCOE) of $1.15/kWh and a capacity factor of 16.7% as an appropriate option. Dayal et al. [4] completed an evaluation of the wind resources in Fiji’s Rakiraki, Nabouwalu, and Udu. For these sites, it was discovered that the Wind Speed (WS) and Wind Power Potential (WPP) are 7.6 m/s, 7.1 m/s, 7.0 m/s, and 401 W/m2, 512 W/m2, and 294 W/m2 correspondingly. These locations have 97–98% wind efficiencies. In their spatial and economic examination of Nepal’s wind potential, Neupane et al. [5] calculated that 1686 MW of wind energy might be captured. Provinces in Karnali and Gandaki have high WPP. For China WPP was estimated at 1783–39 000 TWh by Franke et al. [6]. Weather data set, land use, and wind turbine configuration all affect WPP. Vinhoza and Schaeffer [7] examined Brazil’s offshore WPP. There are three potentials listed: 1064 GW of technical potential, 330 GW of environmental and social potential, and 1688 GW of gross potential. WPP was carried out for South Sudan by Ayik et al. [8]. It is discovered that at 10 m, WPP ranges from 128.36 to 14.39 W/m2, while WS ranges from 5.08 to 2.36 m/s. For the Indian Tirumala region, Kumar et al.’s [9] study estimates Weibull parameters via multiverse optimization. The highest mean wind speeds recorded in December were 5.12 m/s at 10 m and 6.621 m/s at 65 m. Chandel et al. [10] presented Weibull for WPP at 30 m, 50 m, 80 m, and 100 m for the Western Himalayan Indian state of Himachal Pradesh. The findings indicated that the wind speed is highest in the summer and lowest in the winter. In a different study, Chandel et al. [11] used WAsP to evaluate WPP at distances of 30 m, 50 m, 70 m, and 100 m and discovered that the Hamirpur location is most suitable for microturbines. For 10 Indian states at 90 m, Sangroya and Nayak [12] detected WPP using Weibull and determined that the wind’s generation capacity was 21 268.3 MW as of May 2014. For the Uttara Kannada district, Karnataka State, India, Ramachandra et al. [13] estimated the preliminary WPP at 70 m utilizing geospatial tools including remote sensing and GIS. The average annual WS is discovered to be between 2.5 and 3.0 m/s. In order to identify WPP at 80 m for Indian sites, Hossain et al. [14] presented a Geographic Information System (GIS) 4 250 639.912 MW is found to be the predicted wind farm potential. Singh and Prakash [15] identified WPP using Weibull probability distribution at 10 m for Ranchi, Jamshedpur, Devghar, Lohardaga, and Chaibasa of Jharkhand in India. It is suggested that at 10 m in height is not suitable for the wind turbine to generate electricity. At 50 m Gadanki, India, Reddy et al. [16] statistical analysis for WPP detection. The yearly energy yield is 332.8 kWh/m2 and the average WS is 2.9 m/s. WPP at 150 m height for Bheemunipatnam in northern Andhra Pradesh, India, was examined by Murthy and Rahi [17]. They discovered that the site had moderate and consistent wind speeds as well as a gusty wind speed of 13.3 m/s.

From preceding studies, there is no information, publically available regarding WPP and its economics at 150 m to allow enforce more development of wind energy use in different sites of India. This paper is the first effort to make publically available information about the WPP assessment with different methods and its economics at 150 m height for 62 Indian sites, it will allow to local population a valuable insight into the wind resource, its potential development, and its value to a utility utilization of individual use.

The objective of this paper is to present a very comprehensive WPP at different Indian sites using various methods. Section 2 describes briefly the analysis methods while Section 3 presents the results gathered and a discussion of their significance. The conclusions of this paper are given in Section 4.

2 Methodology

2.1 Research plan and program

The suggested method for assessing wind power generation capacity and performing an economic cost analysis for installing wind turbines at various tower heights is shown in Figure 1, which includes the step-wise-step procedure. Based on this and the equation given in the subsection computational analysis is performed.

thumbnail Figure 1

Proposed approach for wind power density evaluation and cost of energy at different tower heights.

