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Multistate Wind Energy Conversion System Models for Adequacy Assessment of Generating Systems Incorporating Wind Energy

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Wind energy is considered to be a very promising alternative for power generation because of its tremendous environmental, social, and economic benefits. Electrical power generation from wind energy behaves quite differently from that of conventional sources. The fundamentally different operating characteristics of those facilities, therefore, affect the power system reliability in a manner different from that of the conventional systems. This paper is focused on the development of suitable models for wind energy conversion systems, in adequacy assessments of generating systems, using wind energy. These analytical models can be used in the conventional generating system adequacy assessment utilizing analytical or Monte Carlo state-sampling techniques. This paper shows that a five-state wind energy conversion system model can be used to provide a reasonable assessment of the practical power system adequacy studies, using an analytical method, or a state-sampling simulation approach.
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Multistate Wind Energy Conversion System Models
for Adequacy Assessment of Generating Systems
Incorporating Wind Energy
Roy Billinton and Yi Gao
Abstract—Wind energy is considered to be a very promising al-
ternative for power generation because of its tremendous environ-
mental, social, and economic benefits. Electrical power generation
from wind energy behaves quite differently from that of conven-
tional sources. The fundamentally different operating characteris-
tics of those facilities, therefore, affect the power system reliability
in a manner different from that of the conventional systems. This
paper is focused on the development of suitable models for wind
energy conversion systems, in adequacy assessments of generating
systems, using wind energy. These analytical models can be used
in the conventional generating system adequacy assessment uti-
lizing analytical or Monte Carlo state-sampling techniques. This
paper shows that a five-state wind energy conversion system model
can be used to provide a reasonable assessment of the practical
power system adequacy studies, using an analytical method, or a
state-sampling simulation approach.
Index Terms—Generation adequacy assessment, reliability eval-
uation, wind system models.
I. INTRODUCTION
T
HE development and utilization of wind energy to satisfy
the electrical demand has received considerable attention
in recent years, owing to the concerns regarding the dwindling
energy resources and enhanced public awareness of the potential
impact of the conventional energy systems on the environment.
Improvements in wind generation technologies will continue to
encourage the use of wind energy in both the grid-connected and
stand-alone systems. Owing to the random nature of the wind,
the wind generators behave quite differently from the conven-
tional generators. Therefore, it is important for the power system
planners and engineers to carefully consider the reliability is-
sues [1] associated with the wind energy sources.
A wind energy conversion system (WECS) converts the natu-
ral energy available at the system location into electrical energy.
Developing an adequacy model for a wind turbine generator
(WTG) requires the consideration of three factors that directly
affect the generator output. The first factor is the random nature
of the site resource, which must be included in an appropri-
ate model to reflect the variable characteristics of the wind at
that particular site. The second factor is the relationship be-
tween the power output and the site resource. This relationship
can be determined using the WTG operational parameters and
specifications. The third factor is the unavailability of the WTG
expressed by the unit forced outage rate (FOR) [2].
Manuscript received February 16, 2006; revised June 2, 2006. Paper no. TEC-
00053-2006.
The authors are with the Power System Research Group, University of
Saskatchewan, Saskatoon, SK S7N 5A5, Canada.
Digital Object Identifier 10.1109/TEC.2006.882415
In this paper, time series models are utilized to simulate hourly
wind speeds. The power output of a WTG unit is then obtained
using the relationship between the power output and the wind
speed. An apportioning method [2] is introduced and used to
create multistate models for a WTG unit, and for a WECS con-
taining multiple WTGs. An analytical procedure that incorpo-
rates the WTG FOR is used to build a multistate WECS model.
Attention is focused on the development and examination of
appropriate multistate WECS models for generating system ad-
equacy evaluation.
