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░ ABSTRACT- The growing demand for electrical energy, coupled with the uneven distribution of natural resources,
necessitates the integration of power plants. Coordinating the operation of interconnected generating units is crucial to meet the
fluctuating load demand efficiently. This research focuses on the Economic Load Dispatch (ELD) problem in hybrid power systems
that incorporate solar thermal and wind energy. Renewable energy resources, such as wind and solar thermal energy, depend on
atmospheric conditions like wind speed, solar radiation, and temperature. This study addresses the ELD problem using a Modified
Grey Wolf Optimization (MGWO) approach to obtain the most optimal solution for generator fuel costs. The Grey Wolf
Optimization (GWO) approach, inspired by natural processes, is utilized but may exhibit both exploratory and exploitative behavior.
To enhance its performance, we propose a novel version called MGWO, integrating memory, evolutionary operators, and a
stochastic local search approach. The suggested MGWO approach is applied to two distinct test systems comprising 13 and 26 units,
respectively, to solve the ELD with variable load requirements. Comparative analyses with other strategies demonstrate the
effectiveness of MGWO in addressing the ELD problem. This modification enhances the GWO method, making it more robust and
efficient for optimizing ELD in hybrid power systems.
Keywords: Economic load Dispatch, Solar thermal and wind energy, Modified grey wolf optimization, exploratory, exploitative,
evolutionary operators, stochastic local search.
░ 1. INTRODUCTION
Research on Economic Load Dispatch (ELD) for Thermal-
Solar-Wind systems has attracted attention. Previous studies
have examined different optimization methods to achieve an
optimal energy source balance. However, conventional
techniques may lead to suboptimal solutions or overlook
complexities like valve-point loading. Soft computing methods
suffer from slow convergence, parameter fine-tuning
requirements, and premature convergence risks, constraining
their ability to fully explore the solution space.
Electricity is essential for modern technology, especially in
India where energy source distribution varies widely across
regions. To address this, an integrated electrical system is
crucial for efficient transfer and distribution based on demand.
Effective scheduling of generating units [1, 2] is vital to align
with demand fluctuations and optimize costs. Challenges like
valve-point loading, multifuel systems, and operational
constraints complicate Economic Load Dispatch (ELD) [3-9],
which aims to minimize costs while meeting various
constraints.
Renewable energies offer numerous benefits such as energy
savings, emission reduction, environmental sustainability, and
significant potential for conservation [13]. Wind power is the
fastest-growing and most economical renewable source, while
solar energy, particularly through photovoltaic panels,
Economic Load Dispatch of Thermal-Solar-Wind System
using Modified Grey Wolf Optimization Technique
Y V Krishna reddy1*, Naga Venkata Ramakrishna G², Prof. (Dr.) Mohammad Israr³, Buddaraju
Revathi4, Dr. Pavithra G5, and Dr Nageswara Rao Lakkimsetty6
1Associate Professor, Department of EEE, SV College of Engineering, Tirupati, India, Email: krishnareddy.yv@svcolleges.edu.in
2Faculty of Engineering, University of Technology and Applied Sciences-Al Musannah, Sultanate of Oman, Email:
Naga.Krishna@utas.edu.com
3President, Maryam Abacha American University of Nigeria, Hotoro GRA, Kano State, Federal Republic of Nigeria, Email:
president@maaun.edu.ng
4Assistant Professor, Department of Electronics and Communication Engineering, SRKR Engineering college, Bhimavaram
534204. Email: buddaraju.revathi@gmail.com
5Associate Professor, Dept. of Electronics & Communication Engineering, Dayananda Sagar College of Engineering Bangalore,
Karnataka, India, Email: dr.pavithrag.8984@gmail.com
6School of Engineering, Department of Chemical Engineering, American University of Ras Al Khaimah, United Ara Emirates.
Email: Lnrao1978@gmail.com
*Correspondence: Y V Krishna Reddy, e-mail: krishnareddy.yv@svcolleges.edu.in
ARTICLE INFORMATION
Author(s): Y V Krishna Reddy, Naga Venkata Ramakrishna G,
Prof. (Dr.) Mohammad Israr, Buddaraju Revathi Dr. Pavithra G,
and Dr Nageswara Rao Lakkimsetty;
Received: 09/04/2024; Accepted: 04/07/2024; Published: 10/08/2024;
E- ISSN: 2347-470X;
Paper Id: IJEER240403;
Citation: 10.37391/ijeer.120324
Webpage-link:
https://ijeer.forexjournal.co.in/archive/volume-12/ijeer-120324.html
Publisher’s Note: FOREX Publication stays neutral with regard to
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International Journal of
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Open Access | Rapid and quality publishing Research Article | Volume 12, Issue 3 | Pages 926-933 | e-ISSN: 2347-470X
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Website: www.ijeer.forexjournal.co.in Economic load dispatch of Thermal-Solar-Wind system using
efficiently converts sunlight into electricity [10-12]. Both wind
and solar energy are abundant and less dependent on specific
geographic locations, making them easier to harness for power
generation.
