Average dynamical frequency behaviour for multi-area islanded micro-grid networks

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Vol. 18, No. 6, December 2020, pp. 3324~3330
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v18i6.16805  3324
Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA
Average dynamical frequency behaviour for multi-area islanded
micro-grid networks
M. Saifuzam Jamri, Muhammad Nizam Kamarudin, Mohd Luqman Mohd Jamil
Centre for Robotics and Industrial Automation (CeRIA), Faculty of Electrical Engineering,
Universiti Teknikal Malaysia Melaka, Malaysia
Article Info ABSTRACT
Article history:
Received May 28, 2020
Revised Aug 24, 2020
Accepted Sep 3, 2020
A micro-grid is a part of power system which able to operates in grid or
islanding mode. The most important variable that able to give us information
about the stability in islanded micro-grid network is the frequency dynamical
responses. The frequency analysis for multi-area micro-grid network model
may involve a complicated of mathematical equations. This makes
the researcher intending to omit several unnecessary parameters in order to
simplify the equations. The purpose of this paper is to show an approach to
derive the mathematical equations to represent the average behavior of
frequency dynamical responses for two different micro-grid areas. Both of
networks are assumed to have non-identical distributed generator behavior
with different parameters. The prime mover and speed governor systems are
augmented with the general swing equation. The tie line model and
the information of rotor angle was considered. Then, in the last section,
the comparison between this technique with the conventional approach using
centre of inertia (COI) technique was defined.
Keywords:
Distributed generator
Frequency responses
Islanded micro-grid
State-space representation
Two area networks
This is an open access article under the CC BY-SA license.
Corresponding Author:
Mohd Saifuzam bin Jamri,
Centre for Robotics and Industrial Automation (CeRIA),
Faculty of Electrical Engineering,
Universiti Teknikal Malaysia Melaka,
Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia.
Email: saifuzam@utem.edu.my
1. INTRODUCTION
Micro-grid is a part of a power system which used an electricity sources consist dispatchable
generation and non-dispatchable renewable energy that are capable of operating in either parallel or
independent towards the main power grid [1-4]. Furthermore, micro-grid can operate in both grid connected
and islanded mode. When the micro-grid is operates in island mode, the crucial situation is an energy balance
between supply and demand because the load is dominantly supplied by the local sources. If there are any
imbalance in the network, the system can lead to significant frequency deviations [5-8]. Many research work
related to the micro-grid power system frequency study with variety of motivation has been done and most of
them involved the large system network with multiple generators and multi area. A lot of mathematical
equations has been derived to model the physical network.
The research work in [9-15] discussed about the investigation about frequency load control for
the power system network with multiple connected distributed generator. The utilized of neural predictive [16]
and linear quadratic regulator (LQR) method [17] in controling the frequency via multi-area microgrid system
basically used the large order of mathematical equations. The power sharing and frequency control strategy via

TELKOMNIKA Telecommun Comput El Control 
Average dynamical frequency behaviour for multi-area islanded micro-grid... (Mohd Saifuzam bin Jamri)
3325
multi-area islanded power system is disccused in [18-20]. These works shows the important of frequency
deviations information in order to analyse the level of stability of the network.
The complicated mathematical models are crucial to implement and make the researcher think about
the way to simplify it by removing the unwanted parameter and put some assumption and approximation. These
situation brings the assumption of coherent generator behaviour when the multiple generators are operating in
the same area network [21]. Furthermore, same approach have been done for the multi area network with the tie
line bias control which interconnected between areas [22-24]. However, the average frequency dynamical
response of the whole system is defined through the center of inertia approach, and not in state-space
representation [25]. This paper shows an approach to derive the state-space mathematical model of multi-area
micro-grid network. The motivation of this research work is to derive and determine the evarage dynamical
behaviour of frequency network by using similarity transformation and decomposition approach. The proposed
micro-grid model was validated with the different load demand profile. At the end of the analysis, the comparison
with the conventional approach using centre of inertia (COI) has been made.
2. RESEARCH METHOD
2.1. Generator model
The main goal is to ensure that the derived mathematical equations must be able to represent
the interconnected, load sharing and frequency control. After that, one generator system was derived by using
several approximation processes to represent their average behavior. Figure 1 show the interconnected of two
areas of micro-grid.
