This document discusses optimal placement of distributed generation in distribution networks. It introduces different types of distributed generation and outlines the advantages of distributed generation integration. It then discusses various approaches for optimally placing type 1 distributed generation, which can only inject real power, to minimize power losses and improve voltage profiles. Particle swarm optimization is applied to determine the optimal size and location of type 1 distributed generation in 33-bus and 69-bus test systems. The document also considers optimal placement of type 1 distributed generation and capacitors together in order to compensate for reactive power losses.

1. 1
Optimal Distributed Generation
Placement Distribution Networks
Presented By
Dr. Satish Kansal
Department of Electrical Engineering
Baba Hira Singh Bhattal Institute of Engg. & Tech. Lehragaga

2. 2
Organization :
 Introduction
 Optimal placement of Type-I distributed generation (DG)
 Optimal placement of Type-I DG in compensated network
 Optimal placement of different type of DG sources
 Hybrid approach for placement of multiple DGs of multiple types
 Optimal placement of DGs & Capacitors based on Cost-benefit
analysis

3. 3
Introduction
 Objective
 Traditional electric power system
 Operation
 Present Challenges
 Distributed Generations

4. 4
Distributed Generation
CIGRE :Define DG as the generation, which has the
following characteristics [1]:
 Not centrally planned
 Not centrally dispatched at present
 Usually connected to the distribution networks
 Smaller then 50-100MW.

5. 5
Distributed Generation
 International Energy Agency (IEA) :
 serving a customer on-site
 providing support to a distribution network,
 connected to the grid
 Ackermann et al.
 DG is an electric power generation source
 connected directly to the distribution network
 small-scale electricity generation.

6. 6
Distributed Generation
 Embedded Generations
 Disperse Generations
 depends upon many technologies
 depends upon many applications

7. 7
 Increasing DG penetration:
 Growing share of distributed generators (DGs)
 Policy initiatives to promote DG throughout the world
Distributed Generation

8. Advantages of DG Integration
 Reduction in line losses
 Improvement in voltage profile
 Deferred network extension
 Improvement in system efficiency
 Enhanced peak shaving capacity
 System reliability and security
8

9. 10
Literature Review
 Literature reviewed can be categorized as follows:
 Problem of optimal placement of distributed generation [4,
11,19]
 Reactive power compensation with capacitors [24, 42, 47]
 Placement of different types of DGs [9,38]
 Various search approaches used [26,32,48]
 Various objectives and constraints

10. 11
Shortcomings in Existing Methodologies
 Minimization of the real power loss only.
 DG supplying real power only.
 analytical method for single DG only.
 optimal power factor of the DG
 maximizing the profits
 DG against centralized generation
 availability in the market

11. 12
 The DG’s can be characterized into different types as [2]:
Type I: DG capable of injecting real power only, like
photovoltaic, fuel cells etc.
Type II: DG capable of injecting reactive power only, e.g. kvar
compensator, synchronous compensator, capacitors etc.
Type III: DG capable of injecting both real and reactive power,
e.g. synchronous machines,
Type IV: DG capable of injecting real but consuming reactive
power, e.g. induction generators.
 In the present work different types of DG’s are considered for optimal
placement

12. Motivation for the Present Work
 India is fastest growing economics
 availability of quality supply is very crucial for the sustained growth
 Electricity demand increasing rapidly
 generating capacity in 1950 is 1,712 MW
 Presently 211,766.22 MW
 per capita per year only 860.72 kWh
 triple by 2020, with 6.3% annual growth.
13

13.  India is in power deficient state
 power deficiency is nearly 12.2% of peak demand.
 results in power cuts, blackouts, etc.
 DG are compulsory for continuous growth
14

14. 15
Optimal Placement of Type-I
DGs

15. 16
 analytical approach and particle swarm optimization (PSO)
technique
 DG supplying real power
 33-bus, and a 69-bus system.
 loss reduction and voltage profile improvement
 operational constraints
Optimal Placement of DG

16. 17
LOCATION AND SIZING ISSUES
0
10
20
30
40
50
60
70
0102030405060708090100
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0.05
0.055
Loss
(MW)
%DG Size Bus No.
Effect of size and location of DG on system loss

17. Mathematical Modelling
 Assumptions:
 Sizing and locations are considered at point load only,
 DG can deliver active power only.
 Optimal Sizing of DG
18
𝜕𝑃𝐿
𝜕𝑃𝑖
= 2𝛼𝑖𝑖 𝑃𝑖 + 2 𝛼𝑖𝑗 𝑃𝑗 − 𝛽𝑖𝑗 𝑄𝑗 = 0
𝑁
𝑗=1
𝑗≠𝑖

18. 
19

19. 20
Problem Formulation
 Objective function to minimize the real power loss
 Constraints :
 power flow equations
 Voltage constraint (±5% )
 Line current constraint

20. Approaches
21
 Analytical approach
 PSO Technique
 Analytical approach
 Optimal size of type-I DG
 Optimal Location

21. Particle Swarm Optimization (PSO)
Technique
22


22. 23
Advantages of PSO
 rapidly developed
 easy implementation.
 few particles required to be tuned
 no overlapping and mutation calculation
 search can be carried out by the speed of the particle.
 only most optimist particle can transmit information onto the other
 researching speed is very fast.

