A new simplified approach for optimum allocation of a distributed generation

International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME
& TECHNOLOGY (IJEET)

ISSN 0976 – 6545(Print)
ISSN 0976 – 6553(Online)
Volume 4, Issue 2, March – April (2013), pp. 165-178
IJEET
© IAEME: www.iaeme.com/ijeet.asp
Journal Impact Factor (2013): 5.5028 (Calculated by GISI) ©IAEME
www.jifactor.com

A NEW SIMPLIFIED APPROACH FOR OPTIMUM ALLOCATION OF
A DISTRIBUTED GENERATION UNIT IN THE DISTRIBUTION
NETWORK FOR VOLTAGE IMPROVEMENT AND LOSS
MINIMIZATION

Dr.T.Ananthapadmanabha1, Maruthi Prasanna.H.A. 2, Veeresha.A.G. 2,
Likith Kumar. M. V 2
1
Professor, Dept of EEE, NIE, Mysore, Karnataka, India.
2
Research Scholar, Dept of EEE, NIE, Mysore, Karnataka, India.

ABSTRACT

In the present energy scenario, increased concerns are shown towards distributed
generation (DG) driven by renewable energy resources. DG is a small scale generation units
that are connected near to customer load center or directly to the distribution network. Such
DGs has the capability of altering power flows, system voltages, and the performance of the
integrated network. When DGs are integrated to existing distribution network, offers many
techno-economical benefits. To maximise the availing benefits, optimal DG planning is
necessary. The two critical issues of DG planning are : Optimal Placement of DG & Optimal
sizing of DG. The problem of optimal allocation of DG in the existing distribution system
plays an important role in planning and operation of Smart Electrical Distribution Systems,
which is the state of the art development in power system. In this paper, the optimal location
of a DG is found out by using a new index called ‘TENVDI’ & the optimal sizing of DG at
the optimal location is decided for loss minimisation. The proposed methodology has been
tested on standard IEEE-33bus radial distribution system & IEEE-69bus radial distribution
system using MATLAB 2008. The method has a potential to be a tool for identifying the best
location and rating of DG to be installed for improving voltage profile and reducing line
losses in a distribution system.

KEYWORDS: RDS (Radial Distribution System), DG (Distributed Generation), TEN (Tail
End Node), VDI (Voltage Deviation Index).

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1. INTRODUCTION

Due to limitation on fossil fuel resources, alternative solutions to traditional large
power stations areunder high priority in recent years to meet growing energy demand of the
future [1]. Distributed Generation (DG) usually refers to the power generation from a few
kilowatts to hundreds of megawatts ( and some proposed restrictions under 50MWs) of the
small scale, distributed, efficient, reliable power generation unit which is arranged around the
user [2].The IEEE defines DG is the generation of electricity by facilities that are sufficiently
smaller than central generating plants so as to allow interconnection at nearly any point in a
power system [2].DG is an approach that employs small scale technologies to produce
electricity close to the end users of power. DG technologies often consist of modular (and
sometimes renewable energy) generators, and they offer a number of potential benefits. In
many cases, DGs can provide lower cost electricity and higher power reliability and security
with fewer environmental consequences than can traditional power generators.DG
technologies include small gas turbines, wind turbines, small combined cycle gas turbines,
micro turbines, solar photovoltaic, fuel cells, biomass and small geothermal generating
plants.
Determining the suitable location and sizing of a DG is important in order to ensure
for maximum benefits to be obtained from the integration of DG with the distribution system.
with proper planning of DG integration the following technical and economical benefits such
as Voltage support and power quality improvement, Utility system reliability improvement,
Voltage profile improvement, Spinning reserve support during generation outages, Reduction
in line losses and hence reduce demand for the grid, Environmental impact in terms of
reduction in polluting emission as compared with traditional power plants, Transmission and
distribution costs can be reduced since the DG units are closer to the customers, DG is
available in small modular units and therefore easier to find for their resulting in sites short
lead times for procurement and installation, DG plants offer good efficiencies especially in
co-generations and combined-cycles (for larger plants) and many more. The main
applications of DG can be found in the applications involving Base load, Standby Power,
Stand alone systems, Peak load shaving, Rural and remote applications, Combined Heat &
Power (CHP), & Grid support.
In literature, there are a number of approaches developed for placement and sizing of
DG units in distribution system. Chiradeja and Ramkumar [3] presented a general approach
and set of indices to assess and quantify the technical benefits of DG in terms of voltage
profile improvement, line loss reduction and environmental impact reduction. Khan and
Choudhry [4] developed an algorithm based on analytical approach to improve the voltage
profile and to reduce the power loss under randomly distributed load conditions with low
power factor for single DG as well as multi DG systems. Hung et al. [5] used an improved
analytical method for identification of the best location and optimal power factor for placing
multiple DGs to achieve loss reduction in large-scale primary distribution networks. For
optimal placement of DG, Mithulanathan et al. [6] presented a genetic algorithm based
approach to minimize the real power loss in the system and found a significant reduction in
the system loss. The optimal sizing and siting of DGs was investigated by Ghosh et al. [7] to
minimize both cost and loss with proper weighing factors using Newton-Raphson (NR) load
flow method. Ziari et al. [8] proposed a discrete particle swarm optimization and genetic
algorithm (GA) based approach for optimal planning of DG in distribution network to
minimize loss and improve reliability. Kamel and Karmanshahi [9] proposed an algorithm for