Figure 1: The interconnected of two areas of micro-grids network
The system network is assumed to have 2 busses and 2 generators while each generator is assumed to
be equipped with the prime mover and speed governor. The model was divided into four main parts which are
rotating mass, prime mover, speed governor and load model. Each part was modeled separately and augmented
all together to become one model. The swing equation can be used to model the generator and the micro-grid
network as shown in (1). This equation describes the deviations of frequency response in per unit base.
2𝐻
𝜔 𝑠
𝑑∆𝜔
𝑑𝑡
= ∆𝑃𝑚 − ∆𝑃𝑒 (1)
Where ∆𝑃𝑚, ∆𝑃𝑒 and ∆𝜔 are the deviations of mechanical power, electrical load demand and frequency
respectively. 𝐻 is the per unit inertia constant which related to the kinetic energy at the synchronous speed with
respect to generator rating. The source of mechanical power for rotating mass is commonly known as the prime
mover. The model for prime mover is related to the changes in mechanical power output ∆𝑃𝑚 to the changes
in steam position ∆𝑃𝑔𝑉. This work, used the simplest prime mover model which can be approximated with
a single time constant 𝜏 𝑇 resulting in the following:
𝑑∆𝑃 𝑚
𝑑𝑡
=
1
𝜏 𝑇
(∆𝑃𝑔𝑣 − ∆𝑃𝑚) (2)
The speed governor system ∆𝑃𝑔𝑣 is depends on the electrical load demand. When the load demand
suddenly increased, the electrical load power exceeds the mechanical power and resulting the power deficiency.
The amount of power deficiency will influence the amount of kinetic energy stored in the rotating system.
The reduction in kinetic energy will cause the turbine speed and consequently the generator frequency to fall.
This change in speed is sensed by the turbine governor and act to adjust the turbine input valve to change
the mechanical power output to bring the speed to a new steady-state. The equation which directly describe
the speed governor behavior is as follows:
𝑑∆𝑃 𝑔𝑣
𝑑𝑡
=
1
𝜏 𝑔𝑣
(𝑘∆𝑃𝑟𝑒𝑓 −
∆𝜔
𝑅
− ∆𝑃𝑔𝑣) (3)

 ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 18, No. 6, December 2020: 3324 - 3330
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The speed governor mechanism act as the comparator whose take the different between the reference set power 𝑃𝑟𝑒𝑓
and the power
𝜔
𝑅
where 𝑅 is represent the slope of the curve in speed governor characteristics. 𝑘 is the integrator gain
constant to satisfy the rate of change of 𝑃𝑟𝑒𝑓 during automatic generation control operation. The load on a power
system consists of variety of electrical devices. It can be divided into two types which are loads independent of
frequency such as lighting and heating loads, and load dependent to frequency such as motor loads. The sensitivity
of such loads is depends on their speed-load characteristics and can be approximately defined by:
∆𝑃𝑒 = ∆𝑃𝐿 + 𝐷∆𝜔 (4)
∆𝑃𝐿 is the non-frequency sensitive load change and 𝐷∆𝜔 is the frequency sensitive load change. 𝐷 is expressed
as percent change in load divide by percent change in frequency.
2.2. Modeling the two micro-grid area
2.2.1. Tie-line model
In this section, each micro-grid area was assumed to have one generator model. The complete
mathematical equation for two micro-grid area take into account the information of generator rotor angle
𝑑𝛿 𝑖
𝑑𝑡
= 𝜔𝑖. Where 𝑖 is represented the number of area. This information is important to ensure the cooperative
behavior between both areas in term of amount of power sharing over the tie-line. During normal operation,
the real power transferred over the tie line is given by:
𝑃𝑖𝑗 =
|𝐸 𝑖||𝐸 𝑗|
𝑋 𝑖𝑗
𝑠𝑖𝑛𝛿𝑖𝑗 (5)
Where 𝑋𝑖𝑗 = 𝑋𝑖 + 𝑋𝑡𝑖𝑒 + 𝑋𝑗 and 𝛿𝑖𝑗 = 𝛿𝑖 − 𝛿𝑗 while 𝑖 and 𝑗 represent the area 1 and area 2 respectively.