The analytical method [2] and the state-sampling simulation
technique [2] are applied to two test systems designated as the
Roy–Billinton test system (RBTS) [3] and the IEEE reliability
test system (IEEE-RTS) [4]. The total installed capacity and
system peak load of the RBTS are 240 and 185 MW, respec-
tively. The IEEE-RTS installed capacity and system peak load
are 3405 and 2850 MW, respectively. The wind site used in the
studies is located in Saskatchewan, Canada.
II. E
VALUATION TECHNIQUES
Considerable work has been done on the development and ap-
plication of models and techniques for generating capacity relia-
bility evaluation, and is documented in [5]–[8]. Certain specific
examples of wind related documentations, are presented in [1]
and [9]–[14]. The most comprehensive approach to incorporate
wind energy in a generating capacity evaluation is to use Monte
Carlo sequential simulation. This can be accomplished using
time series wind models [9], [12]. There is, however, a need to
develop suitable WECS models that can be easily incorporated
in more conventional approaches to generating capacity ade-
quacy assessment, such as analytical methods and the Monte
Carlo state-sampling technique. The detailed chronological na-
ture of the wind energy, modeled in the sequential simulation
approach, is not recognized in these techniques and the wind
variability is represented by a probability distribution.
Analytical techniques represent the system by analytical mod-
els and evaluate the system risk indices from these models using
mathematical solutions [2]. The loss of load expectation (LOLE)
approach is the most common method in use and is also used to
this paper. In this approach, the generating system represented
by the capacity outage probability table (COPT), and the load
represented by the load duration curve (LDC), are convolved to
calculate the LOLE index [2].
In the state-sampling Monte Carlo simulation approach, the
system state is obtained by sampling all the component states.
0885-8969/$25.00 © 2008 IEEE
The basic sampling procedure is conducted by assuming that the
behavior of each component can be categorized by a uniform
distribution under [0, 1]. The component can be represented by
a two-state or a multistate model. One of the advantages of the
system state-sampling method is that the multistate components
can be incorporated in the analysis without a significant increase
in the computing time.
A commercial software designated as Monte Carlo
Evaluation of COmposite system REliability(MECORE) [15],
which utilizes the state-sampling Monte Carlo simulation tech-
nique, was used in part of the studies described in this paper. The
MECORE software was developed to analyze composite gen-
eration and transmission systems. The transmission elements in
the test system are assumed to be 100% reliable, when MECORE
is used in a generating system study. The basic LOLE index used
in the analytical method [2] is the same, as the expected duration
of load curtailment (EDLC) used in MECORE [15].
III. W
IND TURBINE GENERATOR UNIT MODELS
A. Modeling and Simulating Wind Speeds
The wind speed model and data for the Swift Current site
located in the Province of Saskatchewan, Canada, have been
used in this paper. The mean and standard deviation of the
wind speed at the Swift Current site are 19.46 and 9.7 km/h,
respectively. The hourly mean and standard deviation of wind
speeds from a 20-year database (Jan. 1, 1984 to Dec. 31, 2003)
for this location were obtained from Environment Canada. These
data were used to build the auto-regressive and moving average
model (ARMA) time series model [9]. The ARMA(4, 3) model
is the optimal time series model for the Swift Current site, for
which the parameters are shown as
y
t
=1.1772y
t1
+0.1001y
t2
0.3572y
t3
+0.0379y
t4
+ α
t
0.5030α
t1
0.2924α
t2
+0.1317α
t3
α
t
NID
0, 0.524760
2
. (1)
where {α
t
} is a normal white noise process with zero mean and
variance 0.524760
2
.
Once the wind speed time series model is established, the
simulated wind speed SW
t
can be calculated as
SW
t
= µ
t
+ σ
t
y
t
(2)
where σ
t
is the standard deviation of the observed wind speed
at hour t and µ
t
is the observed mean wind speed at hour t.
