Economic Dispatch using Quantum Evolutionary Algorithm in Electrical Power System involving Distributed Generators

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 7, No. 5, October 2017, pp. 2365~2373
ISSN: 2088-8708, DOI: 10.11591/ijece.v7i5.pp2365-2373  2365
Journal homepage: http://iaesjournal.com/online/index.php/IJECE
Economic Dispatch using Quantum Evolutionary Algorithm in
Electrical Power System involving Distributed Generators
Ni Ketut Aryani1
, Adi Soeprijanto2
, I Made Yulistya Negara3
, Mat Syai’in4
Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia
Article Info ABSTRACT
Article history:
Received Oct 25, 2016
Revised Jun 5, 2017
Accepted Jun 19, 2017
Unpredictable increase in power demands will overload the supply
subsystems and insufficiently powered systems will suffer from instabilities,
in which voltages drop below acceptable levels. Additional power sources
are needed to satisfy the demand. Small capacity distributed generators
(DGs) serve for this purpose well. One advantage of DGs is that they can be
installed close to loads, so as to minimise loses. Optimum placements and
sizing of DGs are critical to increase system voltages and to reduce loses.
This will finally increase the overall system efficiency. This work exploits
Quantum Evolutionary Algorithm (QEA) for the placements and sizing. This
optimisation targets the cheapest generation cost. Quantum Evolutionary
Algorithm is an Evolutionary Algorithm running on quantum computing,
which works based on qubits and states superposition of quantum mechanics.
Evolutionary algorithm with qubit representation has a better characteristic of
diversity than classical approaches, since it can represent superposition of
states.
Keywords:
Distributed generation
Economic dispatch
Quantum genetic algorithm
Copyright © 2017 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Ni Ketut Aryani,
Power System Simulation Lab, Department of Electrical Engineering,
Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia.
Email:ketut.aryani@gmail.com
1. INTRODUCTION
Distributed generation has an important role in a modern and complex electrical power system.
DGs are of small sizes, high efficiencies, low investment costs and the most importantly is their ability to
run on renewable energy sources. DGs can also provide stand-alone remote applications with their required
power. However, improper placement of Distributed Generation will result in increased system losses,
resulting in higher costs. Placements and sizings of DG are critical to the overall system efficiency.
The use of distributed generator in the system has some advantages in terms of economic, technical
and environmental. Environmental advantages entail reductions of sound pollution and emission of gases.
The economical advantages are reductions in transmission and distribution costs, electricity prices and
savings on fuel. Technical advantages cover wide varieties of benefits, for examples, line loss reduction,
increased system voltage profiles and increased power stability and reliability. It can also provide stand-alone
remote applications with their power need.
Planning of an electrical power systems involving DGs requires the definition of several factors,
such as: the best technology and method to use, the number and the capacity of the units, the best location,
the network connection way, and alike.
Researchers have used evolutionary computational methods for economic dispatch. Joko Pitono
used the sigmoid decreasing inertia weight PSO for calculating Hybrid Optimization of Emission and
Economic Dispatch [1], Hybrid Optimization of Emission and Economic Dispatch by the Sigmoid
Decreasing Inertia Weight Particle Swarm Optimization, this paper proposed technique of optimization
which combined fuel cost economic optimization and emission dispatch using the Sigmoid Decreasing

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IJECE Vol. 7, No. 5, October 2017 : 2365 – 2373
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Inertia Weight Particle Swarm Optimization algorithm (PSO) to reduce the cost of fuel and pollutants
resulted. Leandro dos Santos Coelho a,*, Chu-Sheng Lee b, using chaotic and Gaussian particle swarm
optimization Solving economic load dispatch problem [2]. Hossein Shahinzadeh used Particle Swarm
Optimization algorithm to solve the economic load dispatch of units in power systems with valve-point
effects consideration [3]. PSO is used for the solution of Dynamic Economic Load Dispatch (DELD)
problem with valve point loading effects and ramp rate limits in the paper writen by G.Sreenivasan [4].
