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IEEE TRANSACTIONS ON POWER SYSTEMS 1
A New Under-Frequency Load Shedding Technique
Based on Combination of Fixed and Random Priority
of Loads for Smart Grid ApplicationsJ. A. Laghari, Hazlie Mokhlis , Member, IEEE , Mazaher Karimi , Member, IEEE ,Abdul Halim Abu Bakar , Member, IEEE , and Hasmaini Mohamad , Member, IEEE
Abstract— This paper presents a new under-frequency load
shedding technique based on the combination of random and
fixed priority of loads. It has been observed that placing all of the loads in the distribution system with fixed priority resultsin un-optimum load shedding. On the other hand, designing theload priority with a combination of random and fixed priority
provides the technique with some sort of flexibility in achieving the
optimal load shedding. The validation of the proposed scheme ondifferent scenarios proves that the proposed technique is capable
of achieving the optimal load shedding and recovering frequencyto nominal value without any overshoot.
Index Terms— Distributed generation (DG), fixed priority loads,
optimum load shedding module (OLSM), random priority loads.
I. I NTRODUCTION
T HE exponential growth in electricity demand and envi-ronmental pollutions has driven the distributed generation(DG) technology to experience a boost in thepower systems [1].
Currently, DG has been widely employed as an alternative op-
tion for electrical power generation, both from the power quality
and system reliability perspectives. The usage of DG benefits
power utilities, DG owners’, and end-users in terms of relia-
bility, improved power quality, power ef ficiency, and economics
[2]. With the utilization of DG, the cost of transmission and dis-
tribution is reduced, consisting of around 30% of the costs re-
lated to electricity supply [3]. Due to these advantages, the in-
terconnection of DG into distribution networks is undergoing a
rapid global expansion.
Currently, most DGs operate parallel to the grid to supply the
increased load demand, and are disconnected from the grid in
the case of islanding. Islanding is a situation where distribution
network looses the grid connection, yet continue to be supplied
Manuscriptreceived February 17,2014; revisedJune 20, 2014and August 20,2014; accepted September 23, 2014. This work was supported in part by Min-
istry of Higher Education Malaysia (HIR-MOHE D000004-16001), Universityof Malaya, and QUEST, Nawabshah, Pakistan. Paper no. TPWRS-00229-2014.
J. A. Laghari is with the Department of Electrical Engineering, Universityof Malaya, Malaysia, and also with QUEST, Nawabshah, Pakistan (e-mail:
H. Mokhlis and M. Karimi arewith the Department of ElectricalEngineering,University of Malaya, Malaysia (e-mail: [email protected]).
A. H. Abu Bakar is with the University of Malaya Power Energy DedicatedAdvanced Centre (UMPEDAC), Level 4, Wisma R&D UM, Jalan Pantai Ba-
haru, 59990 Kuala Lumpur, Malaysia (e-mail: [email protected]).H. Mohamad is with the Faculty of Electrical Engineering, University of
Technology MARA (UiTM), 40450, Shah Alam, Selangor, Malaysia (e-mail:
[email protected]; [email protected]).
Digital Object Identifier 10.1109/TPWRS.2014.2360520
by the DG connected to it. IEEE Std. 1547–2003 [4] stated that
islanding should be prevented, and in the case of islanding, the
DG should detect and disconnect itself from the distribution net-
work within 2 s. However, the benefits of the DG will not be
fully utilized if the DG always needs to be disconnected after
islanding. With the significant penetration of DG and expected
high penetration levels in the near future, the operation of distri-
bution networks in an islanded mode will be inevitable. Several
international standards have been developed that can be used as
guide lines by utilities or independent power producers (IPP)
to operate the island system, such as IEEE Std. 1547 [4], IEEE
Std. 929 [5], UL 1741 standards [6], EEE C37.95-2003 [7], and
IEEE 242-2001 [8].
