Application of Thunderstorm Algorithm for Defining the Committed Power Output Considered Cloud Charges

International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-5, May- 2016]
Infogain Publication (Infogainpublication.com) ISSN : 2454-1311
www.ijaems.com Page | 293
Application of Thunderstorm Algorithm for
Defining the Committed Power Output
Considered Cloud Charges
A.N. Afandi
Electrical Engineering, Universitas Negeri Malang, Indonesia
Abstract— This paper presents an application of
Thunderstorm Algorithm for determining a committed
power output considered cloud charges with various
technical constraints and an environmental requirement.
These works also implemented on IEEE-62 bus system
throughout an operational economic dispatch covered for
economic and emission aspects. The results obtained
show that statistical and numerical performances are
associated with charges. It also presents fast and stable
characteristics for the searching speeds. By considering
the cloud charge parameter, it contributes to
performances and results of Thunderstorm Algorithm. In
addition, the introduced algorithm seems strongly to be a
new promising approach for defining the committed
power output problem.
Keywords—Cloud charge, economic dispatch,
intelligent computation, power system, thunderstorm
algorithm.
I. INTRODUCTION
Presently, technical problems are more complicated than
previous cases included numerous variables for
representing physical systems in suitable models as
closed as its functions in nature with natural
characteristics and behaviours. Many problems have
become crucial topics to solve correctly in feasible
ranges within high qualities under numerous constraints
and environmental requirements for searching the desired
performances. To cover these conditions, the problems
adopted many parameters are expressed in optimization
functions considered potential variables and limitations
in order to obtain better solutions within a period time
operation. Moreover, these functions are conducted to
designed models for presenting real cases in
mathematical statements as the objective function
constrined by technical conditions and environmental
requirements.
By considering mathematical expressions, real problems
are solvable easily using various methods of
computations associated with its defined functions
through traditional or evolutionary approaches. Both
methods are commonly used to carry out the problem and
applied to evaluate its performances. Actually, these
approaches has different characteristics while searching
the optimal solution. In detail, traditional methods use
mathematical programs given in various versions as the
proposed names at the early introduction. As long as the
period implementation, popular classical methods are
linear programming, lambda iteration, quadratic
programming, gradient search, Newton’s method,
dynamic programming, and Lagrangian relaxation [1],
[2], [3], [4]. On the other hand, evolutionary methods use
optimization techniques, such as genetic algorithm,
neural network, simulated annealing, evolutionary
programming, ant colony algorithm, particle swarm
optimization, and harvest season artificial bee colony
algorthm [5], [6], [7], [8], [9]. These methods have been
proposed for replacing classical approaches on the base
of its weaknesses considered many phenomena and
behaviours in nature with mimicking its mechanisms.
Nowadays, evolutionary methods are frequently used to
solve optimization problems, not only for real cases but
also for designed themes [10]. These methods are useful
for breaking out large systems and multi dimensions
constructued using multiple variables and constraints. In
particular, many types have been proposed at different
times as an introduction early based on its inspirations.
Since the first time of the evolutionary idea became a
new computation era out of the classical period, many
works have been done for developing and improving its
performances with modified techniques and phases.
Moreover, these developments are also subjected to
expand computational performances for increasing
abilities to carry out numerous problems with many
proposed procedures.
In this paper, a new intelligent computation application is
introduced to solve the power system operation problem
(PSOP) and it is used to define the balanced power
production. In addition, this paper presents its powerful
for searching the optimal solution of the PSOP associated
with cloud charges.

II. THUNDERSTORM ALGORITHM
At present, the lightning is considered as an atmospheric
discharge during thunderstorms or other possibility
factors produced by several steps in terms of Charge
separation; Leader formation; and Discharge channel.
Moreover, the lightning process is defined as an electric
discharge in the form of a spark in a charged cloud that
the negative and positive charges are deployed at different
positions [11], [12], [13]. In addition, a seat of electrical
processes can be produced by a thunderstorm and it is
rapidly advanced during the continuous lightning in the
thunderstorm. In this phenomenon, the defining
atmospheric material for the thunderstorm is very
important things and urgently observations covered in
moisture; unstable air; and lift.
Many studies have been done to observe these
phenomena with numerous discussions for searching
suitable models and understanding its mechanisms.