2.2 Weibull parameters computation

2.2.1 Wind power

The amount of energy a wind turbine can convert to power depends greatly on the wind speed: The average wind speed’s cube (the third power) affects the wind’s energy content. As seen below, air density, the area swept by a wind turbine’s rotor, and the cube of wind speed all directly relate to the amount of power that may be generated from the available wind. p = 1 2 ρ A v 3 $$ p=\frac{1}{2}{\rho A}{v}^3 $$(1)where A = rotor swept area, ρ = air density, v = velocity of wind in m/s.

2.2.2 Weibull model

Weibull and Cumulative Density Functions, or WDF and CDF, are two probability distributions that are used to compute the frequency distribution of known wind speeds [18]. f ( v )   = ( K C ) ( v C ) K - 1 exp [ - ( v C ) K ] $$ f(v)\mathrm{\enspace }=\left(\frac{K}{C}\right){\left(\frac{v}{C}\right)}^{K-1}\mathrm{exp}\left[-{\left(\frac{v}{C}\right)}^K\right] $$(2) F ( v ) = 1 - exp [ - ( v C ) K ] $$ F(v)=1-\mathrm{exp}\left[-{\left(\frac{v}{C}\right)}^K\right] $$(3)where, f(v) and F(v) are the WDF and CDF corresponding to the wind speed v in m/s, K is the shape parameter which has no units and tells how to peak the distribution of the Weibull plot is, and C is the scale parameter.

2.2.3 Wind profile vertical

Research and applications in boundary layer meteorology, air quality, and numerical weather prediction all depend on wind profiles. The vertical wind profile is defined in equation (4). It is also known as power law. The power law is highly useful for calculating wind speeds in accordance with the necessary hub heights for wind turbines. Below is a mathematical illustration of the power law [19]. v 2 v 1 = ( h 2 h 1 ) α $$ \frac{{v}_2}{{v}_1}={\left(\frac{{h}_2}{h1}\right)}^{\alpha } $$(4)where, v1 and v2 are the wind speeds (in m/s) at corresponding heights h1 and h2 (in meters), and α is the power exponent, which is equal to 0.143. The shape parameter k represents the nature of the wind i.e. 1.5≤ K ≤ 1.99 indicates fairly windy site, if K ≤ 1.5 indicates highly variable or gust wind speed, if K $ K$ ≥ 2 means moderate wind, if K ≥ 3 indicates regular steady wind speed.

The Weibull parameters can be computed by different methods given in Appendix A. The mean Wind Power Density (WPD) is calculated as, P W = 1 2 ρ C 3 γ ( 1 + 3 K ) . $$ {P}_{\mathrm{W}}=\frac{1}{2}\rho {C}^3\gamma \left(1+\frac{3}{K}\right). $$(5)

2.2.4 Statistical analysis

2.2.4.1 Relative power density error (RPDE)

RPDE is computed using the following formula proposed by Mohammadi et al. [27]: RPDE = ( P pdf - P ) P × 100 $$ \mathrm{RPDE}=\frac{\left({P}_{\mathrm{pdf}}-P\right)}{P}\times 100 $$(6)where Ppdf represents the wind power density computed by the considered method and P is the power derived from the data. The best method in terms of power density will provide the smallest RPDE.

2.3 Economic analysis

The unit cost of wind power is calculated by investment in capital and operating costs. Before installation of a Wind Turbine Generator (WTG), it is crucial to find its cost based on references [28, 29]. The Cost Of Energy (COE, $/kWh) calculation are given in Appendix B.

For analysis of the suitability of wind turbines for 21 locations in India, 8 different types of wind turbines are taken as shown in Table 1 [30].

Table 1

Wind turbine details.

2.4 Categorization of wind energy resource

Regional wind resource estimates can be obtained from the Wind Energy Resource Atlas of the United States. Wind resource assessment is expressed in terms of wind class, wind speed, and wind power density as shown in Table 2 [31].

Table 2

Classification of wind energy resource.