When islanding occurs in a distribution network, voltages and
frequencies are severely disturbed due to the imbalance between
the generation and load demands. Thefrequency will rapidly de-
crease in the case of total load demand exceeding the total gener-
ation, and it will be essential that certain amount of load be shed
to restore the frequency of the islanded distribution system to
its nominal value. Controlling the frequency within permissible
limits during islanding operation is the most important tech-nical challenge currently being investigated worldwide. Com-
monly, under-frequency load shedding (UFLS) techniques may
be classified as a conventional, adaptive, and computational in-
telligence-based technique. The operation of conventional tech-
nique is based on the initialization of UFLS relay at a certain fre-
quency threshold to shed fixed amount of loads. However, this
technique failed to achieve the optimal load shedding. This is
due to the fact that conventional techniques shed loads without
estimating the actual power imbalance. This may lead to either
over-shedding, which may result in power quality problems, or
under-shedding, which may result in total power system tripping
[9], [10]. Adaptive load shedding techniques may be applied as
a suitable alternative to conventional techniques. This technique
has the advantage of estimating the amount of power imbalance
by utilizing the power swing equation.
Up to now, various adaptive UFLS schemes have been pro-
posed for load shedding. W. Gu et al. [11] proposed a multi-stage
UFLS approach to restore the frequency of an islanded micro
grid. The authors have compared their results to conventional
techniques, and have shown that their proposed technique shed
lesser loads compared to the conventional technique. However,
the overshoot in the frequency response of the proposed tech-
nique shows that some extra load still has been shed, despite the
fact that it was necessary to shed that load to restorethe frequency
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2 IEEE TRANSACTIONS ON POWER SYSTEMS
to nominal value [11]. Hence,the load shedby theproposed tech-
nique is also not optimal. Similarly, some other optimum load
shedding techniques has been proposed in [12]–[14]. However,
the schemes still possess higher frequency overshoots in their
response, indicating that the load being shed is not optimal.
The effect of frequency overshoot is not only limited to
adaptive UFLS techniques, but the techniques based on arti-
ficial intelligence also suffer from this limitation as well. An
UFLS technique based on Hierarchical Genetic Algorithm
(HGA) to determine the minimum load shed mount is proposed
in [15]. However, it can be observed that despite using HGA,
the frequency still has overshoot in it indicating over shedding
of the loads [15]. Similarly, other intelligent techniques for
optimum load shedding proposed in [16]–[19] also experienced
very high overshoot, which proves that despite using intelligent
load shedding techniques, the amount of load being shed is still
not optimal.
The aforementioned literature review shows that compared to
conventional UFLS techniques, adaptive and intelligent based
UFLS techniques shed lesser loads. However, the frequencyovershoot in those techniques clearly indicates that some extra
loads are being shed, even though some techniques proved that
by shedding one lesser load, the frequency could not be restored
to its nominal value. The smooth frequency response without
overshoot may be used as a factor for the justification of op-
timal load shedding. This over shedding of loads might be due
to the fact that every technique is bounded by fixed priorities
and the amount of loads, which is always somehow lower or
higher than the required amount to be shed. Due tofixed priority,
the technique sheds load by following a sequence, starting from
the first load up to that load, until the frequency recovers to its
nominal value. Hence, when it reaches a certain stage, it needs
to shed only a small amount of load, but the load on the pri-
ority list has a higher value. The technique has to shed that load
anyway in order to recover the frequency, otherwise, it might
result in an overall power collapse. This results in extra load
shedding, leading to a frequency overshoot. However, if some
sort of flexibility is provided to the load priority of the UFLS
technique, it may lead to optimal load shedding. This flexibility
may be achieved by classifying all loads into a combination of
random and fixed load priority. With flexible load priority, the
optimum load shedding can be obtained by comparing the load
shed amount with the total loads of combination of random pri-
ority loads, and shedding the loads of that combination having
minimum error. The proposed UFLS technique is based on theconcept of dividing the loads into a combination of random and
fixed priority loads.
The rest of the paper is organized as follows. Sections II and
III present the proposed methodology and test system mod-
eling. The simulation results and discussions are presented in
Sections IV and V, andthe conclusionis presented in SectionVI.
