Engineering, 2013, 5, 51-55 Published Online September 2013 (
Copyright © 2013 SciRes. ENG
Analysis of Cooperation between Wind Power and
Load Side Resources
Xiaorui Guo, Ke Wang, Yaping Li
China Electric Power Research Institute, Nanjing, China
Email: guoxi a,,
Received July 2013
Development of the intermittent energy is greatly promoted by change in energy, while consumption of large -scale in-
termittent energy is becoming a problem. With the development of smart grid technology, controllability of load side
resources is becoming more and more important. Based on the wave characteristics of wind power, this paper indicates
that wind energy has continuous output characteristics on the hour-time scale. Through ana lysis on loads characteristic
of indu stry, public facility and resident, this paper gets comprehensive response of load side resources. Considering
characteristics of wind power output, combined with different load side resources and DR program, this paper suggests
cooperation between wind power and load side resources on different time scales.
Keywords: Wind Power; Fluctuation Characteristic; Load Side Resources; Cooperation ; Adjustment Featu res
1. Introduction
With the continuous growth of wind power, wind power
is gradually increasing to an alternative energy source as
well as the leading energy and its consumption is be-
coming a huge challenge. In fact, due to the geographical
dispersion effect and the complementary of output, the
output of large scale wind farms is not dramatically
changed [1,2]. The key impact on consumption of wind
power is fluctuant in days, and most with anti-peaking
characteristics [3]. In order to reduce the fluctuation of
wind power to the grid, the researchers have made use of
UHV transmission with wind-fire bale delivery [4], con-
figured and optimized storage capacity storage settled
fluctuation of wind power [5], as well as optimized the
scheduling of wind power to solve the problem [6]. More
studies are from the perspective of power generation or
transmission side. With the development of demand re-
sponse technology, consumption of wind power is not
only from the generation and transmission side but also
considering the ability of load side [7]. Domestic and
foreign scholars have realized the enormous potential of
load side resources in consumption of wind power, and
there are a lot of r esearche s on enhancing the acceptance
of wind power and improving the quality of the grid such
as making use of electric vehicles and wind power coop-
erative scheduling [8,9] and establishing joint modal of
wind power-demand response [10-12]. However, because
of its quantity and widely distributed, the response cha-
racteristics, response capabilities and potential of dif-
ferent load types are not identical, so the key of promot-
ing consumption of wind power is grasping its coopera-
tion with wind po wer. Firstly, taking one regional power
gird for example, this paper researches on wind power
fluctuation in time domain. Then this paper analyses
electricity utilization way and characteristics of industrial
loads, commercial loads and resident loads, and on this
basis, it extracts load side resources that can be used to
balance the fluctuations of wind power.
2. Fluctuation Characteristics of Wind
Power on Time Scales
Fluctuation of wind power cant be simply equated with
a stochastic phenomena, its volatility has had the general
characteristics of random phenomena, uncertainty and
statistical regularity. It means that we can be able to de-
scribe its statistical regularity and variation trend mathe-
matically. Using mean, standard deviation, frequency
analysis and other statistical indicators, this paper ana-
lyses the distribution of the amount of sample characte-
ristics of wind power.
2.1. Statistical Analysis on Outputs of Wind
Based on measured wind power data of certain wind
farms, this paper analyses for fluctuation characteristics
of wind power. As of December 2012, the regions total
wind power installed capacity is 2400 MW, and net total
capacity is 2203 MW. Data collection period is 00:00:00,
Copyright © 2013 SciRes. ENG
November 27, 2011, to 24:00:00, November 30, 2012,
sampling interval is 5 minute; another data collection
period is December 16:00:00, to 16:00 :00, December 17,
2012, sampling interval is 5s.
The mean of statistical index can reflect the overall
level of wind power output within a certain time or pe-
riod. Standard deviation reflects the degree of change,
the greater the standard deviation and the me an of repre-
sentative, the wind power fluctuation is severe; The
smaller the standard deviation, the higher the average
representative, wind power fluctuation is relatively flat.
Analyzes the mean, standard deviation of wind farm
power per unit value on dif ferent sampling time scales (5
second, 5 minute , one hour).
From Table 1, with the time scale increases, the wind
farm output level has increased, when the time interv al is
changed from 5 second to one hour the mean increases
from 0.11 to 0.14, output distribution increases with time
scales. The standard deviation increases from 0.06 to
0.11, indicating that as the time scale increases, wind
power output increa sed volat i lity.
