Energy and Power Engineering, 2013, 5, 393-397
doi:10.4236/epe.2013.54B076 Published Online July 2013 (
Analysis on the Characteristics of Wind Power Output in
Hainan Power Gr i d*
Jianfeng Wang, Dongmei Zhao
College of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China
Received March, 2013
It is of great importance to study the characteristics of wind power output for the healthy and secure & stable of power
grid. Based on the actual operating data, th e probability distribution of the power fluctuations of the wind farm in Hai-
nan and the variation of wind power annual, seasonal, daily active output is analyzed. The study showed that Hainan
Province has obvious seasonal variation of wind power output characteristics, higher levels of output of the year gener-
ally in winter or summer, spring and autumn to contribute small. The av erag e wind power output will contribute to “low
day and high night”, with certain peaking capacity. Shorter time scales, changes in the wind power to smaller amount,
not to bring too much impact on system operation, while a long time fluctuations affect the scheduling and running on
the grid.
Keywords: Wind Power; Power Fluctuation; Probability Distribution
1. Introduction
With the growing energy and environmental problems,
the development of new energy has been a concern
around the globe. China has a vast coastline of wind en-
ergy resources are widely distributed and relatively rich
areas are mainly concentrated in the southeast coast and
nearby islands and the northern. In addition, inshore and
offshore wind energy resources are very rich [1].
The new energy is one of the pillar industries in recent
years to focus on supporting the development of Hainan,
Hainan wind power has been rapid development. As of
the end of June this year, Hainan new energy installed
capacity of 249.5 MW wind power project to dominate.
Last year, Hainan wind power generation of 270 million
Because of intermittent, randomness and volatility
characteristics of wind, with the rising proportion of
wind power installed capacity proportion in the system,
the impact on security, stability and economic operation
of the power system will cannot be ignored[2,3]. Wind
power output change by a variety of geographical and
climatic factors. Usually only by the statistics and analy-
sis of a large number of actual data, can we get the varia-
tion of the wind power in particular areas. Therefore,
based on the actual operating data of Hainan several
wind farms from 2011 to 2012, variation of wind power
annual, seasonal, daily active output is analyzed. Further
with the probability distribution method, the fluctuations
of wind power are quantitative analyzed, as well as the
impact on Hainan power grid.
2. Overview of Wind Power in HAINAN
Hainan Island is located in northern margin of tropical,
with tropical monsoon maritime climate. Winter prevail-
ing northeast monsoon and prevailing southwest mon-
soon in the summer, sometimes blowing southeast mon-
soon and many tropical cyclones occurs. The wind en-
ergy resources which can be developed and used are
mainly distributed in th e coastal areas, offshore areas and
some inland mountainous area.
Off the coast of Hainan is rich in wind energy re-
sources, and the average wind speed in most parts is be-
tween 4.3 m/s to5.2 m/s. As of the end of 2012, the Hai-
nan power gr id has b een pu t into operatio n in w ind f arms
in six, the total installed capacity of 303MW, accounting
for about 7.23% of the installed capacity of Hainan
province. The six grid wind farms are Wenchang wind
farm, E’man wind farm, Gancheng wind farm, Sigeng
wind farm, Gaopai wind farm and Dongfang wind farm,
as shown in Figure 1. The capacity of Dongfang wind
farm is very small, and Gaopai just put into operation in
the end of the yea r, in th is pap er, the f irst fou r wind f arm
will be the research focus.
*The National High Technology Research and Development of China
863 Program ( 2012AA050201).
Copyright © 2013 SciRes. EPE
Figure 1. Diagram of wind power in Hainan.
3. Wind Power Output Fluctuation Analyses
3.1. Output Level under Different Years
Over a period of time, the wind power output level is
constantly in random fluctuations, it is difficult to accu-
rately predict, therefore, need to select an indicator to
study the laws of statistics. Wind power annual utiliza-
tion hours [4], also known as Equivalent full load power
generation hours, Refers to the ratio of the actual gener-
ating capacity of wind power equipment in the year with
annual generating capacity of the power generation
equipment running at rated power. Statistics Hainan wind
farm operating data in the past two years, the annual uti-
lization hours is about 1433 ~ 2340 h, the average value
of 2000h. Annual utilization hours of wind power af-
fected by multiple factors: 1) the impact of meteorologi-
cal factors such as wind conditions, climate, natural dis-
asters, etc; 2) wind turbine failure rate, reliable operation
time of the unit; 3) the transmission, substation capacity
constraints of wind farm area; 4)the electric field losses,
transformer, line losses, and other auxiliary power con-
Wind power annual utilization hours can evaluate the
level of efficiency in the use of the wind farm, the level
of annual utilization hours reflect the relative size of the
level of wind speed from the other side in different years.
