Operation cost reduction in unit commitment problem using improved quantum binary PSO algorithm

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 2, April 2020, pp. 1149~1155
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i2.pp1149-1155  1149
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
Operation cost reduction in unit commitment problem using
improved quantum binary PSO algorithm
Ali Nasser Hussain, Ali Abduladheem Ismail
Department of Electrical Power Engineering Techniques, Electrical Engineering Technical College,
Middle Technical University, Baghdad, Iraq
Article Info ABSTRACT
Article history:
Received May 31, 2019
Revised Oct 10, 2019
Accepted Oct 18, 2019
Unit Commitment (UC) is a nonlinear mixed integer-programming problem.
UC used to minimize the operational cost of the generation units in a power
system by scheduling some of generators in ON state and the other
generators in OFF state according to the total power outputs of generation
units, load demand and the constraints of power system. This paper proposes
an Improved Quantum Binary Particle Swarm Optimization (IQBPSO)
algorithm. The tests have been made on a 10-units simulation system and
the results show the improvement in an operation cost reduction after using
the proposed algorithm compared with the ordinary Quantum Binary Particle
Swarm Optimization (QBPSO) algorithm
Keywords:
Particle swarm optimization
Unit commitment
Quantum computing
Hybrid algorithm
Improved QBPSO algorithm Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Ali Nasser Hussain,
Department of Electrical Power Engineering Techniques,
Electrical Engineering Technical College,
Middle Technical University, Baghdad, Iraq.
Email: alinasser1974@yahoo.com
1. INTRODUCTION
Unit commitment is a hard problem that to be optimized in a power system in order to minimize
the total operation cost of generators for a specified time horizon according to the load demand satisfying
the power outputs of generators and system constraints [1]. UC has to make a decision to properly operate
the generators to get a lower cost by making some generators ON and the others OFF according to
the demand and these generators must be economically dispatched. UC problem is NP-hard problem and it
can be presented as mixed integer nonlinear optimization problem. As the number of the generators grow up,
the solution will take a longer time because the combinations 0-1 that for each hour in the time horizon will
grow exponentially. Two types of constraints must be satisfied in the unit commitment problem solution,
the first one is related to the system such as the transmission constraints and the power reserve constrains in
case of increase the demand or the outage of a generator from the system and the other types of constraints
are related to the generators such as ramp-up limit, ramp-down limit, minimum time up and minimum time
down [2].
Different ideas have been developed to solve UC problem. The solution methods of the UC problem
can be separated into two kinds, the first one is known as deterministic solution techniques such as Priority
List (PL) [3]; Dynamic Programming (DP) [4]; Lagrangian Relaxation (LR) [5]; second order cone
programming [6]; Mixed Integer Programming (MIP) [7]; Mixed Integer Linear Programming (MIP) [8] and
Branch and Bound (BB) [9]. The other solution method is known as stochastic approaches and they were
successful in UC problem solution and as an example for these methods Genetic Algorithm (GA) [10];
Evolutionary Programming (EP) [11]; Simulated Annealing (SA) [12]; Particle Swarm Optimization
(PSO) [13]; Quantum Evolutionary Algorithm (QEA) [14]; Ant Colony Search Algorithm (ACSA) [15];

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 2, April 2020 : 1149 - 1155
1150
Differential Evolution approach (DE) [16]; Artificial Neural Networks (ANN) [17]; Tabu Search (TS) [18];
Eagle strategy based crow search algorithm (ES-CSA) [19] and Grey Wolf Optimizer (GWO) [20]. In this
paper, the proposed algorithm is Improved Quantum Binary Particle Swarm Optimization (IQBPSO)
algorithm which the product of the hybridization of Binary Particle Swarm Optimization (BPSO) algorithm
and the Quantum Computing because the BPSO may fail to find the global minimum therefore this
improvement has been made to achieve the better solution.
2. PROBLEM FORMULATION
The objective function of UC problem was formulated to minimize the total operation cost over
the time horizon [1]. This minmization may be done by selecting the combination of the generation units that
satisfies all the constraints of the power system and the generation unit itself and these combinations 0-1 that
represented the status of each generator ON/OFF. The formulation of cost function for the UC problem that
must be minimized by the sum of the starting cost of the generators and operational cost for each unit over
a specefied period of time and it can be expressed by the following equation:
F = ∑ ∑ [fgk(Pgk) + STCgk(1 − Ug(k−1)]Ugk
N
g=1
T
k=1 (1)
where fgk is the fuel cost function, Ugk is the state of unit g which can be 0 or 1 at the hour k, N is the number
of generation units,T is the time horizon, g is the index of the unit, k is the index of time, Pgk is the power
delivered from the unit g at the hour k and STCgk is the start-up cost of the unit g at the hour k.
