International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 10, October 2013
ISSN 2319 - 4847
Power Generation Schedule for Economical Aspects
Using Evolutionary Technique
S. R. Vyas1, Dr. Rajeev Gupta2
1
2
Research Scholar, Mewar University, Chhitorgrah. India,
Dean EC Dept., University College of Engg. RTU, Kota. India,
Abstract
There are so many important factor for the power plant and power system Economic load dispatch and environmental effect are
important optimization task in power system operation for allocating generation among the committed units such that the
constraints imposed are satisfied and the energy requirement in terms of different variance .Reduction in fuel costs done of power
generation by proper load dispatch schedule. So the overall costing of operation of power system can be reduced. Minimum
generation costs are achieved by economic scheduling of different generating plant of power system by their maximum and
minimum capacity and load demand. By economic load scheduling we mean to find the generation of the different generators or
plants so that the total fuel cost is minimum and at the same time the total losses and demand at any instant must be the total
generation. MATLAB program is used to achieve above requirement and arrange generation plant according to the program
output for the efficient operation of the power system.
Keywords: Economic Load dispatch, Evolutionary technique, Environment, Emission
1. INTRODUCTION
However, economic load dispatch is not so important in the beginning when there were small power generating plants for
each locality, such as urban power system, but now with the growth in the power demand and at the same time guarantee
regarding the continuity of the power supply to the consumer under normal condition have force the power system
engineers to developed grid system. For such system the economic load dispatch problem has became increasingly
important. The definition of economic dispatch provided the operation of generation facilities to produce energy at the
lowest cost to reliably serve consumers, recognizing any operational limits of generation and transmission facilities [3].
The conventional economic load dispatch (ELD) problem of power generation involves allocation of power generation to
different thermal units to minimize the operating cost subject to diverse equality and inequality constraints of the power
system. This makes the ELD problem a large-scale highly nonlinear constrained optimization problem. Allocation of
generation output should be made on economic basis and must be made instantly when load changes.
2. PROBLEM FORMULATION
The objective of solving the economic dispatch problem in electric power system is to determine the generation levels for
all on-line units which minimize the total fuel cost and minimizing the emission level of the system, while satisfying a set
of constraints. It can be formulated as follows[9]
N
FT
=
F1+ F2+ F3+ -------- +Fi (Pi)
=
 Fi(P )
i
i 1
The conventional economic load dispatch (ELD) problem of power generation involves allocation of power generation to
different thermal units to minimize the operating cost subject to diverse physical constraints of the power system. Main
effective constraint from above is primary constraints or Equality constraints and Secondary constraints or Inequality
constraints. Primary constraints or Equality constraints, Secondary constraints or Inequality constraints are as per given
below. Primary constraint or equality constraint is the generator output and equation is as per given [1].
N
 Pg
i
= Pd
i 1
N
 Pg
i
= Pd +PLOSS
i 1
Pd = total load connected to the grid and P LOSS = total loss in the system
Secondary constraints or Inequality constraints is the maximum and minimum capacity of individual power plant[4].
Pmin ≤ Pi ≤ Pmax
The minimal power output is determined by technical conditions or other factors of the boiler or turbine. Generally, the
minimum load at which a unit can operate is influenced more by the steam generator and the regenerative cycle than by
the turbine. The only critical parameters for the turbine are shell and rotor metal differential temperatures, exhaust hood
Volume 2, Issue 10, October 2013
Page 98
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 10, October 2013
ISSN 2319 - 4847
temperature, and rotor and shell expansion. Minimum load limitations of the boiler are generally caused by fuel
combustion stability, and the values, which will differ with different types of boiler and fuel. Minimum load limitations of
the turbine - generator unit are caused by inherent steam generator design. The maximum power output of the generating
unit is determined by the thermal limit of plant, capacity or rate capacity of the boiler, turbine, or generator [8].
3. METHODOLOGY
Evolutionary Algorithms are optimization techniques based on the concept of a population of individuals that evolve and
improve their fitness through probabilistic operators like recombination and mutation. These individuals are evaluated
and those that perform better are selected to compose the population in the next generation. After several generations
these individuals improve their fitness as they explore the solution space for optimal value. The field of evolutionary
computation has experienced significant growth in the optimization area. These algorithms are capable of solving
complex optimization problems such as those with a non-continuous, non-convex and highly nonlinear solution space. In
addition, they can solve problem that feature discrete or binary variables, which are extremely difficult[6]. Several
algorithms have been developed within the field of Evolutionary Computation (EC) being the most studied Genetic
Algorithms were first conceived in the 1960’s when Evolutionary Computation started to get attention. Recently, the
success achieved by EAs in the solution of complex problems and the improvement made in computation such as parallel
co stimulated the development of new algorithms like Differential Evolution (DE), Particle Swarm Optimization (PSO),
Ant Colony Optimization(ACO) and scatter search present great convergence characteristics and capability of
determining global optima. Evolutionary algorithms have been successfully applied to many optimization problems
within the power systems area and to the economic dispatch problem. Some steps for the above algorithm is as per given.
[9]
4. TEST PROBLEM
The economic load dispatch (ELD) problem was solved using the differential evolution algorithm. The simulation was
performed on the Np generators test system described as per equation the parameters used for the different system are
decided as per their technical specification and their limits. And on the base of this we get the output for the Economic
load dispatch problem. Develop Matlab programming for the calculation of the above system and try to evaluate whole
system. Now prepare Flowchart and Algorithms for the programming of our system and calculation. From this flowchart
and Algorithms we develop Matlab base programming [2]. Now compare our programming with classical method of
solution as discussed in the former chapter now we take one simple case for the comparison of system. Here we take
classical method for the solution of Economic load dispatch problem [6]. From the program we get the result for the
Economic schedule of generator for the given load condition. For the running of above program some data of the
generator is required which are as per the given below.
Table 1 Generator data
No. of
Generator
1
2
3
Generator rating
in Mw.
210
210
120
Maximum
Value in Mw.
240
238
100
Minimum
Value in Mw.
90
85
20
Data for the generator is as per the given below
Table 2 Generation coeffecient
Sr.No.
ai
bi
ci
1
0.00524
8.664
328.12
2
0.00608
10.05
136.92
3
0.00592
9.75
59.15
Loss Coefficient for the generator is as per the below. From this we can calculate the cost function for the generation.
5. RESULT
From above calculate the value for the generation cost for the 300MW. Output from the three power plant with total loss
addition and limit total cost of generation is as per given in fig. 1. Result is included with generator limit and loss co
efficient of the individual power plant
Volume 2, Issue 10, October 2013
Page 99
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 10, October 2013
ISSN 2319 - 4847
Fig.1 Graph between iteration and generation cost for the 10 generation
6. CONCLUSION
As per result given in table total generation cost can be reduced with the help of evolutionary technique. At every iteration
total cost for the generation is reduced and then it’s become constant for fixed it’s called minimum value or the optimum
point for the output
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