Wind characteristics and wind energy assessment in the Barents Sea based on ERA-Interim reanalysis

Chenglin Duan; Zhifeng Wang; Sheng Dong; Liao Zhenkun

doi:10.1515/ohs-2018-0039

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Wind characteristics and wind energy assessment in the Barents Sea based on ERA-Interim reanalysis

Oceanological and Hydrobiological Studies

Volume 47 (2018): Issue 4 (December 2018)

By: Chenglin Duan, Zhifeng Wang, Sheng Dong and Liao Zhenkun

Open Access

|Dec 2018

Figures & Tables

Geographical location of the Barents Sea and the sea area within the inner sector is the selected domain for the statistical study

Annual average, mean monthly maximum (April), minimum (September) distributions of the sea ice concentration and the ice zone partitions. In the ice-free zone, the points labeled from P1 to P1 are selected for further wind climate and energy analysis.

Table 1

Four extreme value distribution functions

Distribution type	Probability density distribution function f(x)	Parameters
GEV	$\frac{1}{α} {[1 -β (\frac{v - μ}{α})]}^{1 / β-1} \exp {- {[1 -β (\frac{v - μ}{α})]}^{1 / β}}$	μ-location parameter α-scale parameter β-shape parameter
Gumbel	$\frac{1}{α} \overset{μ}{\exp} [- (\frac{v -}{α}) \overset{μ}{- \exp} (- \frac{v -}{α})]$	μ-location parameter α-scale parameter
Weibull	$\frac{α}{β} {(v - μ)}^{α - 1} \exp [- \frac{{(v - μ)}^{α}}{β}] x \geq μ$	μ-location parameter β-shape parameter α-scale parameter
P-III	$\frac{β^{α}}{Γ (α)} {(v - μ)}^{α-1} e^{-β (x - μ)} v^{3} μ,α > 0$	μ-location parameter β-shape parameter α-scale parameter

Spatial distributions of annual average wind speed at 10 m

Spatial distributions of mean seasonal wind speed at 10 m from winter to autumn

Wind roses from P1 to P7 in the Barents Sea. The color scale represents wind speed classification

Spatial distributions of annual average wind speed and power density at 100 m

Spatial distributions of mean seasonal wind speed at 100 m from winter to autumn

Spatial distributions of mean seasonal wind power density at 100 m from winter to autumn

Table 2

Parameters of model wind turbine generators for CCWE3000D and SL500

WTGs	Rated power (kW)	Hub height (m)	Rotor diameter (m)	Cut-in wind speed (m s⁻¹)	Rated wind speed (m s⁻¹)	Cut-out wind speed (m s⁻¹)
CCWE3000D	3000	100	103	3	12	25
SL500	5000	100	128	3.5	12.5	25

Interannual variations of annual average wind power density at 100 m from P1 to P7 in 1996–2015

Table 3

Locations of the selected points and 100 m annual average wind speeds, wind power density (WPD) and net electric energy output from P1 to P7

Points	Longitude (N)	Latitude (E)	Mean speed (m s⁻¹)	WPD (W m⁻²)	Electric energy (MWhy)
Points	Longitude (N)	Latitude (E)	Mean speed (m s⁻¹)	WPD (W m⁻²)	CCWE3000D	SL500
P1	22	74	10.23	1108	14 384	23 534
P2	22	72	9.99	1041	14 002	22 855
P3	32	74	10.26	1097	14 535	23 776
P4	35	72	10.10	1046	14 312	23 390
P5	42	73	9.91	985	14 011	22 873
P6	42	71	9.95	991	14 121	23 064
P7	37	70	9.96	1002	14 118	23 079

Power output curves for wind turbine generators

Table 4

Comparisons of the K-S test and RMSE between GEV, Gumbel, Weibull and P-III distributions

Distributions	K-S test	Mean	Maximum	Minimum
Distributions	K-S test	RMSE	RMSE	RMSE
GEV	99.97%	0.0484	0.1700	0.0210
Gumbel	99.96%	0.0591	0.1353	0.0174
Weibull	98.56%	0.0473	0.2957	0.0191
P-III	100%^*	0.0434^*	0.1061^*	0.0153^*

[i] Note: Results of the K-S test in this study mean in fact the passing rate of all grids in the whole domain with the significance level of 0.05. The most optimal results are marked with *.

A. Fitting probability density curves and cumulative probability density curves of the annual maximum wind speed series from P1 to P4; B. Fitting probability density curves and cumulative probability density curves of the annual maximum wind speed series from P5 to P7

Table 5

Different return values of extreme wind speeds (m s⁻¹) based on Gumbel and P-III distributions from P1 to P7 for 50-year and 100-year return periods; higher extreme speeds are marked with *

Points	Distribution	5-year	10-year	25-year	50-year	100-year
P1	Gumbel P-III	22.62 22.59	23.17 23.39	24.32 24.27	25.18^* 24.85	26.03^* 25.38
P2	Gumbel P-III	22.35 22.47	23.41 23.27	24.75 24.15	25.74^* 24.73	26.73^* 25.26
P3	Gumbel P-III	22.43 22.86	23.33 23.73	24.48 24.68	25.33^* 25.30	26.17^* 25.88
P4	Gumbel P-III	21.89 22.16	22.73 22.97	23.78 23.87	24.57^* 24.49	25.34^* 25.06
P5	Gumbel P-III	21.57 21.78	22.45 22.58	23.56 23.49	24.38^* 24.11	25.20^* 24.68
P6	Gumbel P-III	21.65 21.76	22.62 22.58	23.85 23.52	24.76^* 24.16	25.67^* 24.76
P7	Gumbel P-III	22.26 22.71	23.26 23.66	24.52 24.71	25.46^* 25.40	26.39^* 26.03

Spatial distributions of extreme wind speeds in 50-year and 100-year return periods based on the P-III distribution function

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DOI: https://doi.org/10.1515/ohs-2018-0039 | Journal eISSN: 1897-3191 | Journal ISSN: 1730-413X

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Language: English

Page range: 415 - 428

Submitted on: Feb 2, 2018

Accepted on: Apr 26, 2018

Published on: Dec 3, 2018

Published by: University of Gdańsk

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

sea ice zoning,

offshore wind,

wind energy,

spatio-temporal variation,

extreme wind parameters,

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© 2018 Chenglin Duan, Zhifeng Wang, Sheng Dong, Liao Zhenkun, published by University of Gdańsk
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 47 (2018): Issue 4 (December 2018)

Wind characteristics and wind energy assessment in the Barents Sea based on ERA-Interim reanalysis

Figures & Tables

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