Figure 2 shows, the average wind speed of each region
in 2012 is basically lower than 2011.
3.2. Wind Power in Different Seasons and
The wind speed of an area is largely influenced by the
local climate, wind energy resources of monsoon climate
region shows apparent regularity in the long-term within
the one-year cycle. Active power and capacity factor [5]
was chosen as indicators in the Statistics of the output
data of the Hainan four wind farms in 2011 and 2012.
The results are shown in Figure 3.
In 2011 and 2012, the maximum outp ut occurs in Jan-
uary and June, with the value 83.3MW and 56.5MW
respectively; Minimum output occurs in August and
September, 14.8 MW and 22.7 MW respectively. The
seasonal maximum peak-to-valley was 68.5 MW and
33.8 MW, accounting for 37.18% and 18.31% of the
wind turbine total installed capacity.
From the wind power output curves in these two years
we can find, generally the largest wind power output
appear in the November, December, January, Under the
influence of the winter monsoon, Hainan have a greater
average wind speed; In the summer, around June, will
also appear larger wind, which is related to the impact of
the monsoon, and tropical cyclones; spring and autumn
wind is small generally.
Figure 4 shows annual capacity factor curve in the
Wenchang, E’man, Gancheng and Sigeng wind farm in
2011. The curve shows, in the winter, the output of all
the wind farms have reached the peak level of the year;
Figure 2. Annual utilization hours in different wind farms.
Figure 3. The total power of the four wind farms in each
Figure 4. Annual capacity factor curve in the four wind
farms in 2011.
Copyright © 2013 SciRes. EPE
J. F. WANG, D. M. ZHAO 395
in spring and autumn, wind power output is lower; in the
summer, different wind farm have different variation.
The geographic distance between Gancheng wind farm
and Sigeng wind farm is very near, at the west of Hainan
Island, has a similar law for the wind power output fluc-
tuations. The curves present bimodal characteristics,
winter and summer wind power output is high, while
spring and autumn to contribute significantly lower,
which exhibit significant characteristics of the monsoon.
On the other hand, Wenchang and E’man, at the north of
Hainan, have a different variation rule. In winter, wind
speed and wind power capacity factor are much higher
than other periods, in spring, summer and autumn wind is
low, and relatively little change, Curve shows unimodal
3.3. Wind Power Output Changes in One Day
For power systems, wind power is an uncontrollable
power source, the increase in power output of wind pow-
er, means that the system equivalents loads is relatively
small, and further affect daily open formulation and ad-
justment of the shutdown plan of the power system.
Therefore, it is necessary to study wind power output
variation in 24 hours.
With the active output data of wind farms in Hainan in
2012 as the foundation, the variation of the capacity fac-
tor of each power plant in one day is calculated, as
shown in Figure 5.
It can be found in a wide range of areas in Hainan , the
capacity factor of wind farms in 24 hours with the same
regularity. Wind power output level is low in the night
and the morning, and little change; Afternoon, the wind
power output level is increasing, and the peak generally
appear in the 14:00 to 17:00. We can find that wind
power output of Hainan, which is unlike in inland areas
of significant anti-peaking[6] characteristics presents the
characteristics of “low day and high night” and has cer-
tain support and added effect to peak load regulation in
power system, on the other hand, it is also conducive to
the elimination of the grid for wind power. In the men-
tioned four wind farm, E’man, Gancheng Sigeng wind
power output shows obvious fluctuations trend in one
day, the peak output level can reach twice of the night,
while power output is more tend to steady in Wenchang,
only a slight increase in the afternoon.
In order to study how the wind farm daily output
curves changes under different seasons, E’man wind
farm is made as an example for analysis on daily output
level in each season, as shown in Figure 6.