The fuel cost function is calculated as follows
fgk(Pgk) = cg(Pgk)² + bg(Pgk) + ag (2)
where cg,bg, ag are the fuel cost coeficients and the start-up are represented by the following equation:
STCgk = {
HSCg if MDTg ≤ Tg
off
≤ MDTg + CSHg
CSCg if Tg
off
> MDTg + CSHg
(3)
where (HSCg,CSCg) are the hot and cold start-up cost of the unit g; CSHg is the cold start hours for the unit g;
(MUTg, MDTg) are the minimum up and downtime of the unit g and (Tg
on
, Tg
off) are the time of the unit g is
continuously ON or OFF
The objective function of UC problem is resterercted by some constraints and these constraints are
the system constraints and the generation unit constraints [1].
1. The demand must be supplied by the generators at each hour.
∑ Pgk
N
g=1 Ugk = Dk (4)
2. The constraint of spinning reserve in case of increase the demand or loss generator unit from the group.
∑ Pg
maxN
g=1 Ugk ≥ Dk+Rk (5)
3. The generator can produce power in the range between the maximum and minimum values.
Pg
max
≥ Pgk ≥Pg
min
(6)
4. The generation unit must be operated at least for a time equals to the minimum up time.
Tg
on
≥ MUTg (7)
5. The generation unit must be shut-down or in the OFF state at least for a time equals to the minimum
downtime.
Tg
off
≥ MDTg (8)
where Dk is the load demand of the system at the hour k; Rk is the spinning reserve of the system at the hour
k and (Pg
max
, Pg
min) are the maximum and minimum power that can be supplied from the unit g.

Int J Elec & Comp Eng ISSN: 2088-8708 
Operation cost reduction in unit commitment problem using improved quantum … (Ali Nasser Hussain)
1151
3. PARTICLE SWARM OPTIMIZATION AGORITHM
Particle Swarm Optimization (PSO) is introduced firstly by James Kennedy and Russell Eberhart in
1995 [21]. PSO algorithm is a heuristic optimization method based on the parallel experience of
the individuals to search for the optimum solution. The PSO particles spread in a search space D of
the problem and each of them has a position vector 𝑿 and speed vector 𝑽 [21]. In this algorithm, the particles
are guided using the personal experience for each particle which is known as 𝑃𝑏𝑒𝑠𝑡 and the overall or
the global experience among all particles that termed as 𝐺𝑏𝑒𝑠𝑡. Then, the velocity and location of each
particle in the population are modified by using the calculation of the current particle velocity and
the distance from 𝑃𝑏𝑒𝑠𝑡 location and 𝐺𝑏𝑒𝑠𝑡 location [22]. Furthermore, the experience can be accelerated by
two set of the acceleration factors, (𝑐1, 𝑐2) are the cognitive and asocial acceleration constant factors
respectively; (𝜑1, 𝜑2) are two random numbers generated between [0, 1]. The movement is also can be
controlled by multiplying it by inertia factor that lies in the range of [⍵ 𝑚𝑎𝑥 , ⍵ 𝑚𝑖𝑛] and the typical range is
⍵ 𝑚𝑎𝑥= 0.9 to ⍵ 𝑚𝑖𝑛 = 0.4. The velocity update is described by the following equation:
𝑉 𝑚+1
= ⍵𝑉 𝑚
+ 𝑐1 𝜑1(𝑃𝑏𝑒𝑠𝑡𝑖
𝑚
− 𝑋𝑖
𝑚) + 𝑐2 𝜑2(𝐺𝑏𝑒𝑠𝑡 𝑚
− 𝑋𝑖
𝑚
) (9)
where m is the current itration. The position of the particles can be updated as follows equation:
𝑋 𝑖
𝑚+1
= 𝑋 𝑖
𝑚
+ 𝑉𝑖
𝑚+1
(10)
the inertia factor is represented by the following equation:
⍵ = ⍵ 𝑚𝑎𝑥 −
(⍵ 𝑚𝑎𝑥−⍵ 𝑚𝑖𝑛)
𝑖𝑡𝑒𝑟 𝑚𝑎𝑥
⤫ 𝑚 (11)
where (⍵ 𝑚𝑎𝑥, ⍵ 𝑚𝑖𝑛) are the initial and final weights, 𝑖𝑡𝑒𝑟 𝑚𝑎𝑥 is the maximum iteration number and 𝑚 is
the current iteration number. The binary version of the PSO (BPSO) has been presented by James Kennedy
and Russell Eberhart to be used in discrete spaces [23]. The update proces of the position for the particles can
be achieved by using a new variable known as Sigmoid Limiting Transformation and can be written as
𝑆(𝑉𝑖𝑗
𝑚+1
) =
1
1−𝑒𝑥𝑝(𝑉𝑖𝑗
𝑚+1)
(12)
By using the sigmoid function, the position update of the particle in the binary version of a PSO algorithm is
done as the following equation
𝑋 𝑖𝑗
𝑚+1
= {
1 if 𝑟𝑖𝑗 < 𝑆(𝑉𝑖𝑗
𝑚+1
)
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
(13)
where 𝑟𝑖𝑗 is a random number distributed uniformly between [0, 1].