23. PSO Parameters
 PSO parameters :
 Population size : 50
number of particles : 10
ωmin : 0.4
ωmax : 0.9
C1 = C2 : 2
Maximum number of iterations : 100
25

24. Results and Discussions
 Test systems
 33-bus with total load of 3.72 MW and 2.3 MVAr
 69-bus with total load of 3.80 MW and 2.69 MVAr
 Beaver conductors
 base voltage is 12.66 kV.
26

25. 27
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
0
0.5
1
1.5
2
2.5
3
3.5
4
Bus Number
SizeofDGinMW
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
0.1
0.12
0.14
0.16
0.18
0.2
0.22
Bus Number
LossinMW

26. Method
Optimum
location
Optimum DG size
(MW)
Power loss (KW)
Without
DG
With DG
Analytical Method Bus 6 3.15 210.97 115.2
PSO approach Bus 7 2.91 210.97 115.1
28
Power loss with and without DG for 33-bus system with constraints
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
0.9
0.95
1
Bus Number
VoltageProfileinp.u.
With DG
Without DG

27. 29
5 10 15 20 25 30 35 40 45 50 55 60 65 70
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Bus Number
SizeofDGinMW
1 6 11 16 21 26 31 36 41 46 51 56 61 66 70
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
Bus Number
LossinMW

28. 30
Method
Optimum
location
Optimum DG size
(MW)
Power loss (KW)
Without
DG
With DG
Analytical Method Bus 61 1.81 225 83.4
PSO approach Bus 61 1.81 225 83.4
Power loss with and without DG for 69-bus system with constraints
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 69
0.9
0.95
1
Bus Number
ViltageProfileinp.u.
With DG
Without DG

29. Conclusions
 minimize the real power loss.
 Improvement in voltage profile
 minimizing the DG size
31

30. 32
OPTIMAL PLACEMENT OF TYPE-I DG
IN REACTIVE POWER COMPENSATED
NETWORK

31. Introduction
 optimal placement of type-I DG in reactive power compensated
network
 reactive power is compensated by the optimal placement of
Capacitor
 minimize the active power loss
 enhance the voltage profile
 also minimize the size of type-I DG,
33

32. Mathematical Modelling
 minimize the real power losses.
 Assumptions:
 DG can inject active power only,
 Capacitor can inject reactive power only,
 DG and Capacitor placement at constant load
34

33. 
35
𝜕𝑃𝐿
𝜕𝑄𝑖
= 2𝛼𝑖𝑖 𝑄𝑖 + 2 𝛼𝑖𝑗 𝑄𝑗 + 𝛽𝑖𝑗 𝑃𝑗 = 0
𝑁
𝑗=1
𝑗≠𝑖

34. Optimal Power Factor

36

35. Objective function
 objective function is to minimize the total system real
power loss
 Constraints:
 power flow equations
 Voltage constraint (±5% )
 Line current constraint
37

36. PSO Approach
 Particle swarm optimization technique
 PSO technique is applied to determine the optimal size of DG
and Capacitor to minimize the real power losses.
 Population size 50
 Number of iterations 200
 Number of particles 10
 Dimension of search space 4
 ωmin 0.4
 ωmax 0.9
 C1 = C2 2
38

37. Results and Discussions
 Results of proposed methodology:
39
Test
system
Optimum
location
Optimum
DG size
(MW)
Optimum
Capacitor
size
(MVAr)
Active Power loss
(KW)
Reactive Power
loss (KVAr)
% Reduction in loss
Without
DG &
Cap.
With DG
& Cap.
Without
DG &
Cap.
With DG
& Cap.
Active Reactive
33 bus Bus 6
2.49 ------- 211 111.17 143.03 81.66 47.31% 42.91%
2.49 1.72 211 67.95 143.03 54.79 67.79% 61.69%
69 bus Bus 61
1.81 ------ 225 83.4 102.2 40.7 62.93% 60.18%
1.81 1.29 225 23.2 102.2 14.4 89.69% 85.91%

38. DG and Capacitor at different Locations
 Results:
40
System PSO Technique
33 Bus System
Cases Bus No.
Capacity
Loss in (kW)
DG (MW)
Capacitor
(MVAr)
Same location 6 2.4908 1.7213 67.95
Different
location
6 2.5317
58.45
30 1.2558
69 Bus System
Same location 61 1.8285 1.3006 23.17
Different
location
61 1.8285
23.17
61 1.3006

39. DG and Capacitor placement with optimal
power factor
 Results:
41
System
Bus
location
Base
case
Fast Analytical Approach [2] Proposed PSO Technique
33 bus 6
Line loss
(kW)
DG size
(MVA)
Optimal
p. f.
Line
loss
(kW)
DG Size
(MW)
Capacitor
Size
(MVAr)
Optimal
p. f.
Line
loss
(kW)
211 3.025 0.85 68.28 2.49 1.72 0.82 67.95
69 bus 61 225 2.243 0.82 23.20 1.83 1.30 0.81 23.17

40. 42
0
1
2
3
4
5
-1
0
1
2
3
4
5
0.05
0.1
0.15
0.2
0.25
0.3
DG size (MW)DG size (Mvar)
Loss(MW)
67.95 kW
1.72
OPF = 0.82 leading
2.49
DG and Capacitor at same bus no. 6 in a 33-bus distribution system
Fig. 3.1

