A New Under-Frequency Load Shedding Tech

8/18/2019 A New Under-Frequency Load Shedding Tech

1/9

This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

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:

[email protected] ).

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

0885-8950 © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-https://www.researchgate.net/publication/264398176_Application_of_computational_intelligence_techniques_for_load_shedding_in_power_systems_A_review?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==https://www.researchgate.net/publication/3928215_Implementation_and_comparison_of_different_under_frequency_load-shedding_schemes?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==https://www.researchgate.net/publication/264398176_Application_of_computational_intelligence_techniques_for_load_shedding_in_power_systems_A_review?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==https://www.researchgate.net/publication/3928215_Implementation_and_comparison_of_different_under_frequency_load-shedding_schemes?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://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


2/9


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://-/?-


3/9


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

http://-/?-http://-/?-http://-/?-http://-/?-


4/9



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.

http://-/?-https://www.researchgate.net/publication/3264152_An_adaptive_method_for_setting_underfrequency_load_shedding_relays?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==http://-/?-https://www.researchgate.net/publication/3264152_An_adaptive_method_for_setting_underfrequency_load_shedding_relays?el=1_x_8&enrichId=rgreq-787b025d-adf6-492c-b7ec-614129c70cba&enrichSource=Y292ZXJQYWdlOzI2Njg1ODEwNDtBUzoxNTI0NDExOTAzNTkwNDBAMTQxMzM1NjIyMDIyNg==http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


5/9



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://-/?-


6/9



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.

http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


7/9



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,

http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


8/9



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.

R EFERENCES

[1] J. A. Laghari, H. Mokhlis, A. H. A. Bakar, and M Karimi, “A new is-

landing detection technique for multiple mini hydro based on rate of change of reactive power and load connecting strategy,” Energy Con-

vers. Manage., vol. 76, pp. 215–224, 2013.[2] A. A. Bayod-Rújula, “Future development of the electricity systems

with distributed generation,” Energy, vol. 34, pp. 377–383, 2009.[3] “Distributed Generation in Liberalized Electricity Market,” Inter-

national Energy Agency, 2002, assessed on Oct. 1, 2013 [Online].Available: http://gasunie.eldoc.ub.rug.nl/FILES/root/2002/3125958/

3125958.pdf

[4] IEEE Standard for Interconnecting Distributed Resources With Elec-tric Power Systems, PP. 0_1-16 , IEEE Std. 1547-2003, 2003.

[5] IEEERecommended Practice for Utility Interface of Photovoltaic(PV)Systems, P. i, IEEE Std. 929-2000, 2000.

[6] Inverter, Converter, and Controllers for Use in Independent Power System, UL1741, 2001.

[7] IEEE Guide for Protective Relaying of Utility-Consumer Interconnec-

tions p. 0_1, IEEE Std. C37.95-2002 (Revision of IEEE Std. C37.95-1989), 2003.

[8] IEEE Recommended Practice for Protection and Coordination of In-dustrial and Commercial Power Systems, pp. 1-710, IEEE Std. 242-

2001 (Revision of IEEE Std. 242-1986) [IEEE Buff Book], 2001.

http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


9/9



[9] J. A. Laghari, H. Mokhlis, A. H. A. Bakar, and H. Mohamad, “Ap- plication of computational intelligence techniques for load shedding

in power systems: A review,” Energy Convers. Manage., vol. 75, pp.

130–140, 2013.[10] B. Delfino, S. Massucco, A. Morini, P. Scalera, and F. Silvestro, “Im-

plementation and comparison of different under frequency load-shed-dingschemes,” in Proc. IEEE Power Eng. Soc. Summer Meeting , 2001,

vol. 1, pp. 307–312.[11] W. Gu, W. Liu, C. Shen, and Z. Wu, “Multi-stage underfrequency load

shedding for islanded microgrid with equivalent inertia constant anal-ysis,” Int. J. Elect. Power Energy Syst., vol. 46, pp. 36–39, 2013.

[12] P. Mahat, Z. Chen, and B. Bak-Jensen, “Underfrequency loadshedding

for an islanded distribution system with distributed generators,” IEEE Trans. Power Del., vol. 25, pp. 911–918, 2010.

[13] A. A. M. Zin, H. M. Hafiz, and W. K. Wong, “Static and dynamicunder-frequency load shedding: a comparison,” in Proc. Int. Conf.

Power System Technol., 2004, vol. 1, pp. 941–945.[14] F. Ceja-Gomez, S. S. Qadri, and F. D. Galiana, “Under-frequency load

shedding via integer programming,” IEEE Trans. Power Syst., vol. 27,

pp. 1387–1394, 2012.[15] H. Ying-Yi and W. Shih-Fan, “Multiobjective underfrequency load

shedding in an autonomous system using hierarchical genetic algo-rithms,” IEEE Trans. Power Del., vol. 25, pp. 1355–1362, 2010.

[16] E. J. Thalassinakis, E. N. Dialynas, and D. Agoris, “Method Com- bining ANNs and Monte Carlo Simulation for the Selection of the Load

Shedding Protection Strategies in Autonomous Power Systems,” IEEE Trans. Power Syst., vol. 21, pp. 1574–1582, 2006.

[17] C.-T. Hsu, H.-J. Chuang, and C.-S. Chen, “Adaptive load shedding for

an industrial petroleum cogeneration system,” Expert Syst. Applicat.,vol. 38, pp. 13967–13974, 2011.

[18] M. A. Mitchell, J. A. P. Lopes, J. N. Fidalgo, and J. D. McCalley,“Using a neural network to predictthe dynamicfrequency responseof a

power sy stem to an under-fr equency load shedding scenario,” in Proc.

IEEE Power Eng. Soc. S ummer Meeting , 2000, vol. 1, pp. 346–351.[19] H. Mokhlis, J. A. Laghari, A. H. A. Bakar, and M. Karimi, “A fuzzy

based under-frequency load s hedding s cheme for islanded d istributionnetwork connected with DG,” Int. Rev. Elect. Eng. (I.R.E.E), vol. 7,

2012.[20] V. V. Terzija, “Adaptive underfrequency load shedding based on the

magnitude of the disturbance estimation,” IEEE Trans. Power Syst.,vol. 21, pp. 1260–1266, 2006.

[21] P. M. Anderson and M. Mirheydar, “An adaptive method for setting

underfrequency load shedding relays,” IEEE Trans. Power Syst., vol.7, pp. 647–655, 1992.

[22] P. Kundur , Power System Stability and Control . New York, NY,USA: McGraw-Hill, 1994.

[23] P. Wang and R. Billinton, “Optimum load-shedding technique to re-duce the total customer interruption cost in a distribution system,” IET

Gener., Transm., Distrib., vol. 147, pp. 51–56, 2000.

[24] A. Mokari-Bolhasan, H. Seyedi, B. Mohammadi-ivatloo, S. Abapour,and S. Ghasemzadeh, “Modified centralized ROCOF based load shed-

ding scheme in an islanded distribution network,” Int. J. Elect. Power Energy Syst., vol. 62, pp. 806–815, 2014.

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.

Documents

A New Under-Frequency Load Shedding Tech