Renewable energy sources offer significant opportunities in
modern grid systems. This study explores a combined system
of solar, wind, and steam units. However, integrating green
energy adds complexity to Economic Dispatch (ED) due to their
unpredictable nature and erratic power outputs [13-16]. This
research focuses on solar and wind power, with wind energy's
future uncertainty stemming from its reliance on random wind
speeds, introducing unpredictability into the ELD problem
formulation [17].
Cost functions in ELD problems often exhibit non-smooth
characteristics, complicating the search for optimal solutions.
Prior research has utilized soft computing methods like Particle
Swarms Optimization [18] and Artificial Bee's Colony
algorithm [19] to tackle optimization challenges. Reliability
indices for ELD were computed in one study [20], while an
Artificial Collaborative Search algorithm [21] addressed non-
convex ELD problems with valve point impacts in another.
Other methods like Shrunken Gaussian Distribution Quantum-
Behaved Optimization [22] have been introduced for multi-
constraint optimization. Various techniques such as
Equilibrium Optimizer, Cuckoo Optimization, and
combinations thereof have also been explored [24-26]. Recent
studies [27-31] have further expanded methodologies to address
ELD with renewable and thermal energy integration,
broadening the range of solutions in this field.
This study introduces a novel grey wolf optimization technique
to address ELD optimization challenges effectively [32, 33].
The approach aims to handle complexities like valve point
loading and integrating renewable sources into the system.
Renewable energy units are assumed to be strategically located
near the load center, reducing transmission losses and
enhancing optimization strategy robustness. The method
provides a simple, adaptable, and precise solution for
optimizing ELD problems.
This research addresses overlooked challenges in ELD for a
Thermal-Solar-Wind system. Conventional methods and soft
computing techniques often yield suboptimal solutions due to
complexities like valve-point loading and slow convergence.
Integrating renewable sources further complicates operations,
requiring coordinated parameter management and robust grid
stability modeling. The Modified Grey Wolf Optimization
(MGWO) method is proposed to efficiently optimize ELD in
this integrated system, offering a novel approach with superior
performance through effective parameter tuning.
░ 2. ELD PROBLEM WITH WIND AND
SOLAR ENERGY INCLUSION
The Economic Load Dispatch (ELD) problem becomes intricate
with the integration of wind energy due to numerous equality and
inequality standards associated with both thermal and wind
energy producing units. Given that solar energy production does
not involve fuel costs, the primary objective is to minimize the
expenses associated with thermal generators and the cost of
electricity produced by wind units (denoted as Ftotal). The
optimization's objective function, in high-level language, aims to
strike a balance by minimizing the overall costs of thermal
generators and wind unit electricity production in the context of
ELD with wind integration.
(1)
The cost for generating thermal electricity via the VPL effect is
stated as:
2 min
th i i i i i i i i i i
F (P) aP bP c d*sin(e*(P P)) ($/hr)= + + + −
(2)
The cost for generating thermal electricity with a cubic function
is represented as follows:
32
th i i i i i i i i
F (P) aP bP cP d ($/hr)= + + +
(3)
The cost of the wind energy output determined with the wind
power coefficient is given equation (4)
(4)
2.1 Equality Constraints
(5)
2.2 Inequality Constraints
(6)
(7)
2.3 Wind power plants modelling
The speed of the wind is inherently unpredictable, and its
correlation with wind energy exhibits a non-linear nature. Data
on wind speeds from various locations are structured to adhere
to the Weibull distribution, a statistical model widely used to
characterize the variability of wind speeds and assess wind
energy potential. Equation (8) represents the probability density
function (pdf) for wind speed. In simpler terms, this approach
entails modeling the unpredictable and variable nature of wind
speed using the Weibull distribution, providing an effective
means to analyze and manage the fluctuations in wind speed
and their implications for wind energy generation.
(8)
Wind energy (Wp) is a random variable that may be
approximated from wind speed, as illustrated by eq. (9).