Fig. 1 shows a comparison of the observed wind speed prob-
ability distribution for the original 20 years of data, and the
simulated wind speed probability distribution obtained using
the ARMA(4, 3) model and a large number (8000) of simulated
years. The observed average wind speed is 19.46 km/h, and the
simulated value is 19.53 km/h. The observed wind speed prob-
ability distribution is not as continuous as the simulated distri-
bution, as it is based on only 20 years of data.
Fig. 1 shows that the ARMA(4, 3) model provides a reason-
able representation of the actual wind regime. Simulation results
are used to generate the wind speed probability distributions in
the system adequacy studies described later in this paper.
Fig. 1 Observed and simulated wind speed distributions for the Swift Current
site.
B. Modeling Wind Turbine Generators
The power-output characteristics of a WTG are quite differ-
ent from those of a conventional generating unit. The output
of a WTG depends strongly on the wind regime as well the
performance characteristics of the generator.
After the hourly wind speed is obtained, the next step is to
determine the power output of the WTG as a function of the wind
speed. This function is described by the operational parameters
of the WTG. The parameters commonly used are the cut-in
wind speed (at which the WTG starts to generate power), the
rated wind speed (at which the WTG generates its rated power),
and the cut-out wind speed (at which the WTG is shut down
for safety reasons). The hourly power output of a WTG can be
obtained from the simulated hourly wind speed using
PP(SW
t
)
=
0, 0 SW
t
<V
ci
(A + B × SW
t
+ C × SW
2
t
) × P
r
,V
ci
SW
t
<V
r
P
r
,V
r
SW
t
<V
co
0,SW
t
V
co
(3)
where P
r
,V
ci
,V
r
and V
co
are the rated power output, the cut-in
wind speed, the rated wind speed, and the cut-out wind speed of
the WTG, respectively. The constants A, B, and C depending
on V
ci
,V
r
, and V
co
are presented in [16].
C. The Capacity Outage Probability Table of the WTG
The hourly mean wind speeds and output power for the WTG
unit, without considering its FOR, are generated based on the
ARMA time series model and the power curve, respectively.
The capacity outage probability table (COPT) of a WTG unit
can be created by applying the hourly wind speed to the power
curve. The procedure is briefly described as follows.
1) Define the output states for a WTG unit as segments of
the rated power.
2) Determine the total number of times that the wind speed
results in a power output, falling within one of the output
states.
3) Divide the total number of occurrences for each output
state by the total number of data points to estimate the
probability of each state.
Fig. 2 Capacity outage probability profile for the WTG unit.
Fig. 3 Comparison of capacity outage probability profiles for the WTG unit.
The WTG COPT is formed using this approach. Two cases
are illustrated in this section. The first case utilizes the actual
observed 20 years of Swift Current site data. The second case
uses the simulated 8000-year data. Fig. 2 shows the two capac-
ity outage probability distributions. The class interval width is
5% in this figure and the indicated capacity-outage level is the
midpoint of the class.
Fig. 2 illustrates that the observed probability profile is dis-
continuous, owing to the limited wind data collection. The sim-
ulated wind data provides a reasonable representation for ade-
quacy assessment. The power-output characteristics of a WTG
are very different from those of the conventional generating
units. The WTG can be considered as a generating unit with
many derated states [2]. Fig. 2 shows that the probability of
having a full WTG output (0% capacity outage), is relatively
low for this wind regime.
As noted earlier, the power output of a WTG unit depends
strongly on the wind resource at the specific location. In order
to illustrate the effect of site resources on the WTG unit, the
average wind speed used in the ARMA model was changed
from 19.46 to 38.92 km/h, using a simple multiplication factor
of 2.0. The results are illustrated graphically in Fig. 3, which
shows that the power output of a WTG is completely dependent
on the wind regime, and will increase if the facilities are located
at a site, where a higher wind velocity is available. Fig. 3 shows
the change in the capacity outage profile, when the mean wind
speed is significantly increased.