II. METHODOLOGY
The aim of the proposed UFLS techniques is to achieve op-
timal load shedding. The proposed technique consists of three
main modules:
1) Center of Inertia Frequency Calculator Module (COIFCM)
2) Load Shed Amount Calculator Module (LSACM)
Fig. 1. Flow chart of COIFCM.
3) Optimum Load Shedding Module (OLSM)
The description of each module is explained as follows:
A. Center of Inertia Frequency Calculator Module (COIFCM)
The operation of COIFCM is presented in Fig. 1. In grid con-
nected mode, the COIFCM uses grid frequency , and
send it to the LSACM module. However, in the case of is-
landing, the COIFCM determines the center of inertia frequencyas follows [20]:
(1)
where
frequency of the center of inertia (Hz);
inertia constant of th generator (seconds);
frequency of th generator (Hz);number of DGs.
https://www.researchgate.net/publication/256970760_Multi-stage_underfrequency_load_shedding_for_islanded_microgrid_with_equivalent_inertia_constant_analysis?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==https://www.researchgate.net/publication/260509471_Under-Frequency_Load_Shedding_Via_Integer_Programming?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==https://www.researchgate.net/publication/224135553_Multiobjective_Underfrequency_Load_Shedding_in_an_Autonomous_System_Using_Hierarchical_Genetic_Algorithms?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==https://www.researchgate.net/publication/224135553_Multiobjective_Underfrequency_Load_Shedding_in_an_Autonomous_System_Using_Hierarchical_Genetic_Algorithms?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==https://www.researchgate.net/publication/3267686_Method_Combining_ANNs_and_Monte_Carlo_Simulation_for_the_Selection_of_the_Load_Shedding_Protection_Strategies_in_Autonomous_Power_Systems?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-https://www.researchgate.net/publication/4263450_Adaptive_Underfrequency_Load_Shedding_Based_on_the_Magnitude_of_the_Disturbance_Estimation?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==https://www.researchgate.net/publication/4263450_Adaptive_Underfrequency_Load_Shedding_Based_on_the_Magnitude_of_the_Disturbance_Estimation?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==https://www.researchgate.net/publication/260509471_Under-Frequency_Load_Shedding_Via_Integer_Programming?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==https://www.researchgate.net/publication/3267686_Method_Combining_ANNs_and_Monte_Carlo_Simulation_for_the_Selection_of_the_Load_Shedding_Protection_Strategies_in_Autonomous_Power_Systems?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==https://www.researchgate.net/publication/224135553_Multiobjective_Underfrequency_Load_Shedding_in_an_Autonomous_System_Using_Hierarchical_Genetic_Algorithms?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==https://www.researchgate.net/publication/224135553_Multiobjective_Underfrequency_Load_Shedding_in_an_Autonomous_System_Using_Hierarchical_Genetic_Algorithms?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==https://www.researchgate.net/publication/264399956_A_Fuzzy_Based_Under-Frequency_Load_Shedding_Scheme_for_Islanded_Distribution_Network_Connected_with_DG?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==https://www.researchgate.net/publication/224082941_Underfrequency_Load_Shedding_for_an_Islanded_Distribution_System_With_Distributed_Generators?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==https://www.researchgate.net/publication/256970760_Multi-stage_underfrequency_load_shedding_for_islanded_microgrid_with_equivalent_inertia_constant_analysis?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-
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LAGHARI et al.: NEW UNDER-FREQUENCY LOAD SHEDDING TECHNIQUE 3
Fig. 2. Flow chart of LSACM.
The COIFCM module transmits this frequency to the LSACM
module via a communication link. Furthermore, COIFCM also
continuously check whether any of the DG in the distribution
system is disconnected. In case any of the DG trips; the module
will again determine the equivalent frequency.
B. Load Shed Amount Calculator Module (LSACM)
The algorithm of LSACM is shown in Fig. 2. The algorithms
will calculate the total generation and total spinning reserve of
the system based on the DGs parameter information. The spin-
ning reserve of individual generators are calculated using the
following equation:
(2)
Similarly, the total spinning reserve of DGs can be calcu-
lated as
(3)
where
number of DGs;
maximum generation capacity of th DG;
generated power of th DG.