4. HYBRIDIZATION OF QUANTUM COMPUTING WITH BPSO ALGORITHM
The quantum bit is known as the smallest unit of information that store in the quantum
computer [24]. The quantum bit can be in two states, the first state is 0 and the second is 1 .These states may
be written as |0⟩ and |1⟩ and the quantum bit state can be reproduced as follows:
|Ѱ⟩ = 𝛼|0⟩ + 𝛽|1⟩ (14)
where 𝛼 and 𝛽 are two complex numbers that identifies the probability amplitude of the relative conditions.
The state of the quantum bit can be normalized to unity to guarantee that |𝛼|2
+ |𝛽|2
= 1. Quantum gates
have been used to change the state of the quantum bit and for examples of these gates, NOT gate, Hadamard
gate and rotation gate [25]. The novel QEA has been proposed by Kim and Han as [24]. This QEA is inspired
from the quantum-computing concept so the quantum bit has been designed to get the binary solutions.
The quantum bit is defined by pair of numbers which are α and β and the quantum bit can be formulated as
a string of
𝑞 = [
𝛼1
𝛽1
|
𝛼2
𝛽2
|
. . .
. . . |
𝛼 𝑛
𝛽𝑛
] (15)

 ISSN: 2088-8708
1152
where |𝛼𝑗|
2
+ |𝛽𝑗|
2
= 1 and j = 1, 2 ……n .
The rotation gate can be used as a variance factor to update the individual of the quantum bit and
the rotation gate is represented by the following equation:
𝑈(𝛥𝜃𝑗) = [
𝑐𝑜𝑠(𝛥𝜃𝑗) − 𝑠𝑖𝑛(𝛥𝜃𝑗)
𝑠𝑖𝑛(𝛥𝜃𝑗) 𝑐𝑜𝑠(𝛥𝜃𝑗)
] (16)
where 𝛥𝜃𝑗 is the jth quantum bit rotation angle that goes to 0 or 1state. A lookup table as shown in Table 1 is
used to determine the value of 𝛥𝜃𝑗 and adjusted as 𝜃1 = 0, 𝜃2 = 0, 𝜃3 = 0.01𝜋, 𝜃4 = 0, 𝜃5 = − 0.01𝜋,
𝜃6 = 0, 𝜃7 = 0, 𝜃8 = 0 and B is the best solution where B = (𝑏1, 𝑏2, 𝑏3, . . . . . . . , 𝑏 𝑛 ) as described in
reference [24].