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(9)
When the wind speed between vci and vr, the wind farm's power
production is deliberated to be an uninterrupted variable, and its
pdf is given by eq. (8). The total yield from all the wind turbines
is treated as a single random variable Pwj, with the pdf supplied
by
(10)
Here
(11)
To characterize the uncertainty of wind power availability, a
probabilistic tolerance is chosen to define the scenario in
which available power is insufficient to meet total power
demand. As a result, the balance of power limitation in equation
(12) with solar and wind energy is being modified as follows.
mn
j
r w i pv D a
j 1 i 1
P P P P (P)
==
+ +
(12)
2.4 The modelling of photovoltaic (PV) system
A PV generator's power production is primarily governed by the
sunlight and temperature. The hourly electrical production of
the PV generator may be calculated using equation (13).
(13)
The average solar radiation (IT) for a PV system on an inclined
surface may be computed as follows equation (14).
(14)
Efficiency of the system () is expressed as follows equation
(15).
(15)
Here, (16)
░ 3. MODIFIED GREY WOLF
OPTIMIZATION
Mirjalli developed the Grey Wolf Optimization (GWO) [32]
method, drawing inspiration from the hierarchical structure and
hunting strategies of grey wolves. The method incorporates
alpha, beta, omega, and delta wolves, mirroring their social
hierarchy. The alpha wolf holds the dominant position, guiding
crucial decisions for the pack. Beta wolves support the alpha
and can take leadership roles in their absence. Omega wolves
ensure the pack's dominance under the alpha's direction, while
delta wolves follow without question. The GWO method
follows the four-stage hunting process of grey wolves:
encircling, herd testing, target selection, and chasing/finishing.
3.1. Looking for food
In the Grey Wolf Optimization method, potential solutions from
the search space, referred to as wolf solutions, are randomly
initiated to commence the search process. Similarly to real grey
wolves hunting, when they locate their prey, they tend to pursue
it individually rather than as a group.
3.2. Encircling prey
Mathematical equations (17) and (18) are provided below to
explain the behaviour of grey wolves circling their prey after
searching it.
p
E O* X (k) X ( k)=−
(17)
p
X(k 1) X ( k ) B * E+ = −
(18)
In this context, k represents the present iteration. Vectors
coefficients are denoted by
O
and
B
.
B
is used to prevent grey wolves (GW) from attacking searchers'
livestock.
O
represents obstacles encountered by the prey during
a hunt. The position vector of grey wolves is illustrated by
X
and
a prey's position indicated by a vector by
p
X
. The vectors
O
and
B
are determined by the following equations:
1
B 2 * l * r l=−
(19)
2
O 2 * r=
(20)
3.3. Hunting
After surrounding their victim, grey wolves become intensely
focused on the kill. wolves are often used as hunters'
guides. The best possible candidate solution is provided by.
Grey wolf chasing habit may be expressed mathematically as
eq. (21)-(27).
(21)
(22)
(23)
(24)
(25)
(26)
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1 2 3
(X X X )
X( k 1) 3
++
+=
(27)
3.4. Attacking prey
Once the chase is done, grey wolves will launch an assault on
their victim. Based on the position of wolves, the
GWO algorithm enables the wolves, to relocate so that they can
more effectively ambush their prey. Two factors,
, need
be taken into account before making a move on the target. Here,
linearly decreases from 2 to 0 as the iterations grows, and the
variability of likewise diminishes as does.
(28)
The modified GWO [33] uses an exponential function to
calculate the decay of an over iterations. Consider
(29)
░ Table 1: Control and Parameter setting for MGWO
Parameter
Range or Value
Search Space
[0,1]
Dimension
13 or 26
No. of Grey Wolfs
40
No. of iterations
2000 or 4000
b
1
a
decreases linearly from 2 to 0
r1, r2
Random number
Alpha-Positions
Optimal of Power Generation
Alpha-Score
Minimal of Cost
░ 4. RESULTS AND DISCUSSION
The evaluation and application of Modified Grey Wolf
Optimization (MGWO) span across three different scenarios
within two test cases, demonstrating its efficacy across diverse
contexts. These scenarios entail optimizing the scheduling of
thermal systems, planning solar-thermal systems, and planning
solar-wind-thermal systems. The code for these test cases is
meticulously crafted and executed using MATLAB 9.6 R2019a.
The programs run on hardware equipped with a 1.90 GHz
Pentium III Processor and 4.0 GB of RAM, facilitating a
thorough assessment of MGWO's performance across varying
configurations.