E’man wind farms wind power output has almost the
same change trend in different season, showed a single
peak characteristic, which usually appear at 14:00 to
17:00, the affection of the seasonal variation on wind
power output is mainly reflected in the size of the spe-
cific values. Clearlywind power capacity factor is basic
above 0.4 in winter, Indicating that higher utilization
efficiency in winter; While in spring, for a very long time,
the wind power capacity factor is less than 0.2.
4. Probability Distribution of Wind Power
4.1. Probability Distribution of Wind Speed
The distribution characteristics of wind speed generally
shows positive skewness, Weibull distribution [7, 8] is
generally considered as a suitable probability density
function for the wind speed statistical description. The
Weibull distribution is a single peak distribution function
cluster, which has two parameters. Its probability density
function can be expressed as:
() exp
kx x
px cc c
 
 
 
where, k is called the shape parameter, c called the scale
There are a variety of methods to estimate parameters
of the Weibull distribution, which is chosen depending
on the wind speed statistics. Three methods are com-
monly used [9]: Least squares method, mean and vari-
ance estimation method, minimum error approximation
Figure 5. Wind power capacity factor in E’man, Gancheng
Sigeng and Wenchang wind farm in 24 h.
Figure 6. Wind power capacity factor in E’man in each
Copyright © 2013 SciRes. EPE
According to the statistics of wind sp eed data in 2012,
combined with the second method, the calculated wind
speed distribution parameters are as follows in Table 1:
4.2. Probability Distribution of Wind Power
At present, the quantitative analysis o f the characteristics
of wind power fluctuations is fewthe probability dis-
tribution of the field is also not very mature. The normal
distribution can be used to describe the distribution of the
first-order differential sequence of wind power [10, 11]
proposed an improved t-distribution to describe the min-
ute level power fluctuation. The probability distribution
of the power differential sequence has an important role
to researcher like wind power forecast, wind farms
equivalents modeling and so on, so following this de-
tailed study:
In order to quan tify the power flu ctuations of the wind
power, this article refers to two numerical feature
amounts to describe the first-order differential sequence
of wind power. Assuming that as the wind power of
a wind farm at a certain moment, where is an
n-dimensional vector, is number of the wind farm
units, the average of wind power is
nP, and using the
standard deviation of wind power ou tput as quantita-
tive indicators to describe the amplitude of the wind
power fluctuation.
Assuming that is to describe the probability of oc-
currence of the first-order differential sequence of wind
power at different amplitude range [12]. The following
formula is:
TN N (3)
N is the number of occurrences of a certain
range of first-order differential sequence, is the to tal
number of wi nd power di ff erenti a l sequen ce.
Statistical analysis is to used on Gancheng wind farm
actual operating data of one day in2011installed ca-
pacity of 49.5 MW. The distribution of the amplitude of
power fluctuation at different time scales (10 s, 1 min, 15
min, 1 h) is shown in Fig u r e 7.
Table 1. Wind speed probability distribution parameters in
four wind farms.
Wind farm Reference
height/m average wind
speed/(m/s-1) c k
E’man 65 6.24 6.12 2.01
Gancheng 65 6.74 5.99 1.63
Sigeng 65 6.12 6.09 2.01
Wenchang 65 6.38 6.14 1.97
Figure 7. Distribution of the amplitude of power fluctuation
at different time scales.
Table 2. Wind power maximum, and standard of the
first-order differential sequence.
Time scale 10s 1min 15min 1h
Power fluctuations
maximum (MW) 0.871 1.875 6.698 6.564
Standard deviation
(MW) 0.1456550.400795 1.8723483.588958
At 1min, 15min scale, the probability of the distribu-
tion in 0.01pu
is 85.25% and 76.0%, respectively; at
10s-15min scale, the probability of the fluctuations of
wind power in 0.01pu
is almost 100%.
With increased sampling time scale, the magnitude of
the wind power fluctuations increases, the distribution
area of power fluctuations will be more widely. Table 2
from the perspective of the active wind power fluctuation
maximum value and the standard deviation, indicating
that wind power fluctuations grows with the larger time
In the very small time scale, power fluctuations is a
smaller amount, does not bring too much impact on the
system operation; However, when the size of the grid-
connected wind power increasing and wind power pe-
netration is high, which will cannot be ignored. The
short-term fluctuations affect system infrequency modu-
lation, and long-term fluctuations have effect on dispatch-
ing and operation of power system.