The algorithm will continuously monitor the islanding event by
checking the status of the incoming grid’s substation breaker,
which is connected to both the grid and distribution system or
the DG tripping event by checking the respective individual DG
breakers. This may occur due to the failure or malfunction of
generators differential protection or transmission line tripping.
The LSACM has two different strategies to estimate the power
imbalance or total generation loss. For Islanding and DG trip-
ping events, the total generation loss is determined by
(4)
where
total generation loss;
grid power supply;
DGs dispatching power;
total load consumption.
The amount of total generation loss after being subtracted from
the total spinning reserve is sent to the OLSM for load shedding.
However, for load increment cases, the power imbalance is es-
timated using the power swing equation. Mathematically, total
power imbalance due to load variation for generators can be
computed by following expression:
(5)
where
inertia constant of th generator (seconds);
rate of change of center of inertia frequency
(H/s);
rated frequency (Hertz);
number of DGs;
power imbalance.
In order to avoid unnecessary activation of the load shed-
ding technique for very small disturbances, a threshold called
is introduced. The threshold value is set according
to smallest value of active power load of distribution system.
The value set for this threshold is 50 kW, which is system
specific, and can be adjusted accordingly. If the estimated
amount exceeds this threshold, the proposed technique begins
its next step; otherwise, the DG unit remains operating without
requiring any load shedding. The proposed technique determine
the amount of load shed by getting the difference of estimated
power imbalance and total spinning reserve from the following
equation:
(6)
which shows the amount of load that needs to be shed in order
to recover the frequency to its nominal value. The LSACM
sends this value to the OLSM via a communication link to de-
termine the best load combination, which provides the optimal
load shedding.
C. Optimum Load Shedding Module (OLSM)
The algorithm of OLSM is shown in Fig. 3. This is the impor-
tant part of the proposed technique, which differentiates it from
other techniques. When OLSM receives the amount of load to be
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4 IEEE TRANSACTIONS ON POWER SYSTEMS
Fig. 3. Flow chart of OLSM.
shed from the LSACM, it first captures the number and values
of the random priority loads. Based upon the number of random
priority loads, it calculates the total number of possible combi-
nations using the following equation:
(7)
where shows the number of random priority loads. The next
step involves calculating the sum of all loads in each combina-
tion and determining the absolute error:
(8)
where is the error of the th combination, and
is the sum of active power of the th combi-
nation.
After this, the proposed algorithm selects the combination
with the minimum absolute error. By shedding the loads of this
minimum absolute error combination, optimal load shedding
can be achieved.
The algorithm directly sends the signal to the breakers of
these loads to disconnect. However, if the amount of load being
shed exceeds the total random priority loads, then the proposed
algorithm will first shed all random priority loads, and then start
shedding the fixed priority loads until the conditions
are met. The delay time, which includes the calculation, com-
munication, and circuit breaker operation time, is assumed to be
Fig. 4. Test system.
100 ms, which is in accordance to practical considerations [4],
[21]. The proposed algorithm performs the load shedding in a
single step. This paper assumes that the distribution network is
equipped with reliable monitoring devices and fast communica-
tion system for transmitting data.
III. TEST SYSTEM MODELING
The test system considered in this paper is a part of an ex-
isting 11-kV Malaysia distribution network. It consists of hy-
brid DG resources having three DG units, two Mini hydro DG
units and one Bio-Mass DG unit. The test system shown in
Fig. 4 is modeled using PSCAD/EMTDC and Matlab interfacetechnologies. The distribution system is modeled in PSCAD,
the agents are simulated in Matlab, and user-defined interface
models are done in PSCAD, which are defined in order to as-
sociate these two platforms together. Through these interface
models; the agents in Matlab can collect and transfer data from
PSCAD. The transmission grid is connected to the distribution
network via two units of step-down transformers (132 kV/11
kV), rated 30 MVA each. The islanding is simulated by opening
the circuit breaker of Bus 2000. The two Mini Hydro
DG units and Bio-Mass DG unit, each rated at a capacity of
2 MVA (maximum power dispatch is 1.8 MW, 1.8 MW, and
1.85 MW, respectively) operate at a voltage level of 3.3 kV, andare connected to a 2-MVA transformer to step-up the voltage
level to 11 kV. Both mini hydro units use synchronous genera-
tors equipped with a governor, a hydraulic turbine with all the
necessary valves to control water flow(s), and an excitation con-
troller. The Bio Mass DG unit consists of a synchronous gener-
ator, which is equipped with a thermal governor, a generic tur-
bine, and an excitation controller.