Various characteristics have been tested and reported for
analyzing these curious issues in many studies in order to
recognize natural behaviours [14], [15], [16], [11], [12],
[17], [18], [19], [20]. In general, the introduced algorithm
entitled Thunderstorm Algorithm (TA) has adopted a
phenomenon in nature for pretending natural processes
performed using several stages to explain the adoption of
the inspiration [21]. Furthermore, this inspiration is
associated with a natural mechanism conducted to define
multiple natural lightning in the computation.
Fig. 1: Thunderstorm Algorithm’s Phases
By considering this phenomenon, its mechanisms are
transferred into certain procedures as the sequencing
computation presented in pseudo-codes in terms of Cloud
Phase; Streamer Phase; and Avalanche Phase [21]. Cloud
Phase is used to produce cloud charges and to evaluate
the clouds before defining the pilot leader. Another step,
Streamer Phase, is supposed to define the prior streamer
and to guide striking directions included the path
evaluation for defining the streaming track. The final
process is Avalanche Phase, which is used to evaluate
channels, replace the streaming track for keeping the
streamer. In detail, these phases are depicted in Fig. 1.
In these phases, the searching mechanism is conducted to
striking processes and channeling avalanches to deploy
the cloud charges, which is populated using (1).
Moreover, TA is also consisted of various distances of the
striking direction related to the hazardous factor for each
position of the striking targets as presented in (2). Each
solution is located randomly based on the generating
random directions of multiple striking targets. In
principle, the sequencing computation of TA is given in
several procedures as presented in following
mathematical main functions.
Cloud charge: Q = (1 + k. c). Q , (1)
Striking path: D = (Q ).b.k, (2)
Charge’s probability: probQsj
Qsj
m
∑ Qs
m for m
Qsj
n
∑ Qs
n for n
, (3)
where Qsj is the current charge, Qmidj is the middle
charges, s is the streaming flow, Dsj is the striking
charge’s position, Qsdep is the deployed distance, n is the
striking direction of the hth
, k is the random number with
[-1 and 1], c is the random within [1 and h], h is the
hazardous factor, b is the random within (1-a), n is the
striking direction, j ∈ (1,2,..,a), a is the number of
variables, m ∈ (1,2,..,h).
III. COMMITTED POWER OUTPUT
The power system operation is able to measure using a
financial aspect for defining the whole operation, such as
fixed cost; maintenance cost; and production cost, in
order to the PSOP can be conditioned in an economic
portion with the suitable budget. Since the operation is
concerned in the technical cost of products and services,
the optimal operation and planning are very important
things for deciding in the balanced power production.
Economically, these problems become urgently issues to
decrease running charges of the electric energy while
supplying load demands at different places. It also needs
to manage using an economic strategy for selecting the
optimal operating cost.
To cover these issues, the committed power output (CPO)
is more complicated problems included all generating
units under technical constraints and environmental
requirements. In this problem, the CPO is focused on the
generating unit participation for supporting power
productions associated with the given load demand. In
addition, the CPO is measured in the optimal total cost for
the fuel consumption and the pollutant compensation [1],
[4], [22], [23], [24], [25]. In detail, this problem is
optimized using an integrated economic dispatch (IED)
with considering the load dispatch (LD) and the emission

dispatch (ED) [23], [24], [26]. Moreover, The IED is
formulated by equation (4) and each fuel cost
participation is expressed in (5) for defining the LD as
given in (6). In particular, the individual pollutant
discharge of generating unit is formed in (7) and the ED’s
function is presented in (8) for all participants in the CPO.
In general, the CPO is commonly approached using main
mathematical functions as follows:
IED: Φ = w. F + (1 − w). h. E , (4)
Fi(Pi) =ci+biPi +aiPi
2
, (5)
LD: F = ∑ (c + b . P + a . P!
),"
#$ (6)
E (P) = γ + β . P + α . P!
, (7)
ED: E = ∑ &γ + β . P + α . P!