Linearizing this equation by assuming the small deviation in the tie line power flow ∆𝑃12 from the nominal value:
∆𝑃𝑖𝑗 =
𝑑𝑃 𝑖𝑗
𝑑𝛿 𝑖𝑗
|
𝛿 𝑖𝑗0
∆𝛿𝑖𝑗 (6)
= 𝑃𝑠∆𝛿𝑖𝑗
𝑃𝑠 is the power angle at the initial operating angle 𝛿𝑖𝑗0
= 𝛿𝑖0
− 𝛿𝑗0
and it was defined as the synchronizing
power coefficient and then the tie line power deviation is take on the form:
∆𝑃𝑖𝑗 = 𝑃𝑠(∆𝛿𝑖 − ∆𝛿𝑗) (7)
The direction of power flow is dictated by the rotor angle difference. As shown in (7) show the example of
power flows from area one to area two when ∆𝑃𝑖𝑗 is positive. As shown in (4) and (7) can be combined into
(1) to become one complete equation for rotating generator as follows:
𝑑∆𝜔 𝑖
𝑑𝑡
=
1
2𝐻 𝑖
(∆𝑃 𝑚𝑖 − 𝑃𝑠(∆𝛿𝑖 − ∆𝛿𝑗) − 𝐷𝑖∆𝜔𝑖 − ∆𝑃𝐿𝑖) (8)
𝑑∆𝜔𝑗
𝑑𝑡
=
1
2𝐻𝑗
(∆𝑃 𝑚𝑗 − 𝑃𝑠(∆𝛿𝑗 − ∆𝛿𝑖) − 𝐷𝑗∆𝜔𝑗 − ∆𝑃𝐿𝑗)
2.2.2. Tie-line bias control
The rule of thumb of micro-grid system operation connected between two areas is to ensure the whole
system operating at the nominal frequency. So, this is able to consider the area control error (ACE) where each
area tends to reduce the frequency error to zero. The 𝐴𝐶𝐸𝑖 was integrated to have the rate of change with
respect to time and its information is used as ∆𝑃𝑟𝑒𝑓𝑖 for combine with speed governor model as shown in (3).
𝑑∆𝑃 𝑟𝑒𝑓𝑖
𝑑𝑡
=
𝑑∆𝐴𝐶𝐸 𝑖
𝑑𝑡
= ∆𝑃𝑖𝑗 +
∆𝜔 𝑖
𝑅 𝑖
+ 𝐷𝑖∆𝜔𝑖 (9)
= ∆𝑃𝑖𝑗 + ∆𝐵𝑖
2.2.3. State-space representation
As shown in (1-3), (8) and (9) can be written into the state-space representation. From the equation
developed, each area will provide five state variables. Since it takes into account all the information required

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for two areas interconnection, the total state variables will become ten. The system can be represented in state
space as follows:
𝑥̇ = 𝐴𝑥 + 𝐵𝑢 (10)
The matrix 𝐴 and 𝐵 is arranged as shown in (11) and (12).
𝐴 =
[
−𝐷1 2𝐻1⁄ 0 − 𝑃𝑠 2𝐻1⁄ 𝑃𝑠 2𝐻1⁄ 1 2𝐻1⁄ 0 0 0 0 0
0 −𝐷2 2𝐻2⁄ 𝑃𝑠 2𝐻2⁄ − 𝑃𝑠 2𝐻2⁄ 0 1 2𝐻1⁄ 0 0 0 0
1 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0
0 0 0 0 −1 𝜏 𝑇1⁄ 0 1 𝜏 𝑇1⁄ 0 0 0
0 0 0 0 0 −1 𝜏 𝑇2⁄ 0 1 𝜏 𝑇2⁄ 0 0
−1 𝜏 𝑔𝑣1 𝑅1⁄ 0 0 0 0 0 −1 𝜏 𝑔𝑣1⁄ 0 𝐾1 𝜏 𝑔𝑣1⁄ 0
0 −1 𝜏 𝑔𝑣2 𝑅2⁄ 0 0 0 0 0 −1 𝜏 𝑔𝑣2⁄ 0 𝐾2 𝜏 𝑔𝑣2⁄
−𝐵1 0 −𝑃𝑠 𝑃𝑠 0 0 0 0 0 0
0 −𝐵2 𝑃𝑠 −𝑃𝑠 0 0 0 0 0 0 ]
(11)
𝐵 = [−
1
2𝐻1
−
1
2𝐻2
0 0 0 . . . . . .0 ]
𝑇(1𝑥10)
(12)
2.2.4. An average behavior
In this section, an average behavior was determined by using similarity transformation method.