Table 1. Lookup table to determine rotation angle
𝑥𝑗 𝑏𝑗 Fitness (X) ≥ Fitness (B) Δθj
0 0 False 𝜃1
0 0 True 𝜃2
0 1 False 𝜃3
0 1 True 𝜃4
1 0 False 𝜃5
1 0 True 𝜃6
1 1 False 𝜃7
1 1 True 𝜃8
A new BPSO inspired by quantum computing which is known as Quantum Binary Particle Swarm
Optimization (QBPSO) [26]. Each element in the particle has a state of 1 or 0 according to the probability of
|𝛼|2
+ |𝛽|2
= 1. The QBPSO proposes a new way to update the velocity of each particle by the use of
Quantum Computing. The inertia factors (⍵ 𝑚𝑎𝑥, ⍵ 𝑚𝑖𝑛) and the acceleration factors (𝑐1, 𝑐2) are omitted in
the QBPSO and replaced by the rotation angle. The update process of the position vector can be done by
using the probability |𝛽|2
that has been stored in the ith quantum bit individual (𝑞𝑖). Therefore, the jth
element of the ith particle takes a value of 1 or 0 as in the following equation [26]:
𝑋 𝑖𝑗
𝑚+1
= {
1 if 𝑟𝑖𝑗 < |𝛽𝑖𝑗|2
(17)
The rotation angle can be determined by using the current position 𝑃𝑏𝑒𝑠𝑡 and the global position
𝐺𝑏𝑒𝑠𝑡 of the swarm as in the following equation:
𝛥𝜃𝑖𝑗 = 𝜃 × {𝛾1𝑖 × (𝑥𝑖𝑗
𝑃
− 𝑥𝑖𝑗) + 𝛾2𝑖 × (𝑥𝑗
𝐺
− 𝑥𝑖𝑗) (18)
where 𝜃 is the rotation angle magnitude and (𝛾1𝑖 , 𝛾2𝑖 ) can be found by a comparison among the fitness of
the current position of the particle i, the fitness of the best position 𝑃𝑏𝑒𝑠𝑡 and the fitness of the global
position 𝐺𝑏𝑒𝑠𝑡 respectively as in equations (19) and (20):
𝛾1𝑖 = {
0 if 𝐹𝑖𝑡𝑛𝑒𝑠𝑠 𝑜𝑓 (𝑋𝑖) ≥ 𝐹𝑖𝑡𝑛𝑒𝑠𝑠(𝑃𝑏𝑒𝑠𝑡 𝑖)
(19)
𝛾2𝑖 = {
0 if 𝐹𝑖𝑡𝑛𝑒𝑠𝑠 𝑜𝑓 (𝑋𝑖) ≥ 𝐹𝑖𝑡𝑛𝑒𝑠𝑠(𝐺𝑏𝑒𝑠𝑡 𝑖)
(20)
The magnitude of the rotation is decreased monotonously from a maximum value 𝜃 𝑚𝑎𝑥 to a minimum value
𝜃 𝑚𝑖𝑛 along the iteration by the following equation:
𝜃 = 𝜃 𝑚𝑎𝑥 −
𝜃 𝑚𝑎𝑥−𝜃 𝑚𝑖𝑛
𝑖𝑡𝑒𝑟 𝑚𝑎𝑥
× 𝑚 (21)

1153
5. IMPROVED QBPSO ALGORITHM
The QBPSO algorithm may fail in finding the optimum value of the solution, therefor;
an improvement is made on the QBPSO to get the better solution. The improvement on QBPSO is to search
for the fitness in the personal best 𝑃𝑏𝑒𝑠𝑡 for the first half of the iterations and after finding it the fitness in
the global best 𝐺𝑏𝑒𝑠𝑡 will be searched in the second half of the iterations. This improvement can be
expressed as the following equations:
𝛾1𝑖 = {
0 if 𝑚 ≥ (𝑖𝑡𝑒𝑟 𝑚𝑎𝑥/2)
(22)
𝛾2𝑖 = {
1 if 𝑚 ≥ (𝑖𝑡𝑒𝑟 𝑚𝑎𝑥/2)
(23)
and the rotation angle is updated as in (18, 21)
6. RESULTS AND ANALYSIS
The simulation is implemented using MATLAB program version (R17b) for test system consist of
10 generation units over a time horizon of 24 hours [26]. The values of 𝜃 𝑚𝑖𝑛 and 𝜃 𝑚𝑎𝑥 are chosen equal to
0.05𝜋 and 0.1𝜋 respectively. The number of population and iterations are 50 and 26 respectively. Table 2
lists the generation of each unit. The total operation cost that has been obtained by the IQBPSO is 563938.4 $
where the total fuel cost of the generation units is 559848.4 $ and the sum of the startup cost for all the units
is 4090 $. The results showed minimum cost has been achieved from using the IQBPSO compared with
the ordinary QBPSO which produces a total cost of 563977$ as in reference [26].