'
"
#$ , (8)
where Φ is the IED ($/h), w is a compromised factor, h is
a penalty factor, Ftc is the total fuel cost ($/h), Et is the
total emission (kg/h), Fi is the fuel cost of the ith
generating unit ($/h), Pi is a power output of the ith
generating unit, ai; bi; ci are fuel cost coefficients of the ith
generating unit, ng is the number of generating unit, Ei is
an emission of the ith
generating unit (kg/h), αi; βi; γi are
emission coefficients of the ith
generating unit.
IV. APPLICATION’S PROCEDURES
In these studies, simulations adopt a standard model of
the power system for demonstrating the impact of the
cloud charges related to the CPO with various technical
constraints. The use of the standard model is commonly
approached by researchers for performing own problems,
even practical systems are also able to apply for the same
problem. In these works, the IEEE-62 bus system is
selected as the sample system, which is consisted of 19
generators; 62 buses; and 89 lines as discussed
completely in [24]. Technically, it data are presented in
Table I; Table II; and Table III for coefficients and power
limits which are given in individual generating units.
Table I. Fuel Cost Coefficients
Gen α β γ Gen α β γ
G1 0.0070 6.80 95 G11 0.00450 1.60 65
G2 0.0055 4.00 30 G12 0.00250 0.85 78
G3 0.0055 4.00 45 G13 0.00500 1.80 75
G4 0.0025 0.85 10 G14 0.00450 1.60 85
G5 0.0060 4.60 20 G15 0.00650 4.70 80
G6 0.0055 4.00 90 G16 0.00450 1.40 90
G7 0.0065 4.70 42 G17 0.00250 0.85 10
G8 0.0075 5.00 46 G18 0.00450 1.60 25
G9 0.0085 6.00 55 G19 0.00800 5.50 90
G10 0.0020 0.50 58 a ($/MWh2
), b ($/MWh)
Table II. Emission Coefficients
Ge
n
a b c
Ge
n
a b c
G1
0.01
8
-
1.8
1
24.3
0
G1
1
0.01
4
-
1.2
5
23.0
1
G2
0.03
3
-
2.5
0
27.0
2
G1
2
0.01
2
-
1.2
7
21.0
9
G3
0.03
3
-
2.5
0
27.0
2
G1
3
0.01
8
-
1.8
1
24.3
0
G4
0.01
4
-
1.3
0
22.0
7
G1
4
0.01
4
-
1.2
0
23.0
6
G5
0.01
8
-
1.8
1
24.3
0
G1
5
0.03
6
-
3.0
0
29.0
0
G6
0.03
3
-
2.5
0
27.0
2
G1
6
0.01
4
-
1.2
5
23.0
1
G7
0.01
3
-
1.3
6
23.0
4
G1
7
0.01
4
-
1.3
0
22.0
7
G8
0.03
6
-
3.0
0
29.0
3
G1
8
0.01
8
-
1.8
1
24.3
0
G9
0.04
0
-
3.2
0
27.0
5
G1
9
0.04
0
-
3.0
0
27.0
1
G1
0
0.01
4
-
1.3
0
22.0
7
α (kg/MWh2
), β
(kg/MWh)
Table III. Power Limits of Generators
Gen
Pmin
(MW)
Pmax
(MW)
Qmin
(MVar)
Qmax
(MVar)
G1 50 300 0 450
G2 50 450 0 500
G3 50 450 -50 500
G4 0 100 0 150
G5 50 300 -50 300
G6 50 450 -50 500
G7 50 200 -50 250
G8 50 500 -100 600
G9 0 600 -100 550
G10 0 100 0 150
G11 50 150 -50 200
G12 0 50 0 75
G13 50 300 -50 300
G14 0 150 -50 200
G15 0 500 -50 550
G16 50 150 -50 200
G17 0 100 0 150

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G18 50 300 -50
G19 100 600 -100
These applications are applied to IEEE-62 bus system as
the power system model using several programs, which
are compiled together in the sequencing processes based
on the pseudo-codes covered the cloud phase; streamer
phase; and avalanche phase. Each phase follows its
mechanism for involving all parameters of TA in the
processes while searching the optimal solution with
various charges in the cloud charge phase.