D. Building a Multistate WECS Model Using the Apportioning
Method
There are many derated states in which the output of a
WTG can reside in the course of its operating history. One
of the requirements of the adequacy assessment is to repre-
sent the WTG by an acceptable reduced number of derated
states.
The apportioning method [17] has been used in this paper to
create the selected multistate models for a WTG and the WECS.
An analytical procedure that incorporates the WTG FOR is
presented and used to build a multistate WECS model. The
probability of a unit, residing in the full down state in a two-state
representation, is known as the derating adjusted forced outage
rate (DAFOR) [8]. The term DAFOR is used by the Canadian
electric power utilities. In the United States, the designation for
this statistic is the “equivalent forced outage rate” (EFOR). The
EFOR or DAFOR is obtained using the apportioning method
in which the residence times of the actual derated states are
apportioned between the up (normal) and down (outage) states,
and there are no assigned derated states.
1) Multistate WTG Models: The WTG COPT, shown graph-
ically in Fig. 2, based on the simulated wind speeds, can be
reduced to form different multistate capacity outage probability
tables using the apportioning method. A state capacity outage
probability table is designated as a SCOPT. A 5 SCOPTW is a
five-state WTG capacity outage probability table. Table I shows
the effects of reducing the COPT in Fig. 2 to a series of different
SCOPTW. These results do not include the WTG FOR. The ef-
fects of wind variability can be aggregated to produce a DAFOR
statistic, similar in form to that used for conventional generat-
ing units. This statistic is designated as DAFORW, which in this
case is 0.76564. The DAFORW is the same for each SCOPTW
shown in Table I.
2) Wind Energy Conversion System Model: A WECS can
contain one or more WTG. A WECS has two basic parts: One
is the wind resource and the other is the actual WTG units. If
the WECS consists of identical WTG units with zero FOR, the
WECS multistate models are the same as those of the single
WTG unit shown in Table I. If the FOR of the WTG units is
not zero, the WECS derated state capacity outage probability
tables are not the same as those of a single WTG unit. An an-
alytical procedure has been used to create WECS multistate
models, including WTG FOR. The designation MSCOPTW is
used to indicate a SCOPTW, modified to include the WTG
FOR. A 2 MSCOPTW is a two-state WECS model, in-
cluding the WTG FOR. The following cases illustrate the
procedure.
Consider a WECS containing one 2-MW WTG unit with a
4% FOR. The wind condition is represented by the two-state
model (2 SCOPTW) shown in Table I. The wind condition and
the actual WTG unit form a simple series system as shown
in Fig. 4. The availability (A) of the WTG unit is 0.96 and
unavailability (U ) or FOR is 0.04.
A 20-MW WECS, containing ten identical 2-MW WTG units,
is represented in Fig. 5. The WTG units are considered to have
either a zero FOR, or a FOR of 4%. The procedure used to
TABLE I
M
ULTISTATE WTG COPT (SCOPTW)
Fig. 4 Single unit model.
Fig. 5 Multiple WTG unit model.
TABLE II
WTG U
NIT COPT WITH DIFFERENT FOR
develop the WECS COPT is similar to the previous two cases
and is briefly described in the following.
Step 1) The wind condition models are represented by the
SCOPTW shown in Table I.
Step 2) The identical WTG units (0% and 4% FOR) are com-
bined to create the COPT shown in Table II.
Step 3) The wind condition and the WTG unit COPT are
combined to create the multistate WECS COPT. The
COPT for 2 SCOPTW and 5 SCOPTW are shown in
Table III as examples.
Step 4) The WECS COPT obtained in Step 3 can be reduced,
if desired, using the apportioning method. When the
FOR is equal to 0, the MSCOPTW is the same as
the SCOPTW shown in Table I. Table IV shows the
MSCOPTW, when the WTG FOR is 4%. The modi-
fied derating adjusted forced outage rate of the WECS
(MDAFORW) is 0.77501. The MDAFORW is the
same for each MSCOPTW as shown in Table IV.
