Maximum Loadability Enhancement with a Hybrid Optimization Method

Bulletin of Electrical Engineering and Informatics
Vol. 7, No. 3, September 2018, pp. 323~330
ISSN: 2302-9285, DOI: 10.11591/eei.v7i3.1168  323
Journal homepage: http://journal.portalgaruda.org/index.php/EEI/index
Maximum Loadability Enhancement with a Hybrid
Optimization Method
E. E. Hassan1
, T. K. A. Rahman2
, Z. Zakaria3
, N. Bahaman4
, M. H. Jifri5
1,5
Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia
2
School of Science and Technology, Asia University, Wisma Subang Jaya, Selangor, Malaysia
3
Faculty of Electrical Engineering, Universiti Teknologi Mara, Shah Alam, Selangor, Malaysia
4
Faculty of Information and Communication Technology, University Teknikal Malaysia Melaka (UTeM), Malaysia
Article Info ABSTRACT
Article history:
Received March 26, 2018
Revised Jul 25, 2018
Accepted Aug 08, 2018
Nowadays, a power system is operating in a stressed condition due to the
increase in demand in addition to constraint in building new power plants.
The economics and environmental constraints to build new power plants and
transmission lines have led the system to operate very close to its stability
limits. Hence, more researches are required to study the important
requirements to maintain stable voltage condition and hence develop new
techniques in order to address the voltage stability problem. As an action,
most Reactive Power Planning (RPP) objective is to minimize the cost of
new reactive resources while satisfying the voltage stability constraints and
labeled as Secured Reactive Power Planning (SCRPP). The new alternative
optimization technique called Adaptive Tumbling Bacterial Foraging
(ATBFO) was introduced to solve the RPP problems in the IEEE 57 bus
system. The comparison common optimization Meta-Heuristic Evolutionary
Programming and original Bacterial Foraging techniques were chosen to
verify the performance using the proposed ATBFO method. As a result, the
ATBFO method is confirmed as the best suitable solution in solving the
identified RPP objective functions.
Keywords:
ATBFO
Meta-heuristic evolutionary
programming
RPP
SCRPP
Copyright © 2018 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
E. E. Hassan,
Faculty of Electrical Engineering,
Universiti Teknikal Malaysia Melaka (UTeM), Malaysia.
Email: erwani@utem.edu.my
1. INTRODUCTION
Many countries have reported that millions of dollars were lost due to voltage collapse incidents.
Failure to progress above the specific voltage magnitude leads to voltage collapse [1]. In other words, voltage
collapse is due to voltage instability that refers to the inability of a power system to keep the steady state
voltages at all buses [2]. Besides that, failure in congestion management may results in blackout of the whole
or parts of power system. This situation is verified by a report which stated that an outage of a 345kV
transmission line has caused blackout in Canada and U.S in August 2003 because the system is unable to
sustain the additional load [3]. Therefore, efficient RPP planning would be able to avoid the occurrence of
voltage collapse. Several objective functions were implemented in SCRPP in order to improve the voltage
stability condition of a power system such as minimizing voltage deviation from specified operating points
and maximizing static stability margin (SM) [4].
In order to obtain optimal solution to SCRPP, the efficient and reliable optimization technique has
become necessary. These advanced and efficient solutions are able to overcome the weakness of the existing
classical methods which are not capable to solve non convex, non-continuous and highly nonlinear solution
such as in SCRPP problems [5]. Thus, today meta-heuristic optimization approaches such as Particle
Swarming Optimization (PSO), Evolutionary Programming (EP), Genetic Algorithm (GA) and Bacterial

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BEEI, Vol. 7, No. 3, September 2018 : 323 – 330
324
Foraging Algorithm (BFA) with advanced search techniques make the problems possible to be solved. These
techniques offered global optimal solutions, however, at the expense of computational time [6]. Therefore,
recent researches are inspired to merge conventional methods and advanced optimization techniques for
better and faster optimization approaches.
This study intended to introduce a new Adaptive Tumbling Bacterial Foraging Optimization
(ATBFO) algorithm which is an improvement to the basic Bacterial Foraging Optimization (BFO) algorithm.
The proposed technique was implemented to solve the single objective SCRPP problems. Finally, the
performances of the newly developed technique ATBFO were compared with that provided by the EP and
the basic BFO. The best solutions were identified based on the smallest total system losses and maximum
loading point that the system can withstand. In addition, the aggregate function method was applied to
confirm the outperformed method among them. The lowest total aggregate value is declared as the excellent
approach for the SCRRP problem.