166


optimal sizing and siting of DGs at any bus in the distribution system to minimize losses and
found that the total losses in the distribution network would reduce by nearly 85%, if DGs
were located at the optimal locations with optimal sizes. Singh et al. [10] discussed a multi-
objective performance indexbased technique using GA for optimal location and sizing of DG
resources in distribution systems.
This paper presents a simple method for voltage profile improvement, real power loss
reduction, substation capacity release and is based on tail end nodes voltage sensitivity
analysis. Power flow analysis is done using the forward-backward sweep method. Test results
carried out on IEEE-33 bus system & IEEE-69 bus system using MATLAB 2008 validates
the suitability of this proposed method.

2. NOMENCLATURE

Nn : Total number of nodes or buses in the given radial distribution system.
TENVDI : Tail End Nodes Voltage Deviation Index (matrix of order Nn X 1)
TENVDIi : Tail End Nodes Voltage Deviation Index evaluated by placing DG at bus
number i.
NTE : Number of Tail End Nodes.
SDG : Complex Power rating of DG in MVA
SDGmin & SDGmax : Minimum & Maximum Complex Power rating of DG in MVA
Ploss, Qloss, & Sloss : Real Power, Reactive Power & Complex Power loss in distribution system
SDGopt : Optimal Size of DG (Complex power rating in MVA)
SDopt : Complex demand at optimal location in MVA
SDG : Incremental value of Size of DG (Complex power rating in MVA)

3. PROPOSED METHODOLOGY

The optimal allocation of DG problem consists of three important steps. Viz Selection
of Load flow analysis technique, finding optimal location and selection of optimal size of DG.

3.1 LOAD FLOW ANALYSIS

Conventional NR and Gauss Seidel (GS) methods may become inefficient in the
analysis of distribution systems, due to the special features of distribution networks, i.e. radial
structure, high R/X ratio and unbalanced loads, etc. These features make the distribution
systems power flow computation different and somewhat difficult to analyze as compared to
the transmission systems. Various methods are available to carry out the analysis of balanced
and unbalanced radial distribution systems and can be divided into two categories. The first
type of methods is utilized by proper modification of existing methods such as NR and GS
methods. On the other hand, the second group of methods is based on backward and forward
sweep processes using Kirchhoff’s laws. Due to its low memory requirements, computational
efficiency and robust convergence characteristic, backward and forward sweep based
algorithms have gained the most popularity for distribution systems load flow analysis. In this
study, Backward and Forward sweep method [11] is used to find out the load flow solution.