4.1 Test case descriptions
Test Case-1: In three scenarios, a standardized setup of 13
generating units with valve point loading [34] was used. Load
demand remained at 2520 MW, and transmission power losses
were omitted for consistency in evaluating proposed
methodologies.
Test Case-2: In this specific test scenario, 26 generating units
with cubic fuel costs [35] were employed across three scenarios.
The constant power demand is set at 2900 MW, with the
intentional exclusion of transmission losses for a focused
assessment.
In case of hybrid solar-thermal system, maximum power of 50
MW generated from solar plants. The additional parameters for
the solar power plant are set to
2
pv
A =90163.04m
,
p.f. 0.92=
,
3
4.7*e−
= −
,
pce 0.91=
,
re 0.1045=
,
0
re
T 25C=
.
In case of hybrid solar- wind-thermal system, power producing
unit's data are included here, alongside with an extra wind farm.
A wind farm has a cost coefficient of krw = 1, kpw = 5, and a
maximum electrical capacity of 155 MW. The remaining
constants are vci=5, vco=45, and vr=15. The shape and scale
factor are both set to one and fifteen. Figure 1 depicts the whole
network used for simulation analysis in this test case.
Figure 1. Thermal-Solar-Wind System
4.2 Thirteen Unit System
The presence of the valve point loading effect introduces a
highly nonlinear and complex multi-model issue, posing a
significant challenge in finding the global optimal solution. In
this context, the Modified Grey Wolf Optimization (MGWO)
method applied to the scheduling of thermal units reveals an
optimal cost of $24,164.8260 per hour. This result surpasses the
performance of the most recently published approach,
establishing MGWO as a more effective solution. Table 2
provides a detailed overview of the optimum dispatch solution
achieved by MGWO, offering a comprehensive comparison of
findings with other methods. Additionally, Table 3 presents a
statistical analysis of the results, further emphasizing the
superiority of the MGWO approach.
By integrating the thermal plant with solar power generation,
the optimal cost achieved is $23,907.1861 per hour. The solar
power generation component contributes 25.3250 MW to the
total power output of 2520 MW, with the negligible cost of
generating this small quantity of power omitted in this particular
scenario. Table 2 provides a comprehensive overview of the
optimal power dispatch for this integrated system; however,
there are currently no existing works available for comparison
with the findings. This signifies a unique contribution to the
field, showcasing the effectiveness of the integrated thermal and
solar power generation approach.
International Journal of
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░ Table 2: Optimal Power Dispatch for 13-unit system
Thermal System
Thermal-Solar
system
Thermal-Solar-
Wind system
Unit
HCRO [36]
BSA [36]
MGWO
MGWO
MGWO
P1
628.3185
628.3185
628.3457
628.2918
628.3031
P2
299.1993
299.1993
299.1964
299.2562
299.3991
P3
294.9957
294.4848
302.8527
317.3192
302.0003
P4
159.7331
159.7331
159.7231
159.7899
160.1677
P5
159.7331
159.7331
159.8903
159.8442
110.1211
P6
159.7331
159.7331
159.8300
159.8192
159.7681
P7
159.7331
159.7330
159.7772
159.7566
160.0072
P8
159.7331
159.7331
160.0487
160.1631
159.9839
P9
159.7331
159.7331
159.8202
109.8888
159.9086
P10
77.3999
77.3999
115.2347
114.8184
40.0434
P11
77.3999
77.3999
40.2900
77.3784
77.4883
P12
92.3999
92.3997
55.0039
92.9790
92.9196
P13
91.8882
92.3997
119.986
55.3701
93.0152
PSolar
NA
NA
NA
25.3250
25.3340
PWind
NA
NA
NA
NA
51.5493
Therm.Cost ($/hr)
24164.8260
24164.0524
24122.0320
23907.1861
23373.3577
Cost of wind
overestimation ($/hr)
NA
NA
NA
NA
108.1418
Cost of wind
underestimation ($/hr)
NA
NA
NA
NA
0.0012
Total Operating Cost
($/hr)
24164.8260
24164.0524
24122.0320
23907.1861
23481.4995
Time (Sec)
7.8698
4.8923
3.4598
3.5897
3.8956
░ Table 3: Statistical data for a 13-unit Thermal ELD system
Methods
HCRO
CRO
IPSO-TVAC
BSA
MGWO
Minimum Cost ($/h)
24164.8260
24165.1664
24166.8000
24164.0524
24164.8260
Average Cost ($/h)
24164.9837
24166.9355
24167.3700
24164.2942
24164.9025
Maximum Cost ($/h)
24165.3402
24169.3642
24169.4100
24166.5831
24165.1835
Avg. time CPU (sec)
5.04
5.56
N.A
5.12
5.02
S.D
0.93
0.94
N.A
0.75
0.56
Through the combined efforts of thermal, solar, and wind power
generation, the optimal cost achieved is $23,481.4995 per hour.