2. SECURED REACTIVE POWER PLANNING
RPP is also known as VAR planning in which reactive power sources are managed and planned
optimally [7]. Reactive power can either inductive or capacitive in nature [8]. RPP is normally solved by
using optimization methods. Various factors and objectives are taken into account in solving RPP in order to
ensure for optimal power flow solution. The main objective of RPP is normally minimization of cost
functions such as variable VAR cost, fixed VAR cost, real power losses and also fuel cost [9]. The authors in
this reference also have explained on the deviation of the operating voltage from a specified voltage schedule
and hence utilized Voltage Stability Margin (VSM). In Secured Reactive Power Planning (SCRPP), voltage
stability criteria are normally treated as the constraints. Therefore, the importance of Load Margin (LM)
assessment is used as a tool to indicate the maximum loading point in order to provide secure operating
margin in power system operation.
2.1. Load Margin Assessment
Load Margin (LM) is broadly accepted in analyzing the closeness of the operating condition of a
power system to its voltage collapse. The LM is defined as the quantity of load increment allowable before a
power system reaches the unsecure voltage condition. The load margin was determined by gradually increase
the load until the load flow failed to give solution.
The relationship between reactive power reserve and Voltage Stability Margin (VSM) was
investigated by researchers in reference [10]. The authors in [11] proposed for re-dispatch of reactive power
in order to improve the voltage stability condition of the power system. However, the total active power
losses were not measured because they believed that the solution is not the optimum one. For that reason,
many researchers have given attention to enhance voltage stability condition by sustaining the reactive power
in a power system [12].
The important steps for load margin estimation that involved the load margin analysis and
enhancement were discussed. Thus, load margin assessment can be classified into two categories in which
the first is to forecast the MLP while the second one is to enhance the voltage stability margin for better
stability condition.
2.2. Objective Functions for SCRPP
The consideration to be an objective function based on Maximum Loadability Point (MLP)
improvement for all load busses in solving SCRPP and also at the improvement of MLP at the
critical bus [13].
2.2.1. Maximizing MLP
MLP for a power networks is the maximum amount of load that could be sustained before it reached
the unstable operating point. As referred to references [14], the LM or also called as VSM could be defined
as the distance from the base case, λ0 load to the maximum loading limit, λmax prior to its imbalance point as
shown in Figure 1. During the assessment, the weakest bus among the network and maximum load that it can
sustain can also be determined. The bus with the smallest margin is identified as the weak or critical bus.
This figure also illustrates the comparison between the MLP before optimizing the reactive power sources
through RPP i.e point A and the MLP after the reactive sources are optimized i.e point B.

BEEI ISSN: 2302-9285 
Maximum Loadability Enhancement with a Hybrid Optimization Method (E. E. Hassan)
325
Figure 1. The comparison graph between pre and post SCRPP implementation
2.2.2. Minimizing Total System Losses
The objective function for total loss minimization is given by Equation 1.
𝑚𝑖𝑛 𝑓𝑄 = ∑ 𝑃𝑘𝐿𝑜𝑠𝑠,(𝑣, 𝜃) = ∑ 𝑔𝑘
𝑘∈𝑁𝐺
𝑘=(𝑖,𝑗)
(𝑉𝑖
2
𝑘∈𝑁𝐺
+ 𝑉
𝑗
2
− 2𝑉𝑖𝑉
𝑗𝑐𝑜𝑠𝜃𝑖𝑗) MW
𝑉𝑖𝑚𝑖𝑛
≤ 𝑉𝑖 ≤ 𝑉𝑖𝑚𝑎𝑥
𝑖 ∈ 𝑁𝐵
𝑄𝐺𝑖𝑚𝑖𝑛
≤ 𝑄𝐺𝑖 ≤ 𝑄𝐺𝑖𝑀𝑎𝑥
𝑖 ∈ {𝑁𝑃𝑉, 𝑛𝑠}
(1)
where, Qi and Qjare reactive power at sending and receiving buses respectively, 𝑄𝐺𝑖 is generated reactive
power of bus i, 𝑉𝑖 𝑎𝑛𝑑 𝑉
𝑗 are voltage magnitude at sending and receiving buses respectively. 𝑃𝑘𝐿𝑜𝑠𝑠, is total
active power loss over the network,𝑁𝐵is load bus, 𝑁𝑃𝑉 is voltage controlled bus and 𝑛𝑠 is reference (slack)
bus.
