Power Quality (Thailand) Ltd., Co. 52/44 Moo. 1, Ramkhamhaeng Rd., Soi 90, Sapansoong,

Bangkok 10240, Thailand

Panida BoonyaritdachochaiPower Quality and Energy Efficiency Engineer

28th September 2010

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Introduction

Objectives

Methodology

Generator Indicator for Congestion Management

Objective Function

PSO Schemes (CPSO, PSO-TVIW and PSO-TVAC)

Numerical Results

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3

Congestion is the overloading in transmission lines. It could be caused by

unexpected outages of generation sudden increase of load tripping of transmission lines failure of other equipment

The SO is responsible for determining the necessary actions to ensure that no violations of the grid constraints occur.

Transmission congestion can cause additional outages, increase the electricity prices in some regions and can threaten system security and reliability.

The cost to relieve congestion can increase to a level that could present a barrier in electricity trading

IntroductionIntroduction

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Network overloading can be relieved by different control: power generation rescheduling operation of FACTS controllers line switching load shedding

The power transferring should satisfy customer requirement with lowest cost while solve the congestion problems.

The installation of equipment should not be first choice for the SO to deal with congestion problems.

Therefore the power redispatching approach is significant as the prior approach for congestion management.

Introduction (Cont.)Introduction (Cont.)

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Indicator techniques of the sensitivity factor are discussed Generator sensitivity (GS) technique proposes in [1] for optimum

selection of participating generators. [2], [3] and [4] introduce transmission congestion distribution factors

(TCDFs). In [5] presents technique based on sensitivity of current flow to

congested line.

[6] and [7] have proved that PSO is the best optimizer among GA, NN and EP.

PSO is appropriate for complex problems as defined in [8] which is due to: discontinuities higher order nonlinearities prohibit operating zone ramprate limits of generators

PSO is increasingly gaining acceptance for solving a variety of power system problems as in [9] due to simplicity, superior convergence, high solution quality.

Introduction (Cont.)Introduction (Cont.)

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To propose active power redispatching to alleviate the overload in transmission system by optimal generators.

The optimal generators are indicated by generator sensitivity (GS) technique. Its aim is to find the most effective participating generators in congestion management.

The minimum adjustment cost and real power redispatching are considered in the problem formulation.

To explore the ability of PSO-TVAC compared with PSO-TVIW and CPSO

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(1)

ggg G

j

j

ji

G

i

i

ij

G

ij

P

θ

θ

P

P

θ

θ

P

ΔP

ΔP

)θ(θBVV)θ(θGVVGVP jiijjijiijjiij2

iij sincos (2)

(3)

ijgGS

11

nnnnΔPHΔθ 1

nnn

n

2

n

n

2

2

2

nn

P

θ

P

θ

P

θ

P

θ

0

0

000

=M

1HM Let;

(4)

II. Objective Function for Congestion Management

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g

Ng

ggg ΔPΔPCMinimize (5)

Subjected to

gmax

ggmin

g ,N1,2,;gΔPΔPΔP

lmax

l

Ng

1g

0lg

ij ,n1,2,;lFFΔPGSg

gmax

gmax

g - PPΔP mingg

ming PPΔP and

(6)

(7)

(8)

Ng

1gg 0ΔP

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The position and velocity of the p particle in d dimensions can be expressed as

The best previous position of a particle is recorded and represented as

If the g particle is the best among all particles in the group, it is presented as

pdp2p1p ,p,,pppbest

gdg2g1g ,g,,gggbest

and

Figure 1: bird flocking and Fish schooling

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B. Particle Swarm Optimization with Time-Varying Inertia Weight (PSO-TVIW)

pdgd22pdpd11kpd

1kpd xgbestrandcxpbestrandcvwCv

4.24.1where,42

2C

2

minmax

maxminmax w

k

kkwww

(10)

Where; and

C. Particle Swarm Optimization with Time-Varying Acceleration Coefficients(PSO-TVAC)

1imax

1i1f1 ck

kccc 2i

max2i2f2 c

k

kccc and

A. Classical Particle Swarm Optimization (CPSO)

pdgd22pdpd11kpd

1kpd xgbestrandcxpbestrandcvwv (9)

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Table 1: Parameters of PSO.

1kpdpd

1kpd vxx (11)

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Figure 2: Flowchart of congestion management by PSO-TVAC.

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Figure 3: IEEE 30-bus system.

Congested Line Real Power Flow (MW) Line Limit (MVA) Over the Limit (MW)

1 to 2 170 130 40

Table 2: Congested line in IEEE 30-bus system.

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Table 3: Solutions by PSO schemes in IEEE 30-bus system.

Figure 5: GS values to power redispatching in IEEE 30-bus system.