A procedure similar to that used to model a 20-MW WECS
containing ten identical 2-MW WTG units, can be used to model
large wind farms. The binomial distribution can be used if the
WTG units are identical. The WTG COPT can be created us-
ing the conventional COPT algorithm [2], if the WTG units are
not identical. The multistate models of a 400-MW WECS con-
taining 200 WTG units of 2 MW with 4% FOR are shown in
Table V. These multistate models are very similar to the 20-MW
WECS multistate models shown in Table IV. The MSCOPTW
models are dominated by the SCOPTW models created for this
wind regime.
The FOR effect is minimal for reasonable FOR values at this
mean wind speed. The FOR effect will increase as the mean
TABLE III
WECS COPT M
ODELS FOR DIFFERENT WIND CONDITION MODELS
wind speed increases. The effect of varying the WTG unit FOR
on the generating system adequacy is analyzed on the RBTS and
the IEEE-RTS systems, using the WECS 5 MSCOPTW models
shown in Tables IV and V, respectively. Fig. 6 shows the annual
system LOLE, with varying WTG FOR for the RBTS and the
RTS using the analytical method and MECORE. The RBTS and
the RTS system peak loads are 185 and 2850 MW, respectively.
It can be seen from Fig. 6 that the changes in the FOR of the
WTG units do not have a significant impact on the calculated
system reliability indices. The WTG FOR can be neglected in
many practical situations without creating unreasonable errors
in the calculated LOLE. The results will, of course, be slightly
optimistic and favor the installation of WTG units. The WECS
models shown in Tables IV and V are used in the following
studies on the RBTS and the IEEE-RTS.
IV. A
PPLICATION OF WECS MULTISTATE MODELS IN
GENERATING CAPACITY ADEQUACY ASSESSMENT
The 20- and 400-MW WECS multistate models shown in
Tables IV and V are used in the RBTS and RTS analyses, re-
spectively. The analytical technique and the state-sampling ap-
proach used in MECORE provide similar results, when the same
load model is used. The MECORE software [15] uses a hybrid
simulation and enumeration procedure to incorporate the vari-
ous load levels in the assigned time period, and therefore, uses
a multistep load model in the analysis. The MECORE software,
as noted earlier, was designed to conduct adequacy evaluation in
composite generation and transmission systems, and to provide
TABLE IV
M
ULTISTATE MODELS FOR A 20-MW WECS WITH 4% WTG FOR
individual load point and system adequacy indices. Composite
system studies have also been conducted using multistate WECS
models in MECORE. The following results are restricted to the
basic generating capacity adequacy assessment.
V. RBTS S
YSTEM ANALYSIS
The WECS multistate models, shown in Table IV, were used
to investigate the impact of different WECS models on the RBTS
generating system adequacy. Both the analytical method and
MECORE were used in this study. The annual system LOLE
TABLE V
M
ULTISTATE MODELS FOR A 400-MW WECS WITH 4% WTG FOR
for a peak load of 185 MW, are presented in Fig. 7. This figure
shows that the LOLE fluctuates slightly owing to the different
number of states used in the analysis, and that the use of a
two-state representation provides a pessimistic appraisal of the
system adequacy. This is consistent with the use of the DAFOR
to represent large conventional generating units. The original
RBTS at a peak load of 185 MW has a LOLE of 1.15 h/year,
and therefore, has a reasonable level of generating adequacy.
The addition of a 20-MW WECS produces a recognizable but
relatively small decrease in the LOLE. The results show that
Fig. 6 Test systems’ HL-I annual system LOLE as a function of the WTG
FOR.
Fig. 7 RBT Sannual system LOLE for a peak load of 185 MW using different
WECS state models.
Fig. 8 HL-I annual system EDLC (LOLE) with WECS multistate models.
the LOLE does not change considerably, when the WECS is
modeled with at least three states. The effect of varying the
peak load on this conclusion is illustrated in Fig. 8.