41. Objective: minimize the real power loss
constraints:
 Size of DG and Capacitor limited to less than 30%
 easily availability.
43
Analytical approach

42. Results and Discussion
44
Summary of the 33-bus and 69-bus base case
Case I: DG and Capacitor are placed at different optimal locations
Test System 33-Bus 69-Bus
Σ kW loss 211 225
Σ kVAr loss 143 102.2
0.9092
1.0000
Test System 33-Bus 69-Bus
DG-Unit 1500 kW, placed at bus 8
Capacitor 900 kVAr, placed at bus 30
1500 kW, placed at bus 61
1200 kVAr, placed at bus 61
Σ kW loss 70.17 27.2
Σ kVAr loss 49.1 17.4
0.9702
1.0000

43.  Case II: DG and Capacitor are placed at same optimal
location.
45
Test System 33-Bus 69-Bus
DG-Unit 1500 kW, placed at bus 30
Capacitor 900 kVAr, placed at bus 30
1500 kW, placed at bus 61
1200 kVAr, placed at bus 61
Σ kW loss 75.65 27.2
Σ kVAr loss 56.13 17.4
Optimal P.f. (Leading) 0.86 0.78
0.9702
1.0000

44. 46
Compensation results of 33-bus and 69-bus system

45. Optimal Power Factor
47
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
Power factor
Totalpowerloss(MW)
Loss With DG & Capacitor
Loss Without DG & Capacitor
Loss at optimal p.f.
Lagging Leading
Fig. 3.3: Variation of power factor on power loss of 33 bus distribution system

46. Optimal Power Factor
48
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
0.19
0.195
0.2
0.205
0.21
0.215
0.22
0.225
0.23
0.235
Power factor
Totalpowerloss(MW)
Loss with DG & Cap.
Loss witout DG & Cap.
Loss at optimal p.f.
Lagging Leading
Fig. 3.4: Variation of power factor on power loss of 69-bus distribution system

47. Comparative Study
 DG supply real power only.
49
Test System 33-bus
Method
GA [6] Proposed approach with DG Proposed Approach
(without Constraints)
Optimal Location 6 6 8 (DG) 30 (Capacitor)
Optimal Size 2380 (kW) 2490 (kW) 1500 (kW) 900 (kVAr)
Σ kW loss Reduction 44.83% 47.29% 66.74%

48. Test System 33-bus 69-bus
Method (IA) [2] Proposed Approach (IA) [2] Proposed Approach
Optimal Location 6 6 61 61
Optimal Size 3.03(MVA) 2.49 MW (DG), 2.22(MVA) 1.81 MW (DG),
1.72 MVAr (Cap) 1.29 MVAr (Cap)
3.03(MVA) 2.22(MVA)
Optimal p.f. (Leading) 0.85 0.82 0.82 0.81
Σ kW loss Reduction 67.67% 67.79% 89.67% 89.69%
50
• integration of DG in reactive power compensated network also
reduces the size of DG
• Less capital cost of Capacitor
• provides more economy to the system.

49. Conclusions
 The main conclusions can be drawn as
 minimize the active power loss,
 maintain the voltage profile of the system,
 reduces the size of DG,
 Less Capacitor cost
 more economy solution
51

50. 52
Optimal Placement of Different
Type of DG Sources in
Distribution Networks

51. 53
Introduction
 Most of work on DG supplying real power only i.e., the type-I DGs.
 In the present work different types of DG’s
 Both PSO technique and analytical approach
 Different types of DGs are:
 Type-I
 Type-II
 Type-III
 Type-IV

52. Problem Formulation
 Objective: Minimization of real power loss
 Approaches:
 PSO technique
 Analytical approach
54
𝑀𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝑃𝐿 = 𝛼𝑖𝑗 𝑃𝑖 𝑃𝑗 + 𝑄𝑖 𝑄𝑗 + 𝛽𝑖𝑗 𝑄𝑖 𝑃𝑗 − 𝑃𝑖 𝑄𝑗
𝑁
𝑗=1
𝑁
𝑖=1

53. PSO technique

55

54.  Constraints:
 power flow equations
 Voltage constraint (±5% )
 Line current constraint
 Right-of-buses are excluded
56

55. PSO Parameters
Swarm size = 50
Number of iterations = 80
c1 = c2 = 2
ωmin = 0.4
ωmax = 0.9.
57

56.  Case-I
 placement of each type of DG independently
 Case-II
 type-I and type-II DG are placed together
 applied on 33-bus and 69-bus test networks
58
Cases

57. Test
system
Optimum
location
DG Type
Optimal Size of Different Types
of DG
Active Power loss (KW) % Reduction in
Active Power
loss(MW) (MVAr)
(MVA,
P.f)
Without
DG
With DG
33 bus
Bus 6 Type-I 3.15 ------ ------ 211 115.29 45.36%
Bus 30 Type-II ------ 1.23 ------ 211 151.41 28.24%
Bus 6 Type-III ------ ------
3.02, 0.82
(leading)
211 67.95 67.79%
69 bus Bus 61
Type-I 1.8078 ------ ------ 225 83.37 62.93%
Type-II ------ 1.29 ------ 225 152.10 32.40%
Type-III ------ ------
2.243,
0.82
(leading)
225 23.18 89.69%
59
 could not find any single type-II DG, which satisfies all the
constraints.
 With exception in the voltage limit i.e., ±8% in place of ±5%.
Case-I