2.5 Site selection

The monthly average wind speed at 10 m height for 62 locations measured by National Aeronautics and Space Administration (NASA) which is averaged for that month over the 30 years (January 1984–December 2014), is shown in Figure 2 [32].

thumbnail Figure 2

Wind speed profile of 62 Indian sites. a) Wind Speed for 10 different locations in Gujarat; b) Wind speed for 9 different locations in Karnataka; c) Wind Speed for 10 different locations in Tamil Nadu; d) Wind Speed for different locations in Andaman and Nicobar, Assam, Jammu and Kashmir and West Bengal; e) Wind Speed for 8 different locations in Madhya Pradesh; f) Wind Speed for 8 different locations in Maharashtra; g) Wind Speed for 9 different locations in Andhra Pradesh; h) Wind Speed for 4 different locations in Rajasthan.

3 Results and discussions

3.1 WPP Estimation at different heights

Wind classes of different sites are shown in Table 3. It is found that Barkheri Bazar of Madhya Pradesh, Raipur, Chakal of Maharashtra, Vajrakarur of Andhra Pradesh, Kamagiri, M.S. Puram, Servallar Hills of Tamil Nadu, Sangasar, Suvarda, of Gujarat comes under Wind Class 2, Mount Harriet, South Bay of Andaman and Nicobar, Nagda of Madhya Pradesh, Jagmin of Maharashtra, Kanyakumari of Tamil Nadu, Mahidad of Gujarat come under Wind Class 3, Kalunirkulam of Tamil Nadu under Wind Class 4 and Brahmanwel of Maharashtra fall under Wind Class 5. The rest sites come under Wind Class 1 which is not suitable for wind power generation.

Table 3

Wind category and class of different Indian sites.

3.2 Comparison of different methods to estimate wind power density

The results of each of the comparison of the methods for each of the 15 sites are presented below. Figure 3 presents the comparison of the Weibull Probability Density Function (PDF) obtained by each method and Figure 4 presents the comparison of the Weibull Cumulative Density Function (CDF) obtained by each method. Table 4 presents the best method for wind regime characteristics for each site. The rank is evaluated by RPDE to find out the best method. It is found that the NEPFM method estimates WPD better for Mahidad, Suvarda, Kanyakumari, Kalunirkulam, Mount Harriet, Barkheri Bazar, Nagda, Chakla. Empirical Method of Justus (EMJ) estimates WPD better for Sansagar, Jagmin. Empirical Method of Mabchour (EMM) estimates WPD better for Kamagiri, M.S. Puram, Savaller Hill, Raipur. Empirical Method of Lysen (EML) estimates WPD better for Brahmanwel.

thumbnail Figure 3

Comparison of Weibull probability density function with different methods. a) Fitting comparison methods of wind speed distribution of Mahidad hill location of Gujarat; b) Fitting comparison methods of wind speed distribution of Suvarda location of Gujarat; c) Fitting comparison methods of wind speed distribution of Sansagar location of Gujarat; d) Fitting comparison methods of wind speed distribution of Kanyakumari location of Tamil Nadu; e) Fitting comparison methods of wind speed distribution of Kainonkarai location of Tamil Nadu; f) Fitting comparison methods of wind speed distribution of Kalunirkulam location of Tamil Nadu; g) Fitting comparison methods of wind speed distribution of M.S.Puram location of Tamil Nadu; h) Fitting comparison methods of wind speed distribution of Sarvallar hill location of Tamil Nadu; i) Fitting comparison methods of wind speed distribution of Mount Harriet location of Andaman Nicobar; j) Fitting comparison methods of wind speed distribution of Barkheri bazaar location of Madhya Pradesh; k) Fitting comparison methods of wind speed distribution of Nagda location of Madhya Pradesh; l) Fitting comparison methods of wind speed distribution of Jagmin location of Maharashtra; m) Fitting comparison methods of wind speed distribution of Chakla location of Maharashtra; n) Fitting comparison methods of wind speed distribution of Raipur location of Maharashtra; o) Fitting comparison methods of wind speed distribution of Brahmanwel location of Maharashtra.
thumbnail Figure 4