Fig. 8 shows the effects of adding different WECS models to
the RBTS at various system peak loads. It also shows that the
benefit associated with adding the 20-MW WECS to the RBTS
increases as the peak load increases. This benefit is relatively
small at the system design peak of 185 MW.
Fig. 9 RTS annual system EDLC for a peak load of 2850 MW with different
WECS multistate models using MECORE.
Fig. 10 HL-I annual system EDLC with different WECS models versus peak
load.
Figs. 7 and 8 show that the annual system indices are relatively
close, using a model with three or more states to represent
the WECS when the peak load is 185 MW. Fig. 8 shows that
additional states are required in the WECS model, when the
peak load increases significantly. The system EDLC in these
situations may be unacceptably high.
VI. IEEE-RTS S
YSTEM ANALYSIS
The IEEE-RTS annual system LOLE (EDLC), obtained us-
ing MECORE and the WECS multistate models, represented in
Table V is shown graphically in Fig. 9. The IEEE-RTS, in its
original form, is considered to be relatively weak from a gen-
eration point of view at the designed peak load of 2850 MW.
The LOLE (EDLC) under this condition is 13.005 h/year. The
addition of a 400-MW WECS, using a two-state representation,
provides a significant benefit under this condition. The benefit
of adding the WECS increases as the number of states in the
multistate WECS model increases. Fig. 9 illustrates that the sys-
tem EDLC at a peak load of 2850 MW is relatively constant,
when the WECS is represented by models containing five or
more states.
Fig. 10 shows the annual system EDLC with different WECS
multistate models as a function of the system peak load level.
It also indicates that the WECS five-state model can be used
to represent a WECS in a practical adequacy assessment of the
RTS.
The conclusion can be drawn based on the analyses of the
RBTS and the RTS that using a five-state WECS model can
provide a reasonable adequacy assessment of similar power
systems containing a WECS. This model can be applied in
practical studies using an analytical method or a state-sampling
procedure, such as that used in MECORE. If the wind regime
varies considerably over the course of a year, a series of multi-
state WECS models can be created, using the relative data for
each time period. The annual LOLE is then determined by sum-
ming up the period values. This procedure is used to incorporate
scheduled maintenance of generating units.
VII. C
ONCLUSION
A comparison of the observed wind speed probability dis-
tribution and the simulated wind speed probability distribution
created by the ARMA model, illustrates that ARMA models
provide a useful representation of the actual wind regimes. A
comparison between the COPT for the observed wind data and
the simulated wind data shows that simulated wind data can
be used to provide a reasonable representation for adequacy
assessment. The effect of wind speed on the WTG power output
shows that the power output of a WTG is totally dependent on
the wind regime, and will increase if the facilities are located at
a site, where higher wind velocities are experienced. Increased
wind speeds will also result in different multistate WTG or
WECS models.
The apportioning method can be used to create selected WTG
multistate models. A WECS multistate model is the same as that
of a single WTG unit, when the FOR of the WTG units is zero.
The DAFOR of the WECS and the single WTG unit are also
the same. An analytical procedure is introduced and used to
create WECS multistate models, when the WTG FOR is in-
corporated. This procedure is applicable to large wind farms,
which are composed of a number of identical or nonidentical
WTG. The studies on the RBTS and the IEEE-RTS LOLE, with
different WTG FOR, indicate that the changes in WTG FOR
do not have a significant impact on the calculated reliability
indices. Using zero FOR will not significantly impact the cal-
culated indices, and can greatly simplify the WECS modeling
procedure.
The analyses of the generating systems, including WECS,
indicate that a five-state WECS model can be used to provide
a reasonable assessment in practical studies, using the analyti-
cal method or a state-sampling procedure such as that used in
MECORE. This is an important observation as it permits WECS
to be incorporated in large practical system studies without re-
quiring a significant increase in computer solution time. This
representation can also be used in composite generation and
transmission system reliability studies.