58. 60
System PSO Technique
33 Bus
System
DG Type Bus No.
DG Capacity
Loss in
(kW)
CPU
Time
(s)
(MW) (MVAr)
Simultaneous
Type-I &
Type-II DG
placement
6 2.5317
58.45 1.97
30 1.2258
69 Bus
system
Simultaneous
Type-I &
Type-II DG
placement
61 1.8285
23.17 3.66
61 1.3006
Case-II: Different locations

59.  Power loss curves for different types of DGs
61
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
Bus Number
MinimumPowerLoss(MW)
Type-I DG
Type-II DG
Type-III DG
Type-I & II DGs
at Diff. Opt.Loc.
Total Power Loss of 33 bus distribution system

60.  Power loss curves for different types of DGs
62
Total Power Loss of 69 bus distribution system
1 6 11 16 21 26 31 36 41 46 51 56 61 6666 6969
0
0.05
0.1
0.15
0.2
0.25
Bus Number
MinimumPowerLoss(MW)
Type-I DG
Type-II DG
Type-III DG
Type-I & II DGs
at Diff. Opt. Locs.

61.  Results and Discussions:
63
System Analytical Approach
33 Bus
System
DG Type Bus No.
DG Capacity
Loss in
(kW)(MW) (MVAr)
MVA, P.f.
(leading)
Type-III DG 6
3.027,
0.82
67.95
Simultaneous
Type-I & Type-II
DG placement
6 2.4829
58.45
30 1.2232
69 Bus
system
Type-III DG 61
2.224,
0.81
23.19
Simultaneous
Type-I & Type-II
DG placement
61 1.8078
23.19
61 1.292
Analytical approach

62.  In case of type-I and type-II DGs similar results
 type-III DG results are slightly different due to heuristic nature of
the PSO.
 Power factor is same in both the cases
 In 69-bus system due to difference in the size and power factor,
the real power loss obtained by both the approaches is slightly
different.
64

63. Comparative Study
 proposed approach results were compared artificial bee colony
(ABC) algorithm [8] and GA method [6]
 The DG-unit supplying real power only.
65
Test System 69-bus
Method ABC[8] GA[6] Proposed PSO
Optimal Location 61 61 61
Optimal Size 1900 (kW) 1827 (kW) 1808 (kW)
Σ kW loss Reduction 62.97% 62.91% 62.95%

64.  The convergence characteristics of different types of DGs by
proposed PSO approach
66
0 50 100 150 200 250 300 350 400 450 500
92
92.2
92.4
92.6
92.8
93
93.2
93.4
Nuber of iterations
FitnessFunction(kW)
0 50 100 150 200 250 300 350 400 450 500
155.3
155.4
155.5
155.6
155.7
155.8
155.9
156
156.1
156.2
156.3
Number of iterations
Fitnessfunction(kW)
0 50 100 150 200 250 300 350 400 450 500
20
25
30
35
40
45
50
55
60
65
Number of Iterations
Fitnessfunction(kW)

65. Voltage profiles
 Improvement in voltage
67
System
Voltage @bus before DG Voltage @bus after DG
Min Max Min Max
33 bus 0.9038@18 1.0000@1 0.9502@18 1.0000@1
69 bus 0.9092@65 1.0000@1 0.9679@27 1.0000@1
Voltage profile before and after Type-I DG
System
Voltage @bus before DG Voltage @bus after DG
Min Max Min Max
33 bus 0.9038@18 1.0000@1
0.92@18 (±5%
Voltage violation)
1.0000@1
69 bus 0.9092@65 1.0000@1
0.93@65 (±5%
Voltage violation)
1.0000@1
Voltage profile before and after Type-II DG

66. System
Voltage @bus before
DG
Voltage @bus after DG
Min Max Min Max
33 bus 0.9038@18 1.0000@1 0.9570@18 1.0002@6
69 bus 0.9092@65 1.0000@1 0.9724@27 1.0000@1
68
Voltage profile before and after Type-III DG
System
Voltage @bus before
DG
Voltage @bus after DG
Min Max Min Max
33 bus 0.9038@18 1.0000@1 0.9570@18 1.0002@6
69 bus 0.9092@65 1.0000@1 0.9724@27 1.0000@1
Voltage profile before and after simultaneous Type-I & Type-II
DGs placement
 in all the cases the voltage profile improves significantly after optimal
placement of DGs.