5. Conclusions
Based on Hainan wind farm actual operating data, from
the two aspects of the time scale and th e probability den-
sity distribution, in this paper, the wind power output in
different situations are compared and studied, the con-
clusion is as follows:
1) In Hainan, wind farms are mainly located on the
west coast and Wenchang, belongs to the offshore wind
power; the annual utiliz ati on hours i s about 1433 ~ 234 0 h,
the average value of 2000 h, wind energy resources are
relatively abundant.
2) The output of wind power has obvious seasonal
Copyright © 2013 SciRes. EPE
Copyright © 2013 SciRes. EPE
variation characteristics: in winter, wind power output is
at the peak, and there is a clear correlation; the output of
spring an d fall of eac h wind farm is low;
3) Unlike inland wind power’s "high day and low
night" feature, each wind farm, in every season, the wind
power output is basically the same, peak generally appear
in the afternoon from 2 o'clock to five o'clock, showed a
single peak characteristics conducive to the system
peaking wind power consumption;
4) Shorter time scales, changes in the wind power to
smaller amount, not to bring too much impact on system
operation, long time fluctuations affect the scheduling
and running on the grid, the need for furthe r res ea rch.
[1] J. F. Li, “China Wind Power Outlook 2012,” Beijing:
China Environmental Science Press, 2012.
[2] W. Y. Li, B. H. Zhang and Bagen, “Reliability Impacts of
Large Scale Utilization of Wind Energy on Electric
Power Systems,” Proceedings of the CSEE, Vol. 28, No.
1, 2008, pp.100-105.
[3] J. Guo, “Study on Impact of Grid-connected Wind Tur-
bine & Wind Farm on Power System,” The New Energy
Power Control Technology, Vol. 32, No. 1, 2010, pp.
[4] Z. S. Lin and L. Q. Luo, “Analysis of Wind Power Output
Characteristics in Fujian Andits Impact on Power Grid,”
Electric Power Construction, Vol. 32, No. 12, 2011, pp.
[5] D. Y. Yu, X. S. Han, J. Liang and S. G. Song, “Study on
the Profiling of China’s Regional Wind Power Fluctua-
tion Using GEOS-5 Data Assimilation System of Na-
tional Aeronautics and Space Administration of Amer-
ica,” Automation of Electric Power System, Vol. 35, No.
5, 2011, pp. 77-81.
[6] L. D. Yi, M. Y. Zhu, L. Wei, N. Jiang and G. L. Yu, “A
Computing Method for Peak Load Regulation Ability of
Northwest China Power Grid Connected With
Large-Scale Wind Farms,” Power System Technology,
Vol. 34, No. 2, 2010, pp. 129-132.
[7] G. Yang, Y. X. Du and M. Chen, “The Calculation of
Energy Provided by Wind Turbine Based on Its Power
Characteristic and the Wind Frequency Weibull Distribu-
tion,” Journal of Electric Power, Vol. 23, No. 4, 2008, pp.
[8] M. Ding, W. Wu, H. B. Wu, M. Q. Mao and Y. Y. Zhang,
“Research on Forecasting of Probabilistic Distribution
Parameters of Wind Speed and Its Application,” Power
System Technology, Vol. 32, No. 14, 2008, pp. 10-14.
[9] M. Ding, Y. C. Wu and L. J. Zhang, “Study on the Algo-
rithm to the Probabilistic Distribution Parameters of Wind
Speed in Wind Farms,” Proceedings of the CSEE, Vol. 25,
No. 10, 2005, pp. 107-110.
[10] L. Xiang, K. Chang, X. Y. Li and F. Xue, “Probe into
Temporal-spatial Characteristics of Wind Power Output
in Ningxia,” Shaanxi Electric Power, 2011, pp. 43-49.
[11] W. X. Lin, J. Y. Wen, X. M. Ai, S. J. Cheng and W. J.
Lee, “Probabil ity Density Function of Wind Power Varia-
tions,” Proceedings of the CSEE, Vol. 32, No. 1, 2012, pp.
[12] Y. Cui, G. Mu, Y. Liu and G. H. Yan, “Spatiotemporal
Distribution Characteristic of Wind Power Fluctuation,”
Power System Technology, Vol. 35, No. 2, 2011, pp.