Table 2. Results of UC problem in the simulation system
hour Unit1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Unit 7 Unit 8 Unit 9 Unit 10 Demand MW
1 455 245 0 0 0 0 0 0 0 0 700
2 455 295 0 0 0 0 0 0 0 0 750
3 455 370 0 0 25 0 0 0 0 0 850
4 455 455 0 0 40 0 0 0 0 0 950
5 455 390 0 130 25 0 0 0 0 0 1000
6 455 360 130 130 25 0 0 0 0 0 1100
7 455 410 130 130 25 0 0 0 0 0 1150
8 455 455 130 130 30 0 0 0 0 0 1200
9 455 455 130 130 85 20 25 0 0 0 1300
10 455 455 130 130 162 33 25 10 0 0 1400
11 455 455 130 130 162 72.8 25 10.2 10 0 1450
12 455 455 130 130 162 80 25 43 10 10 1500
13 455 455 130 130 162 33 25 10 0 0 1400
14 455 455 130 130 85 20 25 0 0 0 1300
15 455 455 130 130 30 0 0 0 0 0 1200
16 455 310 130 130 25 0 0 0 0 0 1050
17 455 260 130 130 25 0 0 0 0 0 1000
18 455 360 130 130 25 0 0 0 0 0 1100
19 455 455 130 130 30 0 0 0 0 0 1200
20 455 455 130 130 162 33 25 10 0 0 1400
21 455 455 130 130 85 20 25 0 0 0 1300
22 455 455 0 0 145 20 25 0 0 0 1100
23 455 425 0 0 0 20 0 0 0 0 900
24 454.79 345.21 0 0 0 0 0 0 0 0 800
7. CONCLUSION
The unit commitment problem is a hard optimization process that the power system has to be deal
with it to get a minimum operation cost. This paper proposes an improved quantum binary particle swarm
optimization (IQBPSO) algorithm to solve the unit commitment problem. The algorithm has been tested on
10 unit simulation system during 24-hour time horizon and a minimized operational cost has been achieved.

 ISSN: 2088-8708
1154
REFERENCES
[1] Wood and B. Wollenberg, “Power generation, operation, and control”. New York: J. Wiley & Sons, 1996.
[2] Hobbs, et al., The Next Generation of Electric Power Unit Commitment Models. Boston, MA: Springer US, 2002.
[3] R. M. Burns and C. A. Gibson, “Optimization of priority lists for a unit commitment program,” presented at
the IEEE Power Engineering Society Summer Meeting, 1975, Paper A, 75 453-1.
[4] W. L. Snyder, et al., “Dynamic programming approach to unit commitment,” IEEE Trans. Power Syst., vol. 2, no.
2, pp. 339–350, May 1987.
[5] F. Zhuang and F. D. Galiana, “Toward a more rigorous and practical unit commitment by Lagrangian relaxation,”
IEEE Trans. Power Syst., vol. 3, no. 2, pp. 763–770, May 1988.
[6] Yuan, Xiaohui, et al., “Second-order cone programming for solving unit commitment strategy of thermal
generators”. Energy Conversion and Management. vol. 76, pp. 20-25, 2013. 10.1016/j.enconman.2013.07.019.
[7] A. I. Cohen and M. Yoshimura, “A branch-and-bound algorithm for unit commitment,” IEEE Trans. Power App.
Syst., vol. PAS-102, pp. 444–451, Feb. 1983.
[8] M. Shaaban, H. Zeynal, and K. Nor, “MILP-based short-term thermal unit commitment and hydrothermal
scheduling including cascaded reservoirs and fuel constraints,” International Journal of Electrical and Computer
Engineering (IJECE), vol. 9, no. 4, pp. 2732–2742, Aug. 2019.
[9] J. A. Muckstadt and R. C. Wilson, “An application of mixed-integer programming duality to scheduling thermal
generating systems,” IEEE Trans. Power App. Syst., vol. PAS-87, pp. 1968–1978, 1968.
[10] K. S. Swarup and S. Yamashiro, “Unit commitment solution methodology using genetic algorithm,” IEEE Trans.
Power Syst., vol. 17, no. 1, pp. 87–91, Feb. 2002.
[11] H. Chen and X. Wang, “Cooperative coevolutionary algorithm for unit commitment,” IEEE Trans. Power Syst.,
vol. 16, no. 1, pp. 128–133, Feb. 2002.
[12] D. N. Simopoulos, et al.,, “Unit commitment by an enhanced simulated annealing algorithm,” IEEE Trans. Power
Syst., vol. 21, no. 1, pp. 68–76, Feb. 2006.