As shown in Figure 1, from 5 second to 5 minute scales,
wind wave is small, fluctuation of wind power are main-
ly embodied in hours and above time scale, such fluctua-
tions will affect generation scheme of conventional unit,
fluctuations of large- scale wind power may cause the
unit operating in a low output and uneconomical state.
2.2. Fluctuation Characteristics of Wind Power
The template is used to format your paper and style the
text. All margins, column widths, line spaces, and text
fonts are prescribed; please do not alter them. You may
note peculiarities. For example, the head margin in this
template measures proportionately more than its custom-
ary. This measurement and others are deliberate, using
specifications that an ticip a te yo ur paper as one part of the
entire journals, and not as an independent document.
Please do not revis e an y of the current design a tions.
Table 1. Statistics for different time scales (S = 2203 MW).
Time scale 5 second 5 minute 1 hour
Mean 0.111206 0.138001 0.147557
standard deviation 0.06242 0.08088 0.108315
Figure 1. Fluctuation statistics in different time scales.
In the second and minute time scale, fluctuations of
wind power output are usually small, but a longer time
scale (hours and above) are greater. As time scale in-
creases, the fluctuations of wind power output distribu-
tion has a certain trend.
Wind power output is random within a week, but the
random variation in a certain time frame. From Figure 2,
wind power output can maintain a certain level in 24
hours, more below the range 8 hours but always over one
3. Analysis on Load Side Resources
According to City electric power plan specification,
the typical user is divided into industry, public facility
and resident. Through the survey on electricity consump-
tion of different loads, this paper gives demand response
type for different load side resources.
3.1. Characteristics of Industrial Loads
Industrial users generally can be divided into heavy and
light class. Most of the heavy loads are continuous pro-
duction-oriented enterprises, the loading rate is high and
remained stable throughout the year. There are a consi-
derable number of heavy users which are one or two
loads, requiring higher power supply reliability. The light
is the production of consumption-oriented processing
industries, the characteristics of the users are quite dif-
ferent and the reliability of po wer supply requirements is
not identical. The loading rate is high, above 90%. Load
curve is stable, there have more equipment in which
stand-alone capacity is small and the total load is large,
requiring higher power supply reliability as well.
In actual operation, the industries which are price-
sensitive have already adjusted the production line to
avoid time-of-use price and peak tariff. Limited to power
supply reliability of industry load, the heavy load is suit-
able for interruptible load, and the light load can partici-
pate in interruptible load and emergency demand re-
sponse. Combining with load characteristics of morning
and evening peak, Tables 2 and 3 show the controlling
characteristics of the industr y.
1 2 3 4 5 6 7
Average output of a week
8h 8h
Figure 2. Peripheral curve of wind power output.
Copyright © 2013 SciRes. ENG
Table 2. Adjustment features of heavy industries.
Industry Proportion of interruptible load Remark
Heavy industry
Steel- Rolling production line (0.75 - 0.85)*0.8
8 hours interr upt suitable for early peak, 4 hours interrupt
suitable for evening peak
Nonferrous Metals Processing 0.35 - 0.5 Low production continuity, 4 hours interrupt
Building Materials(cement) (0.75 - 0.85)*0.8 Notice for a long time, 8 hours interrupt
Table 3. Adjustment features of light industries.
Industry Proportion of interruptible load Proportion of emergency demand response
Light industry
Spinning Cotton production: 0.54 /
Manufacturing on appliances, electronics,
daily necessities
025 - 0.4 025 - 0.4
Pharmaceutical industry Chinese medicine: 0.5 0.5
Table 4. Adjustment features of public utilities.
Proportion of HVAC Proportion of lighting load
20 minutes 2 hours
Day-time Night-time Day-time Night-time Day-time Night-time
Office 0.24 0.01 0.18 0.01 0.033 0.005
Catering 0.008 0.001 0.006 0.001 0 0
Retail 0.101 0.01 0.101 0.01 0.083 0.01
Refrigeration 0.064 0.004 0.048 0.004 0.01 0.001
School 0.32 0.01 0.24 0.01 0.043 0.006
3.2. Characteristics of Public Facilities Loads
Users of public facilities are divided into administrative
offices, cultural and entertainment, sports, education and
scientific research, health classes, and commercial, fi-
nancial, services, etc. This class of load is mainly using
heating, ventilation, air-conditioning and cooling (HVAC)
and lighting load, can be divided into HVAC and lighting
reduced capacity. The users who have energy manage-
ment and control system (EMCS) or system control and
data acquisition (SCADA) can participate in the regula-
tion of HVAC and lighting. According to electricity
consumption, the regulation time period is divided into
daytime (6:00 -22:00) and night time (22:00-6:00 the next
day). According responsiveness HVAC has two ways:
one has response speed of 15 minute and sustained re-
ductions time of 2 hours; the other has response speed of
5second to 5 minute and sustained reductions time of 20
minutes. The lighting load has the same responsiveness
whether 2 hours or 20 minutes.