67. Size & Site allocation of Type-IV DG

69
𝑀𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝑃𝐿 = 𝛼𝑖𝑗 𝑃𝑖 𝑃𝑗 + 𝑄𝑖 𝑄𝑗 + 𝛽𝑖𝑗 𝑄𝑖 𝑃𝑗 − 𝑃𝑖 𝑄𝑗
𝑁
𝑗=1
𝑁
𝑖=1

68. 
70

69. Results and Discussions
71
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Bus Number
OptimumRealPowerProduction(MW)
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33
0.16
0.17
0.18
0.19
0.2
0.21
0.22
0.23
Bus Number
RealPowerLoss(MW)

70.  best location is 12 with a total power loss of 163.3 kW and 113.7
KVAR respectively.
 Similarly for 69-bus system
72
1 6 11 16 21 26 31 36 41 46 51 56 61 66
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Bus Number
OptimumRealPowerGeneration(MW)

71.  DG producing 1.36 MW and consuming 0.574 MVAR when installed
at bus No. 56 to minimize the loss.
73
0 5 10 15 20 25 30 35 40 45 50 55 60 65
0.18
0.2
0.22
0.24
0.26
0.28
0.3
0.32
0.34
0.36
Bus Number
RealPowerLoss(MW)

72. Case
Test
system
Optimal
location
Optimum DG size
Real Power loss
(KW)
Reactive Power
loss (KVAr)
% reduction in
loss
(MW) (MVAr)
Without
DG
With
DG
Without
DG
With
DG
Real Reactive
Analytical
33 bus Bus 12
1.52 0.592 211 163.3 143 113.7 22.61% 20.49%
PSO 2.18 0.691 211 155.3 143 109.4 26.40% 23.49%
Analytical
69 bus Bus 56
1.36 0.574 266.5 199 119.4 89.5 25.33% 25.04%
PSO 1.72 0.618 266.5 193.4 119.4 86.5 27.43% 27.55%
74
 The Real and Reactive Power loss with and without DG for 33 bus and
69 bus systems with Analytical and PSO techniques

73. 75
Conclusions
 Placement of different types of DGs using PSO technique
 optimal power factor is evaluated
 PSO approach results are verified with analytical approach
 analytical approach are suitable for finding the location in smaller
systems.
 heuristic approaches are more suitable for large systems because
searches converge to solution fast.

74. 76
Hybrid Approach for placement
of Multiple DGs

75. 77
Introduction
 combination of analytical approach and PSO technique
 size of multiple DGs supplying real and reactive powers by
analytical approach.
 locations and optimal power factors by PSO application.
 voltage profile enhancement is also examined.
 results of Proposed hybrid approach are verified with PSO technique
and fast analytical method

76. Hybrid approach

78

77. Hybrid approach

79

78. 
80

79. 
81

80. Optimal Locations of DGs
 single DG placement, it is possible to calculate DG size and to
evaluate the loss at every bus.
 For n DGs and N buses in the same network, the numbers of
combinations be NCn,
 Hence, a search technique or a heuristic method is needed
 locations power factors are determined by using PSO technique,
82

81. 83
 Objective is to minimize the active power and reactive power loss
subject to the following constraints
 subject to
 operational constraints as given by load flow equations,
 DG & Capacitor supplying real power & reactive power,
 sizing and locations at peak load,
 Line loading and voltage limits.
Problem Formulation

82. Results and Discussions
 Size and Site allocation of type-I multiple DGs
 The results are discussed as given in table
84

83. 85
Case Approach Installed DG schedule
Total DG
capacity
(MW)
Ploss
(kW)
Loss
reduction
(%)
No DG 211 0.00
I DG
Hybrid
Bus 6
Size 2.49 2.49 111.17 47.31
PSO
Bus 6
Size 2.59 2.59 111.03 47.38
IA [9]
Bus 6
Size 2.60 2.60 111.10 47.39
2 DG
Hybrid
Bus 13 30
Size 0.83 1.11 1.94 87.28 58.64
PSO
Bus 13 30
Size 0.85 1.16 2.01 87.17 58.69
IA [9]
Bus 6 14
Size 1.80 0.72 2.52 91.63 56.61
3 DG
Hybrid
Bus 13 24 30
Size 0.79 1.07 1.01 2.87 72.89 65.45
PSO
Bus 14 24 30
Size 0.77 1.09 1.07 2.93 72.79 65.50
IA [9]
Bus 6 12 31
Size 0.90 0.90 0.72 2.52 81.05 61.62

84.  Size and Site allocation of type-II multiple DGs
 helps in enhancement of voltage profiles of the systems.
86
System Case Installed DG schedule
DG capacity
(MVAr)
Ploss
(kW)
Loss
reduction (%)
33-bus
No DG 211 0.00
I DG
Bus 30
Size 1.23 1.23 151.41 28.24
2 DG
Bus 12 30
Size 0.43 1.04 1.47 141.94 32.73
3 DG
Bus 13 24 30
Size 0.36 0.51 1.02 1.89 138.37 34.42

85.  Size and Site allocation of type-III multiple DGs
 The results are discussed as given in table
87

86. 88
Case Approach
Bus
Location
DG size
(MVA)
Optimal
p.f.
Power
loss (kW)
% Loss
Reduction
No DG 211 0
1 DG
Hybrid 6 3.028 0.82 67.9 67.82
PSO 6 3.035 0.82 67.9 67.82
IA [9] 6 3.107 0.82 67.9 67.82
2 DG
Hybrid
13 1.039 0.91
28.6 86.44
30 1.508 0.72
PSO
13 0.914 0.91
28.6 86.44
30 1.535 0.73
IA [9]
6 2.195 0.82
44.39 78.98
30 1.098 0.82
3 DG
Hybrid
13 0.873 0.90
11.7 94.4524 1.186 0.89
30 1.439 0.71
PSO
13 0.863 0.91
11.8 94.4124 1.188 0.90
30 1.431 0.71
IA [9]
6 1.098 0.82
22.29 89.4530 1.098 0.82
14 0.768 0.82