To model the different mini hydro and Bio-Mass DG compo-
nents, the standard models for exciter, governor, and hydraulic
turbine in PSCAD/EMTDC library have been used. The exciter
chosen for all DGs is IEEE type AC1A standard model. For
mini hydro governor and turbine models, the PID controller, in-
cluding pilot and servo dynamics, and hydraulic turbine with
non-elastic water column without surge tank models, are used.
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LAGHARI et al.: NEW UNDER-FREQUENCY LOAD SHEDDING TECHNIQUE 5
TABLE ILOAD DATA AND THEIR PRIORITY
Similarly, for the Bio-Mass governor and turbine models, the
mechanical hydraulic governor and Generic Turbine Model, in-
cluding the Intercept Valve (IV) effect, are used.
The distribution network consists of 28 buses and 20 lumped
loads. In a real system, the loads are always frequency and
voltage dependent. The active power and reactive power load
dependency is given by [22]
(9)
(10)
where
active and reactive power at new voltage and
frequency;
active and reactive power at base voltage and
frequency;
coef ficient of active and reactive power load
dependency on frequency;
coef ficient of active and reactive power load
dependency on voltage;
frequency deviation and voltage deviation.
In this study, the value for , , , and are set to
1.0, , 1.0, and 2.0, respectively. By selecting these values,
the loads are regarded as voltage and frequency dependent. The
power consumption of each load for the test system is shown
in Table VIII. For load shedding purposes, 8 loads of the dis-
tribution system have been selected. The loads are prioritized
on the basis of their importance. Typically, loads are consists of
commercial, industrial, municipal and residential types. Among
these loads, commercial and industrial loads are more importantcompared to residential loads. Hence, commercial loads (loads
ranked 7 and 8) are given fixed priority and residential loads
(loads ranked 1–6) are given random priority. The loads, with
their priority rankings, are shown in Table I. However, loads can
also be prioritized based upon other factors depending upon the
nature of the distribution network. Some works have used load
priority based on customers willingness to pay [12], customer
interruption cost [23], and customer’s willingness to pay and
minimizing system penalty paid by distribution network oper-
ator [24].
All loads with random priority have the advantage of being
able to be disconnected withoutthe need to follow any sequence.
Based upon the amount of load being shed, the proposed algo-
rithm will shed those random priority loads that are equal to or
TABLE II8-STAGE CONVENTIONAL UFLS SCHEME
close to that value. Real-time measurement and remote circuit
breaker (RCB) is facilitated at all eight loads for wireless com-
munication and disconnecting the breakers.
A. Modeling of Conventional UFLS Technique
As previously mentioned, the proposed load shedding will be
compared to the conventional and adaptive load shedding tech-
niques. In addition, it is known that conventional UFLS oper-
ation fully rely on the under-frequency relay performing load
shedding in predefined intervals of time. The overall 8-stageload shedding plan is designed for all three types of load shed-
ding techniques. The conventional UFLS technique will begin
when the frequency falls below the 49.5-Hz limit and trip sig-
nificant loads at every frequency threshold. The total stages of
conventional load shedding techniques with different load pri-
ority are shown in Table II.
B. Modeling of Adaptive UFLS Technique
The modeling of the adaptive UFLS technique is slightly dif-
ferent than conventional UFLS. In this, whenever a disturbance
occurs, the technique checks the first frequency limit of 49.5 Hz.
After that, its procedure is similar to the first two sections of the proposed technique. However, after determining the amount of
load to be shed, it uses fixed priority loads, as shown in Table I.
