Journal of International Council on Electrical Engineering
ISSN: (Print) 2234-8972 (Online) Journal homepage: https://www.tandfonline.com/loi/tjee20
Reconfiguration for load balancing of feeder
in distribution system including distributed
generation
Hye Ji Kim & Yong Tae Yoon
To cite this article: Hye Ji Kim & Yong Tae Yoon (2016) Reconfiguration for load balancing of
feeder in distribution system including distributed generation, Journal of International Council on
Electrical Engineering, 6:1, 166-170, DOI: 10.1080/22348972.2016.1217815
To link to this article: https://doi.org/10.1080/22348972.2016.1217815
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Published online: 05 Aug 2016.
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Journal of International Council on Electrical Engineering, 2016
VOL. 6, NO. 1, 166–170
http://dx.doi.org/10.1080/22348972.2016.1217815
OPEN ACCESS
Reconfiguration for load balancing of feeder in distribution system including
distributed generation
Hye Ji Kim and Yong Tae Yoon
Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
ABSTRACT
There are several types of load in distribution system. The load value that represents the varying
load with time is needed to be estimated for reconfiguration because the system is reconfigured in
non-real time. Specially, as the power system develops as the smart grid, the amount of distributed
generations is increased. Since the variability of distributed generations’ output is higher compared
to normal generations, it is important that which representative value is used for variable output
when an algorithm performs. In addition, the reversed current caused by distributed generation
should be considered. In this paper, we propose a method to choose representative value considering
distribution generation and variable load and also a constraint considering the reversed current. By
this method, we study the distribution system reconfiguration for reliable and efficient operating.
1. Introduction
Distribution system reconfiguration is to operate the system efficiently and reliably by change the status of the
normally open points (NOP) on the distribution line. It
controls the loading of the respective feeder. In this paper,
we propose a reconfiguration algorithm for the purpose of
taking into account the distributed generation.
In order to operate distribution system reliably and
economically, system operator should consider load balancing between feeders and voltage maintaining within
acceptable range.[1] When the load of the distribution line
exceeds the capacity, it can damage the power system. In
addition, the customers might be supplied the low quality
electricity such as low voltage power. Therefore, the overload should be prevented and voltage of the system should
be within the limited range for the stable power supply.
[2] Considering the volatility of the load, it is necessary
to equalize the load of each feeder in order to prevent
overload. It makes the system reliable.[3]
As distribution power system changes to smart grid, the
penetration of distributed generation grows.[4] It makes
system complicate and affects system such as changing
voltage. It increases the importance of reconfiguration for
reliable and efficient operation considering distributed
generations.[5,6]
There are many kinds of loads in the current distribution system. These loads are volatile but the
CONTACT Yong Tae Yoon
ARTICLE HISTORY
Received 2 March 2016
Accepted 25 July 2016
KEYWORDS
Reconfiguration; distributed
generation; load balancing;
malfunction of protective
machine; representative load
system reconfiguration is not operated in real-time yet.[7]
Therefore, the representative value is needed for representing the load value which changes in the period between
reconfiguration.[8] A data value for a system reconfiguration usually uses heavy-value. Prior to introduction of
distributed generation, overload and low voltage was main
problem. Therefore, the heavy load was selected as the
representative load. However, as linkages of distributed
generation have been increasing, the over voltage problem has been considered. When the output of distributed
generation is max and the load is light load, the problem
occurs. In conventional papers, light load has been used
for testing proposed algorithm as one of the system cases.
However, in this paper, we select not only heavy load but
also light load as representative load in the algorithm. This
makes it possible to solve problems that occur when load
value is light by the algorithm.
This paper also proposes a constraint to prevent a
reversed power flow caused by the distributed generation.