87. Type-I and Type-II DGs placed at different locations
Approach
DG Type
Bus
Location
DG Capacity
Power
loss (kW)
% Loss
Reduction(MW) (MVAr)
No DG 211 0
Hybrid
Type-I & II
DGs
6 2.483
58.51 72.27
30 1.223
PSO
Type-I & II
DGs
6 2.532
58.45 72.29
30 1.256
Hybrid
Type-I & II
DGs
12 0.436
28.49 86.4913 0.828
30 1.114 1.036
PSO
Type-I &
Type-II
DGs
12 0.449
28.49 86.4913 0.846
30 1.138 1.044
Hybrid
Type-I &
Type-II
DGs
13 0.364
11.7 94.45
14 0.753
24 1.075 0.516
30 1.028 1.008
PSO
Type-I &
Type-II
DGs
13 0.364
11.8 94.41
14 0.753
24 1.075 0.516
30 1.028 1.008
89

88. Voltage Profiles
 Voltage profile before and after 1DG of Type-III
 Voltage profile before and after 2DG of Type-III
 Voltage profile before and after 3DG of Type-III
90
System Voltage @bus before DG Voltage @bus after DG
Min Max Min Max
33 bus 0.9038@18 1.0000@1 0.9572@18 1.0004@6
69 bus 0.9092@65 1.0000@1 0.9725@27 1.0000@1-3,28,36
System Voltage @bus before DG Voltage @bus after DG
Min Max Min Max
33 bus 0.9038@18 1.0000@1 0.9572@18 1.0004@6
69 bus 0.9092@65 1.0000@1 0.9725@27 1.0000@1-3,28,36
System Voltage @bus before DG Voltage @bus after DG
Min Max Min Max
33 bus 0.9038@18 1.0000@1 0.9919@8 1.0003@30
69 bus 0.9092@65 1.0000@1 0.9943@50 1.0000@1-4,28,36,61

89. Conclusion
 allocation of multiple DGs of multiple types minimizes the line
losses.
 Number of DG units reduces the losses to a considerable amount.
 optimal power factor results minimum power loss has also been
evaluated.
 proposed approach minimize the sizes of DGs.
 Improvement in voltage profiles of the systems.
91

90. 92
Cost Benefit Analysis for DG
Placement

91. 93
Introduction
 Design, operate and maintain reliable power system with lowest cost
and highest benefit,
 objective is to minimize the real power loss to maximize the benefits,
 Distributions companies are responsible for providing customer
demand at lowest cost,
 optimal placement of real and reactive power sources in the
distribution systems to maximize the profit.
 Various technical and economic factors are considered to achieve
the objective.

92. Mathematical Modelling

94

93. 
95

94. 
96

95. Objective Function

97

96.  Subject to the constraints:
 Power flow equations must be satisfied,
 DGs & Capacitors are supplying real power & reactive power
respectively,
 voltage must be kept within standard limits,
 Thermal limit of distribution lines for the network must not exceed,
 Sizes of DGs and Capacitors are equal to or less than 30% of
substation rated capacity.
98

97. Case Study
 DG unit is considered out of service 10% of the time due to both
predicted and unpredicted (O & M) reasons,
Expected hours unavailable = 0.1 x 8760 = 876 hours consist of
170 hours for scheduled maintenance,
171.8 hours expected joint fuel system
534.2 unexpected failures.
i.e., DG will be available for 7884 hours of operation during the year
99

98. Commercial data regarding DGs and
Capacitors

100

99. Results Analysis and Discussions
 DG and Capacitor placement is carried out for a 10-year study
period on 33-Bus System.
101
Network condition Optimal size at optimal location Costs ( )
DG allocation 1.5 MW at node 8
Capacitor allocation 0.9 MVAr at node 30
Initial investment on DG ( ) 375 x 105
Initial Investment on Capacitor ( ) 9 x 104
Benefits of loss reduction ( ) 4.35 x 107
Benefits of reduction in
purchased energy ( )
4.99 x 108
Operational costs of DG ( ) 2.49 x 108
Maintenance cost of DG ( ) 6.34 x 107
Maintenance cost of Capacitor ( ) 1.94 x 105
Total benefits ( ) 1937.94 x 105

100.  Table shows acquired benefit during the planning period
 Time to execute comes out to be 30.81 second.
 total benefits are Rs.1937.94 lacks in planning period of 10 years
 planning period of 2 years, placement of DG and Capacitor
evaluates the profit of Rs.143.14 lacks
 DG of 1.5 MW at node 8 gives the benefits Rs. 315.23 lacks in a
planning period of 3 years
 Capacitor of 0.9 MVAr in combination with DG, the benefit increases
from Rs.315.23 lacks to Rs. 396.62 lacks.
102

101.  additional investment of Rs.0.9 lacks on Capacitor, provide the
benefit of 81.39 lacks.
 The total initial investment for the optimal placement of DG and
Capacitor comes to be Rs.375.9 lacks.
 initial investment will be recovered in less than 3 years
 The payback period is 3 year.
103

