Coyote multi-objective optimization algorithm for optimal location and sizing of renewable distributed generators

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 11, No. 2, April 2021, pp. 975~983
ISSN: 2088-8708, DOI: 10.11591/ijece.v11i2.pp975-983  975
Journal homepage: http://ijece.iaescore.com
Coyote multi-objective optimization algorithm for optimal
location and sizing of renewable distributed generators
E. M. Abdallah1
, M. I. Elsayed2
, M. M. ELgazzer3
, Amal A. Hassan4
1,2,3
Department of Electrical and Machines, Faculty of Engineering, Al-Azhar University, Egypt
4
Electronics Research Institute, Cairo, Egypt
Article Info ABSTRACT
Article history:
Received Feb 17, 2020
Revised Aug 14, 2020
Accepted Sep 30, 2020
Research on the integration of renewable distributed generators (RDGs) in
radial distribution systems (RDS) is increased to satisfy the growing load
demand, reducing power losses, enhancing voltage profile, and voltage
stability index (VSI) of distribution network. This paper presents the application
of a new algorithm called ‘coyote optimization algorithm (COA)’ to obtain
the optimal location and size of RDGs in RDS at different power factors. The
objectives are minimization of power losses, enhancement of voltage
stability index, and reduction total operation cost. A detailed performance
analysis is implemented on IEEE 33 bus and IEEE 69 bus to demonstrate the
effectiveness of the proposed algorithm. The results are found to be in a very
good agreement.
Keywords:
Coyote optimization algorithm
renewable energy
Distributed generators
Power loss reduction
Voltage stability index This is an open access article under the CC BY-SA license.
Corresponding Author:
E. M. Abdallah
Department of Electrical and Machines, Faculty of Engineering
Al-Azhar University
Nasr City, Cairo, Egypt
Email: eng.eman1928@yahoo.com
1. INTRODUCTION
Generally, the electrical distribution network (DN) is the final stage for electrical connection
between the enormous power supply and the electricity users. The DN is a complex system and it is
characterized by high power losses due to high (R/X) ratio [1]. To overcome this problem many researches
are performed on the integration of distributed generators (DGs) in DN [2]. DGs known as a small scale
electrical generation unit (typically 1 kW-50 MW) it is located near to load side. DGs may depend on
conventional and/or non-conventional sources. Renewable energy power generation is increasing rapidly.
Solar and wind resources are the most readily available sources. Also, DGs plays significant role in
decreasing power losses, enhancing voltage stability and voltage profile of all busses [3]. In order To benefit
from installation DGs in DN; placement and size of DGs must be optimized Considering DGs capacity and
voltage limit. The inappropriate siting and sizing of DG units in the RDS will adversely affect the system,
which is increased power loss and voltage instability [4]. Thus, several research has been done to evaluate the
advantages of integration RDGs on DN by optimally sizing and placing for these unites through solving a
single or several objectives problems. Many algorithms are used to solve this problem to enhance the
performance of electrical DN. In [5], performance improvement of distribution systems is proposed by
solving multi-objective functions using the genetic algorithm (GA). In [6], an approach is presented for
optimum DGs siting to enhance voltage stability for all buses of network and less power losses. In [7],
genetic and particle swarm optimization are implemented to find the optimum size and location of DGs to
reduce power losses and to enhance voltage regulation and voltage stability of DN. In [8], multi-objective

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 2, April 2021 : 975 - 983
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optimization is proposed to find optimal sizing and placement of DGs using Pareto frontier differential
evolution algorithm. In [9] a strategy for programming goals using GA was proposed for solving a multi-
objective DGs planning in distribution power system. In [10], firefly algorithm is implemented to obtain an
optimal siting of multiple DGs in the DN. Some researches take into account the economical perspectives of
DGs allocation problems such as in [11] that presented optimal sizing and placement of DGs for reducing
power losses and total investment cost using probabilistic multi-objective optimization algorithm. In [12],
RDGs are integrated into a distribution system for power losses reduction using a honey bee mating
optimization algorithm.
This paper introduce application of new effective algorithm called “coyote optimization algorithm
(COA)” to find the optimal size and location of DGs based renewable energy by solving multi-objective
function. The objectives are minimizing power losses, enhancement of VSI for all buses of network, and
decreasing the total operation cost at constant load power. By solving these objectives, the performance of
electrical networks will be improved. Two types of DGs are used; type I deliver active power only like
photovoltaic and type II deliver active and reactive power at different power factors 0.95 and 0.85 such as
wind turbine. The proposed COA algorithm is implemented on the IEEE RDS including IEEE 33 bus and
IEEE 69 bus. COA algorithm gives better results compared to other algorithms.