2.2.3. The Important Control Variables
The control variables considered are capacitor or reactor switching transformer tap changing [15]
and active power of generator, to facilitate the requirement of SCRPP.
3. METHODOLOGY
3.1. New Adaptive Bacterial Foraging Optimization (ATBFO) Algorithm
This recent Bacterial Foraging Optimization (BFO) searching algorithm invented by K.M. Passino,
is supported by the fact that natural selection tends to eliminate animals with poor foraging strategies against
those with attractive foraging [16]. These poor hunters will be either eliminated or sometimes reshaped to
good ones through a repeated generation process. Several processes of E. coli foraging that are present in our
intestines are called chemotaxis, swarming, reproduction and elimination and dispersal [17]. Using the E.coli
foraging strategy as in BFO, the global searching space is improved by modifying the tumbling approach by
adapting the mutation technique applied in Meta-EP into tumbling expression implemented in basic BFO
thus represented by new Equation 2 to 4 in ATBFO algorithm.
𝜃𝑖(𝑗 + 1, 𝑘, 𝑙) = 𝜃𝑖(𝑗, 𝑘, 𝑙) + 𝐶(𝑖)Ø(𝑖) (2)
Hence: Ø(𝑖) =
∆(𝒊)
√∆𝑻(𝒊)∆(𝒊)
, where ∆(𝑖)= random vector for each bacterium, ∆𝑇(𝑖)= transpose of random vector
for each bacterium. Then, mutate the new position of 𝐽𝑙𝑎𝑠𝑡 by using given by Equation 2.
∅′
𝑖(𝑗) = ∅(𝑗) exp 𝜏′
𝑁(0,1) + 𝜏𝑁𝑖(0,1) (3)
𝑃′
𝑖(𝑗) = 𝑃𝑖(𝑗) + ∅′
𝑖(𝑗)𝑁𝑗(0,1) (4)
where 𝜏 = √
1
√2𝑛
, 𝜏′ =
1
√2𝑛
, 𝑃′
𝑖(𝑗), 𝑃𝑖(𝑗), ∅′
𝑖(𝑗) and ∅(𝑗) is a ith
component of respective vector, 𝑁𝑖(0,1) is
normally distributed one dimensional random number with mean 0 and 1. 𝑁𝑗(0,1) indicates the random
number will be new for each value of j.
λ0 λmax
pre
λmax
post Load

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3.2. A New ATBFO Algorithm for Single Objective Function SCRPP
An intelligence heuristic technique named as ATBFO algorithm was implemented as an
optimization mechanism for solving SCRPP problems with single objective solution. This single objective is
either to maximize the Maximum Loadability Point (MLP) or minimize system losses while satisfying the
operational constraints. The corresponding objective function is calculated while the value of the other is
observed. The simulations were tested under tested on the IEEE 57 bus system for unstressed and stressed
conditions as illustrated in Figure 2. The task also covered all possibilities of load increments as following:
a. Reactive load increment or Q increment
b. Real load increment or P increment and
c. Reactive and Real load increment or Q and P load increased simultaneously.
In addition, the ATBFO method was also executed on identified critical load bus growth called as
Case 1. While, in Case 2 was when the load at all busses were increased simultaneously. During the
implementation, different sizes of control variables were determined, such as Reactive Power Dispatch
(RPD) Qgs, Capacitor Placement (CP), Qinj and Transformer Tap Change Setting (TTCS), Xmer. The solution
in searching for optimal sizes of control variables were also categories into different group of RPP techniques
such as Xmer ,Qinj, Qgs&Qinj, Qgs&Xmer, Qinj&Xmer or Qinj, Qgs&Xmer as RPP technique respectively as referred
in [32, 33]. The overall implementations of the structure covered throughout the contribution were explained
in depth by the subsequent Figure 2.
Start
Generation of
control variables
Run load flow
Comply initial
condition?
Calculate maximum
load margin
Enter the pool
Pool full?
Tumble
Run load flow
Comply initial
condition?
Calculate maximum
load margin
All parents
tumbled?
Swim
Run load flow
Comply initial
condition?
Rank the
descending results
Select the best 10
readings
Duplicate the 10
readings
Probability
<0.25?
Tumble
Take the
reading
All readings been
checked?
