The proposed methodology to solve MAED problem begins with finding range of area power demands for each area by incorporating the tie line limits.

1. Project 2 Presentation
Multi-Area Economic Dispatch Performance Using Swarm
Intelligence Technique
Student Name: Kelvin Yong Hong Chien
Student ID : MKE161159
Supervisor : Dr. Mohd Hafiz Bin Habibuddin

2. Content
1. Introduction
2. Problem Statement
3. Literature Review
4. Research Objectives
5. Scope of work
6. Research Methodology
7. Result
8. Conclusion

3. Introduction
Single Area Economic Dispatch Multi Area Economic Dispatch
Minimised fuel cost Minimised fuel cost without violating
tie-line power transfer
No interconnection between areas Tie-line constraint and area power
balance requirement
Depends on stability of generators Less gen. cost, max reliability, reserve
sharing and improved stability

4. Introduction
• Multi Area Economic Dispatch determine optimal
value of power generation and interchange of power
through tie-lines interconnecting areas minimized
fuel cost.
• In previous paper is using PSO but consideration is
only on single area economic dispatch. The tie-line is
not implemented.
• Number of tie-line and power transfer limit will
determine the stability and cost of production.

5. Problem Statement
• In paper [1], multi area economic dispatch using PSO
yields better generation cost compared to
Evolutionary Programming (EP) only. There is no
comparison between Genetic Algorithm (GA) without
consideration of losses.
• In paper [2], PSO is used to solve non-linear problem
with comparison between GA but without
considering the multi area.

6. Literature Review
Author/ Year/ Method Analysis Advantage/ Disadvantage
(M. K. M. Zamani, et. al.
2017)
Multi-Area Economic
Dispatch Performance
Using Swarm Intelligence
Technique Considering
Voltage Stability
PSO is employed. The algorithm is
tested on a 2-area 48-bus power
system with different case studies.
Variation in active power loading in
achieving an optimal solution is also
considered in this study.
PSO yields better results
as compared to the EP.
There is no comparison
between GA and PSO.
(Zwe-Lee Gaing, 2003)
Particle Swarm
Optimization to Solving the
Economic Dispatch
Considering the Generator
Constraints
Comparison between GA and PSO
using test set of 6 units, 15 units and
40 units with consideration of ramp
rate limits, prohibited operating zone
and non smooth cost function.
PSO yields better results
as compared to the GA.
There is no element of
multi area.

7. Literature Review
Author/ Year/ Method Analysis Advantage/ Disadvantage
(M. Mohammadian. et. al.
2018)
Optimization of single and
multi-areas economic
dispatch problems based
on evolutionary particle
swarm optimization
algorithm.
E PSO is designed to solve constraints
namely: valve-point effects,
prohibited operating zones, multiple
fuel usage, dynamic ramp rate limits,
transmission losses, tie-line capacity
and spinning reserve. MAED cases:
10 units, 3 tie-line and 40 units, 6 tie-
line
E PSO yields better results
as compared to the EP,
RCGA and PSO. There is no
direct comparison
between GA and PSO.
(A. V. V. Sudhakar, et. al.
2014)
Differential evolution for
solving multi area
economic dispatch
DE is employed. The algorithm is
tested on a 2-area 2units each, 4-area
4 units each and 120 units.
Comparison with Hybrid Neural
Network and EP.
DE yields best result.
However, the transmission
losses is not considered

8. Literature Review
Author/ Year/ Method Analysis Advantage/ Disadvantage
(M. P. Musau, N. A. O. C. W.
Wekesa 2010)
Multi area multi objective
dynamic economic dispatch
with renewable energy and
emissions
5 objective includes thermal,
renewable, tie line losses and
emissions, other constraints
RE is modeled using scenario based
method (SBM). Modified Firefly
Algorithm with Levy Flights and
Derived Mutation (MFA-LF-DM)
which is applied to validate
optimality condition decomposition
(OCD) in terms of cost and emission
reduction.
Frequent cycling by
thermal generating unit
causes thermal and
pressure stresses.
Renewable energy reserve
is for high penetration of
RE which in Malaysia still
not suitable.
(M. Basu / 2016 )
Quasi-oppositional group
search optimization for
multi-area dynamic
economic dispatch
2 case studies:
(a) multi area dynamic economic
dispatch with valve point loading,
and transmission losses
(b) multi area dynamic economic
dispatch with valve point loading
multiple fuel sources, and
transmission losses.
Method: QOGSO, GSO, GSA, BBO, DE,
Cost function of PSO is not
cheap, but the processing
time is faster.