3.2 OPTIMAL PLACEMENT OF DG USING TENVDI :

In order to restrict solution space to few buses, tail end nodes are first identified by
viewing the distribution network topology. By penetrating DG with 50% of the total feeder

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loading capacity at each node at a time, the Tail End Nodes Voltage Deviation Index
(TENVDI) is calculated using (1). When DG is connected at bus i, TENVDI for bus i is
defined as:

ሺ௏௡௢௠௜௡௔௟ି௏௠ሻ మ
TENVDIi = ∑ே்ா
௠ୀଵ ே்ா
--- (1)

Where, ‘m’ corresponds to the each tail end node element of Tail End Nodes (TEN) matrix of
order NTE X 1 ;
Vnominal is taken as 1.0 Pu ;
TENVDIi gives the total deviation of voltages of all tail end nodes of the network with
respect to the nominal voltage. The bus corresponding to the minimum TENVDI value when
DG is inserted at the same bus is the optimal location of DG in the distribution system. The
flowchart for finding optimal location for DG placement is shown in fig1.

Figure 1: Flowchart for finding optimal location of DG in distribution system using
TENVDI

168


3.3 OPTIMAL SIZING OF DG AT OPTIMAL LOCATION:

For deciding the optimal size of DG to be placed at the optimal location obtained
from TENVDI, the DG is inserted at the optimal bus, size is varied from minimum value
(SDGmin) to maximum value (SDGmax) with step size of ( SDG). The size which gives the
minimum complex power loss is the optimal size of DG to be placed at optimal location. The
flowchart for determining the optimal size of the DG to be placed at optimal location for loss
minimisation is shown in fig2.

Figure 2: Flowchart for determinign optimal size of DG at optimal location for loss
minimisation

169


4. SIMULATION RESULTS AND DISCUSSION

4.1 IEEE-33 BUS RADIAL DISTRIBUTION SYSTEM

The distribution system characteristics: Number of buses=33; Number of lines=32;Slack Bus
no=1; Base Voltage=12.66KV; Base MVA=100 MVA; The test system is simulated in MATLAB
2008 & the proposed methodology has been tested, whose results are as shown below.

Figure 3: Single line diagram of standard IEEE-33 Bus system

Table 1: Tail End Node matrix elements

Sl.no Tail End Nodes
1 18
2 22
3 25
4 33

Table 2:Base case Bus Voltages for IEEE-33BUS test system

Bus Bus Bus Bus Bus Bus
no Voltage no Voltage no Voltage
(Pu) (Pu) (Pu)
1 1.0000 12 0.9177 23 0.9793
2 0.9970 13 0.9115 24 0.9726
3 0.9829 14 0.9093 25 0.9693
4 0.9754 15 0.9078 26 0.9475
5 0.9679 16 0.9064 27 0.9450
6 0.9495 17 0.9044 28 0.9335
7 0.9459 18 0.9038 29 0.9253
8 0.9323 19 0.9965 30 0.9218
9 0.9260 20 0.9929 31 0.9176
10 0.9201 21 0.9922 32 0.9167
11 0.9192 22 0.9916 33 0.9164

170


Figure 4: Basecase Voltage profile for IEEE-33bus system

Table 3: Variation of TENVDI with DG Placement

Bus TENVDI Bus TENVDI Bus TENVDI
no (x10-4) no (x10-4) no (x10-4)
1 5.231 12 0.913 23 3.969
2 5.028 13 1.378 24 3.914
3 4.049 14 1.681 25 4.005
4 3.471 15 2.009 26 1.668
5 2.918 16 2.452 27 1.558
6 1.755 17 3.593 28 1.229
7 1.525 18 4.201 29 1.137
8 0.894 19 5.019 30 1.141
9 0.775 20 5.172 31 1.289
10 0.832 21 5.297 32 1.378
11 0.856 22 5.611 33 1.513