In particular, these processes are run in designed
programs in terms of main program; evaluate program;
cloud charge program; streamer program; avalanche
program; and dead track program. N addition, TA
performed using 1 of the avalanche; 100 of the streaming
flows; and 4 of the hazardous factor. Moreover, the tested
system feeds the power production for 2,766.7 MW and
1,206.1 MVar of load demands constrained by 10% of the
total loss; 0.5 of the weighting factor; 0.85 kg/h of the
standard emission; ± 5% of voltage violations at each bus;
and 95% of the power transfer capability for the line.
V. RESULTS AND DISCUSSIONS
As given in the previous section, these works consider
2,766.7 MW for the load constrained by various technical
limitations. By considering 10% of the total loss; 0.85
kg/h of the standard emission; the equilibrium
demand and the power production, the cloud charge
distributions are illustrated in following
figures are presented for each cloud size
and 100 charges, which are deployed at different positions
randomly in Fig. 2; Fig. 3; Fig. 4; and Fig.
to these figures, charges affect to the cloud’s
characteristics and charged density within
desired locations. In detail, the highest size has the
highest density for the charge.
Fig. 2: Cloud’s profile with 25 charges
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400
600
62 bus system as
the power system model using several programs, which
are compiled together in the sequencing processes based
codes covered the cloud phase; streamer
phase; and avalanche phase. Each phase follows its
for involving all parameters of TA in the
processes while searching the optimal solution with
hese processes are run in designed
programs in terms of main program; evaluate program;
rogram; streamer program; avalanche
N addition, TA is
performed using 1 of the avalanche; 100 of the streaming
flows; and 4 of the hazardous factor. Moreover, the tested
system feeds the power production for 2,766.7 MW and
1,206.1 MVar of load demands constrained by 10% of the
total loss; 0.5 of the weighting factor; 0.85 kg/h of the
5% of voltage violations at each bus;
and 95% of the power transfer capability for the line.
DISCUSSIONS
As given in the previous section, these works consider
2,766.7 MW for the load constrained by various technical
10% of the total loss; 0.85
equilibrium of the load
on, the cloud charge
distributions are illustrated in following figures. These
s are presented for each cloud size for 25; 50; 75;
and 100 charges, which are deployed at different positions
Fig. 5. According
s, charges affect to the cloud’s
within different
desired locations. In detail, the highest size has the
Cloud’s profile with 25 charges
Table IV. Statistical Results Based on
N
o
Parameters
25
1
Max point
($/h)
17,15
1
2
Min point
($/h)
16,72
0
3 Range ($/h) 431
4 Mean ($/h)
16,75
1
5 Median ($/h)
16,72
0
[Vol-2, Issue-5, May- 2016]
ISSN : 2454-1311
Page | 296
Table IV. Statistical Results Based on the Charges
Cloud charges
50 75 100
17,15 16,63
3
17,66
6
16,53
9
16,72 16,12
0
16,45
5
15,84
1
431 513 1,211 698
16,75 16,18
7
16,61
3
15,91
5
16,72 16,12
0
16,45
5
15,84
1

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6 Streaming 14 19
7 Opt. time (s) 2.6 3.5
8 Run time (s) 16.9 17.2
Graphically, TA’s abilities are give in Fig.
for streaming flows and time consumptions associated
with cloud charges. Fig. 6 presents convergence speeds of
computations while finishing all processes for
determining optimal solutions in 100 streaming flows
with its individual time usage for each process as
illustrated in Fig. 7. Moreover, the processes have
different started points for searching solutions of the
as similar as the obtained streaming flows of the optimal
points remained in different speeds. For 25 charges, the
computation is started at 17,151 $/h before declining to
16,720 for the optimal position obtained in
consuming 2.6 s of the running time. This execution
needs around 16.9 s for completing 100 of the streaming
flow. In general, the solution is searched in smooth and
fast even the cloud charges used different amounts. In
detail, its statistical performances are listed in Table IV
covered in maximum points; minimum points; range; and
median.
Furthermore, various time consumptions are depicted in
Fig. 7 related to cloud charges. This figure
random time consumptions, which are used to search the
optimal solutions and to complete the processes of the
IED problem considered LD and ED. By considering
these compilations, all results are also provided in Table
IV for the optimal time usage and the running time for
streaming flows. According to these results, the higher
cloud size has longer time consumptions, which are 6.2 s
for obtaining the solution and 18.8 s for completing the
computation associated with 100 of charges.