Important issues in electric power industries are effectively economically optimum operations of
electric power generation systems. The main utility target is to achieve the minimum operating cost and
system stability. As well as to find the minimum operating cost using economic dispatch method. There are
several kinds of methods to calculate the operating costs. Lagrange is the most commonly used to calculate
the minimum cost of electrical energy generation [5]. In addition to this method there are several others that
have been developed by researchers.
Quantum theory has been used widely in the field of electrical energy systems. In several
publications, quantum EAs are utilized to calculate economic dispatch. For example in the references [6], [7]
and [8]. Quantum-Inspired Evolutionary Algorithm for Real and Reactive Power Dispatch, in this paper,
QEA determines the settings of control variables, such as generator outputs, generator voltages, transformer
taps and shunt VAR compensation devices for optimal P-Q dispatch considering the bid-offered cost [6].
Chaotic Quantum Evolutionary Algorithm is used to solve Environmental Economic Dispatch of
Smart Microgrid Containing Distributed Generation System Problems. Quantum evolutionary algorithm is
used to confirm the accuracy and validity of the mathematical model used for determining environment and
economic dispatch of Smart Micro Grid, which is considered as generation cost and emission cost [9].
In this work, quantum evolutionary algorithm is used for the placement of DGs in the network and
for determining the capacity required to raise the voltage on the buses, so that the voltage at each bus is in
safe condition and increases the system load ability. Finally, we perform the calculation for the economic
dispatch to get the cheapest cost of generation. Quantum evolutionary algorithm is also used in similar way in
reference [10],[11].
2. RESEARCH METHOD
2.1. Economic Dispatch Theory
The mathematical formulation of the total cost function is formulated as follows:
min 𝐶 = ∑ 𝐶𝑖(𝑃𝑖)𝑛
𝑖=1 + ∑ 𝐶𝑔(𝑃𝑔)ℎ
𝑔=1 (1)
where 𝐶𝑖 is the total fuel cost for the i th generator (in $/h).
Generally, the fuel cost of thermal generating unit is represented in polynomial function,
𝐶𝑖(𝑃𝑖) = 𝑎𝑖 + 𝑏𝑖 𝑃𝑖 + 𝑐𝑖 𝑃𝑖
2
(2)
where 𝑎𝑖, 𝑏𝑖 and 𝑐𝑖 are cost coefficients of generator 𝑖.
The DG generation cost.
𝐶𝑔(𝑃𝑔) = 𝑑 𝑔 𝐺𝑔 (3)
where 𝑑 𝑔 is cost coefficients of DG
Conventionally, there is no or negligible DG capacity existing in the power system. In economic
dispatch, the system demand and delivery loss are served by utility generators only. With the outspread of
DG's in distribution networks, the non DG system evolves into a hybrid generation environment. The DG
generation capacity has to be taken into account in the new environment. To achieve this, a modification of
the conventional economic dispatch. In addition, N generators are connected to the system . The power
balance constraint becomes:
∑ 𝑃𝑖𝑁 + ∑ 𝑃𝑔ℎ = 𝑃𝐷 + 𝑃𝑙𝑜𝑠𝑠 (4)
where ∑ 𝑃𝑖𝑖 is the total power generated by utility generators and ∑ 𝑃𝑔𝑔 is the total power from DG installed
in buses, while 𝑃𝐷 is total load and 𝑃𝑙𝑜𝑠𝑠 is total losses of the system.
Consequently, the generation cost consists of two parts: the power generated from existing and power from
DG in load bus.

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𝐶 𝑇𝑜𝑡 = ∑ 𝐶𝑖 + ∑ 𝐶𝑔ℎ𝑁 (5)
𝐶 𝑇 is obtained by economically dispatching load and delivery loss among these online generators and DGs.