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Appl. Power Syst., Ames, IA, Sep. 2004.
Roy Billinton (S’59–M’64–SM’73–F’78) received the B.Sc. and M.Sc. degrees
from the University of Manitoba, Winnipeg, MB, Canada, in 1960 and 1963,
respectively. He received the Ph.D. and D.Sc. degrees, both in electrical engi-
neering, from the University of Saskatchewan, Saskatoon, SK, Canada, in 1967
and 1975, respectively.
He was with the System Planning and Production Divisions of Manitoba Hy-
dro, MB, Canada. Since 1964, he has been with the University of Saskatchewan.
He is the author or coauthor of eight books on reliability evaluation and over
850 papers on power system reliability evaluation, economic system operation,
and power system analysis.
Dr. Billinton is a Fellow of the CAE and the Royal Society of Canada. He is
a Registered Professional Engineer in the Province of Saskatchewan, Canada.
Yi Gao received the B.Sc. and M.Sc. degrees from the Zhengzhou University,
Henan, China, in 1996 and 2006, respectively. She is currently working toward
the Ph.D. degree at the University of Saskatchewan Saskatoon, SK, Canada.
She was an Instructor at the Zhengzhou Electric Power College, Henan.
Since January 2004, she has been with the Power System Research Group at the
University of Saskatchewan.
... In Billinton, Y. Gao (2008), analytical models were used to assess wind energy generating system, Monte Carlo state sampling techniques have shown that five-state model of wind energy conversion system can provide an efficient assessment of the power system adequacy studies. The problem of temporally variability in renewable electric production was addressed in Goyena et al. (2009). ...
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... In Billinton, Y. Gao (2008), analytical models were used to assess wind energy generating system, Monte Carlo state sampling techniques have shown that five-state model of wind energy conversion system can provide an efficient assessment of the power system adequacy studies. The problem of temporally variability in renewable electric production was addressed in Goyena et al. (2009). ...
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The challenge of Big data is fundamentally concerned with performing data analytics for large amount of heterogeneous data. This data can be collected from different and/or uncorrelated sources. Due to the complexity of such technology; there are still various possible applications and integrations under study particularly in the fields of smart systems with using trending technologies such as Internet of Things and Cloud computing and utilizing relevant tools and equipment such as advanced sensors and smart meters. The application of big data in the energy sector is considered as one of the main elements of Energy Internet. Crucial and promising challenges exist especially with the integration of renewable energy sources and smart grids. The ability to collect data and to properly use it for better decision-making is a key feature of smart grids. For this purpose, the convenient storing, processing, provision and analysis of information on the renewable energy system behavior is addressed. In this work, the benefits and challenges of implementing big data analytics (BDA) for renewable energy power stations are addressed. The framework and recommendations for this implementation are proposed. Data from a decentralized smart grid data system consisting of 60,000 instances and 12 attributes was used to predict the stability of the system through three different machine learning methods. The results of fitting the penalized linear regression model show the accuracy of 96% for the regression model implemented using 70% of the data as a training set. Using the random forest tree model showed as well 84% accuracy and 78% accuracy for the decision tree model and 87% for the conventional neural network.
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Book
This volume evaluates the different concepts, models, and techniques used to measure the reliability of power systems in both planning and operating phases. Applications of the techniques presented in the text are illustrated in numerical examples and diagrams. Areas discussed include basic probability plus frequency and duration methods for determining generating capacity, interconnected systems, operating reserve, composite generation and transmission systems, and plant and station availability.
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The following bibliography of published material dealing with the application of probability techniques in the evaluation of power system reliability does not contain all the material available on this subject. It does, however, include most of the publications readily available in a power utility or technical reference library together with some of the earlier papers in the field. The many excellent publications clearly indicate the increasing usage and interest in the application of probability methods in the evaluation of power system reliability.