102.  Planning period of 3 years
104
Network condition Optimal size at optimal location Costs ( )
DG allocation 1.5 MW at node 8
Capacitor allocation 0.9 MVAr at node 30
Benefits of loss reduction ( ) 1.52 x 107
Benefits of reduction in
purchased energy ( )
1.66 x 108
Operational costs of DG ( ) 8.33 x 107
Maintenance cost of DG ( ) 2.12 x 107
Maintenance cost of Capacitor ( ) 6.48 x 104
Total benefits ( ) 396.52 x 105

103.  DG and Capacitor placement is carried out for a 10-year
study period on 69-Bus System.
105
Network condition Optimal size at optimal location Costs ( )
DG allocation 1.5 MW at node 61
Capacitor allocation 1.2 MVAr at node 61
Initial investment on DG ( ) 375 x 105
Initial Investment on Capacitor ( ) 1.2 x 105
Benefits of loss reduction ( ) 6.52 x 107
Benefits of reduction in
purchased energy ( )
4.99 x 108
Operational costs of DG ( ) 2.49 x 108
Maintenance cost of DG ( ) 6.34 x 107
Maintenance cost of Capacitor ( ) 2.45 x 105
Total benefits ( ) 2137.19 x 105

104.  planning period of 10 years, a maximum benefits of Rs.2137.19
lacks is achieved
 time taken to execute the optimisation is 51.76 seconds.
 Total initial investment on DG and Capacitor are of Rs.376.2 lacks
 For the planning period of 3 years a benefit of Rs.462.83 lacks can
be obtained.
 total initial investment can be recovered less than 3 years.
 payback period is 3-years.
106

105.  operational costs of Capacitor are nil
 maintenance costs of Capacitor are also too low
 small investment on Capacitor installation maximizes the benefit
107

106.  optimal placement of DG and Capacitor also improves the voltage
profile of test systems,
 another advantage of capacitor placement in addition to maximize
the profit to distribution owner.
108
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
0.9
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0.98
0.99
1
Bus Number
VoltageProfilep.u.
With DG and Capacitor Base Case Voltage

107. 109
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69
0.9
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0.98
0.99
1
Bus Number
VoltageProfilep.u.
With DG and Capacitor Base Case Voltage

108. Conclusions
 presented approach maximizing the profit taking various technical
and economic factors,
 Problem has been optimized considering for different years of
planning periods,
 Installation of Capacitor with DG reduces the loss of the network
drastically,
 initial investments and maintenance costs of capacitor are too less
and having no operational costs,
 The initial investments can be recovered in shorter time period,
110

109.  Installation of DG and capacitor provides more economic solution to
the distribution owner,
 improvement in voltage profile
 Reducing of power flow in conductors because of compensating
loss,
 Decreases stress on the conductors which increases duration of life
time.
111

110. 112
Future Scope of the Work
 work carried out may also be extended for congestion management
 work presented may be extended to mitigate the intermittency of
renewable energy sources.
 Economic dispatch problem of smart microgrid including distributed
generation may be explore.
 Contribution of distributed generation to ancillary services may be
explored.

111. 113
 Distributed generation allocation may be extended for service
restoration
 DG allocation problem may be extended to see the impact on
transient stability of power system.
 optimal DG allocation problem may be extended to other FACTS
components.

112. 114
Author’s Research Publications
 Satish Kansal, Vishal Kumar, Barjeev Tyagi, “Optimal Placement of Different type
of DG Sources in Distribution Networks” International Journal of Electrical
Power and Energy Systems (Accepted), May 2013.
 Satish Kansal, B.B.R.Sai, Barjeev Tyagi, Vishal Kumar “Optimal placement of
Distributed Generation in distribution networks” International Journal of
Engineering, Science and Technology, vol. 3, no. 3, pp. 47-55, April 2012.
 Satish Kansal, Vishal Kumar, Barjeev Tyagi, “Optimal Placement of Distributed
Generator and Capacitor for Power Compensation in Distribution Network”
Electric Power Systems and Components , Under Review.
 Satish Kansal, Vishal Kumar, Barjeev Tyagi, “Hybrid Approach for Placement of
Multiple DGs of Multiple Type in Primary Distribution Networks” Electrical Power
Systems Research , Under Review.
 Satish Kansal, Vishal Kumar, Barjeev Tyagi, “DG and Capacitor Integration in
Power Distribution Systems” IET Generation, Transmission & Distribution
Under Review.

113. 115
 Satish Kansal, Vishal Kumar, Barjeev Taygi, “Multiple Distributed Generators
Placement in Compensated Primary Distribution Networks” 1st Annual International
Conference on Power, Energy and Electrical Engineering (PEEE-2013), 25-26th
August, 2013, Singapore (Accepted).
 Satish Kansal, Vishal Kumar, Barjeev Taygi, “Hybrid Approach for Placement of
Multiple Distributed Generators in Distribution Network” 17th National Power
Systems Conference, (NPSC-2012), IIT-BHU Varanasi, 12 - 14 December, 2012
 Satish Kansal, Vishal Kumar, Barjeev Taygi, “Composite Active and Reactive Power
Compensation of distribution networks” 7th IEEE International conference on
Industrial and Information Systems,(ICIIS-2012), IIT Madras, 6 - 9 August 2012.
 Satish Kansal, B.B.R.Sai, Barjeev Taygi, Vishal Kumar “Optimal placement of Wind-
Based Generation in distribution networks” IET International conference on
Renewable Power Generation (RPG-2011), Edinburgh, United Kingdom, 6 - 8
September 2011.
 †Satish Kansal, B.B.R.Sai, Barjeev Taygi, Vishal Kumar “Optimal placement of
Distributed Generation in distribution networks” National conference on Recent
Advantages in Electrical Power and Energy System Management (RAEPSM-
2011), M.M.M. Engineering College Gorakhpur, 25-26 March 2011.