Some constraints for each generator must be also satisfied. Generation power of each generator should be
laid between maximum and minimum limits. The constraint for each generator is
𝑃𝑖
𝑚𝑖𝑛
≤ 𝑃𝑖 ≤ 𝑃𝑖
𝑚𝑎𝑥
(6)
where 𝑃𝑖
𝑚𝑖𝑛
and 𝑃𝑖
𝑚𝑎𝑥
are the output of the minimum and maximum operation of the generating unit i (in
MW), respectively.
Voltage at load buses
𝑉𝑖
𝑚𝑖𝑛
< 𝑉𝑖 < 𝑉𝑖
𝑚𝑎𝑥
(7)
where 𝑉𝑖
𝑚𝑖𝑛
and 𝑉𝑖
𝑚𝑎𝑥
are voltage minimum and maximum each buses.
Line Power Flow
Newthon Rapson (NR) is a very common method used to calculate the power flow on the system.
The NR power flow is used to calculate the losses in the system and the voltage on each bus. The theory of
power flow used in this paper refers to the reference [5].
Injected Power
The complex power at bus 'i ' is:
𝑆𝑖
∗
= 𝑉𝑖
∗
∑ 𝑌𝑖𝑘 𝑉𝑘
𝑛
𝑘=1 (8)
The variables updated after k th iteration are given as:
∆𝛿𝑖
(𝑘+1)
= ∆𝛿𝑖
(𝑘)
+ ∆𝛿𝑖 (9)
|𝑉𝑖|(𝑘+1)
= |𝑉𝑖|(𝑘)
+ ∆|𝑉𝑖| (10)
Power flow from ith bus to j th bus through the line connected between these buses is given by:
𝑆𝑖𝑗 = 𝑉𝑖 𝐼𝑖𝑗
∗
= 𝑉𝑖 (
𝑉 𝑖−𝑉 𝑗
𝑍 𝑖𝑗
+ 𝑉𝑖 𝑌𝑖𝑗0) (11)
The power flow from the j th bus to ith bus is:
𝑆𝑗𝑖 = 𝑉𝑗 𝐼𝑗𝑖
∗
= 𝑉𝑗 (
𝑉 𝑗−𝑉 𝑗𝑖
𝑍 𝑗𝑖
+ 𝑉𝑗 𝑌𝑗𝑖0) (12)
Line Losses
𝑷𝒍 = ∑ ∑ (𝑺𝒊𝒋 + 𝑺𝒋𝒊)𝒃𝒖𝒔 𝒏𝒐
𝒋=𝟏
𝒃𝒖𝒔 𝒏𝒐
𝒊=𝟏 (13)
= ∑ ∑ {(𝑷𝒊𝒋 + 𝒋𝑸𝒊𝒋) + (𝑷𝒋𝒊 + 𝒋𝑸𝒋𝒊)}𝒃𝒖𝒔 𝒏𝒐
𝒋=𝟏
𝒃𝒖𝒔 𝒏𝒐
𝒊=𝟏 (14)
2.2. Quantum Evolutionary Algorithm (QEA)
The base theories of Quantum Evolutionary Algorithm method are the concepts of qubits and the
superposition of states of quantum mechanics. Qubit is the smallest unit of information stored in a two-state
quantum computer. A qubit may be in the state ‘1’ or in the state ‘0’ or in any superposition of the two. The
state of a qubit can be represented as,
|𝜓〉 = |𝛼|2
+ |𝛽|2
(15)

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As also ilustrated in figure 1, α and β are complex numbers specifying the probability amplitudes of the state
‘0’ and ‘1’ respectively. |𝛼|2
gives the probability that the qubit will be found in ‘0’ state and |𝛽|2
gives the
probability that the qubit will be found in the ‘1’ state. Normalization of the state to unity guarantees,
|𝛼|2
+ |𝛽|2
= 1 (16)
2 𝑚
states can be represented at the same time by a system of m qubits. QGA is based on the concept of
qubits. One qubit is defined with a pair of complex numbers (α,β) as,
[
𝛼
𝛽] (17)
which is characterized by (8) and (9). And an m-qubits representation is defined as
[
𝛼1
𝛽1
|
𝛼2
𝛽2
|
…
… |
𝛼 𝑚
𝛽 𝑚
] (18)
where |𝛼𝑖|2
+ |𝛽𝑖|2
= 1, i = 1,2,3...m
The advantage of this representation is that it can represent any superposition of states. For instance, in a
three qubits system with three pairs of amplitudes such as,
[
1
√2
1
√2
|
√3
2
1
2
|
1.0
0
] (19)
The state of the system can be represented as,
√3
2√2
|000〉 + 0 |001〉 +
1
2√2
|010〉 + 0|011〉 +
√3
2√2
|100〉 + 0 |101〉 +
1
2√2
|110〉 + 0|111〉
The above result means that the probabilities to represent the state |000〉, |010〉, |100〉 and |110〉 are
3
8
,
1
8
,
3
8
and
1
8
respectively. The three qubits system of (11) has four states information at the same time.