The power current flows from transformer bank to terminal load in the radial distribution system. However, the
reversed power flow can occur because of the connection
of the distributed generation in the terminal.[9] Reversed
power flow causes malfunction of the one-way protective
devices.[10] When the distributed generation connects to
the line initially, the connection possibilities is confirmed
and the equipment of that distribution line is replaced.
ytyoon@snu.ac.kr
© 2016 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
Journal of International Council on Electrical Engineering
However, if the structure of the distribution system or the
feeder connected with distributed generation is changed
by reconfiguration, it might cause malfunction of the protective devices. Therefore, we add the constraint to prevent
reverse current to flow into the line which is not prepare
for reverse current. This constraint enables system to be
reconfigure with distributed generation when all distribution feeder do not have the two way protective devices.
2. Proposed methodology
2.1. Representative light load selection
In this paper, we propose to use light load data in the algorithm to solve the over voltage problem which has become
a new issue due to the distributed power. Existing reconfiguration algorithms use heavy load value for load data. The
proposed method is to assume the worst case caused by
the light load and maximum output of the distributed generator so it prevents over voltage problem conservatively.
System reconfiguration is operated for a certain period,
not a real-time operation. Calculating load, representing
the load value of the period, is required to perform the
reconfiguration algorithm. It used to be limited to get load
data. The load of each feeder was obtained by calculating the
current of each feeder. However, it was hard to get the data
of section loads for each time interval. Nowadays, acquisition of sectional load data for each time interval is made
possible by advance in smart metering technology such
as data acquisition and data transmission. Traditionally,
section loads were estimated by dividing the feeder load
with the interval length or the number of intervals. Now we
know both feeders and section loads of each time intervals.
We add the algorithm to prevent over voltage by using
the learned sectional light load. Over voltage is generated
on the feeder linked with distributed generator. Therefore,
the constraint for the over voltage is applied only to the
feeders which have distributed generator. The algorithm
calculates two representative values per a division. One is
a heavy load and the other one is a light load. As used in
the existing reconstruction algorithm heavy load is used
for objective function to avoid overload and to equalize
each feeder load and for the constraint condition to prevent low voltage. The other hand, light load is used for the
constraint condition to prevent over voltage.
The following is the representative light load calculation method proposed in this paper. The sum of the load
for all sections in a feeder for each time interval is the
load of the feeder for each time interval. The algorithm
lists the feeder load of each time period by size, and select
the load among the list for the percent that user set. The
selected feeder load of one period becomes the representative feeder load. Also, the section loads in the selected
167
Figure 1. Example of distribution system.
feeder load become representative section loads. This is
different from selecting the representative values of each
section load independently without regard for sum of section loads which is the feeder load. The case that all section loads are light value is rare. Therefore, the algorithm
selects the each section loads based on the feeder load
value which is the sum of section load in consideration
of the correlation of each section load.
This is an example to obtain section light loads of feeder
1 and feeder 2, as shown in Figure 1. The virtual data of
January 1, 1:00 to March 31, 24:00 is used. It is assumed
that user set 5 percent light load as representative load.
There are 92 days from January 1 to March 31 so there are
2208 data for a feeder load which is sum of section loads
(92 × 24 = 2208). The load corresponding to the lower 5
percent of that is 2097th data among the summation data.
When the rank is a duplicate, the data of latest time is used.
The representative section loads of all feeders are selected
in the same way. Selected time interval for each feeder is
different. The data of January 1, 3:00 is selected for the
section loads of feeder 1 and the data of March 31, 23:00
is selected for the section loads of feeder 2 as the light representative loads based on the above method (Table 1–4).
When the NOP moves and the section load switches
the connected feeder, the switched section load’s value
does not change. For example, feeder 1 uses the section
load data of January 1, 3:00 as a representative loads, and
feeder 2 uses the section load data of March 31, 23:00 as
a representative loads. Even if the load of sec 4 is moved
to the feeder 1 from the feeder 2 as shown in Figure 2, the
representative value of the section load maintains the data
of March 31, 23:00 which is selected at first time.
Therefore, a feeder becomes to consist of the section
loads of the various time intervals after the section load
changes the linked feeder. Although the section load
changes the linked feeder, the section load is unchanged
and still represents the light load or heavy load for own
section. Objective function value for each configure cannot be compared if the section load is re-calculated, which
changes the condition for objective function. It can make
that the objective function value comparison is meaningless to find the optimal solution.