The adaptive technique performs load shedding in a single step.
IV. SIMULATION R ESULTS
The proposed under-frequency load shedding scheme is
tested on various intentional islanding, DG tripping, and load
increment cases to demonstrate its effectiveness. The fol-
lowing four different scenarios are considered to validate its
performance.
A. Islanding Operation at 0.6-MW Power Mismatch
This case is simulated for intentional islanding operation of
the distribution network. The islanding is performed by discon-
necting the grid breaker at time . In this case, the is-
landing is simulated at a power mismatch of 0.6 MW between
the generation and load demand. The total load demand for this
case is 5.91 MW, and the power supplied by mini hydro unit 1, 2,
and Bio Mass DG is 1.73 MW, 1.73 MW, and 1.85 MW, respec-
tively. Hence, total power supplied by all DGs is 5.31 MW, and
the grid supplies the remaining power. The distribution system
has a spinning reserve of 0.14 MW.
When the grid is disconnected, the proposed algorithm
estimates the power imbalance and calculates the amount of
load being shed by subtracting it from the total spinning reserve
https://www.researchgate.net/publication/233530434_Power_System_Stability_and_Control?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==http://-/?-http://-/?-https://www.researchgate.net/publication/224082941_Underfrequency_Load_Shedding_for_an_Islanded_Distribution_System_With_Distributed_Generators?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==https://www.researchgate.net/publication/3354602_Optimum_load-shedding_technique_to_reduce_the_total_customer_interruption_cost_in_a_distribution_system?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==http://-/?-http://-/?-http://-/?-https://www.researchgate.net/publication/233530434_Power_System_Stability_and_Control?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==https://www.researchgate.net/publication/3354602_Optimum_load-shedding_technique_to_reduce_the_total_customer_interruption_cost_in_a_distribution_system?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==https://www.researchgate.net/publication/224082941_Underfrequency_Load_Shedding_for_an_Islanded_Distribution_System_With_Distributed_Generators?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-
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6 IEEE TRANSACTIONS ON POWER SYSTEMS
TABLE IIIPROCEDURE FOR FINDING LOADS OF BEST COMBINATION
Fig. 5. Frequency response for islanding event at 0.6-MW power mismatch.
using the LSACM module. For a 0.6 power imbalance, the pro-
posed LSACM algorithm determines 0.46 MW as the load shed
amount, and send that value to the OLSM. OLSM first deter-
mines that out of 8 loads, 6 loads have random priority. Based
TABLE IV
UFLS PARAMETERS FOR ISLANDING EVENT AT 0.6-MW POWER MISMATCH
upon this number, the total number of possible combinations is
63. All 63 combinations are shown in Table III. After this, the
OLSM calculates the sum of each combination, and determine
the absolute error and the combination with minimum error.
From Table III, it can be observed that combination no. 16 has
the minimum error (0.004), which is the sum of loads 3 and 4.
Hence, the proposed technique sends signals to directly shed
loads 3 and 4 to recover the frequency. The frequency response
of the proposed technique with the conventional and adaptive
technique is shown in Fig. 5, whereas, the amount of load being
shed and other parameters are shown in Table IV.
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LAGHARI et al.: NEW UNDER-FREQUENCY LOAD SHEDDING TECHNIQUE 7
Fig. 6. Frequency response for islanding event at 1.0-MW power mismatch.
It can be noticed from Fig. 5 that the conventional and adap-
tive technique has a slightly high undershoot and overshootcompared to the proposed technique.
The proposed technique has no overshoot, which clearly jus-
tifies that it has performed the optimal load shedding. More-
over, despite accurately estimating power imbalance, the adap-
tive technique also shed extra loads, and has an overshoot of
50.0214 Hz. This is due to the limitation of fixed priority load,
which results in the extra shedding of loads. However, the pro-
posed technique response shows that due to the flexibility of
random priority, the proposed technique sheds 0.464 MW, and
the frequency recovers to the nominal value without any over-
shoot. Hence, a load shedding technique provided with a com-
bination of fixed and random priority load leads to optimum
load shedding. The conventional and adaptive technique shedthe load up to fourth load ranked. However, the proposed tech-
nique shed only loads ranked third and fourth. Hence, it can be
observed that providing random priority to some loads results
in some sort of flexibility that helps to achieve the optimal load
shedding.