In this scenario, the cost of generating a modest amount of
electricity is disregarded, resulting in solar power contributing
25.3340 MW to the total power output of 2520 MW.
Additionally, wind power generation contributes 51.5493 MW
to the overall power generation, incurring a cost of $108.1430
per hour. The thermal power plant complements this mix by
generating 2443.12 MW of power at an operational cost of
$23,373.3577 per hour. Given the uniqueness of this integrated
approach, where multiple sources contribute to power
generation, there are currently no ongoing works available for
direct comparison. Table 2 serves as a detailed representation
of the optimal power dispatch for this specific case, showcasing
the effectiveness of the combined thermal, solar, and wind
power generation system.
Figure 2 provides a graphical representation of the convergence
characteristics observed in a 13-unit system under three
different test scenarios. The chart distinctly communicates that
the inclusion of renewable energy sources results in a notable
decrease in operational costs required to meet the overall power
demands of 2520 MW. This visual presentation effectively
emphasizes the positive influence of integrating renewable
sources, showcasing enhanced cost efficiency within the power
generation system.
Fuel Cost ($/hr)
Iterations
Figure 2. Convergence of a 13-unit system, with a power
demand of 2520MW
0200 400 600 800 1000 1200 1400 1600 1800 2000
23988.3292
25118.8643
26302.6799
27542.287
28840.315
THREMAL
THREMAL-SOLAR
THERMAL-SOLAR-WIND
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4.2.1. Twenty-Six Unit System
In this scenario, the Modified Grey Wolf Optimization
(MGWO) technique identifies an optimal cost of $43,436.5297
per hour to meet the power demand of 2900 MW through the
scheduling of thermal Economic Load Dispatch (ELD). This
result surpasses the performance of the most recently published
technique. Table 3 present detailed insights into the optimal
dispatch solution achieved by MGWO, alongside a
comprehensive comparison of the results.
Through the integration of the thermal plant with solar power
generation, the achieved optimal cost is $42,114.5083 per hour.
In this specific scenario, solar power generation contributes
49.5786 MW to the total power output of 2900 MW, with the
negligible expense of generating a minor amount of power
overlooked. The optimal power dispatch for this case is
meticulously presented in Table 4, and the results are rigorously
compared to those obtained using the BSA technique. This
comparison sheds light on the effectiveness of the integrated
thermal and solar power generation approach in achieving cost
efficiency.
░ Table 4: Optimal Power Dispatch for 26-unit system
Unit
Thermal system
Thermal-Solar system
Thermal-Solar-Wind system
BSA
MGWO
BSA
MGWO
BSA
MGWO
P1
2.4001
2.4019
2.4084
2.4002
12.0000
2.4024
P2
2.4002
2.4029
2.4119
2.4000
2.4014
2.4006
P3
2.4000
2.4023
2.4011
2.4040
2.4118
2.4000
P4
2.4004
2.4028
2.4063
2.4101
2.4012
2.4000
P5
2.4001
2.4003
2.4000
2.4000
2.4059
2.4003
P6
4.0003
4.0003
4.0002
4.0000
4.0006
4.0000
P7
4.0003
4.0003
4.0004
4.0000
4.0000
4.0005
P8
4.0004
4.0005
4.0012
4.0002
4.0008
4.0007
P9
4.0002
4.0005
4.0000
4.0000
4.0000
4.0000
P10
76.0000
75.9999
76.0000
75.9991
76.0000
75.9993
P11
76.0000
75.9999
75.9999
75.9996
75.9999
76.0000
P12
76.0000
75.9999
76.0000
75.9997
76.0000
75.9985
P13
76.0000
75.9999
75.9998
76.0000
76.0000
75.9998
P14
100.0000
100.0000
75.9962
99.9996
99.9988
100.0000
P15
100.0000
99.9999
99.9995
99.9995
99.9984
100.0000
P16
100.0000
99.9998
99.9997
100.0000
99.9979
99.9991
P17
155.0000
155.0000
154.9998
155.0000
155.0000
154.9999
P18
155.0000
155.0000
154.9986
154.9998
154.9998
154.9999
P19
155.0000
155.0000
155.0000
154.9996
154.9988
155.0000
P20
155.0000
155.0000
154.9996
154.9999
155.0000
154.9992
P21
190.9990
175.1190
192.3459
175.8760
119.1194
121.8040
P22
166.0000
148.2370
164.8986
149.4340
93.2265
100.8790
P23
141.0010
124.6710
140.7339
123.1003
71.0914
71.3260
P24
350.0000
350.0000
350.0000
350.0000
349.9996
349.9986
P25
400.0000