End
Calculate maximum
load margin
Calculate maximum
load margin
Comply initial
condition?
Satisfied
swimming
iteration?
Figure 2. Flowchart of ATBFO process for SCRPP for stressed and unstressed condition
The proposed ATBFO was tested on the IEEE 57 bus system for each Single Objective SCRPP
functions as the following:
a. SOSCRPP1=maximum MLP
b. SOSCRPP2=minimum total losses

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The similar optimization process using this ATBFO method which to minimize the total system
losses SOSCRPP2 solutions were also obtained from Case 1 and Case 2 i.e during unstressed and stressed
situations.
3.3. Aggregate Function Method
The aggregate function is introduced in this study as an alternative to describe the results obtained
from optimization methods to meaningful evaluation and conclusion. From the results obtained, the least
answers bring the smallest aggregate value among others objective functions and vice versa. At the end, the
total aggregates are calculated and the smallest sum value as the finest solution.
4. RESULTS AND DISCUSSION
This section discusses the comparison between two individual objective functions namely
SOSCRPP1 and SOSCRPP2 which are to maximize the MLP and to minimize the total losses. Table 1 shows
the improved voltages and their corresponding losses after the implementation SCRPP by optimizing
RPD+TTCS+CP using ATBFO (Point A’). Similarly, the less total loss was determined from SOSCRPP1 as
compared to SOSCRPP2 at the same Point A’. Initially, the Pre-SCRPP (Point A) has 0.849V (Vmin),
30.4575MW (Losses) and 195% (MLP).
Table 1. Comparison between SOSCRPP1 and SOSCRPP2 at Point A’ (After the Implementation of SCRPP)
for Case 1
Single objective of SCRPP for Case 1 using (RPD+TTCS+CP) technique at Point A’
Types
of
load
increment
Objective function SOSCRPP 1 SOSCRPP 2 SOSCRPP 1 SOSCRPP 2
Minimum
Voltage, (p.u)
Minimum
Voltage, (p.u)
Losses
(MW)
Losses
(MW)
P load-unstressed condition 0.957 0.877 31.2383 31.9231
P load-stressed condition 0.940 0.912 30.9819 31.8038
Q load-unstressed condition 0.971 0.866 28.2897 28.6808
Q load-stressed condition 0.973 0.942 27.9983 27.9994
Q & P load-unstressed condition 0.948 0.885 29.5578 30.1719
Q & P load-stressed condition 0.951 0.885 29.2530 30.1169
Table 1 highlights that SOSCRPP 1 resulted in the highest minimum voltage improvement for all
types of load increments at the critical load bus 31. The SOSCRPP1 is solved through the improved ATBFO
which optimized the RPD+RPP+CP with minimizing total losses and maximizing MLP as objective
functions.
While in case 2, the results obtained from SOSCRPP1 (objective function: maximizing MLP) and
SOSCRPP2 (objective function: minimizing total losses) for P load, Q load and Q with P load increments
during the unstressed and stressed situations are compared as shown in Table 2. The table also tabulates the
minimum voltages after of the implementation of SCRPP.
Table 2. Comparison between SOSCRPP1 and SOSCRPP2 at Point A’ (post optimization)for Case 2
Single objective of SCRPP for Case 2 using (RPD+TTCS+CP) technique
Types
of
load
increment
Objective function SOSCRPP1 SOSCRPP2 SOSCRPP1 SOSCRPP2
Minimum
Voltage, (p.u)
Minimum
Voltage, (p.u)
Losses
(MW)
Losses
(MW)
P load-unstressed condition 0.931 0.906 70.6513 71.6664
P load-stressed condition 0.935 0.898 66.4320 67.7000
Q load-unstressed condition 0.932 0.919 29.3769 29.7674
Q load-stressed condition 0.924 0.913 29.9849 29.7363
Q & P load-unstressed condition 0.925 0.899 48.2148 48.5307
Q & P load-stressed condition 0.939 0.887 46.4769 46.6924
The results gained from SOSCRPP1 show higher minimum voltage as compared to that obtained by
SOSCRPP2. In addition, SOSCRPP 1 also leads to lower total losses. Hence, SOSCRPP1 is better in
performance as compared to SOSCRPP2 for Case 1 and Case 2.