9. Research Objectives
1
• To develop the multi-area economic model considering
tie-line capacity, generator limit, transmission line losses
and power balance as constraints.
2
• To optimize the model using algorithm of Particle Swarm
Optimization (PSO) using 2 area network with 3 different
case studies.
3
• To validate the efficiency by analysing between PSO and
GA in producing the generation cost and convergence
time

10. Scope of Work
• Proposed method to consider duration of simulation,
CPU time (Sec.) as one of the benchmark in
determining the efficiency of the system.
• Conduct the optimization of the model using Particle
Swarm Optimization (PSO).
• Conduct experiment using data of 4 units, 6 units
and 40 units system and simulate in MATLAB R2013a.
• For comparison, the same database are tested on
simple PSO and GA.

11. Methodology – Design MAED
Start
Determine the area
power demand
Sets the amount of power
flow through tie-line
(import/export) from Area A
Calculate output
power by PSO / GA
Model
simulation
error?
Yes
No
Evaluation:
Comparison of PSO and GA in
fuel cost and power output
generated
Check if
result can
be accept?
End
* All three case studies are using the same flow

12. Methodology –
Flow Chart for PSO

13. Case Studies
Case
studies
No. of Gen
(Area 1)
No. of Gen
(Area 2)
Tie-line limit
Transmission
line losses
Case 1 2 Units 2 Units 200MW No
Case 2 3 Units 3 Units 80MW Yes
Case 3 25 Units 15 Units 1000MW No

14. PSO & GA Parameters
Parameters Value
Initial position Random
Population size 50
Number of iterations 300
Acceleration constants:
C1, C2
2,2
Inertia weight:
Wmin - Wmax
0.4,0.9
Particle Swarm Optimization Genetic Algorithm
Parameters Value
Population size 50
Number of generations 300
Crossover rate, Pc 0.8
Mute rate, Pm 0.01
Crossover parameter, a 0.5

15. Result – Case 1: 4 Units [4]
Area Unit a
(RM)
b
(RM/MW)
c
(RM/MW2)
Pmin
(MW)
Pmax
(MW)
1 1 561 7.92 0.001562 150 600
2 78 7.97 0.00482 50 200
2 1 310 7.85 0.00194 100 400
2 250 7.50 0.00184 70 340
Fuel Cost
Tie-line: 200MW
B to A
Area A Area B
GenGen
Gen
Gen
PD1=721MW
P1=521MW
PD2=309MW
P2=509MW
Area A is buying 200 MW from Area B.

16. Result – Case 1: 4 Units [4]
Case 1
Area 1 Area 2
P1(MW) P2(MW) P1(MW) P2(MW)
Output powers (MW) – PSO 402.14 118.86 224.2 284.8
Output powers (MW) - GA 396.66 124.34 201.27 307.73
Generation (MW) 521 509
Area power demand (MW) 721 309
Fuel cost (RM/MW) per area – PSO 5092 4702.7
Fuel cost (RM/MW) per area - GA 5092 4700
Power flow (MW) Import (200MW) Export (200MW)
Items PSO GA
Total Fuel Cost (RM) 9785.97 9792.00
Time Convergence (s) 1.20 3.25
Summary – Result Simulation

17. Result – Case 2: 6 Units with losses
Area Unit a
(RM)
b
(RM/MW)
c
(RM/MW2)
Pmin
(MW)
Pmax
(MW)
1 1 550 8.10 0.00028 100 500
2 350 7.50 0.00056 50 200
3 310 8.10 0.00056 50 150
2 1 240 7.74 0.00324 80 300
2 200 8.00 0.00254 50 200
3 126 8.60 0.00284 50 120
Fuel Cost
B Coefficient Area 1 B Coefficient Area 2

18. Result – Case 2: 6 Units with losses
Tie-line: 80MW
A to B
Area A Area B
GenGen
Gen
Gen
PD1=770MW
P1=850MW
PD2=700MW
P2=620MW
GenGen
Losses
Losses
Area A is selling 80 MW to Area B.

19. Result – Case 2: 6 Units with losses
Case 2
Area 1 Area 2
P1(MW) P2(MW) P3(MW) P1(MW) P2(MW) P3(MW)
Output powers (MW) – PSO 500 200 150 300 200 120
Output powers (MW) - GA 500 200 150 300 200 120
Generation (MW) 850 620
Area power demand (MW) 770 700
Fuel cost (RM/MW) per area – PSO 8084.5 5959.7
Fuel cost (RM/MW) per area - GA 9022.7 6766.6
Power flow (MW) Export (80MW) Import (80MW)
Power losses (MW) – PSO
Power losses (MW) – GA
9.3818
9.4268
8.0693
8.1253
Items PSO GA
Total Fuel Cost (RM) 14044.20 15789.30
Total Power Losses (MW) 17.4511 17.5521
Time Convergence (s) 1.56 3.60
Summary – Result Simulation

20. Result – Case 3 [2]: 40 Units (Taipower)
Tie-line: 1000MW
A to B
Area A Area B
Gen
27
Gen
3
Gen
40
Gen
25
PD1=6350MW
P1=7350MW
PD2=3150MW
P2=2150MW
Gen
26Gen
2
Gen
1
.
.
.
.
.
.
Area A is selling 1000 MW to Area B.