2.2. Switching limit constraints
Distributed generation may cause malfunction of the oneway protective devices. For example, if the directional
168
H. J. Kim and Y. T. Yoon
Table 1. Example of selecting loads data for feeder1.
Section
Time
Sec1
3
3
3
2
…
3
3
3
January 1, 1:00
January 1, 2:00
January 1, 3:00
January 1, 4:00
…
March 31, 23:00
March 31, 24:00
Selected data
Sec2
3
3.5
2
1
…
2
2
2
Sec3
2.4
2
1
1
…
2
3
1
Sum
8.4
8.5
6
4
…
7
8
6
Rank
108
120
2097
2145
…
1309
839
2097th
Table 2. Example of selecting loads data for feeder2.
Section
Time
Sec4
2
3
4
3
…
1
3
1
January 1, 1:00
January 1, 2:00
January 1, 3:00
January 1, 4:00
…
March 31, 23:00
March 31, 24:00
Selected data
Sec5
3.2
3
2
1
…
2
2
2
Sec6
2
2.5
1
1
…
2
3
2
Table 3. Example of changed loads data for feeder1.
Section
Time
January 1, 1:00
January 1, 2:00
January 1, 3:00
January 1, 4:00
…
March 31, 23:00
March 31, 24:00
Selected data
Sec1
3
3
3
2
…
3
3
3
Sec2
3
3.5
2
1
…
2
2
2
Sec3
2.4
2
1
1
…
2
3
1
Sec4
2
3
4
3
…
1
3
1
Table 4. Example of changed loads data for feeder2.
Section
Time
January 1, 1:00
January 1, 2:00
January 1, 3:00
January 1, 4:00
…
March 31, 23:00
March 31, 24:00
Selected data
Sec5
3.2
3
2
1
…
2
2
2
Sec6
2
2.5
1
1
…
2
3
2
Sec7
4
3
4
5
…
3
2.2
3
Figure 2. Normally open point switching example.
Sec7
4
3
4
5
3
2.2
3
Sum
11.2
11.5
11
10
…
8
10.2
8
Rank
439
154
639
1293
…
2097
1032
2097th
This paper proposes the constraints to limit the moving of NOP which locate on the line having distributed
generator. This limitation of moving NOP prevents the
distributed generation to be connected on other lines. The
following figure shows the movement limits of NOP on
the line with the distributed generator.
In the Figure 3 below, NOP 1, 3, 4 can be moved only
within the switch X displayed. Otherwise, if the NOP 4
moves to SW4, the linkage feeder of the distributed generator is changed to feeder 4 from feeder 2. If the relay of
the feeder 4 is not replaced as bi-directional relay at that
time, incorrect operation of circuit breaker may occur due
to the reversed current. This movement can be prevented
by above constraints.
3. Algorithm
3.1. Algorithm equation
The following is the objective function, constraints, and
voltage calculation formula. Flow calculation may be executed repeatedly from tens to hundreds of times in the
reconfiguration algorithm. Therefore, this paper uses the
following formula which calculates the voltage drop of
the grid to calculate node voltage instead of power flow
equation using iteration method in consideration of the
simulation time.
DropV = (IPi − (Iload ∕2)) × Zline
power relay for automatic failure recovery is installed in
the system, adding a distributed power source may cause
a malfunction of such relay.
Zline = (R × 0.9) + (X ×
√
1 − 0.92 )
(1)
(2)
Journal of International Council on Electrical Engineering
169
Figure 3. Switching limit constraints.
where, Ipi: Pull-in current of section, Iload: Load current of
section, Zline: Line impedance.
The node voltage can be calculated by subtracting the
voltage drop from transmitting voltage in order.
The objective function is to equalize the loads of the
target lines. The loads used in the objective function calculation are selected heavy loads.
Min
N
∑
(DLObj − DLi )2
(3)
i=1
The following is a voltage constraints and capacity
constraints.