Comparison of cumulative wind distribution for different sites. a) Fitting comparison methods of cumulative wind distribution of Mahidad hill location of Gujarat; b) Fitting comparison methods of cumulative wind distribution of Suvarda location of Gujarat; c) Fitting comparison methods of cumulative wind distribution of Sansagar location of Gujarat; d) Fitting comparison methods of cumulative wind distribution of Kanyakumari location of Tamil Nadu; e) Fitting comparison methods of cumulative wind distribution of Kainonkarai location of Tamil Nadu; f) Fitting comparison methods of cumulative wind distribution of Kalunirkulam location of Tamil Nadu; g) Fitting comparison methods of cumulative wind distribution of M.S.Puram location of Tamil Nadu; h) Fitting comparison methods of cumulative wind distribution of Sarvallar hill location of Tamil Nadu; i) Fitting comparison methods of cumulative wind distribution of Mount Harriet location of Andaman Nicobar; j) Fitting comparison methods of cumulative wind distribution of Barkheri Bazar location of MP; k) Fitting comparison methods of cumulative wind distribution of Nagda location of Madhya Pradesh; l) Fitting comparison methods of cumulative wind distribution of Jagmin location of Maharashtra; m) Fitting comparison methods of cumulative wind distribution of Chakla location of Maharashtra; n) Fitting comparison methods of cumulative wind distribution of Jaipur location of Maharashtra; o) Fitting comparison methods of cumulative wind distribution of Brahmanwel location of Maharashtra.
Table 4

Best methods and wind regime characteristics at 150 m height.

3.3. Cost of energy evaluation for different wind turbines

The cost of energy is calculated using data shown in Table 1 of all the sites of different locations is shown in Table 5. The cost of energy (COE, $/kWh) varies from 0.28 to 15.31 $/kWh at the 10 m hub height of the wind turbine and at the 150 m hub height of the wind turbine, COE varies from 0.10 to 3.53 $/kWh. Wind turbine E53/800 has minimum COE variation and E101/3000 has maximum COE.

Table 5

Cost of energy of different sites with different wind models.

4 Conclusions

The WPP assessment with different methods and economics of wind power generation has been done for different locations in India. The most important outcomes of the study can be summarized as follows:

  1. The best method to estimate the wind power density is NEPFM followed by EMM, EMJ, and EML.

  2. Shape parameters and cubic factors do not change with the hub height of the wind turbine. The wind speed and wind power density increase exponentially with the heights.

  3. Weibull shape parameter k ranges from 1.52 to 4.65 while the scale parameter c is between 2.39 m/s and 9.73 m/s.

  4. It is found that Kalunirkulam of Tamil Nadu comes under the Wind Class 4 category and Brahmanwel of Maharashtra falls under Wind Class 5.

  5. At 10 m hub height cost of energy varies from 0.28 to 15.31 $/kWh whereas at 150 m hub height cost of energy varies from 0.10 to 3.53 $/kWh, showing a decrease in COE from 64.28% to 76.94%.

  6. The outcome of this study is important for the wind industry.

Future research work will be focused on the development and comparison of different forecasting models accuracy for wind speed in Indian diverse climatic conditions.

Acknowledgments

The authors would like to acknowledge the National Aeronautics and Space Administration (NASA) for providing of wind speed data for this study.

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Appendix A

A.1. Cubic factor (CF)

The Weibull parameters can be obtained as follows: K = 1 + 3.69 ( CF ) 2 $$ K=1+\frac{3.69}{{\left(\mathrm{CF}\right)}^2} $$(A.1)where CF is the wind energy pattern factor = Average   of   cubes   of   wind   speeds Cube   of   the   average   of   wind   speeds $$ =\frac{\mathrm{Average}\enspace \mathrm{of}\enspace \mathrm{cubes}\enspace \mathrm{of}\enspace \mathrm{wind}\enspace \mathrm{speeds}}{\mathrm{Cube}\enspace \mathrm{of}\enspace \mathrm{the}\enspace \mathrm{average}\enspace \mathrm{of}\enspace \mathrm{wind}\enspace \mathrm{speeds}} $$(A.2) C = v Γ ( 1 + 1 K ) . $$ C=\frac{v}{\mathrm{\Gamma }\left(1+\frac{1}{K}\right)}. $$(A.3)

A.2 Empirical method of Justus (EMJ)

This approach was proposed by Justus et al. [20] in 1977. It present a direct solution based on average wind speed and standard deviation. It is a moment-based estimate of the form factor. This approach results in the Weibull shape factor being written as, K = ( Vm σ ) - 1.086 $$ K={\left(\frac{{Vm}}{\sigma }\right)}^{-1.086} $$(A.4) σ = ∑ i = 1 n ( V i - Vm ) 2 n $$ \sigma =\sqrt{\frac{{\sum }_{i=1}^n ({V}_i-{Vm}{)}^2}{n}} $$(A.5) C = Vm Γ ( 1 + 1 k ) $$ C=\frac{{Vm}}{\mathrm{\Gamma }\left(1+\frac{1}{k}\right)} $$(A.6)where Vm is average mean wind speed and V i is a wind speeds.