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4.1. Comparison of Single Objective Function in SCRPP among Optimization Techniques
The single objective results for maximizing MLP obtained by ATBFO were compared with those
obtained from the original BFO and Meta-EP approaches. Thus, Table 3 highlights the comparison of the
results obtained after solving SCRPP using the above approaches i.e at Point A’ and Point B.
Aggregate function was introduced in the comparative study in order to identify the technique which
gives the best optimization performance as in Table 4. At Point A’, the observed performances are the
minimum voltage improvement and total losses minimization. While at point B, MLP enhancement is
observed.
In Table 4, the performance of each optimization technique is ranked and value 1 is given to the best
result, while value 3 is given to the worst. The least total aggregate indicates the best performance overall.
From this table, it shows that ATBFO always resulted in the best overall performance. Hence, it can be
concluded that ATBFO outperformed the other two optimization technique. This conclusion is summarized
in Table 5.
Therefore, the outstanding optimization computational tool is recorded by the new ATBFO,
followed by Meta-EP and finally the original BFO algorithm.
Table 3. Comparison between ATBFO and Others Optimization Techniques for SOSCRPP1
RPP technique -(RPD+TTCS+CP)
Point B ( Post-optimization) Point A' ( Post-optimization)
Optimization
techniques
Vmin
(p.u)
Vmax
(p.u)
Losses
(MW)
MLP
(%)
Vmin
(p.u)
Vmax
(p.u)
Losses
(MW)
MLP
(%)
Case1
P load –
unstressed
ATBFO 0.855 1.064 43.439 705 0.957 1.092 31.238 325
BFO 0.847 1.067 41.241 600 0.916 1.067 32.409 325
Meta-EP 0.847 1.066 41.278 635 0.929 1.077 31.387 325
P load -
stressed
ATBFO 0.852 1.096 41.550 570 0.940 1.100 30.982 285
BFO 0.855 1.076 38.865 495 0.917 1.073 31.685 285
Meta-EP 0.846 1.071 40.250 535 0.937 1.071 31.237 285
Q load-
unstressed
ATBFO 0.853 1.075 32.362 905 0.971 1.099 28.290 350
BFO 0.850 1.051 31.083 765 0.925 1.067 28.423 350
Meta-EP 0.849 1.075 30.893 795 0.959 1.074 27.977 350
Q load -
stressed
ATBFO 0.850 1.086 31.615 765 0.973 1.100 27.998 305
BFO 0.848 1.069 30.768 655 0.958 1.077 28.335 305
Meta-EP 0.849 1.098 31.285 655 0.946 1.070 28.628 305
Q&P load-
unstressed
ATBFO 0.846 1.082 36.297 455 0.948 1.099 29.558 225
BFO 0.850 1.065 35.737 425 0.940 1.070 29.961 225
Meta-EP 0.846 1.075 34.493 405 0.947 1.053 29.566 225
Q&P load -
stressed
ATBFO 0.856 1.091 35.755 390 0.951 1.095 29.253 195
BFO 0.844 1.046 34.510 335 0.909 1.048 30.010 195
Meta-EP 0.843 1.069 35.346 365 0.938 1.068 29.769 195
Case2
P load-
unstressed
ATBFO 0.843 1.074 159.430 235 0.931 1.089 70.651 165
BFO 0.847 1.040 89.111 180 0.855 1.040 73.946 165
Meta-EP 0.850 1.056 122.053 210 0.907 1.051 66.686 165
P load -
stressed
ATBFO 0.840 1.066 159.298 205 0.935 1.097 66.432 140
BFO 0.844 1.040 80.660 150 0.846 1.040 69.740 140
Meta-EP 0.847 1.069 126.100 185 0.906 1.054 67.641 140
Q load-
unstressed
ATBFO 0.855 1.045 35.709 265 0.932 1.100 29.377 160
BFO 0.843 1.040 33.404 205 0.881 1.040 31.287 160
Meta-EP 0.844 1.040 36.000 260 0.924 1.058 29.728 160
Q load -
stressed
ATBFO 0.858 1.040 35.020 245 0.924 1.053 29.985 140
BFO 0.852 1.040 33.003 165 0.866 1.040 31.759 140
Meta-EP 0.840 1.040 35.945 215 0.913 1.043 30.498 140