  GS CPSOPSO-TVIW

PSO-TVAC

ΔP1 (MW) 0.0000 -55.9 -50.13 -49.25

ΔP2 (MW) -0.8908 22.6 18.88 17.51

ΔP5 (MW) -0.8527 16.2 13.21 14.02

ΔP8 (MW) -0.7394 10.5 9.15 9.88

ΔP11 (MW) -0.7258 5.6 5.87 6.8

ΔP13 (MW) -0.6869 2.6 4.14 3.01

Total power redispatch (MW)  

113.2 101.4 100.5

Cost ($/hr) 287.1 253.1 247.5

Figure 4: GS values in IEEE 30-bus system.

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Figure 6: IEEE 118-bus system.

Congested Line Active Power Flow (MW) Line Limit (MVA) Over the Limit (MW)

89 to 90 260 200 60

Table 4: Congested line in IEEE 118-bus system.

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Gen no.

GS (10-3)Gen no.

GS (10-3)Gen no.

GS (10-3)

1 0 42 -0.0375 80 -0.92504 -0.0005 46 -0.0242 85 50.0686 -0.0001 49 -0.0460 87 50.6548 -0.0014 54 -0.0838 89 74.455

10 -0.0014 55 -0.0871 90 -701.1512 0.0004 56 -0.0854 91 -427.9015 0.0021 59 -0.1100 92 -28.41118 0.0051 61 -0.1160 99 -9.39119 0.0046 62 -0.1130 100 -12.91524 0.1350 65 -0.1350 103 -12.73725 0.0484 66 -0.0983 104 -12.85426 0.0337 69 0.2120 105 -12.77227 0.0451 70 0.3690 107 -12.20231 0.0339 72 0.2326 110 -12.27432 0.0477 73 0.3400 111 -12.0734 -0.0323 74 0.5410 112 -11.74736 -0.0329 76 0.8650 113 0.011040 -0.0343 77 0.0012 116 -0.1750

Table 5: GS values of 54 generators in IEEE 118-bus system.

Figure 7: The GS values of 54 generators in IEEE 118-bus system.

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Table 6: Solutions by PSO schemes in IEEE 118-bus system.

  GS CPSOPSO-TVIW

PSO-TVAC

ΔP1 (MW) 0 -5.9 -5.5 -4.4

ΔP85 (MW) 0.05007 -12.1 -12.1 -10.3

ΔP87 (MW) 0.05065 -31.5 -28.2 -22.0

ΔP89 (MW) 0.07446 -62.0 -59.8 -58.5

ΔP90 (MW) -0.70150 65.1 76.4 69.4

ΔP91 (MW) -0.42790 26.8 29.8 24.7

Total power redispatch (MW)  

226.6 211.7 189.3

Cost ($/hr) 1183.8 1108.4 907.7

Figure 8: GS values to real power re-dispatching in IEEE 118-bus system.

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The optimal power redispatching approach based on PSO-TVAC is superior to CPSO and PSO-TVIW in providing the better congestion management for both IEEE 30 and 118 bus systems.

GS technique uses to select participating generators for real power adjustment. It could reduce computational effort.

The proposed approach is useful for the SO to manage the congestion in electricity market environment.

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1. Sudipta Dutta & S.P. Singh. (2008). Optimal rescheduling of generators for congestion management based on particle swarm optimization. Power system, IEEE transactions on, 23(4), 1560-1569.

2. Kumar. A, Srivastava. S.C & Singh. S.N. (2004). A zonal congestion management approach using real and reactive power rescheduling. Power system, IEEE transactions on,19(1), 554-562.

3. Kumar. A. Srivastava. S.C. & Singh. S.N. (2007). A zonal congestion management approach using ac transmission congestion distribution factors. Electric power system Research, 72(1), 85-95.

4. Meena. T. & Selvi. K. (2005). Cluster Based Congestion Management in Deregulated Electricity Market Using PSO. Indicon 2005 conference, (pp. 627-630). India: Chennai,

5. B.K. Talukdar, A.K. Sinha, S. Mukhopadyay & A. Bose. (2005). A computationally simple method for cost-efficient generation rescheduling and load shedding for congestion management. Electrical Power and Energy Systems, 27(1), 379-388.

6. Eberhart. T. & Kenedy. J. (1995). A New Optimizer using particle swarm theory. Micro Machine and Human Science, Proceedings of the sixth international symposium on, (pp 39-42). Japan: Nagova.

7. Yuhui Shi & Russell C. (1999). Eberhart. Empirical study of particle swarm optimization. Proceedings of the 1999 congress on evolutionary computation. (pp 1945-1950).

8. AlHajri. M.F. & El-Hawary. (2007). M.E. Pattern search optimization applied to convex and non-convex economic dispatch. System, Man and Cybernetics, ISIC. IEEE International Conference on, (pp 2674-2678).

9. Chaturvedi. K.T, Pandit. M & Srivastave. L. (2008). Self-Organizing Hierarchical Particle Swarm Optimization for Nonconvex Economic Dispatch. Power system, IEEE transactions on, 23(3), 1079-1087.

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