Table A1

NEPFM coefficients.

A.3 Empirical method of Lysen (EML)

This approach also came from the Moment method. It uses equation (A.6) to determine K. C is computed as follows [21]: C = Vm ( 0.568 + 0.433 K ) 1 K . $$ C={Vm}{\left(0.568+\frac{0.433}{K}\right)}^{\frac{1}{K}}. $$(A.7)

A.4 Empirical method of Mabchour (Mabch)

This approach was used to assess Morocco’s potential for wind energy by Mabchour. K is expressed using the method of Mabchour [22, 23] as K = 1 + ( 0.483 ( Vm - 2 ) ) 0.51 . $$ K=1+{\left(0.483({Vm}-2)\right)}^{0.51}. $$(A.8)

Once K is calculated, C is obtained using equation (A.6).

A.5 Energy pattern factor method (EPF)

It is an easy method to estimate Weibull parameters. It is based on wind speed average. Once Epf is calculated, the value of K and C is given by the following equations [24]: Epf = V 3 ̅ V m 3 $$ \mathrm{Epf}=\frac{\overline{{V}^3}}{V{m}^3} $$(A.9) K = 1 + 3.69 Epf 2 . $$ K=1+\frac{3.69}{{\mathrm{Epf}}^2}. $$(A.10)

Once K is calculated, C is obtained using equation (A.7).

A.6 Energy pattern factor method of Sathyajith (EPFMS)

This method is another approximation method of estimating Weibull parameters based on the Epf. With this method, K is calculated as follows [25]: K = 3.957 × Epf - 0.898 . $$ K=3.957\times {\mathrm{Epf}}^{-0.898}. $$(A.11)

A.7 Novel energy pattern factor method (NEPFM)

This method is also an approximation of the energy density method to determine the Weibull shape factor and the scale parameter. The parameters were determined using the following equations [26]. K = a 4 Ep f 4 + a 3 Ep f 3 + a 2 Ep f 2 + a 1 Epf + a 0 b 4 Ep f 4 + b 3 Ep f 3 + b 2 Ep f 2 + b 1 Epf + b 0 $$ K=\frac{a4\mathrm{Ep}{\mathrm{f}}^4+a3\mathrm{Ep}{\mathrm{f}}^3+a2\mathrm{Ep}{\mathrm{f}}^2+a1\mathrm{Epf}+a0}{b4\mathrm{Ep}{\mathrm{f}}^4+b3\mathrm{Ep}{\mathrm{f}}^3+b2\mathrm{Ep}{\mathrm{f}}^2+b1\mathrm{Epf}+b0} $$(A.12) C = Vm ( K 2 + c 1 K + c 0 ) K 2 + d 1 K + d 0 . $$ C=\frac{{Vm}({K}^2+c1K+c0)}{{K}^2+d1K+d0}. $$(A.13)

Appendix B

The COE ($/kWh) can be defined by equation (B.1), where CTAC is the total annual cost ($) and EAEO is the yearly electrical energy output of WTG (kWh): COE = C TAC E AEO . $$ \mathrm{COE}=\frac{{C}_{\mathrm{TAC}}}{{E}_{\mathrm{AEO}}}. $$(B.1)