B. Islanding Operation Due to 3-Phase Fault at 1.0-MW
Power Mismatch
In this case, the occurrence of islanding due to short circuit
(3-phase fault) at 1 MW power mismatch between generation
and load demand is simulated. The total load demand in thiscaseis 6.31 MW, from which 5.31 MW is supplied by all DGs. At
first, a three-phase fault occurred in thetie lineof the grid, which
resulted in the grid disconnection. The duration of three-phase
fault is taken as 60 ms, and the fault clearing time is assumed
to be 140 ms, as per practical considerations. When the grid is
disconnected, the proposed algorithm is activated to perform op-
timal load shedding. The frequency responses of all three tech-
niques are shown in Fig. 6, with other par ameter values shown
in Table V.
It can be noticed that due to the three-phase fault, frequency
experiences severe transient, as highlighted in the dotted circle
in Fig. 6. The response of conventional and adaptive techniques
shows high undershoot and overshoot compared to the proposed
technique.
TABLE VUFLS PARAMETERS FOR ISLANDING EVENT AT 1.0-MW POWER MISMATCH
Fig. 7. Frequency response for Bio-Mass DG tripping event.
TABLE VIUFLS PARAMETERS FOR BIO-MASS DG TRIPPING EVENT
Moreover, the proposed technique has no overshoot, which
clearly justifies that the proposed technique has performed
the optimal load shedding. The overall power imbalance was
1.0 MW, and conventional, adaptive, and proposed technique
sheds 1.16 MW, 1.16 MW, and 0.887 MW loads, respectively.
The conventional and adaptive technique due to fixed priority
sheds up to fifth load ranked. However, the proposed technique,
by using the advantage of random priority, shed only loads
ranked second and sixth for optimal load shedding.
C. Bio-Mass DG Tripping Case
In this case, the system operates in islanded m ode. The power
supplied by all the DGs and load demand is 5.31 M W. In prac-
tice, to prevent the power system from collapse,the biggest gen-
erator is disconnected to check whether the under-frequency
load shedding technique is capable of withstanding the loads.
Hence, in this case, Bio-Mass DG tripping case is simulated
to test the effectiveness of the conventional, adaptive, and pro-
posed UFLS technique. The frequency response of all three
techniques is shown in Fig. 7, while other parameter values are
shown in Table VI.
Fig. 7 and Table VI show that conventional and adaptive tech-
niques due to fixed load priority disconnected the loads ranked
first until sixth (1.978 MW). Due to this large load shedding,
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8 IEEE TRANSACTIONS ON POWER SYSTEMS
Fig. 8. Frequency response for load increment case.
TABLE VII
UFLS PARAMETERS FOR LOAD I NCREMENT CASE
for both conventional and adaptive technique, the frequency has
very high overshoots of 50.875 Hz and 50.58 Hz, respectively.
However, due to random load priority, the proposed technique
disconnects the load ranked fourth, fifth, and sixth only. The
frequency recovers to a nominal value without any overshoot,
justifying that the optimum amount of load is shed.
D. Load Increment of 0.8 MW in Islanded System
In this case, the distribution system is operating in islanded
mode, and the load increment scenario is simulated by con-
necting a new load feeder rated 0.8 MW to the bus number 1012
at simulation time . Upon the addition of this load in-
crement, the total load increased from 5.31 MW to 6.11 MW.
The frequency response of DGs for this case is shown in Fig. 8,
and other parameter values are shown in Table VII.
Fig. 8 and Table VII shows that both the conventional and
adaptive techniques, due to fixed load priority, disconnected
loads ranked first until fifth (1.16 MW), and both suffer from
very high overshoot of 50.752 Hz and 50.736 Hz, respectively.
