This document presents a study that uses the Mine Blast Algorithm (MBA) and Bare Bones Teaching Learning Based Optimization (BBTLBO) algorithm to solve the combined heat and power economic dispatch (CHPED) problem. The CHPED problem involves determining the optimal power and heat allocation among generation units to minimize costs while considering constraints. The document describes the mathematical formulation of the CHPED problem and provides an example simulation on a 7 generator system. The results show that both MBA and BBTLBO algorithms find low-cost solutions for the CHPED problem and outperform other algorithms in terms of solution quality and convergence speed.

1. Solution of Combined Heat and Power Economic
Dispatch Problem Using Different Optimization
Technique
Presented By
Arkadev Ghosh
M.Tech in Power Systems Engineering
Under the Guidance of
Dr. Sumit Banerjee
Head of the Department of Electrical Engineering
Dr. B. C. Roy Engineering College, Durgapur
1

2. 2
ABSTRACT
 In this article economic load dispatch problem with co-generation units has
been solved using mine blast algorithm and bare bones teaching learning based
optimization algorithm with some non-linearities like valve point loading effect etc.
The primary objective of CHPED is to determine power and heat allocation
among power only, co-generation and heat only units where cost will be minimum.
 This newly proposed algorithm is built on the basis of bomb explosion.
This technique searches for the global optimum. It based on distance and
direction of shrapnel species. Traditional TLBO has two phases. In first phase
learners improve their knowledge by absorbing teacher’s knowledge and in second
phase by sharing knowledge with themselves.
 This method has been tested on co-generation systems with equality and
in- equality constraints and advantage of this algorithm proves by comparing
with other algorithms.

3. 3
Various investigations on ELD have been undertaken till date, as better solutions would
result in significant economical benefits.
Previously a number of conventional approach such as Lambda method, Gradient
method, Newton method, Linear programming, Interior point method and dynamic
programming have been used in ELD problem.
To overcome this problem, several artificial intelligence methods such as GA, ANN, SA,
TS, EP, PSO, ACO, DE have applied successfully to ELD problem.
In micro-grid, economic load dispatch had been performed using PSO and DE by A K
Basu.
Pothiya et al. proposed a novel and efficient optimization approach based on the
ant colony optimization for solving the economic dispatch problem with non-smooth
cost functions.
Meng proposed quantum-inspired particle swarm optimization to solve the ELD problem.
LITERATURE REVIEW

4. .
BASIC MATHEMATICAL FORMULATION OF
CHPED PROBLEMS
4
The CHPED problem consists two types of Constraints. They are
 Equality Constraint and
 Inequality Constants
Equality Constants considers;
 Power Balance Constants,
To provide the completeness, Inequality Constants are considered for practical ELD
problem.
Inequality Constants are considered;
 Generating Capacity Limits.
 Ramp-rate limits,
 Prohibited operating zones,
 Valve-point effects,
 Multi-fuel options.

5. CHPED with Quadratic Cost function
The quadratic cost function Ft of ELD may be written as
Power Output, (MW)
Figure: 1 The cost of
Generation vs Power
Output
5
TheCostofGeneration
($/hr)
(1) M in . C   Fi Pi   Fi P h i   Fi H i 
i 1 i  N  1 i  m  1

 N m k
The fuel cost function of ith unit of thermal
power generators can be defined by
The fuel cost of ith unit of co-generation units can be defined
by
(2)
FPh aP2
bP c xH y H2
z PH
i i i i i i i i i i i i i i
(3)
The fuel cost of ith unit of heat only units can be
defined by
F H  p H 2
 q H  r
i i i i i i i
(4)
  iiiiiii cPbPaPF 
2

6. CHPED with Valve point loading
Generation cost represent more complex due to Valve point loading
F  ( Fi(Pi)) ( ai biPi  ciPi
2
 ei Sin{fi (Pi
min
 Pi)})
i1 i1
N N
T  
6
(5)
E
D
B
A
C
Without valve point
With valve point
Power Output, (MW)
TheCostofGeneration,($/hr)
Figure: 2 The cost of Generation vs Power Output