21. Result –
Case 3 [2]:
40 Units
(Taipower)
Case 3
Area 1 Area 2
No. Gen
Generation
(MW) - PSO
Generation
(MW) - GA
No. Gen
Generation
(MW) - PSO
Generation
(MW) - GA
Output powers
(MW)
1 113.30 92.65 26 453.13 398.04
2 98.48 94.71 27 14.95 44.14
3 116.88 200.03 28 21.32 63.76
4 142.73 28.63 29 37.55 5.82
5 86.50 10.73 30 80.29 148.89
6 74.66 210.38 31 153.86 222.92
7 240.29 31.12 32 163.78 222.92
8 228.35 88.79 33 70.64 222.92
9 181.60 88.47 34 155.05 92.24
10 255.92 43.89 35 197.26 116.58
11 154.81 125.04 36 144.15 116.58
12 227.72 340.06 37 52.31 143.74
13 250.84 247.15 38 37.99 96.00
14 181.70 772.98 39 81.97 35.42
15 396.65 635.99 40 485.74 220.03
16 231.15 564.02
17 499.00 565.98
18 480.54 283.52
19 484.12 175.08
20 445.35 65.52
21 545.37 623.71
22 548.03 720.45
23 434.25 119.82
24 547.00 419.96
25 384.77 801.32
Generation (MW) 7350 2150
Area power demand
(MW)
6350 3150
Fuel cost (RM/MW) 87342 96100 24010 35097
Power flow (MW) Export (1000MW) Import (1000MW)

22. Result – Case 3 [2]: 40 Units
(Taipower)
Items PSO GA
Total Fuel Cost (RM) 112388.57 120000.00
Time Convergence (s) 4.10 6.23
Summary – Result Simulation

23. Result – Case 3: 40 Units
Area 1 Area 2
PSO
GA

24. Simulation Result - Summary
Items PSO GA
Total Fuel Cost (RM) 9785.97 9792.00
Time Convergence (s) 1.20 3.25
Items PSO GA
Total Fuel Cost (RM) 112388.57 120000.00
Time Convergence (s) 4.10 6.23
Items PSO GA
Total Fuel Cost (RM) 14044.20 15789.30
Total Power Losses (MW) 17.4511 17.5521
Time Convergence (s) 1.56 3.60
Case 1
Case 2
Case 3

25. Simulation Result - Summary
0
20000
40000
60000
80000
100000
120000
140000
4 Unit 6 Unit 40 Unit
Comparison of Fuel Cost for Three
Case Studies
PSO GA
0
1
2
3
4
5
6
7
4 Unit 6 Unit 40 Unit
Comparison of Convergence Time for
Three Case Studies
PSO GA

26. Discussion
• PSO perform better than GA. PSO in some cases
provides not accurate fuel cost due to effect of ramp
rate
• Case studies above only considered transmission
losses because this study focus on getting fast
estimated value where PSO has performed well

27. Conclusion
• Proposed method (PSO economic dispatch)
performs better performance than GA in multi
area environment
• PSO has short convergence time as compared
to GA
• Suggestion to conduct the experiment by
considering: ramp rate, valve-point effect,
reserve, distributed generation (wind turbine
or solar PV) in future work

28. References
1. M. K. M. Zamani, et. al., “Multi-Area Economic Dispatch Performance
Using Swarm Intelligence Technique Considering Voltage Stability”, in
Advanced Science Engineering Information Technology, Vol 7. 2017.
2. Zwe-Lee Gaing, “Particle Swarm Optimization to Solving the Economic
Dispatch Considering the Generator Constraints”, IEEE Transaction, 2003.
3. M. Mohammadian. et. al., “Optimization of single and multi-areas
economic dispatch problems based on evolutionary particle swarm
optimization algorithm.”, IEEE Transaction, 2018.
4. A. V. V. Sudhakar, et. al. “Differential evolution for solving multi area
economic dispatch”, 2014.
5. M. P. Musau, N. A. O. C. W. Wekesa, “Multi Area Multi Objective Dynamic
Economic Dispatch with Renewable Energy and Emissions”, in IEEE, 2016.

29. References
6. X. Xia, A. M. Elaiw., “Optimal dynamic economic dispatch of generation:
A review”, in Electric Power System Research, 2010.
7. M. Pandit, et. al., “Large Scale Multi-area Static / Dynamic Economic
Dispatch using Nature Inspired Optimization”, Springer, 2016.
8. M. Basu, “Quasi-oppositional group search optimization for multi-area
dynamic economic dispatch”, in Electrical Power and Energy System,
Science direct, 2016.
9. Vinay K. Jadouna et. al., “Multi-area Economic Dispatch using Improved
Particle Swarm Optimization”, The 7th International Conference on
Applied Energy – ICAE2015, Science direct, 2015.

30. Thank You
Question & Answer