Vmin < ∀Vnode < Vmax
∀CDL < Cmax
where, N: Number of feeder, DLi: Load of feeder i, Vmin:
Voltage lower limit, Vmax: Voltage upper limit, Vnode:
Voltage of node, CDL : Capacity of feeder, Cmax: Capacity
upper limit.
DLObj
N
1 ∑
DLi
=
N i=1
(4)
The constraints for the movement path of the NOP for the
line with distributed generation is added.
System reconfiguration algorithm basically calculates
the load equalization as objective function and checks the
voltage constraints using the heavy load. In the case of
line that contains the distributed power, the over voltage
constraint which uses selected light load and capacity of
distributed generation and moving constraint for NOP
are carried out.
3.2. Algorithm flow chart
Flow chart of the algorithm is as follows. At first, the algorithm calculates the representative loads, heavy loads and
Figure 4. Algorithm flow chart.
light loads for each section. Then the algorithm selects one
of the NOP and makes certain the constraints as moving
the NOP. It searches for a NOP until the configuration satisfies the constraints and minimizes the objective function. If
searching location of the target point is completed, it starts
to navigate the location of the other points. After navigating
location for all NOP finishes, the algorithm re-searches the
location of all NOP as an existing order. Due to a change in a
normally open position, the best position of the other NOP
could be changed. The algorithm in this way repeatedly
searches the position of the NOP. If, despite repeating the
search, there is no longer any change in the positions of all
NOPs, the set of the positions is the final solution (Figure 4).
In this paper, selecting light loads as representative section loads for over voltage constraints, and NOP switching path constraints are proposed to solve the problems
which occur due to distributed generations. Selecting the
light loads as representative section loads is carried out at
‘Selection of Representative Load Data’ of the algorithm.
Proposed overvoltage constraints and movement limit of
NOP constraints are used in the process of ‘Constraint
Satisfaction’ of the algorithm when the target NOP is on
the feeder connected with a distributed generation.
4. Conclusion
In this paper, we propose reconfiguration algorithm for
a distribution system with distributed generators. If the
distributed generation is connected to the system, the
voltage of the system could be increased above the rated
range and malfunction of the relay can occur. In this
paper, the constraints are added to reconfigure the distribution system considering distributed generators. If the
load of the feeder with distributed generator is reduced by
170
H. J. Kim and Y. T. Yoon
reconfiguration, the problem may come up such as over
voltage and malfunction of circuit breakers. Therefore, we
add the constraints that the NOP on the line linked with
distributed generation can move only when the over voltage does not occur in the conservative case which assumes
that the load is small and the output of distributed generator is maximum. In addition, the constraint condition to
limit the moving of NOP is added to avoid malfunction
of a protective machine. The constraint prevents the NOP
poaching the straight path between the distributed generation and the start point of the feeder. It is expected that
the distribution system with distributed generators can be
safely reconfigured by using this algorithm.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This study was supported by the Korea Electric Power Research
Institute based Electric Power Research Institute.
Notes on contributors
Hye Ji Kim received her BS in Electrical
Engineering from SungKyunKwan University
of Korea in 2012. Currently, she studies for the
degree of MEng and PhD in Electrical and
Computer Engineering at Seoul National
University from 2012. She is a member of
the Electric Power Network Economics
Laboratory [EPNEL] and her adviser is Yong
Tae Yoon. Her major research interests
are Micro-grid, Reconfiguration and Power System
Interconnection. She is a student member of IEEE and KIEE.
Yong Tae Yoon received his BS in applied
mathematics and BS, MS and PhD in electrical engineering from Massachusetts Institute
of Technology, Cambridge MA, U.S.A, in
1995, 1995, 1997 and 2001, respectively. Since
2008, he has been a professor in School of
Electrical and Computer Science in the Seoul
National University. His research focuses on
Smart Grid Architecture, Electric Power Network Economics:
Real-time Pricing, Demand Response, System Operating and
Micro-grid. He is a member of IEEE and KIEE.
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