Q&P load-
unstressed
ATBFO 0.842 1.044 91.411 180 0.925 1.085 48.215 135
BFO 0.848 1.040 67.629 155 0.878 1.040 50.383 135
Meta-EP 0.844 1.049 80.010 170 0.905 1.060 48.383 135
Q&P load -
stressed
ATBFO 0.857 1.095 89.123 155 0.939 1.100 46.477 115
BFO 0.841 1.040 63.136 130 0.867 1.040 48.992 115
Meta-EP 0.835 1.070 77.541 145 0.902 1.060 47.225 115

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Table 4. Comparison between ATBFO and Others Optimization Techniques for SOSCRPP1 Using
Aggregate Performance
Aggregate Function
Point A’ Point B
Optimization techniques Vmin Losses MLP Total Aggregates
Case1
P load-unstressed
ATBFO 1.0 1.0 1.0 3.0
BFO 3.0 3.0 3.0 9.0
Meta-EP 2.0 2.0 2.0 6.0
P load -stressed
ATBFO 1.0 1.0 1.0 3.0
BFO 3.0 3.0 3.0 9.0
Meta-EP 2.0 2.0 2.0 6.0
Q load- unstressed
ATBFO 1.0 2.0 1.0 4.0
BFO 3.0 3.0 3.0 9.0
Meta-EP 2.0 1.0 2.0 5.0
Q load -stressed
ATBFO 1.0 1.0 1.0 3.0
BFO 2.0 2.0 2.0 6.0
Meta-EP 3.0 3.0 3.0 9.0
Q&P load-unstressed
ATBFO 1.0 2.0 1.0 4.0
BFO 3.0 3.0 2.0 8.0
Meta-EP 2.0 1.0 3.0 6.0
Q&P load -stressed
ATBFO 1.0 1.0 1.0 3.0
BFO 3.0 3.0 3.0 9.0
Meta-EP 2.0 2.0 2.0 6.0
Case2
P load-unstressed
ATBFO 1.0 2.0 1.0 4.0
BFO 3.0 3.0 3.0 9.0
Meta-EP 2.0 1.0 2.0 5.0
P load -stressed
ATBFO 1.0 1.0 1.0 3.0
BFO 3.0 3.0 3.0 9.0
Meta-EP 2.0 2.0 2.0 6.0
Q load-unstressed
ATBFO 1.0 1.0 1.0 3.0
BFO 3.0 3.0 3.0 9.0
Meta-EP 2.0 2.0 2.0 6.0
Q load -stressed
ATBFO 1.0 1.0 1.0 3.0
BFO 3.0 3.0 3.0 9.0
Meta-EP 2.0 2.0 2.0 6.0
Q&P load-unstressed
ATBFO 1.0 1.0 1.0 3.0
BFO 3.0 3.0 3.0 9.0
Meta-EP 2.0 2.0 2.0 6.0
Q&P load -stressed
ATBFO 1.0 1.0 1.0 3.0
BFO 3.0 3.0 3.0 9.0
Meta-EP 2.0 2.0 2.0 6.0
Table 5. Comparison between ATBFO and Others Optimization Techniques for SOSCRPP1 for Overall
Performance
Optimization Techniques ATBFO BFO MetaEP
Case1
P load-unstressed 3.0 9.0 6.0
P load -stressed 3.0 9.0 6.0
Q load- unstressed 4.0 9.0 5.0
Q load -stressed 3.0 6.0 9.0
Q&P load-unstressed 4.0 8.0 6.0
Q&P load -stressed 3.0 9.0 6.0
Case2
P load-unstressed 4.0 9.0 5.0
P load -stressed 3.0 9.0 6.0
Q load- unstressed 3.0 9.0 6.0
Q load -stressed 3.0 9.0 6.0
Q&P load-unstressed 3.0 9.0 6.0
Q&P load -stressed 3.0 9.0 6.0
Overall Aggregates 39.0 104.0 73.0
5. CONCLUSION
The objective of SCRPP was to maximize the MLP. In other words, the system has the capability to
support extra loads before going into the voltage instability point. Hence, the number of voltage collapse
events could be reduced. The MLP considered in the study were P, Q and P & Q load increases, while two
cases were analyzed, which were MLP at the critical bus (case 1) and MLP for all load buses simultaneously
(case 2). Single objective functions namely, total losses minimization and MLP improvement were

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implemented and analyzed in solving the SCRPP problems. Several RPP approaches were studied and it was
found that optimizing RPD, CP and TTCS simultaneously gave the best results. Hence, ATBFO was utilized
in SCRPP in order to optimize the RPD, CP and TTCS simultaneously so that the required optimal results
would be obtained. The performance ATBFO was compared with that obtained by BFO and Meta-EP. Based
on the analysis, it was found that ATBFO performed better in terms of MLP improvement, minimum voltage
improvement and total losses minimization.