Fig. 6: Convergences considered the charges
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25 30
5.6 6.2
18.5 18.8
Fig. 6 and Fig. 7
for streaming flows and time consumptions associated
6 presents convergence speeds of
computations while finishing all processes for
determining optimal solutions in 100 streaming flows
with its individual time usage for each process as
7. Moreover, the processes have
oints for searching solutions of the IED
as similar as the obtained streaming flows of the optimal
points remained in different speeds. For 25 charges, the
computation is started at 17,151 $/h before declining to
16,720 for the optimal position obtained in 14 steps with
consuming 2.6 s of the running time. This execution also
needs around 16.9 s for completing 100 of the streaming
flow. In general, the solution is searched in smooth and
fast even the cloud charges used different amounts. In
tistical performances are listed in Table IV
maximum points; minimum points; range; and
, various time consumptions are depicted in
figure illustrates the
re used to search the
optimal solutions and to complete the processes of the
problem considered LD and ED. By considering
these compilations, all results are also provided in Table
IV for the optimal time usage and the running time for
According to these results, the higher
cloud size has longer time consumptions, which are 6.2 s
for obtaining the solution and 18.8 s for completing the
computation associated with 100 of charges.
considered the charges
Fig. 7: Time consumptions considered the charges
Table V. Power Productions Based on the Charges
Gen
Power outputs (MW)
25 50
G1 105.7 105.7
G2 200.0 265.7
G3 227.2 78.4
G4 99.6 91.9
G5 294.2 105.7
G6) 395.9 395.2
G7 108.6 108.6
G8 234.9 227.7
G9 87.9 273.6
G10 91.9 91.9
G11 80.1 147.2
G12 105.3 105.3
G13 149.3 287.8
G14 137.0 150.0
G15 90.2 90.2
G16 149.6 104.6
G17 91.9 91.9
G18 105.8 200.8
G19 240.7 100.0
Total 2,995.8 3,022.1
Load 2,766.7 2,766.7
Loss 229.1 255.4
Refer to multiple directions as presented as the hazardous
factor in TA’s processes, all numerous statistical results
are provided in Table IV associated with cloud charges as
depicted in Fig. 2 to Fig. 5 for the cloud charge’s profiles.
In addition, Table IV has been performed by each
procedure of TA while determining optimal solutions to
meet 2,776.7 MW of the load. This table shows that the
cloud charges give impacts on various aspects, such as,
maximum points; optimal points; and times
[Vol-2, Issue-5, May- 2016]
ISSN : 2454-1311
Page | 297
Time consumptions considered the charges
Table V. Power Productions Based on the Charges
Power outputs (MW)
50 75 100
105.7 105.7 105.7
265.7 376.6 343.4
78.4 132.1 78.4
91.9 93.1 91.9
105.7 190.7 174.6
395.2 291.0 186.1
108.6 166.3 108.6
227.7 278.8 266.2
273.6 87.9 239.0
91.9 91.9 91.9
147.2 149.1 83.5
105.3 105.3 105.3
287.8 105.7 252.7
150.0 70.8 146.9
90.2 99.6 90.2
104.6 104.6 150.0
91.9 91.9 91.9
200.8 270.3 292.8
100.0 210.5 100.0
3,022.1 3,021.9 2,999.0
2,766.7 2,766.7 2,766.7
255.4 255.2 232.3
Refer to multiple directions as presented as the hazardous
factor in TA’s processes, all numerous statistical results
are provided in Table IV associated with cloud charges as
5 for the cloud charge’s profiles.
IV has been performed by each
procedure of TA while determining optimal solutions to
meet 2,776.7 MW of the load. This table shows that the
cloud charges give impacts on various aspects, such as,
s; optimal points; and times.