The new state variables are introduced which described the plus-minus average behavior as follows:
𝑧 𝑎 =
1
2
𝜐1 +
1
2
𝜐2 (13)
𝑧 𝑏 =
1
2
𝜐1 −
1
2
𝜐2 (14)
𝑧 is the new state variables while 𝜐 is the generator state variable. This equation would generate
the transformation matrix which would be used to generate the new vector matrix. A system in previous section
can be transformed to a similar system,
𝑧̇ = 𝑇𝐴𝑇−1
𝑧 + 𝑇𝐵𝑢 (15)
𝑇 is the transformation matrix where the columns are the coordinates of the basis vectors of the new state
vectors 𝑧 space.
3. SIMULATION SETUP
Area 1 and area 2 are assumed to have different parameters as shown in Table 1 so that their behavior
towards any load change is not identical.
Table 1. Parameter of two area micro-grid
Area 1 2
Speed regulation, R 0.05 0.0625
Frequency-sensitivity, D 0.6 0.9
Inertia constant, H 5 4
Governor time constant, 𝜏 𝑔𝑣 0.2 0.3
Turbine time constant, 𝜏 𝑇 0.5 0.6
Integrator gain, Ki = 0.3
By utilizing the matrix as shown in section 2.3 and 2.4, then substitute the parameters in Table 1, the state-space
representation is composed and transformed into plus-minus averaging model as follows:
[
𝜔 𝑎
𝛿 𝑎
𝑃𝑚𝑎
𝑃𝑔𝑣𝑎
𝑃𝑟𝑒𝑓𝑎
𝜔 𝑏
𝛿 𝑏
𝑃 𝑚𝑏
𝑃𝑔𝑣𝑏
𝑃𝑟𝑒𝑓𝑏]
=
[
−0.0862
1
0
−76.6650
−18.75
0.0263
0
0
−23.3350
−1.85
0
0
0
0
0
0
0
0
0
0
0.1125
0
−1.8350
0
0
−0.0125
0
−0.1650
0
0
0
0
1.8350
−4.1650
0
0
0
0.1650
−0.8350
0
0
0
0
1.25
0
0
0
0
0.25
0
0.0263
0
0
−23.3350
−1.85
−0.0862
1
0
−76.6650
−18.75
0.05
0
0
0
0
−0.45
0
0
0
−4
−0.0125
0
−0.1650
0
0
0.1125
0
−1.8350
0
0
0
0
0.1650
−0.8350
0
0
0
1.8350
−4.1650
0
0
0
0
0.25
0
0
0
0
1.25
0 ]
(16)

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3328
This is the state-space representation for describing the average behavior of two areas system. It can
be seen that the state vectors of the equation are divided into two partitions which related to the plus-minus
average. In order to get an average behavior of the whole network, it is possible to reduce the system order by
only taking the upper half of the state vectors and state matrix and ignore the minus average model equations
at the lower half of state vectors. The proposed model was tested under two conditions which are single sudden
load change in area 1 with 0.2 per unit deviation. The equation is reduced as follow:
𝑍 𝑎𝑣 =̇ 𝐴 𝑎𝑣 𝑍 𝑎𝑣 + 𝐵𝑎𝑣 𝑢
𝐴 𝑎𝑣 =
[
−0.0862
1
0
−76.6650
−18.75
0
0
0
0
0
0.1125
0
−1.8350
0
0
0
0
1.8350
−4.1650
0
0
0
0
1.25
0 ]
(17)
𝐵𝑎𝑣 = [−0.05 0 0 0 0] 𝑇
(18)
4. RESULTS AND ANALYSIS
4.1. Simulation results for frequency dynamical behavior
Figure 2 shows the deviations of per unit frequency dynamical response under load change in area 1
respectively. From the observations, the frequency deviation in area 1 gives more oscillations compare to area
2 since the sudden load change is highly dominant in area 1. The micro-grid system in area 2 gives a back-up
role to the micro-grid system in area 1. It clearly be seen that area 2 only contribute several amount of power
during the first moment of load change because of small frequency deviations. This phenomenon happened
due to the power flow sharing between two areas through the tie-line. Since the model considered the ACE,
the system frequency of each area will go back to the nominal after several second. The average frequency
response for the whole network can be seen in Figure 3.