Evolutionary algorithm with qubit representation has a better characteristic of diversity than classical
approaches, since it can represent superposition of states. One qubit string such as (11) is enough to represent
four states. Convergence can be also obtained with the qubit representation. As |𝛼|2
or |𝛽|2
approaches to 1
or 0, the qubit string converges to a single state and the property of diversity disappears gradually. That is,
simultaneously, the qubit representation has both the characteristic of exploration and the characteristic of
exploitation.
Rotation Gate [10]
Q-gate (rotation gate) is defined as a variation operator of QEA, by which operation any updated
qubit should at all time satisfy the normalization condition, |𝛼′|2
+ |𝛽′|2
= 1 , where 𝛼′
and 𝛽′
are the
values of the updated qubit.
The following rotation gate is used as a qubit:
[
𝛼𝑗(𝑡 + 1)
𝛽𝑗(𝑡 + 1)
] = 𝑅𝑖(𝑡) × [
𝛼𝑗(𝑡)
𝛽𝑗(𝑡)
] (𝑗 = 1,2, … , 𝑛) (20)
𝑅𝑖(𝑡) = [
cos ∆𝜃𝑖 − sin ∆𝜃𝑖
sin ∆𝜃𝑖 cos ∆𝜃𝑖
] (𝑖 = 1, 2, … , 𝑛) (21)
∆𝜃𝑖is a rotation angle (Fig.1) of each member of the population. The rotation angle ∆𝜃𝑖is related to the
normalized difference ∆𝑓𝑖 between achievement of each of member of population and the global best
optimum.
∆𝜃𝑖 = ∆𝑓𝑖 × 𝑠𝑖𝑔𝑛 (𝛼 𝑏 − 𝛼𝑗) × 𝑠𝑖𝑔𝑛[𝛽𝑗 × sin ∆𝑓𝑖 − 𝛼𝑗 × (1 − cos〖∆𝑓𝑖)〗] (22)
where :

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∆𝑓𝑖 = 𝜋 × (1 −
𝑓(𝑏)
𝑓(𝑝 𝑖(𝑡)
) (𝑖 = 1,2, … , 𝑛) (23)
𝑠𝑖𝑔𝑛 (𝛼 𝑏 − 𝛼𝑗) = {
+1 𝑖𝑓 𝛼 𝑏 ≥ 𝛼𝑗
−1 𝑖𝑓 𝛼 𝑏 < 𝛼𝑗
(24)
𝑠𝑖𝑔𝑛 [𝛽𝑗 × sin ∆𝑓𝑖 − 𝛼𝑗 × (1 − 𝑐𝑜𝑠∆𝑓𝑖)] = {
+1 𝑖𝑓 𝛽𝑗 × sin ∆𝑓𝑖 ≥ 𝛼𝑗 × (1 − 𝑐𝑜𝑠∆𝑓𝑖)
−1 𝑖𝑓 𝛽𝑗 × sin ∆𝑓𝑖 < 𝛼𝑗 × (1 − 𝑐𝑜𝑠∆𝑓𝑖)
(25)
Figure 1.Basic Quantum-bit (qubit)
2.3. Problem Formulation
The objective function of this proposed method is :
min 𝐶 = ∑ 𝐶𝑖(𝑃𝑖)𝑛
𝑖=1 + ∑ 𝐶𝑔(𝑃𝑔)ℎ
𝑔=1 (26)
where 𝐶𝑖 is the total fuel cost for the i th thermal generator ($/h), 𝐶𝑔 is the total cost for generated power
of DG ($/h).