7. OVERVIEW OF MINE BLAST OPTIMIZATION
ALGORITHM (MBA)
• The proposed algorithm is based on the examination of a mine bomb explosion. In this explosion one
piece of shrapnel is thrown which is collided with other mine bombs near the explosion area. Consider a
mine region where the purpose is to clear the mines. Hence, the goal is to find the mines, while to find the
one with the most explosive effect located at the optimal point. Every shrapnel pieces have
particular direction and distance to collide them with each other to calculate explosion time and effect.
• This method start with first point called first shot point. It is aligned within their operating limits like
initialization process any other optimization technique. It generates a number of shrapnel pieces.
The number of population represents the number of shrapnel pieces.
 rand X  X max min
X 0  X min
(6)
location and again shrapnel pieces are
7
X new
0
d 1
m 1
1X 1  X ' e xp  X


 



Now another mine is exploded with previous mine
at produced at new location using eq. (7)
(7)

8. The position of new mine is bomb is defined as following eq. (8)
X 1 '  d 0  r a n d  c o s  (8)
3600
/ population size
d  X  X  Obj. function  Obj. function 201 1 0 1
X 1  X 0
F1  F 0
1m 
From the explosion point every shrapnel pieces has uneven distance. This is measured by theta which
equals to
From eq. 10, the exponential term can be calculated as eq. (9) and (10).
(9)
(10)
result, a new factor is introduced that is exploration factor 
number then the eq (8) replaced with eq (11)
8
2
d 1  d 0  r a n d n
X 1 '  d 1  c o s 
For taking initial distance it will be subtraction of upper bound and lower bound. To get optimum
When it is higher that iteration
.
The square of a normally distributed random number is incorporated in eq (12). It has advantage
of search ability at smaller and larger distances which provides better exploration in early
iteration.
(11)
(12)

9. BARE BONES TEACHING LEARNING BASED
OPTIMIZATION TECHNIQUE
Teacher Phase
The main parameter of each subject of the learners in the class at generation g
calculated. An improved interactive learning strategy is presented in teacher phase to
balance the global and local search ability. In this phase, each learner applies an
interactive learning strategy based on neighborhood search. To get a new population set
of learners a vector is formed using (13).
 
 
  jj
g
i
g
Teacher
g
Teacher
j
g
FTeacherjij
PuuPXnew
MX
MX
NP
MTXrandXP
,2,1
,2
,,1
1
)13()*
2
(
**






 


u called the hybridization factor and it is random value within 0 and 1. N is the gaussian
distribution of with mean and standard deviation.
Here mean is
2
pbestgbest   pbestgbest , and standard deviation is
pbest (s) and gbest represent their symbolic meaning as hold in particle swarm
optimization technique.
9

10. g
iX
g
rX ri 
    )14(** g
r
g
i
g
iTeacher
g
i
g
i XXrandXXrandXXnew 
Learner Phase
In learner phase the students can develop their knowledge by interaction of students or sharing
their knowledge. To set a new vector in learner phase eq. (14) is to be understood. For a learner
, randomly select another learner as
.When the stopping criteria is satisfied and means after completion of all iteration, optimum result
is got. Here selection procedure of optimization techniques is also performed after learner phase.
10

11. EXAMPLE AND SIMULATION RESULT
The essential codes has been written in MATLAB-7 language and executed on a 2.0
GHz Intel Pentium (R) Dual Core personal computer with 1-GB RAM.
 Consider seven generators system consists of four power generation units, two co-
generation units and three heat only units. The feasible operating regions of the two
cogenerations units are given in Figures 3 and 4.
Fig 3: Power vs Heat characteristics for co-
generation unit I
11
Fig 4: Power vs Heat characteristics for
co- generation unit II