Figure 2. Dynamical frequency responses for each
area networks
Figure 3. An average frequency deviation of the
whole network.
4.2. Comparison with convetional approach
The conventional approach basically only utilizes the information of inertia constant and frequency
deviations in each area to form an equation called as the center of inertia (COI) frequency.
∆𝜔 𝑐𝑜𝑖 =
∑ 𝐻 𝑖 𝜔 𝑖𝑖=1,2..
∑ 𝐻 𝑖𝑖=1,2..
(19)
Figure 4 shows the results comparison between proposed average state-space model and COI approach
as shown in (19). The result shows that the proposed average state-space model yields the similar shape like
the conventional. However, the proposed method gives a little bit lower oscillation and goes to steady-state
faster compared to COI method.

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Figure 4. Comparison between proposed model and COI approach
5. CONCLUSION
The state-space mathematical model for two areas micro-grid system have been derived which
considered the tie-line model and bias control. The state-space mathematical model for average frequency
dynamical behaviour for the whole system have been derived by utilized the similarity transformation and model
decomposition technique. Compare with the aveage frequency computed using COI approach, the results shows
that the proposed technique is possible to use if the state-space representation model is needed. The proposed
average model as shown in (17) is marginally stable due to the zero eigenvalue in the state matrix which would
caused the feasiblity problem for the certain cases. However, this zero eigenvalue can be neglected since the other
state variables does not depend on it.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the Center for Robotics and Industrial Automation, Universiti
Teknikal Malaysia Melaka (CeRIA) for research facilities and support, to the Center for Research and
Innovation Management (CRIM) for funding and publication facility.
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BIOGRAPHIES OF AUTHORS
M.Saifuzam Jamri was born in October 1984. He received his bachelor degree in Electrical
Engineering (Power Electronics & Drives) from Universiti Teknikal Malaysia Melaka,
Malaysia in 2007 and received the Master Degree in Electrical Power Engineering from
Universiti Teknologi Malaysia in 2009. He is currently worked with the Universiti Teknikal
Malaysia Melaka (UTeM) as a lecturer and belongs to Centre for Robotics and Industrial
Automation (CeRIA) group. His interest is in power system study and micro-grid including
load frequency control (LFC) and renewable energy integration. He is currently persuing the
PhD in University Tekninal Malaysia Melaka under Faculty of Electrical Engineering.
Muhammad Nizam Kamarudin was born in Selangor, Malaysia. He received the B.Eng
(Hons.) Electrical from the Universiti Teknologi MARA, Malaysian in 2002, and M.Sc in
Automation and Control from the University of Newcastle Upon Tyne, United Kingdom in
2007. He received the Doctor of Philosophy in Electrical Engineering from the Universiti
Teknologi Malaysia in 2015. He is currently with the Universiti Teknikal Malaysia Melaka
(UTeM). He is the member of the Board of Engineers, Malaysia and Institute of Engineers,
Malaysia. His research interests include nonlinear controls and robust control systems. Before
joining UTeM, he worked as a Technical Engineer at the magnetron department of Samsung
Electronics Malaysia.
Mohd Luqman Mohd Jamil received B.Eng. degree from the Universiti Teknologi MARA,
Shah Alam, Malaysia, in 2000, M.Sc. degree from Univeristy of Newcastle upon Tyne, U.K.,
in 2003, and Ph.D. degree from The University of Sheffield, Sheffield, U.K., in 2011, all in
electrical engineering. He is currently an academician in Faculty of Electrical Engineering,
University Teknikal Malaysia Melaka, Melaka, Malaysia. He is also an active researcher in
Power Electronics and Drives Research Group (PEDG) that established under the same
faculty. .His research interests include the design, control and analysis of permanent-magnet
machines.

Average dynamical frequency behaviour for multi-area islanded micro-grid networks

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Average dynamical frequency behaviour for multi-area islanded micro-grid networks