Bus voltage constrain:
|𝑉𝑖 𝑚𝑖𝑛| ≤ |𝑉𝑖| ≤ |𝑉𝑖 𝑚𝑎𝑥| (27)
Capacity DG constrain :
𝑃𝑔 𝑚𝑖𝑛 ≤ 𝑃𝑔 ≤ 𝑃𝑔 𝑚𝑎𝑥(28)
2.4. The Proposed Method
The proposed method can be described in the following steps and the flowchart of the algorithm is
in figure 2.
1. Start with t = 0
2. Initialize a population of n members (qubit string) :
𝑄 (0) = {𝑞1(0), 𝑞2(0), … , 𝑞 𝑛(0)}
3. Each qubit string is represented:
𝑞1(0) = [|
𝛼1(0)
𝛽1(0)
| |
𝛼2(0)
𝛽2(0)
| . . . |
𝛼 𝑚(0)
𝛽 𝑚(0)
|]
4. For all qubit strings initialize the amplitude amplification at :
𝑞1(0) = |
1
√2
1
√2
…
1
√2
1
√2
…
|
5. Randomly, make a set binary solution P(0) by observing Q(0) state :
𝑃(0) = {𝑝1(0), 𝑝2(0), … , 𝑝 𝑛(0) }
6. Each DG is represented by 8 bit binary number. The MSB (Most Significant Bit) signifies whether
the DG is present. If this bit is 1, DG is present at the bus and this DG’s output power is determined

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by the rest 7 bits and also based on the 𝑃𝑚𝑖𝑛 and 𝑃𝑚𝑎𝑥 specified in the input parameter matrix. If
the MSB is 0, DG is not present and the bus is considered as load bus.
7. Evaluate each 𝑝𝑖(0) (𝑖 = 1,2, … , 𝑛), prepare bus data matrix
8. Using the bus data, run load flow and obtain power generated by each generator.
9. Calculate the objective function and store the fitness value. For this economic dispatch, we use (26)
as the objective function.
10. Perform steps (5) – (7) for each population member.
11. Find population member with the highest fitness value and store this as the best string of the
iteration.
12. Next iteration ( t = t +1 )
13. Generate the next population from the current population by rotating each member such that the new
generation is closer to the best string. See fig 1. Rotation is done by rotator matrix that is calculated
as per equations (20) - (21).
14. Perform steps (4) - (10) as many times as needed.
15. The most optimum solution is the best string of the last iteration.
START
FOR EACH MEMBER: COMPOSE BUSDATA, RUN
POWERFLOW, OBTAIN POWER OF ALL GENERATORS,
USING THE SPECIFIED COST FUNCTIONS CALCULATE
THE FITNESS VALUE
GENERATE INITIAL
POPULATION OF N
MEMBERS (QUBIT STRINGS)
FIND MEMBER WITH THE
HIGHEST FITNESS VALUE
NUMBER OF ITERATION
REACHED?