Table VI. Emissions Based on the Charges
Gen
Pollution productions (kg/h)
25 50 75 100
G1 34.1 34.1 34.1 34.1
G2 847.2 1,692.3 3,765.9 3,059.3
G3 1,162.8 33.9 272.6 33.9
G4 31.5 20.8 22.4 20.8
G5 1,049.9 34.1 333.5 257.0
G6) 4,209.8 4,193.0 2,093.8 704.3
G7 28.7 28.7 156.5 28.7
G8 1,310.2 1,211.9 1,991.3 1,782.1
G9 54.8 2,146.3 54.8 1,547.6
G10 20.8 20.8 20.8 20.8
G11 12.7 142.4 147.7 16.2
G12 20.4 20.4 20.4 20.4
G13 155.1 994.4 34.1 716.1
G14 121.5 158.1 8.3 148.9
G15 51.3 51.3 87.4 51.3
G16 149.2 45.4 45.4 150.5
G17 20.8 20.8 20.8 20.8
G18 34.3 386.4 850.4 1,037.2
G19 1,622.8 127.0 1,167.5 127.0
Total 10,938.0 11,362.2 11,127.8 9,777.0
Table VII. Operational Fees Based on the Charges
Gen
Operating costs (kg/h)
25 50 75 100
G1 909.0 909.0 909.0 909.0
G2 1,473.7 2,327.2 4,199.4 3,581.6
G3 1,819.3 409.3 805.6 409.3
G4 135.3 119.6 122.0 119.6
G5 2,417.7 590.3 1,281.9 1,134.6
G6) 4,640.6 4,626.3 2,766.6 1,376.8
G7 643.4 643.4 1,081.8 643.4
G8 2,289.1 2,179.0 3,018.9 2,799.9
G9 675.5 3,406.3 675.5 2,748.6
G10 131.3 131.3 131.3 131.3
G11 228.4 469.3 477.3 238.0
G12 205.4 205.4 205.4 205.4
G13 532.6 1,504.5 338.2 1,207.0
G14 449.4 505.3 225.0 491.6
G15 582.5 582.5 656.3 582.5
G16 474.6 308.3 308.4 476.5
G17 119.6 119.6 119.6 119.6
G18 261.8 720.8 1,211.5 1,397.8
G19 2,689.0 783.5 2,185.7 783.5
Total 20,678.3 20,540.9 20,719.6 19,356.1
Final results of the PSOP based on the CPO are presented
in the IED as provided in Table V covered cloud charges
for the individual power production. This table also
provides the committed power output and the total loss to
meet the load. According to this table, it is known that
generating units contribute to the power procurement with
different capacities as own scheduled power productions.
Its pollutant productions are listed in Table VI for 19
generating units. Specifically G10 feeds to the power to
the system in the constant amount of 91.9 MW. This
condition is also given by G1 and G17 produced in 105.7
MW and 91.9 MW. In total, generating units deliver the
power to the load center from 2,995.8 MW to 3,022.1
MW with various amounts of the power loss related to the
each cloud charge as given in Table V. As the impact of
the environmental requirement, these power productions
also discharge pollutants around 9,777.0 kg/h to 11,362.2
kg/h corresponded to cloud charges with individual
contributions for the emissions as given in Table VI. In
detail, the higher pollutant contributors are G2; G3; G5;
G6; G8; and G19.
By considering the whole selections for determining the
optimal solutions of the IED problem, the cheapest
operation is determined using the higher cloud charge as
provided in Table VII presented totally for fuel costs and
emission cost compensations. This operation needs
around 19,356.1 $ for existing generating units during
producing power outputs to meet the load demand. In
accordance to individual power productions, several
generators spent the budget in high procurement.
Practically, power outputs of generating units are
associated with the load to set fixed power outputs. The
least operating cost becomes a very crucial decision for
operating the system in the cheapest budget. In this case,
the expensive operations are belonged to several
generating units while producing powers, such as, G2;
G3; G5; G6; and G19, even these payments are depended
on cloud charges. For all compositions of cloud charges,
the cheapest operation is existed by G17 with spent in
119.6 $/h.
VI. CONCLUSIONS
This paper evaluates cloud charge impacts of
Thunderstorm Algorithm on the power system operation
problem presented in the operational economic dispatch
based on load and emission dispatches. By considering
technical constraints and the cloud charges, the results
demonstrated successful application this algorithm for
solving the problem using the IEEE-62 bus system. The
performances indicate that the small size of the cloud
charge has faster iteration and shorter time consumption.
Moreover, cloud charges influenced to the committed
power output combination for 19 generating units.
Finally, from these works, implementations on real and
larger systems are subjected to the future studies.

REFERENCES
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Constrained ED using the Lagrangian Relaxation
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