1. Panida Boonyaritdachochai
Energy Management for Transmission Congestion Using Particle Swarm Optimization with Time Varying Acceleration Coefficients
Panida Boonyaritdachochai
Power Quality and Energy Efficiency Engineer
28th September 2010
Power Quality (Thailand) Ltd., Co.
52/44 Moo. 1, Ramkhamhaeng Rd., Soi 90, Sapansoong,
Bangkok 10240, Thailand

2. Contents Introduction Objectives Methodology Numerical Results
Generator Indicator for Congestion Management
Objective Function
PSO Schemes (CPSO, PSO-TVIW and PSO-TVAC)
Numerical Results

3. Introduction
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

4. Introduction (Cont.)
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.

5. Introduction (Cont.)
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.

6. Objectives
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
I think you should “rescheduled generators” instead “participating”

7. Methodology
Generator Sensitivity (GS)
(1)
(2)
(3)
Let;
(4)

8. Methodology (Cont.) Objective Function for Congestion Management and
(5)
Subjected to
and
(6)
(7)
(8)

9. Methodology (Cont.) III. Particle Swarm Optimization (PSO) Schemes
Figure 1: bird flocking and Fish schooling
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
and

10. Methodology (Cont.) (9) (10)
Classical Particle Swarm Optimization (CPSO)
(9)
Particle Swarm Optimization with Time-Varying Inertia Weight (PSO-TVIW)
(10)
Where;
and
Particle Swarm Optimization with Time-Varying Acceleration Coefficients
(PSO-TVAC)
and

11. Methodology (Cont.)
(11)
Table 1: Parameters of PSO.

12. Methodology (Cont.)
Figure 2: Flowchart of congestion management by PSO-TVAC.

13. Numerical Results Figure 3: IEEE 30-bus system.
Table 2: Congested line in IEEE 30-bus system.
Congested Line
Real Power Flow (MW)
Line Limit (MVA)
Over the Limit (MW)
1 to 2
170
130 40

14. Numerical Results (Cont.)
Figure 4: GS values in IEEE 30-bus system.
Table 3: Solutions by PSO schemes
in IEEE 30-bus system. GS
CPSO
PSO- TVIW
PSO- TVAC
ΔP1 (MW)
0.0000
-55.9
-50.13
-49.25
ΔP2 (MW)
22.6
18.88
17.51
ΔP5 (MW)
16.2
13.21
14.02
ΔP8 (MW)
10.5
9.15
9.88
ΔP11 (MW)
5.6
5.87
6.8
ΔP13 (MW)
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 5: GS values to power redispatching
in IEEE 30-bus system.

15. Numerical Results (Cont.)
Figure 6: IEEE 118-bus system.
Table 4: Congested line in IEEE 118-bus system.
Congested Line
Active Power Flow (MW)
Line Limit (MVA)
Over the Limit (MW)
89 to 90
260
200 60

16. Numerical Results (Cont.)
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.
Gen no.
GS (10-3) 1 42 80 4 46 85
50.068 6 49 87
50.654 8 54 89
74.455 10 55 90 12
0.0004 56 91 15
0.0021 59 92 18
0.0051 61 99
-9.391 19
0.0046 62
100 24
0.1350 65
103 25
0.0484 66
104 26
0.0337 69
0.2120
105 27
0.0451 70
0.3690
107 31
0.0339 72
0.2326
110 32
0.0477 73
0.3400
111
-12.07 34 74
0.5410
112 36 76
0.8650
113
0.0110 40 77
0.0012
116

17. Numerical Results (Cont.)
Table 6: Solutions by PSO schemes
in IEEE 118-bus system. GS
CPSO
PSO-TVIW
PSO-TVAC
ΔP1 (MW)
-5.9
-5.5
-4.4
ΔP85 (MW)
-12.1
-10.3
ΔP87 (MW)
-31.5
-28.2
-22.0
ΔP89 (MW)
-62.0
-59.8
-58.5
ΔP90 (MW)
65.1
76.4
69.4
ΔP91 (MW)
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.

18. Conclusion
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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20. THANK YOU