PRINT OUT THE BEST SYSTEM
CONFIGURATION COMPRISED BY
THE POPULATION MEMBER WITH
THE HIGHEST FITNESS VALUE
STOP
BASED ON THE BEST POPULATION
MEMBER, GENERATE THE NEXT
POPULATION BY ROTATING EACH
MEMBER OF CURRENT POPULATION
QUBITS
READ IN SYSTEM PARAMETERS MATRICES: BUSDATA,
LINEDATA, POSSIBLE BUSES FOR DGs, POSSIBLE DGs
POWERS (pmin-pmax), COST FUNCTION OF EACH DG
READ IN ALGORITHM PARAMETERS:
NUMBER OF POPULATIONS, NUMBER OF
ITERATIONS, NUMBER OF BITS
Figure 2.The Flowchart
3. RESULTS AND ANALYSIS
This work exploits IEEE 30-bus systems (system with 100MVA base, 135 KV base and frequency
as 60Hz) to evaluate the performance of the proposed algorithm. In this system, the buses 1, 2, 5, 8, 11, and
13 are generator buses and others are load buses as shown in Figure 3. The generators parameters are in table
1. Power Generation Limits And Cost Generated Coefficients
Table 1. Generator data
Bus 𝑃𝑖
𝑚𝑖𝑛 𝑃𝑖
𝑚𝑎𝑥
𝑄𝑖
𝑚𝑖𝑛 𝑄𝑖
𝑚𝑎𝑥
𝑎 𝑏 𝑐

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1 50.00 200.0 - - 0 2.00 49.5 10-4
2 20.00 80.00 -40.00 50.00 0 1.75 175.0 10-4
5 15.00 50.00 -40.00 40.00 0 1.00 625.0 10-4
8 10.00 35.00 -10.00 60.00 0 3.25 83.0 10-4
11 10.00 30.00 -6.00 24.00 0 3.00 250.0 10-4
13 12.00 40.00 -6.00 24.00 0 3.00 250.0 10-4
In this simulation, system is operated in critical condition, in which the voltage of some of the buses
approaches the minimum allowable value. This is achieved by increasing the initial load by a small step
every time and then performing power flow calculation to get the voltage. As soon as the voltage falls out of
allowable range of 0.9 ≤ 𝑉𝑖 ≤ 1.1 𝑝𝑢 (as per IEEE standard for voltages on the buses), the load can be
considered as the maximum load of the system. At this maximum load, DGs start participating.
DGs are meant to enhance the capability of the power system (system load ability) and to improve
voltage profile. So as to bring voltages on the buses back up in the normal range of 0.9 ≤ 𝑉𝑖 ≤ 1.1 𝑝𝑢
again. Apart from voltage profile improvement, renewable generation placement and sizing are important
part of the economic dispatch strategy to minimize the overall generation cost. In this work, QEA is used to
devise the placement and the size of the generators in the system. The solution is near optimum and still
meets standard voltage profile requirement. DG posted on this simulation has a rating of 5 - 10 MW and the
cost equation for DG was y=4.50P. Simulations were performed before and after the renewable generators
installed.
Figure 5. Voltage profile of the buses
Table 2. Simulation result
Bus Load
(MW)
Bus
Voltage
(pu)
Generation
In initial
condition
(MW)
Bus Voltage
ED_QEA
with DG
(pu)
Generation
ED_QEA
with DG
(MW)
Bus Voltage
ED_NR
with DG
(pu)
Generation
ED_NR
with DG
(MW)
1 0.000 1.050 200 1.060 198.3 1.060 199.90
2 34.440 1.033 80 1.043 80.0 1.043 80.00
3 3.809 1.011 0 1.029 0.0 1.022 0.00
0.000
0.200
0.400
0.600
0.800
1.000
1.200
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
V_initial
V_QEA
V_NR
Figure 3. Single Line Diagram of The IEEE 30 Bus
Power System
Figure 4 . The Convergence of Economic Dispatch
0 10 20 30 40 50 60 70 80 90 100
1261.5
1262
1262.5
1263
1263.5
1264
1264.5
1265
> > Behaviour of Quantum Genetic Algoritm < <
Iteration
GeneratingCost(Rp/Hour)

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4 12.060 1.003 0 1.021 0.0 1.013 0.00