2. PROBLEM FORMULATION
2.1. Power flow analysis
In RDS Power flow and voltage corresponding to each bus can be calculated using forward-
backward sweep algorithm [13], a single line diagram of the sample RDS is shown in Figure 1.
Figure 1. Single line diagram of the sample RDS
From Figure 1, the injected current at node m is calculated from:
Im = (
Pm+jQm
Vm
)
∗
(1)
The voltage at bus m+1 can be determine as in (2):
Vm+1 = Vm − Im,m+1 ∗ (Rmm+1 + jXm,m+1) (2)
The branch current between bus m and bus m+1 is determined as follow:
Im,m+1 = Im+1 + Im+2 (3)
Power loss in line section between buses m and m+1 is determined as follow:
𝑃𝑙𝑜𝑠𝑠𝑚,𝑚+1 = 𝑅𝑚,𝑚+1 ∗ (
𝑃𝑚,𝑚+1
2+𝑗𝑄𝑚,𝑚+1
2
𝑉𝑚
2 ) (4)
The network total power losses can be calculated through summing losses in all branches of the network
which is given as:
𝑃𝑡𝑜𝑡𝑎𝑙 𝑙𝑜𝑠𝑠𝑒𝑠 = ∑ 𝑃𝑙𝑜𝑠𝑠𝑚,𝑚+1
𝑏
𝑚=1 (5)
where b is total number of branches

Int J Elec & Comp Eng ISSN: 2088-8708 
Coyote multi-objective optimization algorithm for optimal location… (E. M. Abdallah)
977
2.2. Power loss minimization
After DGs installation at an optimal location, the power losses will be decrees and the voltage
stability index will be enhanced. The power losses for the line section between buses m and m+1 can be
determine as written in (6) [14].
𝑃𝑙𝑜𝑠𝑠𝑒𝑠 𝐷𝐺(𝑚,𝑚+1)
= 𝑅𝑚,𝑚+1 ∗ (
𝑃𝐷𝐺𝑚,𝑚+1
2+𝑗𝑄𝐷𝐺𝑚,𝑚+1
2
𝑉𝑚
2 ) (6)
After DGs installation, the total power loss is determined as follows:
𝑃𝐷𝐺𝑡𝑜𝑡𝑎𝑙 𝑙𝑜𝑠𝑠𝑒𝑠
= ∑ 𝑃𝑙𝑜𝑠𝑠𝐷𝐺𝑚,𝑚+1
𝑏
𝑚=1 (7)
Power loss index (PLI) can be determined as given in [15]:
𝑓1 = 𝑃𝐿𝐼 =
𝑃𝐷𝐺𝑡𝑜𝑡𝑎𝑙 𝑙𝑜𝑠𝑠𝑒𝑠
𝑃𝑡𝑜𝑡𝑎𝑙 𝑙𝑜𝑠𝑠𝑒𝑠
(8)
where: 𝑃𝐷𝐺 𝑡𝑜𝑡𝑎𝑙 𝑙𝑜𝑠𝑠𝑒𝑠 is total power loss if there is DGs.
𝑃𝑡𝑜𝑡𝑎𝑙 𝑙𝑜𝑠𝑠𝑒𝑠 is total power loss in absence of DGs.
By installation DGs in RDS the power losses can be minimize, so PLI will be minimized.
2.3. Voltage stability index (VSI) improvement
It is extremely necessary to maintain the DN in stable operation under heavy load conditions, so it is
important to calculate VSI as shown in (9) [16].
𝑉𝑆𝐼𝑖 = |𝑉𝐽|
4
− 4 ∗ [𝑃𝑖(𝑖)𝑅𝑖𝑗 + 𝑄𝑖(𝑖)𝑋𝑖𝑗]|𝑉𝐽|
2
− 4 ∗ |𝑃𝑖(𝑖)𝑅𝑖𝑗 + 𝑄𝑖(𝑖)𝑋𝑖𝑗|
2
(9)
where 𝑃𝑖, is load active power at bus𝑖, and 𝑄𝑖 is load reactive power bus 𝑖, 𝑅𝑖𝑗 and 𝑋𝑖𝑗 are the resistance and
reactance of branch 𝑖𝑗.