According to the survey data of literature [13], consi-
dering the popularizing rate of EMCS/SCADA system,
this paper gives the control characteristics of the public
utilities in Table 4.
Public facilities can be suitable for fast and flexible
load regulation, and can be used as a flexible scheduling
resource to participate in the distr ibution of the load.
3.3. Characteristics of Residential Loads
Intelligent use of electricity can provide information and
have features to facilitate the residents of household
electricity and it will be a trend for electricity consump-
tion of resident. Having small electricity consumption for
individual and large for whole, residents are important
participator in demand response under the smart grid.
Project of demand response in which resident participate
is in residential area. Regulation of resident is mainly the
energy storage capacity load (refrigerators , air conditio n-
ers, heaters and water heaters, etc.) and transfer load
(washing machine, dishwasher). Considering participa-
tion degree of users and electrical usage in different time
periods, this paper gives the control characteristics of the
resident in Table 5.
3.4. Characteristics of Comprehen sive Response
Different electricity Characteristics of industr ies, public
facilities and residents determine the difference between
Copyright © 2013 SciRes. ENG
their participation in load regulation. Most of industrial
companies require higher power supply reliability. It is
obvious that industrial load regulation characteristics of
the load on the system, and its adjustment time is rela-
tively long, need set aside one to ten hours advance no-
tice of the time. Characteristics of public facilities and
residential load is more flexible and can provide a wide
range of load adjustment, and need set aside one hour
advance notice of time.
One regional load is divided into industry, public fa-
cilities and resident firstly, and then Table 6 is given,
which describes adjustment features of different types of
loads on different time scales.
4. Exponential Analysis
Wave characteristics of scale in hours wind power cant
be ignored, while load side resources have adjustable
features in minutes, ho urs or larger time scale. Therefore
it is possible for using load side resources to balance
fluctuations of wind power.
Figure 3 shows wind power output of someday, we
can see that output of morning has obvious anti-peaking
characteristics. The mean of Wind power is 0.173, setting
0.173 as the desired output of wind power. Wind power
output roughly divided into four stages: stage I, from
0:00 to 3:00, the overall load is at its valley, but wind
power output is high; stage II, from 3:00 to 15:00, the
overall load is at its flat or peak, but wind power is low;
stage III, from 15:00 to 21:00, the overall load is at its
peak, and wind power is high; stage IV, from 21:00 to
24:00, consumption of resident is at its peak, wind powe r
output is close to expectations.
As shown in Tab le 7, in stage I, the system load is at
its valley, industrial loads which are price-sensitive are
generally full load operation, it can increase electrical
load of public facilities and resident by means of Auto
DR, such as increasing the cooling rate of the refrigera-
tion/freezer or the storage capacity of resident. In stage II,
the wind power output decreases, before eight oclock,
when system load is low, it can be balanced by control-
ling the load which have been adjusted in stage I and
with interruptible load of 8 hours; after eight oclock,
when system load is at its morning peak with the load of
resident and public facilities higher electric power, it can
be balanced by adding a interruptible load of 4 hours of
industry; during this time it will be fluctuant, so it needs
to supply some Auto DR. In stage III and stage IV, wind
power output has a positive peaking characteristic, the
load of resident and public facilities can mod ify load rate
by means of Auto DR.
5. Conclusion
With the development of smart grid technology, the load
is not just a simple one-way load. With the he l p of demand
Table 5. Adjustment features of resident.
Time period
7:00-11:00 0.0077 Flat section, load of cooking transfer to trough
11:00-14:00 0.0045 Peak load, some load transfer to flat section
14:00-17:00 0.0034 Flat section, reduce air conditioning usage, appliances convert from standby to close
17:00-22:00 0.0011 Peak load, reduce part of lighting, Flexible electrical appliances transfer to flat or trough
22:00-7:00 the next day / Trough section, increase usage time of some appliances
Table 6. Adjustment features of comprehensive response.