The total annual cost of a WTG is the sum of its operation, maintenance expenses and annual repayments on its capital. It can be demonstrated by the following: C TAC = ( b + 0.025 ) C ICC $$ {C}_{\mathrm{TAC}}=(b+0.025){C}_{\mathrm{ICC}} $$(B.2) b = c 1 - ( 1 + c ) - n $$ b=\frac{c}{1-(1+c{)}^{-n}} $$(B.3)where c is the discount rate (%) and often takes about 10%; b is the discount factor; n is the lifetime of project, often taken to be 20 years; Initial capital cost of building in WTG ($) (CICC) and is given by following equation: C blade = 0.5582 ( D 2 ) 2 + 3.8118 ( D 2 ) 2.5025 - 955.24 $$ {C}_{\mathrm{blade}}=0.5582{\left(\frac{D}{2}\right)}^2+3.8118{\left(\frac{D}{2}\right)}^{2.5025}-955.24 $$(B.4) Mass   of   blade = 0.1452 ( D 2 ) 2.9158 $$ \mathrm{Mass}\enspace \mathrm{of}\enspace \mathrm{blade}=0.1452{\left(\frac{D}{2}\right)}^{2.9158} $$(B.5) C hub = 4.05 × Mass   of   blade ( D 2 ) 2.9158 + 24141 $$ {C}_{\mathrm{hub}}=4.05\times \mathrm{Mass}\enspace \mathrm{of}\enspace \mathrm{blade}{\left(\frac{D}{2}\right)}^{2.9158}+24141 $$(B.6) C pith   system = 0.4802 ( D ) 2.6578 $$ {C}_{\mathrm{pith}\enspace \mathrm{system}}=0.4802{(D)}^{2.6578} $$(B.7) C nose   cone = 103 ( D ) - 2899 $$ {C}_{\mathrm{nose}\enspace \mathrm{cone}}=103(D)-2899 $$(B.8) C low   speed   shaft = 0.1 ( D ) 2.887 $$ {C}_{\mathrm{low}\enspace \mathrm{speed}\enspace \mathrm{shaft}}=0.1{(D)}^{2.887} $$(B.9) C bearing   system = D 2.5 ( 0.0043 D - 0.011 ) $$ {C}_{\mathrm{bearing}\enspace \mathrm{system}}={D}^{2.5}\left(0.0043D-0.011\right) $$(B.10) C gear   box = 16.45 N p 1.249 $$ {C}_{\mathrm{gear}\enspace \mathrm{box}}=16.45{{N}_p}^{1.249} $$(B.11) C electronics = 79 N p $$ {C}_{\mathrm{electronics}}=79{N}_p $$(B.12) C yaw   system = 0.0678 ( D ) 2.964 $$ {C}_{\mathrm{yaw}\enspace \mathrm{system}}=0.0678{(D)}^{2.964} $$(B.13) C main   frame = 9.489 ( D ) 1.953 $$ {C}_{\mathrm{main}\enspace \mathrm{frame}}=9.489{(D)}^{1.953} $$(B.14) Mas s main   frame = 2.233 ( D ) 1.953 $$ \mathrm{Mas}{\mathrm{s}}_{\mathrm{main}\enspace \mathrm{frame}}=2.233{(D)}^{1.953} $$(B.15) C platform   and   railing = 1.09 × Mas s main   frame $$ {C}_{\mathrm{platform}\enspace \mathrm{and}\enspace \mathrm{railing}}=1.09\times \mathrm{Mas}{\mathrm{s}}_{\mathrm{main}\enspace \mathrm{frame}} $$(B.16) C hydraulic   and   cooling = 12 N p $$ {C}_{\mathrm{hydraulic}\enspace \mathrm{and}\enspace \mathrm{cooling}}=12{N}_p $$(B.17) C electrical   connection = 40 N p $$ {C}_{\mathrm{electrical}\enspace \mathrm{connection}}=40{N}_p $$(B.18) C nacelle   cover = 11.537 N p + 3849.7 $$ {C}_{\mathrm{nacelle}\enspace \mathrm{cover}}=11.537{N}_p+3849.7 $$(B.19) C foundation = 303.24 × ( Area × hub   height ) a $$ {C}_{\mathrm{foundation}}=303.24\times {\left(\mathrm{Area}\times \mathrm{hub}\enspace \mathrm{height}\right)}^a $$(B.20) C assembly   and   installation = 1.965 × ( D × hub   height ) b $$ {C}_{\mathrm{assembly}\enspace \mathrm{and}\enspace \mathrm{installation}}=1.965\times {\left(D\times \mathrm{hub}\enspace \mathrm{height}\right)}^b $$(B.21) C tower = ( 0.596 × Area × hub   height ) - 2121 $$ {C}_{\mathrm{tower}}=(0.596\times \mathrm{Area}\times \mathrm{hub}\enspace \mathrm{height})-2121 $$(B.22)where a = 0.4037, b = 1.1736, D = rotor radius (m), Np = nominal power (kW).