12. 12
Table 1: Comparison of optimal power output for seven generator system from MBA
Unit Power Output MBA BCO [12] EP [12] PSO [12] RCGA [12]
P1(MW ) 69.0640 43.9457 61.3610 18.4626 74.6834
P2 (MW ) 95.2102 98.5888 95.1205 124.2602 97.9578
P3 (MW ) 78.4768 112.9320 99.9427 112.7794 167.2308
P4 (MW ) 119.4942 209.7719 208.7319 209.8158 124.9079
P5 (MW ) 179.0077 98.8000 98.8000 98.8140 98.8008
P6 (MW ) 58.7472 44.00 44.0000 44.0107 44.0001
H1(MWth) 21.2991 12.0974 18.0713 57.9236 58.0965
H2 (MWth) 39.0086 78.0236 77.5548 32.7603 32.4112
H3 (MWth) 89.6923 59.8790 54.3739 59.3161 59.4919
Total Generation
Cost($/h)
10547 10594 10611 10613 10667

13. Table 2: Optimal power output for seven generator system with VPL effect
13
Unit Power Output BBTLBO
47.6761
36.7091
50.8170
222.6796
139.7606
102.3576
51.5881
44.7244
53.6875
Total Generation Cost($/h) 10,443
)(1 MWP
)(2 MWP
)(3 MWP
)(4 MWP
)(5 MWP
)(6 MWP
)(1 MWthH
)(2 MWthH
)(3 MWthH
   MWthHMWP DD 150600 

14. Table 3: Comparison of optimal power output for seven generator system from BBO
Unit Power Output BBTLBO PSO
[31]
69.8117 18.4626
67.2493 124.2602
53.4476 112.7794
207.7660 209.8158
111.8889 98.8140
89.8365 44.0107
17.2874 57.9236
64.4665 32.7603
68.2461 59.3161
Total Generation Cost($/h) 10,341 10,613
)(1 MWP
)(2 MWP
)(3 MWP
)(4 MWP
)(5 MWP
)(6 MWP
)(1 MWthH
)(2 MWthH
)(3 MWthH
14

15. 0 10 20 30 40 50
iteration no
60 70 80 90 100
1.05
1.095
1.09
1.085
1.08
1.075
1.07
1.065
1.06
1.055
1.1
x 10
4
fuel
cost
15
Fig 5: Convergence characteristic for Co-generation units assisted by MBA

16. 0 10 20 30 40 50 60 70 80 90 100
1.04
1.045
1.05
1.055
1.06
1.065
1.07
1.075
1.08
1.085
x 10
4
iteration no
fuelcost
Fig: 6 Convergence characteristic for seven generator system for solving
CHPED using BBTLBO
16

17. CONCLUSION
 The mine blast algorithm method has been successfully implemented to solve
CHPED problem with non-linear function like VPL effect.
 This method has very fast computational time due to optimal
convergence characteristic. From the comparison it shows the superiority of the
proposed methods. It can be accomplished that this proposed method is a very optimally
potential method for solving CHPED problem in economic operation.
17
 The proposed BBTLBO algorithm applied successfully to solve heat and power
problem with co-generation units by considering non-linear constraints i.e loading
effect by opening valve.
 The BBTLBO algorithm proves its superioty property for getting optimum solution
due to best optimal characteristics, fastest efficiency etc. So this algorithm is a very
efficient algorithm for solving power and heat dispatch with co-generation units.

18. 18
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20

21. PAPER PUBLISHED
1. Title: Solution of economic problem with co-generation units using mine blast
algorithm.
Deblina Maity; Arkadev Ghosh; Sumit Banerjee; Chandan Kumar Chanda
2017 IEEE Calcutta Conference (CALCON)
Paper ID - 165
Publisher: IEEE
Date of Conference: 2-3 December. 2017
Year: 2017
Location: Kolkata
2. Title: Economic dispatch solution for co-generation unit assisted by bare bones
teaching learning optimization technique.
Arkadev Ghosh; Sumit Banerjee; Deblina Maity; Chandan Kumar Chanda
2018 National Power Engineering Conference (NPEC)
Paper ID - NPEC_93_PID5255953
Publisher: IEEE
Date of Conference: 9-10 March. 2018
Year: 2018
Location: Tamilnadu

22. 22
Thank You