114.  †Best Paper Award the paper presented at National Conference RAEPSEM-2011 at
MMMEC Gorakhpur (UP) on “Optimal Placement of Distributed Generation in
Distribution Networks” held on 25-26 March 2011.
116

115. 33-Bus Test System
117
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
26 27 28 29 30 31 32 33
19 20 21 22
23 24 25S/S

116. 69-Bus Test System
118

117. 119

118. 120

119. 121

120. 122

121. Illustration of PSO algorithm
This presentation is for the
understanding of PSO method applied
in DG Placement.

122. Step 1 : Initialize random values into particles which correspond to
bus numbers(or locations of DGs) and sizes to be kept at respective
locations of the chosen network
For Ex. Assume
 there are 3 DGs to be placed and
 the number of particles be 10
 33 bus data taken into consideration
then,
Note : All the values are assumed. They don't correspond to original values

123. Step 1 : Initialize random values into particles which correspond to
bus numbers(or locations of DGs) and sizes to be kept at respective
locations of the chosen network
For Ex. Assume
 there are 3 DGs to be placed and
 the number of particles be 10
 33 bus data taken into consideration
then,
Note : All the values are assumed. They don't correspond to original values
Locations of 3 DGs Sizes of 3 DGs

124. Step 1 : Initialize random values into particles which correspond to
bus numbers(or locations of DGs) and sizes to be kept at respective
locations of the chosen network
For Ex. Assume
 there are 3 DGs to be placed and
 the number of particles be 10
 33 bus data taken into consideration
then,
Note : All the values are assumed. They don't correspond to original values
Locations of 3 DGs Sizes of 3 DGs
10 Combinations
Or
10 particles

125. Step 1 : Initialize random values into particles which correspond to
bus numbers(or locations of DGs) and sizes to be kept at respective
locations of the chosen network
For Ex. Assume
 there are 3 DGs to be placed and
 the number of particles be 10
 33 bus data taken into consideration
then,
Note : All the values are assumed. They don't correspond to original values
1.1MW at 5th bus

126. Step 1 : Initialize random values into particles which correspond to
bus numbers(or locations of DGs) and sizes to be kept at respective
locations of the chosen network
For Ex. Assume
 there are 3 DGs to be placed and
 the number of particles be 10
 33 bus data taken into consideration
then,
Note : All the values are assumed. They don't correspond to original values
0.4MW at 4th bus

127. Step 1 : Initialize random values into particles which correspond to
bus numbers(or locations of DGs) and sizes to be kept at respective
locations of the chosen network
For Ex. Assume
 there are 3 DGs to be placed and
 the number of particles be 10
 33 bus data taken into consideration
then,
Note : All the values are assumed. They don't correspond to original values
2.1MW at 31st bus

128. Step 2 : For each Particle (or each combination of Buses), apply DG
sizes in the particle at locations given in the particle and calculate
loss using exact loss formula.
Sizes of
DGs
Locations of
DGs
Apply Exact
Loss equation PL = 0.132
Note : All the values are assumed. They don't correspond to original values

129. Step 2 : For each Particle (or each combination of Buses), apply DG
sizes in the particle at locations given in the particle and calculate
loss using exact loss formula.
Sizes of
DGs
Locations of
DGs
Apply Exact
Loss equation PL = 0.132
Note : All the values are assumed. They don't correspond to original values
Apply Exact
Loss equation PL = 0.114
Apply Exact
Loss equation PL = 0.122
Apply Exact
Loss equation PL = 0.199
. . .
. . .
. . .
. . .
. . .
. . .
. .
. . .
. . .
. . .
. . .
. . .
. . .
. .

130. Step 2 : For each Particle (or each combination of Buses), apply DG
sizes in the particle at locations given in the particle and calculate
loss using exact loss formula.
Note : All the values are assumed. They don't correspond to original values
Apply Exact
Loss equation PL = 0.114

131. Step 3 : Depending on the respective loss choose the minimum one as
global best.
update the personal best also.
Note : All the values are assumed. They don't correspond to original values
Assume That the following combination has the best value i.e. lowest
PL
Then,
Global
Best
Particle
Fitness of
Global
Best
Apply Exact
Loss equation PL = 0.114

132. Step 3 : Depending on the respective loss choose the minimum one as
global best.
update the personal best also.
Note : All the values are assumed. They don't correspond to original values
Global
Best
Particle
Fitness of
Global
Best
Apply Exact
Loss equation PL = 0.114
Personal Best is also updated similarly. The only change is that it is
compared to its own previous value of the respective Particle.

133. Step 4 : Update the velocities and positions of the Particles using PSO
update equations.
Note : All the values are assumed. They don't correspond to original values
After using
both
equations
and
updating,
The array
transforms
into

134. Step 5: Do steps 2,3,4 until the particles converge to a point where
Global best does not get updated.
Note : All the values are assumed. They don't correspond to original values