Heavy industry Light industry public facilities Resident
Time scale 8 hours 4 hours 8 hours 4 hours 1 hour 2 hours 20 minute Minute - hours
DR program IL IL EDR Auto DR Auto DR
Figure 3. Out put curve of a typical wind power.
Copyright © 2013 SciRes. ENG
Table 7. Control measure of load side resources.
The output stage of wind power Load side resources DR program
Stage I Public facilities, Resident Auto DR
Stage II Valley load Public facilities, Resident, Industry Auto DR; interruptible load of 4 hours and 8 hours
Peak load Industry
Stage III Public facilities, Resident Auto DR
Stage IV
response techniques, load-side resources can also be used
as a flexible grid scheduling resource. On the basis of
analyzing the fluctuation characteristics of wind power,
this paper shows the time scale fluctuation characteristic
of wind power output by means of statistic indicators,
and then it indicates that wind power has continuous
output in hours. After analyzing production characteristic
of different load side resources, it gets adjustment fea-
tures of industrial loads, public facilities and residential
loads in class. It is observed that load side resources can
balance wind power fluctuation by means of interaction
with different load types and DR programs.
6. Acknowledgements
This work is supported by Beijing Natural Science
Foundation (3132035), as well as technology project of
State Gr id Corporation of China: research on demand
response regulatory mechanism for promoting accep-
tance capacity of wind power.
[1] C. Y. Xiao, N. B. Wa ng, K. Ding, et al., “System Power
Regulation Scheme for Jiuquan Wind Power Base,” Pro-
ceeding of the CSEE, Vol. 30, No. 10, 2010, pp. 1-7.
[2] C. Y. Xiao, N. B. Wang, J, Zhi and K. Ding, “Power
Characteristic of Jiuquan Wind power Base,” Automation
of Electric Power System, Vol. 34, No. 17, 2010, pp.
[3] N. Zhang, T. R. Zhou, C. G. Duan, et al., “Impact of
Large-Scale Wind Farm Connecting with Power Grid on
Peak Load Regulation Demand,” Power System Technol-
ogy, Vol. 34, No. 1, 2010, pp. 152-158.
[4] Z. H. Chen, Y. H. Chen, Z. Xing, et al., “A Control
Strategy of Active Power Intelligent Control System for
Large Cluster of Wind Farms Part Two Coordination
Control for Shared Transmission of Wind Power and
Thermal Power,” Automation of Electric Power System,
Vol. 35, No. 21, 2011, pp. 12-15
[5] T. Han, J. P. Lu, L. Qiao, et al., “Optimized Scheme of
Energy-Storage Capacity for Grid-Connected Large-Scale
Wind Farm,” Power System Technology, Vol. 34, No. 1,
2010, pp. 169-173.
[6] W. Zhou, Y. Peng, H. Sun and Q. H. Wei, “Dynamic
Economic Dispatch in Wind Power Integrated System,”
Proceeding of the CSEE, Vol. 29, No. 25, 2009, pp. 13-
[7] X. Ai and X. Liu, “Chance Constrained Model for Wind
Power Usage Based on Demand Response,” Journal of
North China Electric Power University, Vol. 38, No. 3,
2011, pp. 17-23.
[8] A. Shortt and M. O’Malley, “Quantifying the Long-term
Power System Benefits of Electric Vehicles,” IEEE ISGT
Conference 2012.
[9] D. Y. Yu, S. G. Song, B. Zhang and X. S. Han, “Sy ner-
gistic Dispatch of PEVs Charging and Wind Power in
Chinese Regional Power Grids,” Automation of Electric
Power System, Vol. 35, No. 14, 2011, pp. 24-29.
[10] R. Sioshansi, “Evaluating the Impacts of Real-Time Pric-
ing on the Cost and Value of Wind Generation,” IEEE
Transactions on Power Systems, Accepted for Future
[11] Q. R. Wang, G. H. Xie and L. Z. Zhang, “An Integrated
Generation Consumption Dispatch Model with Wind
Power,” Automation of Electric Power System, Vol. 35,
No. 5, 2011, pp. 15-18.
[12] X. Liu, X. Ai and Q. Peng, Optimal Dispatch Coordi-
nating Power Generation with Carbon Emission Permit
for Wind Farms Integrated Power Grid Considering De-
mand Response,” Power System Technology, Vol. 36, No.
1, 2012, pp. 213-218.
[13] D. S. Watson, N. Maston and J. Page, “Fast Automated
Demand Response to Enable the Integration of Renewa-
ble Resources,” Lawrence Berkeley National Laboratory,
June 2012.