The bus which has a minimum value of VSI is the most sensetivity bus to voltage collapse under
increasing load these lead to instability of the voltage. To maintain the system operation in a stable limit, it is
required to maintain VSI at a higher value. As shown in (10) shows the objective function for improving VSI:
𝑓2 = 𝑚𝑖𝑛 1
𝑉𝑆𝐼
⁄ (10)
2.4. Operation cost minimization
One of the benefits of optimum allocation and sizing of DGs in the DN is minimizing overall
operating costs. The total operation cost (TOC) comprises two element ; the first element is cost of the real
active power drawn from electrical substation that reduced by reducing the total power losses and the second
element is cost of active power drown from the DGs which can be minimized by minimizing DGS size [17]:
TOC = (𝑋1𝑃𝐷𝐺 𝑡𝑜𝑡𝑎𝑙 𝑙𝑜𝑠𝑠𝑒𝑠) + (𝑋2𝑃𝐷𝐺𝑇) (11)
where 𝑋1 and 𝑋2 are active power cost coefficient in $/KW supplied from substation and DGs.
The net operation cost can be calculated as:
𝑓3 = ∆𝑂𝐶 =
𝑇𝑂𝐶
𝑋2𝑃𝐷𝐺𝑇
𝑀𝑎𝑥 (12)
The TOC will be minimized by minimizing net operation costs.
2.5. Formulation of multi-objective function and constraints
The proposed objective functions aim to minimize power losses, TOC and maximize VSI as shown in (13).
minimize OF = min(𝑤1𝑓1 + 𝑤2𝑓2 + 𝑤3𝑓3) (13)
where,
𝑤1 + 𝑤2 + 𝑤3 = 1 (14)

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where 𝑤 is the weight factor and its value is chosen corresponding to the importance of power losses, voltage
stability index, and operation cost. The minimization of objective functions must satisfy the operation and planning
constraints to meet the electrical power system requirement. These constraints are presented as follows:
Power balance constraint:
∑ 𝑃
𝑔𝑚
𝑛
𝑚=2 = ∑ 𝑃𝑑𝑚
𝑛
𝑚=2 + ∑ 𝑃𝑙𝑜𝑠𝑠𝑚,𝑚+1
𝑏
𝑚=1 (15)
where: n is total number of buses
∑ 𝑄𝑔𝑚
𝑛
𝑚=2 = ∑ 𝑄𝑑𝑚
𝑛
𝑚=2 + ∑ 𝑄𝑙𝑜𝑠𝑠𝑒𝑠
𝑏
𝑚=1 (16)
Bus voltage limit:
|𝑉
𝑚
𝑀𝑖𝑛
| ≤ |𝑉
𝑚| ≤ |𝑉
𝑚
𝑀𝑎𝑥
| (17)
where |𝑉
𝑚
𝑀𝑖𝑛
| and |𝑉
𝑚
𝑀𝑎𝑥
| is the lower and upper bounder of the voltage |𝑉
𝑚|
|𝑉
𝑚
𝑀𝑖𝑛
| = 0.95𝑝𝑢 and |𝑉
𝑚
𝑀𝑎𝑥
| = 1.05 𝑝𝑢 (18)
Thermal limits:
I(m,m+1) ≤ I(m,m+1)Max (19)
DGs capacity limits:
𝑃𝐷𝐺𝑇
𝑀𝑖𝑛
≤ 𝑃𝐷𝐺𝑇 ≤ 𝑃𝐷𝐺𝑇
𝑀𝑎𝑥
(20)
where,
𝑃𝐷𝐺𝑇
𝑀𝑖𝑛
= 0.1 ∗ ∑ 𝑃𝑑𝑚
𝑛
𝑚=1 & 𝑃𝐷𝐺𝑇
𝑀𝑎𝑥
= 0.6 ∗ ∑ 𝑃𝑑𝑚
𝑛
𝑚=1 (21)
The resultant solution will be accepted if all the above constraints satisfied otherwise it should be rejected.
3. COYOTE OPTIMIZATION ALGORITHM (COA)
The proposed (COA) population focused on the coyote's behavior, Canis latrans species identified as
swarm intelligence and evolutionary heuristic species [18, 19]. Coyote population classified into Np ∈ N∗
packs with Nc ∈ N∗ coyotes each. The total algorithm population is determined by Np and Nc multiplication. For
optimization problem each coyote is a potential solution and its social status is the cost of the objective function [20].
