This document discusses optimal placement of photovoltaic distributed generation (DG) in distribution networks. It begins with an introduction to distributed generation and the types of DG systems. It then discusses methods for optimally determining the location and sizing of photovoltaic DG, including analytical and particle swarm optimization approaches. Several case studies are presented applying these methods to 33-bus and 69-bus test systems to minimize losses and improve voltage profiles with DG integration.

1. ANALYSIS AND DESIGN OF
PHOTOVOLTAIC TYPE DG IN
DISTRIBUTION NETWORKS
Presented By:
Dr. Satish Kansal
Department of Electrical Engineering
BHSBIET Lehragaga

2. 2
Introduction
 Objective-to meet the demand at all the locations within
power network as economically and reliably as possible.
 Traditional electric power system- utilize the
conventional energy resources for electricity generation
 Operation-such traditional generation systems is based on
centralized control utility generators to deliver power to widely
dispersed users through an extensive transmission and
distribution network
 Present Environment- the justification for large central-station
plants is weakening due to depleting conventional energy
sources.

3. 3
Distributed Generation
Distributed Generation (DG), a term commonly used for
small-scale generations, offer solution to many of these
new challenges
Recent developments in small renewable generation
technologies such as wind turbines, photovoltaic, fuel cells,
micro turbines and so on has drawn distribution utilities’
attention to possible changes in the distribution system
infrastructure.

4. 4
 The DG’s can be characterized into different types as:
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.

5.  CIGRE :Define DG as the generation, which has the
following characteristics
 Not centrally planned
 Not centrally dispatched at present
 Usually connected to the distribution networks
 Smaller then 50 MW.
 International Energy Agency (IEA) :
 serving a customer on-site
 providing support to a distribution network,
 connected to the grid
5

6. 6
Distributed Generation
 Embedded Generations
 Disperse Generations
 depends upon many technologies
 depends upon many applications
 Increasing DG penetration- Growing share of
distributed generators (DGs)
 Policy initiatives to promote DG throughout the world

7. 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
7

8. 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 2,11,766.22 MW
 per capita per year only 860.72 kWh
 triple by 2020, with 6.3% annual growth.
9

9.  India is in power deficient state
 power deficiency is nearly 12.2% of peak demand.
 In UP, MP, Maharashtra, Bihar, and Punjab; it is more than 20%.
 results in power cuts, blackouts, etc.
 The above mentioned causes make the Distribution Generation from
fuel cells, wind turbines, photovoltaic and small/micro hydro plants
for continuous growth of the country.
10

10. 11
Optimal Placement of
Photovoltaic Type DG

11. 12
 DG supplying real power
 loss reduction and voltage profile improvement
 operational constraints
Optimal Placement of DG

12. 13
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

13. Approaches
14
 Analytical approach
 PSO Technique
 Analytical approach
 Optimal size of PV
 Optimal Location

14. 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.
15

15. 16
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

16. 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
17
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

17. 18
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

18. 19
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

19. Conclusions
 minimize the real power loss.
 Improvement in voltage profile
 minimizing the DG size
20

20. Renewable and Non- Renewable
Energy Sources.
21

21. Energy Scenario In India
22

22. Energy source Estimated Potential Cumulative
Installed capacity/ number
Wind power 45,000 MW 18321.10 MW
Small Hydro
(upto 25 MW)
15,000 MW 3464.59 MW
Biomass Power 16,000 MW 1242.60 MW
Bagasse Cogeneration 3,500 MW 2199.33 MW
Waste to energy 2,700 MW 93.68 MW
Solar Power (SPV) ----- 1047.16 MW
Family size Biogas Plants 12 million 45.45 Lakh
Solar street lighting system ----- 1,19,634 nos.
Home lighting system ----- 6,03,307 nos.
Solar Lanterns ----- 7, 97,344 nos.
Solar photovoltaic power plants 2.92 MWp
Solar water heating systems 140 million m2
of collector area
5.63 Million m2 of collector area
Solar photovoltaic pumps ----- 7334 nos.
Biomass gasifiers ----- 153.04 MW
23
Table 1.1 Renewable Energy Potential in India AND actual progress achieved up to 30.11.2012

23.  Renewable sources already contribute to about 5% of
the total power generating capacity in the country.
 Prospects for renewable are steadily improving in India
(% of total installed capacity is expected to be 10% by
2020).
24