However, the proposed technique disconnects loads ranked
second and fifth only, and frequency recovers to a nominal
value without any overshoot. This proves that the proposed
technique has performed the optimal load shedding.
V. DISCUSSIONS
From the simulation results, it can be concluded that load
shedding technique with fixed load priority leads to an un-op-
timum load shedding. On the contrary, the load shedding tech-
nique, provided with a combination of random and fixed load
priority, can help to perform optimal load shedding. The results
of the proposed technique for various cases showed that the pro-
posed technique sheds lesser loads compared to conventional
TABLE VIIILOAD DATA FOR THE TEST SYSTEM
and adaptive techniques. Moreover, the frequency response in
the proposed technique recovers to the nominal value smoothly
without any overshoot. Thus, the frequency response of the pro-
posed technique without overshoot clearly proves that the pro-
posed technique has performed optimal load shedding.
The monitoring of the required data for the proposed tech-
nique can be obtained by utilizing the current smart gridmonitoring technology. Due to this, the proposed method is
promising for practical implementation.
VI. CONCLUSION
This paper has presented a new under-frequency load shed-
ding technique based on the combination of random and fixed
priority loads. The proposed scheme uses frequency, rate of
change of frequency, and combination of random and fixed pri-
ority loads to develop the load shedding strategy. The effective-
ness and robustness of this scheme has been investigated on is-
landing events, DG tripping event, and load increment case. The
frequency response of the proposed technique is compared with
both conventional and adaptive UFLS techniques. The simula-
tion results showed that despite the accurate estimation of power
imbalance, the adaptive technique performs un-optimum load
shedding due to fixed load priority. The simulation results of
the proposed technique showed that the adaptation of random
and fixed load priority combination in the UFLS technique has
lead to achieve optimal load shedding. This proves that load pri-
ority plays an important role in optimum load shedding.
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J. A. Laghari received the B.Eng. degree in elec-
trical engineering from BUET Khuzdar, Pakistan, in
2007 and the M.Eng. degree in electrical engineering
from the University of Malaya, Malaysia, in 2012.
Currently he is pursuing the Ph.D. degree at the Uni-versity of Malaya.
He joined Quaid-e-Awam University of Engi-
neering Science and Technology, Nawabshah, Sindh,
Pakistan, as a Lecturer in 2008. His main research
interests are intelligent power system control,
power system optimization, islanding operation in
distributed generation, and smart grid.
Hazlie Mokhlis (M’01) received the B.Eng. degreein electrical engineering and the M.Eng.Sc. degree
from the University of Malaya, Malaysia, in 1999
and 2002, respectively, and the Ph.D. degree from theUniversity of Manchester, U.K., in 2009.
Currently he is an Associate Professor in theDepartment of Electrical Engineering, University of
Malaya. His research interests are fault location, loadshedding, power system optimization, renewable
energy, and smart grid.
Mazaher Karimi (M’11) received the B.Eng. degree
from Islamic Azad University, Iran, and the M.Eng.degree in electrical engineering from the University
of Malaya, Malaysia, in 2002 and 2011, respectively,
and the Ph.D. degree in 2013 from the University of Malaya, Malaysia.
He is currently a post doctoral research fellowin the University of Malaya, Malaysia. His main
research interest is in distributed generation and
electric power system stability.
Abdul Halim Abu Bakar (M’04) received the B.Sc.
degree in electrical engineering from SouthamptonUniversity, U.K., in 1976 and the M.Eng. and Ph.D.
degrees from the University Technology Malaysia in
1996 and 2003, respectively.He has 30 years of utility experience in Malaysia
before joining academ ia. Currently he is a Lecturer inthe Department of Electrical Engineering, University
of Malaya, Malaysia.
Hasmaini Mohamad (M’07) received the B.Eng.,M.Eng., and Ph.D. degrees from the University of
Malaya, Malaysia, in 1999, 2004, and 2012, respec-tively.
Currently she is senior lecturer in the University
of Technology Mara (UiTM), Malaysia. Her major research interest includes islanding operation of
distributed generation, hydro power system, loadsharing technique, and load shedding scheme.