3.1. Algorithm steps
 Initialization
In COA the first step is initializing global coyote population as written in (22):
𝑠𝑜𝑐𝑐𝑗
𝑝,𝑡
= 𝑙𝑝𝑗 + 𝑟𝑗 ∗ (𝑢𝑏𝑗 + 𝑙𝑏𝑗) (22)
where, lbj is the lower boundary , ubj is upper boundary of the jth
decision variable, D is defined as the search
space and 𝑟𝑗 is a real random number generated within the range [0, 1].
 Verify the adaptation of the coyote according to (23):
𝑓𝑖𝑡𝑐
𝑝,𝑡
= 𝑓(𝑠𝑜𝑐𝑐
𝑝,𝑡) (23)
 Defines the pack's Alpha coyote
The pth
pack alpha coyote in the tth
instant of time is determined as in (24):
𝑎𝑙𝑝ℎ𝑎𝑝,𝑡
= {𝑠𝑜𝑐𝑝𝑡
𝐶|𝑎𝑟𝑔𝑐 = {1,2, … … . . , 𝑁𝐶}𝑚𝑖𝑛𝑓(𝑠𝑜𝑐𝑝,𝑡
𝐶)} (24)
 Calculate the pack 's social tendencies

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 Update Coyote's social condition
Using alpha and pack affect the social condition of coyote can be obtained through the following
equation:
𝑛𝑒𝑤𝑠𝑜𝑐𝑝,𝑡
𝐶 = 𝑠𝑜𝑐𝑝,𝑡
𝐶 + 𝑟1 ∗ 𝛿1 + 𝑟2 ∗ 𝛿2 (25)
where, r1 is weight of the alpha ,r2 is weight of pack influence., r1 and r2 are random numbers with in the
generated range [0, 1].
 Evaluating new social condition:
𝑛𝑒𝑤𝑓𝑖𝑡𝑝,𝑡
𝐶
= 𝑓(𝑛𝑒𝑤𝑠𝑜𝑐𝑝,𝑡
𝐶) (26)
 Adaptation
Adaptation means maintaining the new social condition better than the old one as in (27):
𝑠𝑜𝑐𝑝,𝑡+1
𝐶 = {
𝑛𝑒𝑤𝑠𝑜𝑐𝑝,𝑡
𝐶, 𝑛𝑒𝑤𝑓𝑖𝑡𝑝,𝑡
𝐶
< 𝑓𝑖𝑡𝑝,𝑡
𝐶
𝑠𝑜𝑐𝑝,𝑡
𝐶 𝑜𝑡ℎ𝑒𝑟 𝑤𝑖𝑠𝑒
} (27)
 Transition between packs
Sometimes the coyotes abandon their packs and become lonely or join in a pack. The possibility
of leaving coyote its back will be:
𝑃𝑒 = .005 ∗ 𝑁2
𝐶 (28)
number of coyotes per pack is restricted to 14, given that Pe may expect values higher than 1 for Nc
≤√200 diversify interaction of all population's coyotes, meaning cultural exchange among the global
population.
 Update the coyotes’ ages.
 Select the most adapted coyote (best size and location).
The flowchart of COA for optimal location and size of DG is shown in Figure 2.
Figure 2. Flowchart of COA for optimal location and size of DGs

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4. SIMULATION RESULTS AND DISCUSSION
Two distribution systems are used to verify the effectiveness of the COA; IEEE 33 bus and IEEE 69
bus. The objective functions are to minimize PLI and TOC and maximize VSI. For multi-objective
optimization highly importance are given to power loss, VSI and TOC, respectively, according to weight
factors W1 ,W2 and W3 which are taken as 0.5, 0.4, 0.1, respectively, 𝑋1and 𝑋2 are the cost coefficient and
taken for the test systems as 4$/kW and 5$/kW respectively . 𝑋2 is slightly higher than 𝑋1 because it includes
the installation and maintenance cost of DGS [17]. The proposed algorithm is implemented for two types of
RDGs (PV & wind turbine) at different power factors. In the simulation, the load model is considered as a
constant load power (CP). The proposed method is implemented using MATLAB 16 software running on a
computer with Intel®_ Core_ i7 CPU @ 2.4 GHz and 8 GB of RAM.