24. Solar power as a solution to the
Indian power scenario
25

25. Why Solar Renewable energy
Sources Preferred
26

26. Solar PV Applications in PUNJAB
27

27. Converting Solar Energy into
Electrical Energy
28

28. 29

29. 30
Small units in the kilowatt range.
Economical point of view less
aggressive .
No cost reduction
Covers a wide range from less than one Watt to
several megawatts.
more aggressive .
Its cost decreasing day by day

30. Photovoltaic Cell
 “Photo” meaning light.
 “voltaic,” which refers to producing electricity.
 A device that produces an electric reaction to light,
producing electricity.
 A typical PV cell made of crystalline silicon is 12
centimetres in diameter and 0.25 millimetres thick. In full
sunlight, it generates 4 amperes of direct current at 0.5
volts or 2 watts of electrical power.
31

31. Photovoltaic Module
 Cells are interconnected and their
electrical connections are then
sandwiched between a top layer of
glass or clear plastic and a lower
level of plastic or plastic and metal.
An outer frame is attached to
increase mechanical strength, and
to provide a way to mount the unit.
This package is called a "module" or
"panel".
32

32. Photovoltaic Modules Performance
33

33. Impact of solar radiation on V-I characteristic
curve of Photovoltaic Module
34

34. Impact of temperature on V-I
characteristic curve of Photovoltaic
Module
35

35. PHOTOVOLAIC ARRAY
36

36. Types of Solar Photovoltaic System
37
 Stand alone PV system
 Grid Connected PV Systems
 Hybrid System

37. Stand alone PV system
38

38. Grid Connected PV Systems
39

39. Hybrid System
40

40. Grid Connected PV Systems is
Preferred
 No use of battery reduces its capital cost
 More reliable than other PV system.
 To install Grid connected SPV system the
following points are taken into consideration
41

41. GRID CONNECTED SOLAR
PHOTOVOLTAIC SYSTEM
42

42. Grid Connected PV Systems
 Estimate the solar potential available
 Develop a system based on the potential estimations
made for a chosen area of 100, 200, 500 m².
 Annual energy generation by designed plant.
 In the last cost estimation of grid connected SPV power
plant to show whether it is economically viable or not.
43

43. Estimation of Solar Potential
 The solar radiation over different months measured.
 The diurnal variations, average monthly output , yearly
output are find out and related graphs are plot for
showing the variation in different season and time.
 Peak variation and possible plant rating also calculated.
44

44. How Solar Radiation measured
 Estimation the solar potential
 Reading of solar radiation of given site.
 It should have the ability to store the data which
it measure for at least three months.
45

45. METHODOLOGY
 Calculated the daily energy output and monthly
energy output for different months .
 For better understanding, the measured solar
radiation data sheet for the month of April 2010
has been given as a sample of Electrical
Engineering Department, IIT Roorkee site.
46

46. Time 9:00am
47

47. Time10:00am
48

48. Time11:00am and so on
49

49. Variations for April Month 2010
50

50. Variations for the month of Jan 2010 to Feb
2010

51. Variations for the month of
September to December 2010
52

52. Total Output
53

53. Daily & Monthly Energy Outputs
(Watt/ mtr²-hr)
54

54. Peak Variation and Possible Plant
Rating
55

55. Condition for Grid inter facing
 Phase sequence matching: For a three phase system
three phases should be 120 deg phase apart from each
other for both the system.
 Frequency matching : Frequency of the SPV system
should be same as grid. Generally grid is of 50 Hz
frequency capacity, now if SPV systems frequency is
slightly higher than grid frequency (0.1 to 0.5)
synchronization is possible but SPV system frequency
should not be less than grid frequency
 Voltage matching: Voltage level of both the system
should same, otherwise synchronization is not possible.
56

56. 9 kWp Grid connected PV system
 Solar Panel Specification
57

57. Solar PV Power Plant Specification
58

58. Cost analysis for 9kWp SPV Grid
Connected Power Plant
 Total cost for 50 panels : Rs.14,40000/-
 Cost of 3-φ Inverter : Rs. 2,50000/-
 Cost of 3-φ step up Transformer : Rs. 2,00000/-
 Subtotal: Rs.18,90000/-
 Multiply the subtotal above by 0.2 (20%) to cover
balance of system cost. Cost Estimate for Balance of
System: (1890000 × 0.2) Rs. 3,78000/-.
 Total Estimated PV System Cost is Rs.22,68000/-.
59

59. Thanks
60

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

61. 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

62. 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

63. 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

64. 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

65. 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

66. 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

67. 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

68. 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
. . .
. . .
. . .
. . .
. . .
. . .
. .
. . .
. . .
. . .
. . .
. . .
. . .
. .

69. 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

70. 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

71. 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.

72. 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

73. 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