ACKNOWLEDGEMENT
We thank you to Universiti Teknikal Malaysia Melaka (UTeM) and Kementerian Pengajian Tinggi
(KPT) by funding this research paper successful through the grant of RAGS/1/2015/TK0/ FKE/03/B00094.
REFERENCES
[1] C.M. Affonso, L.C.P. da Silva, F.G.M. Lima, S. Soares. Optimal MW/MVAR dispatch and minimal load shedding
strategy for improving voltage stability margin. Power Engineering Society General Meeting, 2003, IEEE, 2003. 2:
895.
[2] A. Laifa, M. Boudour. Facts allocation for power systems voltage stability enhancement using MOPSO. Systems,
Signals and Devices, 2008. IEEE SSD 2008. 5th International Multi-Conference on, 2008. 1-6.
[3] E.E Hassan, Z. Zakaria, T.K.A. Rahman. Improved Adaptive Tumbling Bacterial Foraging Optimization (ATBFO)
for emission constrained economic dispatch problem. Proceedings of the World Congress on Engineering. 2012; 2:
1-4.
[4] F.I.H. Hassim, I. Musirin, T.K.A. Rahman. Voltage Stability Margin Enhancement Using Evolutionary
Programming (EP). Research and Development, 2006. SCOReD 2006. 4th Student Conference on, 2006. 2006;
235-240.
[5] W. Yurong, L. Fangxing, W. Qiulan, C. Hao. Reactive Power Planning Based on Fuzzy Clustering, Gray Code, and
Simulated Annealing. Power Systems, IEEE Transactions. 2011; 26: 2246-2255.
[6] S. Mishra. Hybrid least-square adaptive bacterial foraging strategy for harmonic estimation. Generation,
Transmission and Distribution, IEE Proceedings-. 2005; 152: 379-389.
[7] Z. Wenjuan, L. Fangxing, L.M. Tolbert. Voltage stability constrained optimal power flow (VSCOPF) with two sets
of variables (TSV) for reactive power planning. Transmission and Distribution Conference and Exposition, 2008.
2008; 1-6.
[8] H. Barot, K. Bhattacharya. Optimal Reactive Power Planning and Compensation Effects on Transmission Loss
Components.Power Engineering Society General Meeting, IEEE. 2007; pp. 1-7.
[9] Z. Wenjuan, L. Fangxing, L.M. Tolbert. Review of Reactive Power Planning: Objectives, Constraints, and
Algorithms. Power Systems, IEEE Transactions. 2007; 22: 2177-2186.
[10] B. Lixin, H. Zhenyu, X. Wilsun. Online voltage stability monitoring using VAR reserves. Power Systems, IEEE
Transactions. 2003; 18: 1461-1469.
[11] M. Yao, J.L. Mathieu, D. K. Molzahn. Using demand response to improve power system voltage stability margins.
2017 IEEE Manchester Power Tech. 2017; 1-6.
[12] J. T. Ma, L.L. Lai. Evolutionary programming approach to reactive power planning.Generation, Transmission and
Distribution, IEE Proceedings-.1996; 143: 365-370.
[13] N. Aminudin, T.K.A. Rahman, I. Musirin. Optimal Power Flow for Load Margin Improvement using Evolutionary
Programming.Research and Development, 2007. SCOReD 2007. 5th Student Conference on, 2007; 1-6.
[14] N. Aminudin, T.K.A. Rahman, I. Musirin. Optimal reactive Power Planning for Load margin Improvement using
Multi Agent Immune EP. 2010 Ieee World Congress On Computational Intelligence, 2010; 2010.
[15] L.L. Garver, "Transmission Network Estimation Using Linear Programming," Power Apparatus and Systems, IEEE
Transactions on, vol. PAS-89, pp. 1688-1697, 1970.
[16] Z. Zakaria, T.K.A. Rahman, E.E. Hassan. Economic load dispatch via an improved Bacterial Foraging
Optimization. Power Engineering and Optimization Conference (PEOCO), 2014 IEEE 8th International. 2014; 380-
385.
[17] W. Zhao, L. Wang. An effective bacterial foraging optimizer for global optimization. Information Sciences. 2016;
329: 719-735.

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