400.0000
399.9996
399.9999
399.9980
400.0000
P26
400.0000
400.0000
399.9996
400.0000
399.9980
399.9989
PSolar
NA
NA
NA
49.5786
49.9521
49.3456
PWind
NA
NA
NA
NA
154.9992
154.6478
Therm.Cost ($/hr)
43436.5297
42250.8926
43426.6799
42114.5083
40283.6778
38616.6916
Cost of wind
overestimation
($/hr)
NA
NA
NA
NA
325.1642
324.4272
Cost of wind
underestimation
($/hr)
NA
NA
NA
NA
0.0015
0.0016
Total Operating
Cost ($/hr)
43436.5297
42250.8926
43426.6799
42114.5083
40608.8435
38941.1186
Time (Sec)
5.5672
4.5893
5.9845
4.7962
6.0278
4.9868
By combining thermal, solar, and wind power generation, the
system achieves an optimal cost of $38,941.1186 per hour. In
this integrated setup, solar power contributes 49.3456 MW to
the overall power output of 2900 MW. Additionally, wind
power adds 154.6478 MW to the generation capacity, incurring
a cost of $324.4288 per hour. The thermal power plant
complements this diverse mix by generating 2696.1 MW at an
operational cost of $38,616.6916 per hour. This comprehensive
integration showcases a balanced utilization of different energy
sources, contributing to cost-efficient and sustainable power
International Journal of
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Open Access | Rapid and quality publishing Research Article | Volume 12, Issue 3 | Pages 926-933 | e-ISSN: 2347-470X
932
Website: www.ijeer.forexjournal.co.in Economic load dispatch of Thermal-Solar-Wind system using
generation. Table 4 provides a comprehensive presentation of
the optimal dispatch for a 26-unit system, allowing for an in-
depth comparison with the BSA technique. Simultaneously,
Figure 3 visually captures the convergence characteristics
observed in the 26-unit system under three distinct test
scenarios. The graph compellingly communicates that the
integration of renewable sources results in a notable decrease in
operating expenses needed to fulfill the entire power
requirement of 2900 MW. This graphical representation serves
as a compelling visual testament to the positive impact of
incorporating renewable sources, underscoring their
contribution to enhancing the cost efficiency of the power
generation system.
Fuel Cost ($/hr)
Iterations
Figure 3. Convergence of a 26-unit system, with a power demand of
2900MW
░ 5. CONCLUSIONS
This study introduces Modified Grey Wolf Optimization
(MGWO) for solving the Economic Load Dispatch (ELD)
problem, accommodating scenarios with and without solar
power integration. It employs a probability density function
(pdf) for modeling wind power and deterministic methods for
solar photovoltaic systems. Inspired by grey wolves' hunting
behavior, MGWO uses an exponentially decreasing function to
dynamically balance exploration and exploitation, thereby
enhancing its efficiency in finding optimal ELD solutions
across diverse scenarios. The study highlights MGWO's
superior performance compared to established methods like
BSA, CRO, HCRO, and IPSO-TVAC. MGWO consistently
meets operational criteria and efficiently identifies optimal
dispatch solutions across varied test cases of different
complexities. Its scalability and user-friendly nature position
MGWO as a robust tool for addressing intricate optimization
challenges in power system management and control,
particularly in large-scale applications. But MGWO exhibits
sensitivity to control parameter selection, impacting
performance across varied scenarios and requiring meticulous
tuning. Future enhancements could focus on integrating
stochastic elements to better manage uncertainties in renewable
energy sources, enhancing MGWO's resilience to real-world
variability.
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Electrical and Electronics Research (IJEER)
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© 2024 by the Y V Krishna Reddy, Naga
Venkata Ramakrishna G, Prof. (Dr.)
Mohammad Israr, Buddaraju Revathi Dr.
Pavithra G, and Dr Nageswara Rao Lakkimsetty. Submitted for
possible open access publication under the terms and conditions of the
Creative Commons Attribution (CC BY) license
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