4.1. Optimization results for IEEE 33 bus
The first test system is the IEEE 33 bus that has a total load of 3.72 MW and 2.3 MVAr at voltage
12.66 KV [21]. Forward-backward sweep algorithm is used to determine total power losses for base case
which is 202.6771 KW with minimum voltage 0.9131 p.u at bus 18. Optimization results are presented in
Table 1. It is clear that the percentage of power loss reduction is increased; VSI and voltage profile are more
enhanced when installation DGs operate at 0.85 pf. This means that the reactive power substantially effect on
power losses minimization and improving voltage profile and voltage stability index. Simulation results
obtained by COA are compared with results obtained from numerous other algorithms previously published
such as GA, PSO, FA, and SA to prove the effectiveness of the proposed algorithm. Comparison results are
tabulated in Table 2 (see in appendix). It is clear from the comparison table that COA gives a good agreement
in case of power loss reduction. Moreover, in the case of VSI and voltage profile improvement, COA gives
better results than other algorithms for DGs size at the same range. For 0.95 pf, COA gives better results
regarding the voltage profile and VSI as indicated in Table 2 (see in appendix). The percentage reduction in
power loss is 76.72% and the VSI is 0.9093. Figure 3 represent the voltage profile for the IEEE 33 bus at
different pf.
Table 1. Optimization result of IEEE 33 bus after DGs installation at different power factors
Item CP
Without Unity pf With DG 0.95 Pf With DG 0.85 pf With DG
Optimal DG
Size (kW) (bus)
742.8868 (14)
1260.0998 (30)
749.8989 (14)
1199.3960 (30)
679.6554 (14)
1182.6635 (30)
Total Size (KW) 2002.98 1949.29 1862.3
Power loss (kW) 202.6771 86.345 47.1844 32.7278
% Reduction of power loss 57.39% 76.72% 83.85%
VSI min (p.u.) 0.6940 0.8858 (18) 0.9093 0.912
Minimum (p.u) (bus) 0.9131 0.9703 (18) 0.9786 0. 9793pu
TOC ($) 10360 9935.2 9442.5
Table 2. Comparison optimization results of IEEE 33 bus after DGs installation at different power factors
Method PDG,TLoss
(kW)
% Loss
reduction
Vworst (p.u.)
(bus)
DG
location
DG size
(KW)
SDG,T
(KVA)
VSImin
(p.u.)
TOC ($) Power
factor
FA [10] 87.83 58.37 0.9695 (30) 13
17
31
623.1
261.3
1012
1896.4 0.8820 9833.2 unity
SA [22] 82.03 61.12 0.9676 (14) 6
18
30
1112.4
487.4
876.8
2467.7 ------ 12666.6 unity
GA/PSO
[7]
103.40 50.99 0.9808 (25) 32
16
11
1.2000
0.8630
0.9250
2.9880 ------- 15353.6 unity
PSO [23] 114.89 45.5498 ------ 7 2.8951 2.8951 -------- --------- unity
GA [5] 84.35 58.9% 0.9648 30
14
998.51
833.7
1882.2 .866 ------- unity
QOTLBO
[24]
103.409 50.99 0.9827 (25) 13
26
30
1083.4
1187.6
1199.2
3470.2 0.9240 17764.6 unity
GOA [9] 94 53 ---------- 6
11
1500
1000
3500 -------- -------- unity
COA 86.345 57.39 0.9703 (18) 14
30
742.8868
1260.0998
2000 0.8858 10360 unity
GA [5] 47.971 76.33 --------- 30
14
1100
750
1850 .902 ------ 0.95
COA 47.1844 76.72 0.9786 14
30
749.8989
1199.3960
1949.29 0.9093 9935.2 0.95

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Figure 3. Voltage profile for 33-bus test system at different pf
4.2. Optimization results for IEEE 69 bus
The second test system is IEEE 69 bus that has a total load of 3.8 MW and 2.69 MVAr at 12.6 kV.
Forward-backward sweep algorithm is used to calculate total power losses for the base case which is
225.0028 KW with minimum voltage 0.9092 p.u [25]. Optimization and comparison results are tabulated in
Tables 3 and 4. It is clear from the comparison that COA gives a good agreement in case of power loss
reduction and VSI at unity power factor. Moreover, in case of 0.95 pf. COA gives better results than other
algorithms for power loss reduction, VSI, and voltage profile improvement. Figure 4 show the voltage profile
for IEEE 69 bus at different pf.
