The document discusses a new approach to multi-area economic dispatch and emission dispatch in power systems, utilizing enhanced multi-objective particle swarm optimization (PSO) algorithms. The proposed PSO demonstrates advantages in modeling flexibility, rapid convergence, and reduced computational time compared to other optimization methods. Implementation examples include optimizing generator schedules in an IEEE 30-bus interconnected system while ensuring security constraints.

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Multi–Area Security Constrained
Economic Dispatch and Emission
Dispatch with Valve Point Effects
Using Particle Swam Optimization
Authors
K.K.Swarnkar Dr.S.Wadhwani Dr.A.K.Wadhwani
CERA- 2009

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Economic load dispatch
 Economic load dispatch problem is one of the fundamental
issues of power system operation and planning.
 The Goal of an Economic load dispatch is to determine the
“best” way to instantaneously operate a The Power System.
 Usually “best” = Minimizing the total Operating Cost.
 Operating Costs of the Power plant could be varying from
time to time.
 Real and Reactive Power output of the Power plant are
allowed to vary within limits so that Operating Costs can be
minimized.

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Power Plants
OPTIMIZATION
PROBLEAMS
ECONOMIC
DISPATCH PROBLEAM
EMISSION
DISPATCH PROBLEAM

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Traditional Methods for Optimization
 Lambda iteration method
 Gradient method
 Newton’s method
 Linear programming method
 Interior point method

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Stochastic Methods for Optimization
 Genetic algorithm (GA)
 Differential evolution (DE)
 Chaos optimization algorithm (COA)
 Ant colony (AC)
 Particle Swarm optimization (PSO)
 Other methods

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Particle Swarm Optimization
 PSO is a robust stochastic optimization technique based on the
movement and intelligence of swarms.
 PSO applies the concept of social interaction to problem solving.
 It was developed in 1995 by James Kennedy and Russell
Eberhart .
 It uses a number of particles that constitute a swarm moving
around in the search space looking for the best solution.
 In PSO, each potential solution is assigned a randomized velocity,
and the potential solutions, called particles, fly through the
problem space by following the current best particles.

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Particle Swarm Optimization….
 Unlike other EA, PSO is capable of evolving toward global
optimum with a random velocity by its memory mechanism and
has better global search performance with faster convergence.
 PSO is very similar to GA, but it does not have genetic
operators (crossover and mutation).
 A particle moves with the velocity:
 its own experience,
 experience from all particles.
 The idea is similar to bird flocks searching for food.

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Generator Coefficients of
IEEE 30-BUS Interconnected Three Area system with 8 Generator
BU
S
NO.
ACTIVE POWER
OUTPUT
LIMIT(MW)
COST COEFFICIENTS EMISSION COEFFICIENTS
Min Max a b C d.10-2
e.10-4
f.10-6
1 50 200 0 2.00 0.00375 4.041 -5.554 6.490
2 20 80 0 1.75 0.01750 2.543 -6.047 5.638
5 15 50 0 1.00 0.06250 4.258 -5.094 4.586
8 10 35 0 3.25 0.00834 5.326 -3.550 3.380
11 10 30 0 3.00 0.02500 4.258 -5.094 4.586
13 12 40 0 3.00 0.02500 6.131 -5.555 5.151
31 10 100 0 650 325 0 0 0
32 10 100 0 30 100 0 0 0

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Optimum generation schedule for MAEED
Without security constraints
Algorithm PSO(P.U) EMEP(P.U) TS(P.U) FGTU(P.U)
Area(A1)
Pg1 1.64 1.64 1.63 1.63
Pg2 0.41 0.41 0.42 0.41
Pg3 0.19 0.19 0.18 0.18
Pg4 0.23 0.22 0.22 0.22
Pg5 0.16 0.16 0.16 0.16
Pg6 0.15 0.15 0.15 0.14
Area(A2)
Pg31 0.1 0.1 0.1 0.1
Area(A3)
Pg32 0.9 0.9 0.9 0.9
Total
Gen power
3.78 3.77 3.76 3.76
Emission (ton/hr) 0.368 n.a n.a n.a

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Optimum generation schedule for MAEED
With security constraints
Algorithm PSO(P.U) EMEP(P.U) TS(P.U) FGTU(P.U)
Area(A1)
Pg1 1.60 1.65 1.63 1.63
Pg2 0.43 0.46 0.46 0.46
Pg3 0.20 0.16 0.16 0.16
Pg4 0.29 0.16 0.16 0.16
Pg5 0.14 0.20 0.20 0.20
Pg6 0.13 0.16 0.16 0.16
Area(A2)
Pg31 0.1 0.1 0.1 0.1
Area(A3)
Pg32 0.9 0.9 0.9 0.9
Total
Gen power
3.79 3.79 3.78 3.78
Emission (ton/hr) 0.368 n.a n.a n.a

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The Comparison of various techniques for Optimization
THREE AREAS 32-BUS SYSTEM
Pg1 Pg2 Pg3 Pg4 Pg5 Pg6 Pg31 Pg32
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Generation Levels After Process
#REF!
#REF!
#REF!
#REF!
Bus No.
Output
Power(MW)
E M E P
T S
F G T U
P S O

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Conclusion
 In this paper a new concept termed Multi – Area Economic
Dispatch and Emission Dispatch with Valve Point Effects is
proposed and an enhanced Multi objective Particle Swam
Optimization algorithm is used to derive a set of Pareto –
optimal solutions.
 The main advantage of PSO over other modern heuristics is
modeling flexibility, sure and fast convergence, and loss
computational time than other heuristic method.
 The Tie – line transfer limits between areas are considered to
ensure the power system security. The flexibility of the
proposed algorithms is demonstrated on an IEEE – 30 Bus
Interconnected Three Area systems with 8 generator system.

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Thank you for your attention!
Any questions?

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