5 149.500 0.990 50 1.000 50.0 1.000 34.79
6 0.000 1.000 0 1.015 0.0 1.011 0.00
7 36.180 0.986 0 1.000 5.0 1.000 10.00
8 47.610 1.010 35 1.010 35.0 1.010 35.00
9 0.000 0.981 0 1.029 0.0 1.034 0.00
10 9.204 0.957 0 1.010 0.0 1.012 0.00
11 0.000 1.012 30 1.082 30.0 1.082 30.00
12 17.770 0.978 0 1.025 0.0 1.049 0.00
13 0.000 1.000 40 1.051 40.0 1.071 40.00
14 9.839 0.956 0 1.008 0.0 1.027 0.00
15 13.010 0.948 0 1.005 0.0 1.021 0.00
16 5.554 0.958 0 1.007 0.0 1.019 0.00
17 14.280 0.951 0 1.000 5.0 1.000 5.00
18 5.078 0.933 0 0.999 0.0 1.005 0.00
19 15.080 0.928 0 1.000 5.0 1.000 10.00
20 3.491 0.934 0 1.001 0.0 1.002 0.00
21 27.770 0.939 0 1.000 5.3 1.000 5.00
22 0.000 0.940 0 1.001 0.0 1.002 0.00
23 5.078 0.932 0 0.998 0.0 1.007 0.00
24 13.810 0.922 0 1.000 5.0 1.000 10.00
25 0.000 0.932 0 0.998 0.0 1.004 0.00
26 5.554 0.906 0 1.000 5.3 1.000 5.00
27 0.000 0.950 0 0.996 0.0 1.010 0.00
28 0.000 0.997 0 1.013 0.0 1.008 0.00
29 3.809 0.918 0 0.967 0.0 0.980 0.00
30 16.800 0.899 0 0.949 0.0 0.963 0.00
Tot. load
(MW)
449.900
Tot. Gen
(MW)
435.000 463.942 464.693
Losses (MW) 14.042 14.793
Cost ($/Hour) 1593.63 1558.90
By using Newton-Raphson Power Flow, from Table 2, we can see that when the total load of the
system is reaching maximum at 449.9 MW, the voltages at bus 30 are below standard (0.899 pu). New
generators need to be added in order to sufficiently satisfy power demand and to shoulder line losses and also
to increase the voltages of the buses under standard (bus30). Small capacity distributed generators (DGs) can
supplement the existing thermal generators in improving voltage profiles of the buses. QEA method is used
in combination with power flow for calculating generation cost of the thermal generators and for the
placement and sizing of DGs. The typical iteration convergence is in figure 4.
We can see from tabel 2 that placing DGs of sizes 5, 5, 5, 5.3, 5 , 5.3 MW on buses 7, 17, 19, 21,
24 and 26 respectively raises the voltages at some of them, so that the voltage on each bus remains at the
level permitted as seen in figure 5. The cheapest generating cost as obtained from the calculation is 1593.63$/
h with losses of 14.042 MW.
In order to confirm that QEA is effective enough in this optimisation, lambda iteration of the
Economic Dispatch was performed. In the iteration, DGs are placed on the same buses as the buses obtained
from the calculation done using QEA (the buses 7, 17, 19, 21, 24 and 26) and with the DGs capacities of 5-10
MW and at maximum load of 499.9 MW. The resulting generation cost is 1558.90 $/h with losses of 14.793
MW. The overall system stability is better, where the voltages of all buses are at levels permitted.
4. CONCLUSION
This is a study of Economic Dispatch using Quantum Evolutionary Algorithm (QEA) in Electrical
Power System involving Distributed Generators. QEA can be used for the placements, sizing and the
calculation of thermal power generation and distributed generation to get the cheapest cost of generation.
ACKNOWLEDGEMENTS
The authors thank the Power System Simulation Laboratory, Department of Electrical Engineering,
Institut Teknologi Sepuluh Nopember for all facilities and supports for this research.

IJECE ISSN: 2088-8708 
2373
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