Table 3. Optimization result of IEEE 69 bus after DGs installation at different power factors
Item CP
Unity pf 0.95Pf 0.85PF
Without With DG With DG With DG
Optimal DG
size(kW)(bus)
158.0401 (25)
1745.1869 (50)
365.4609 ( 21)
1806.2857 (50)
351.0718 (21)
1654.0789 (50)
Total Size (KW) 1903.227 2171.746 2005.1
Power loss (kW) 225.0028 77.5752 26.5529 11.3494
% Reduction of
power loss
65.5% 88.1% 94.95%
VSImin (p.u.) 0.6823pu .9041 (27) .9532 .9584
Vminimum(p.u) 0.9092pu 0.9755 0.9884 0.9894
TOC ($) 9826.4 10965e 10071
Table 4. Comparison optimization results of IEEE 69 bus after installation DGs at different power factors
Method PDG,
TLoss
(kW)
% Loss
reduction
Vworst (p.u.)
(bus)
DG
location
DG size
(KW
SDG,T
(KVA)
VSI
min(p.u.)
TOC
($)
Power
factor
FA [10] 74.43 66.90 0.9775 (61) 61
64
27
1142
542
366
2050 0.9100 10547.7 unity
(BFOA)
[17]
89.90 57.38 0.9705 (29) 14
18
32
652.1
198.4
1067.2
1917.6 ------ 9948.1 Unit
GA [5] 76.98 65.73 ----------- 24
62
223
1738
1961 .9096 ------ unity
QOTLBO
[24]
80.58 64.14 0.9945 (65) 15
61
63
929.7
1075.2
992.5
2960.6 0.9585 15125.3 unity
COA 77.5752 65.5 0.9755 25
50
158.0401
1745.1869
1903.227 .9041 9826.4 unity
GA [5] 29.47 77.9 ----------- 61
23
1804
330
2134 .9389 --------- 0.95
COA 26.5529 88.1 0.9786 21
50
365.4609
1806.2857
2171.746 0.9884 10965 0.95

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Figure 4. Voltage profile for IEEE 69 bus at different pf
5. CONCLUSION
This paper introduces implementation of new optimization algorithm (COA) to obtain optimum size
and placement of RDGs that achieve increasing percentage of power loss reduction, voltage profile and
voltage stability of all buses of the DN enhancement. the proposed algorithm is implemented for two test
systems IEEE 33 and 69 bus RDS with constant load power at different power factors. DGs operating at
unity, 0.95 and 0.85 power factor. The simulation result obtained by COA was compared with other popular
algorithms FA, BFOA, and QOTLBO, GA. The proposed algorithm is extremely accurate for evaluating an
optimal solution for location and size of DGs that give more power losses reduction and better result in
improving voltage profile and VSI when compared with other algorithms.
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BIOGRAPHIES OF AUTHORS
E. M. Abdallah received her B.Sc. in electrical power and machines from the Faculty of
Engineering, Al-Azhar university in 2012. She awarded her M.Sc. in electrical power and
machines from the Faculty of Engineering, Al-Azhar university in 2016. Currently, she is a
lecturer Assistant at electrical power and machines dept., Faculty of Engineering, Al-Azhar
University. Her research interests include renewable energy systems, power systems,
optimization and control of smart grid.
M. I. ELsayed received his B.Sc. in electrical power and machines from the Faculty of
Engineering, Al-Azhar university in 1997. He awarded his M.Sc, PhD, ASS Prof and Prof. In
electrical power and machines from the Faculty of Engineering, Al-Azhar university in 2003,
2007, 2012 and 2017, respectively. Currently, He is Vice Dean of Faculty of Engineering
(femal), Al-Azhar university. His research interests control of power systema and reliability and
stability of electric power systems.
M. M. ELgazzer received his B.Sc. in electrical power and machines from the Faculty of
Engineering, Al-Azhar university in 1971. He awarded his M.Sc, PhD, and ASS Prof. in
electrical power and machines from the Faculty of Engineering, Al-Azhar university in 1976,
1983, 1988 and 1993, respectively. His research interests control of power system and
reliability and stability of electric power systems.
Amal A. Hassan received her B.Sc. in electrical power and machines from the Faculty of
Engineering, Cairo University in 2003. She awarded her M.Sc. and Ph.D. in electrical power and
machines from the Faculty of Engineering, Cairo University in 2009, and 2016, respectively.
Currently, she is a researcher in Photovoltaic Cells Dept., Electronics Research Institute (ERI),
and Cairo, Egypt. Her research interests include grid-connected PV systems, smart power grid,
optimization, and control of distributed generation based on renewable energy and microgrid.

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