[13] B. Zhao, et al., “An Improved Particle Swarm Optimization Algorithm for Unit Commitment,” Elect. Power
Energy Syst., Vol. 28, No. 7, pp. 482–490, 2006.
[14] T. W. Lau, et al., “Quantum-inspired Evolutionary Algorithm Approach for Unit Commitment,” IEEE Trans.
Power Syst., Vol. 24, No. 3, pp. 1503–1512, Aug. 2009.
[15] PS. Sishaj, et al., “An Ant Colony System Approach for Unit Commitment Problem,” Int J Electr Power Energy
Syst, Vol. 28, No. 5, pp. 315–23, 2006.
[16] X. Yuan, et al., “Application of Enhanced Discrete Differential Evolution Approach to Unit Commitment
Problem,” Energy Convers Manage, Vol. 50, pp. 2449–2456, 2009. 28
[17] H. Sasaki, et al., “A Solution Method of Unit Commitment by Artificial Neural Networks,” IEEE Trans Power
Syst, Vol. 7, No. 3, pp. 974–81, 1992.
[18] A. Borghetti, et al., “Lagrangian Relaxation and Tabu Search Approaches for Unit Commitment Problem,” IEEE
porto power tech conference, Portugal, 2001.
[19] R. Habachi, A. Touil, A. Charkaoui, and A. Echchatbi, “Eagle strategy based crow search algorithm for solving
unit commitment problem in smart grid system,” Indonesian Journal of Electrical Engineering and Computer
Science., vol. 12, no. 1, pp. 17–29, 2018.
[20] S. S. Sakthi, R. K. Santhi, N. Murali Krishnan, S. Ganesan, and S. Subramanian, “Wind Integrated Thermal Unit
Commitment Solution Using Grey Wolf Optimizer,” International Journal of Electrical and Computer Engineering
(IJECE), vol. 7, no. 5, p. 2309, Oct. 2017.
[21] J. Kennedy, R. Eberhart, “Particle swarm optimization,” in: IEEE International Conference on Neural Networks,
Vol. 4, 1995, pp. 1942-1948.
[22] R. Eberhart, J. Kennedy, “A new optimizer using particle swarm theory,” in: IEEE Proceedings of the Sixth
International Symposium on Micro Machine and Human Science, 1995, pp. 39-43.
[23] J. Kennedy and R. C. Eberhart, “A discrete binary version of the particle swarm algorithm,” IEEE International
Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, Orlando, FL, USA,
1997, pp. 4104-4108 vol.5.
[24] Han K.H and Kim J.H, “Quantum-inspired evolutionary algorithm for a class of combinatorial optimization,” in
IEEE Transactions on Evolutionary Computation, vol. 6, no. 6, pp. 580-593, Dec. 2002.
[25] L. Spector, et al., “Finding a better-than-classical quantum AND/OR algorithm using genetic programming, ”
Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), Washington, DC,
USA, 1999, pp. 2239-2246 Vol. 3.
[26] Y. Jeong, et al., “A New Quantum-Inspired Binary PSO: Application to Unit Commitment Problems for Power
Systems, ” in IEEE Transactions on Power Systems, vol. 25, no. 3, pp. 1486-1495, Aug. 2010.

1155
BIOGRAPHIES OF AUTHORS
Ali Nasser Hussain was born in Iraq on April 30, 1974. He received his B.Sc. and M.Sc. in
Electrical & Electronics Engineering, University of Technology, Baghdad, Iraq, in 1998 and in
2005 respectively and his PhD degrees in Electrical Engineering from University Malaysia Perlis
(UniMAP), Perlis, Malaysia in 2014. Since 2004 he is a senior lecturer in the Electrical
Engineering Technical College at Middle Technical University. His current research interests
include power system operation and control, electrical power system stability and intelligent
optimization, renewable energy, robust control.
Ali Abduladheem Ismail was born in Baghdad-Iraq on February 17, 1989. He received his
B.Sc. in Electrical Power Engineering Techniques, Electrical Engineering Technical College,
Middle Technical University, Baghdad, Iraq, in 2010.He is a shift engineer in National Dispatch
Control Center (NDCC), Ministry of electricity, Iraq. He is pursuing the M.Sc. degree at
Electrical Engineering Technical College, Middle Technical University. His current research
interest includes optimal operation of power system and economics.

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