All Tables

Table 1

Wind turbine details.

In the text
Table 2

Classification of wind energy resource.

In the text
Table 3

Wind category and class of different Indian sites.

In the text
Table 4

Best methods and wind regime characteristics at 150 m height.

In the text
Table 5

Cost of energy of different sites with different wind models.

In the text
Table A1

NEPFM coefficients.

In the text

All Figures

thumbnail Figure 1

Proposed approach for wind power density evaluation and cost of energy at different tower heights.
In the text
thumbnail Figure 2

Wind speed profile of 62 Indian sites. a) Wind Speed for 10 different locations in Gujarat; b) Wind speed for 9 different locations in Karnataka; c) Wind Speed for 10 different locations in Tamil Nadu; d) Wind Speed for different locations in Andaman and Nicobar, Assam, Jammu and Kashmir and West Bengal; e) Wind Speed for 8 different locations in Madhya Pradesh; f) Wind Speed for 8 different locations in Maharashtra; g) Wind Speed for 9 different locations in Andhra Pradesh; h) Wind Speed for 4 different locations in Rajasthan.
In the text
thumbnail Figure 3

Comparison of Weibull probability density function with different methods. a) Fitting comparison methods of wind speed distribution of Mahidad hill location of Gujarat; b) Fitting comparison methods of wind speed distribution of Suvarda location of Gujarat; c) Fitting comparison methods of wind speed distribution of Sansagar location of Gujarat; d) Fitting comparison methods of wind speed distribution of Kanyakumari location of Tamil Nadu; e) Fitting comparison methods of wind speed distribution of Kainonkarai location of Tamil Nadu; f) Fitting comparison methods of wind speed distribution of Kalunirkulam location of Tamil Nadu; g) Fitting comparison methods of wind speed distribution of M.S.Puram location of Tamil Nadu; h) Fitting comparison methods of wind speed distribution of Sarvallar hill location of Tamil Nadu; i) Fitting comparison methods of wind speed distribution of Mount Harriet location of Andaman Nicobar; j) Fitting comparison methods of wind speed distribution of Barkheri bazaar location of Madhya Pradesh; k) Fitting comparison methods of wind speed distribution of Nagda location of Madhya Pradesh; l) Fitting comparison methods of wind speed distribution of Jagmin location of Maharashtra; m) Fitting comparison methods of wind speed distribution of Chakla location of Maharashtra; n) Fitting comparison methods of wind speed distribution of Raipur location of Maharashtra; o) Fitting comparison methods of wind speed distribution of Brahmanwel location of Maharashtra.
In the text
thumbnail Figure 4

Comparison of cumulative wind distribution for different sites. a) Fitting comparison methods of cumulative wind distribution of Mahidad hill location of Gujarat; b) Fitting comparison methods of cumulative wind distribution of Suvarda location of Gujarat; c) Fitting comparison methods of cumulative wind distribution of Sansagar location of Gujarat; d) Fitting comparison methods of cumulative wind distribution of Kanyakumari location of Tamil Nadu; e) Fitting comparison methods of cumulative wind distribution of Kainonkarai location of Tamil Nadu; f) Fitting comparison methods of cumulative wind distribution of Kalunirkulam location of Tamil Nadu; g) Fitting comparison methods of cumulative wind distribution of M.S.Puram location of Tamil Nadu; h) Fitting comparison methods of cumulative wind distribution of Sarvallar hill location of Tamil Nadu; i) Fitting comparison methods of cumulative wind distribution of Mount Harriet location of Andaman Nicobar; j) Fitting comparison methods of cumulative wind distribution of Barkheri Bazar location of MP; k) Fitting comparison methods of cumulative wind distribution of Nagda location of Madhya Pradesh; l) Fitting comparison methods of cumulative wind distribution of Jagmin location of Maharashtra; m) Fitting comparison methods of cumulative wind distribution of Chakla location of Maharashtra; n) Fitting comparison methods of cumulative wind distribution of Jaipur location of Maharashtra; o) Fitting comparison methods of cumulative wind distribution of Brahmanwel location of Maharashtra.
In the text

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