Figure 5: Variation of TENVDI with DG Placement Figure 6:Variation of Tail End Node Voltage with
DG Placement

171


Table 4 : Comparison of Complex Power Losses for
Optimal sizing of DG at Optimal location: Bus 9

Optimal Complex Power Loss (Sloss) in
Location = Bus KVA
9
DG Rating in Case1 Case2 Case3
MVA (Unity Pf) (0.9Pf lag) (0.8Pf lag)
0.5 193.9777 182.1617 182.1227
1.0 159.0668 136.2883 136.1958
1.5 147.3413 113.8010 113.5875
2.0 156.6458 112.0736 111.6356
2.5 185.1807 128.9607 128.1659
3.0 231.3957 162.6299 161.3234
3.5 293.8651 211.3234 209.3202
4.0 371.4385 273.8590 270.9834
Minimum Loss 147.3413 112.0736 111.6356
Optimal DG 1.5 2.0 2.0
capacity (SDGopt)
in MVA

Figure 7: Comparison of complex power losses after placement of DG for different cases

Figure 8: Comparison of System Voltage Profile after DG placement (3 cases) with base case

172


Table 5: Improvement of system parameters with optimal allocation of DG
Parameters Base Case Case I Case II CaseIII
Active Power losses in Pu 0.211 0.1215 0.0908 0.0902
Reactive Power losses in Pu 0.143 0.0834 0.0643 0.0644
Active Power drawn from Substation in Pu
3.926 2.3365 2.0058 2.2052
Reactive Power drawn from Substation in Pu
2.443 2.3834 1.4925 1.1644

As per the flowchart of fig.1, the optimal location for DG having rating of 50% of total
complex demand of distribution system found to be Bus No: 9 (corresponding to minimum
TENVDI). At this optimal location the optimum size of DG for loss minimisation for various
cases is given in table4. From fig 8, it is evident the optimal allocation of DG results in improved
voltage profile..

4.2 IEEE-69 BUS RADIAL DISTRIBUTION SYSTEM:
The distribution system characteristics: Number of buses=69; Number of lines=68;Slack
Bus no=1; Base Voltage=12.66KV; Base MVA=100 MVA; The test system is simulated in
MATLAB 2008 & the proposed methodology has been tested, whose results are as shown below.

Table 6: Tail End Node matrix
elements
Sl.no Tail End Nodes
1 27
2 35
3 46
4 50
5 52
6 65
7 67
Figure 9: Single line diagram of standard IEEE-69 Bus system
8 69

Figure 10: Basecase Voltage profile for IEEE-69bus system

173


Table 7:Base case Bus Voltages for IEEE-69 BUS test system

Bus Bus Bus Bus Bus Bus
no Voltage no Voltage no Voltage
(Pu) (Pu) (Pu)
1 1.0000 24 0.9565 47 0.9998
2 1.0000 25 0.9564 48 0.9985
3 0.9999 26 0.9563 49 0.9947
4 0.9998 27 0.9563 50 0.9942
5 0.9991 28 0.9999 51 0.9785
6 0.9901 29 0.9999 52 0.9737
7 0.9808 30 0.9998 53 0.9746
8 0.9786 31 0.9997 54 0.9714
9 0.9774 32 0.9997 55 0.9669
10 0.9724 33 0.9995 56 0.9626
11 0.9713 34 0.9992 57 0.9401
12 0.9681 35 0.9992 58 0.9290
13 0.9652 36 0.9999 59 0.9248
14 0.9623 37 0.9997 60 0.9197
15 0.9594 38 0.9995 61 0.9123
16 0.9589 39 0.9994 62 0.9120
17 0.9580 40 0.9994 63 0.9117
18 0.9580 41 0.9983 64 0.9098
19 0.9576 42 0.9980 65 0.9092
20 0.9573 43 0.9979 66 0.9091
21 0.9568 44 0.9979 67 0.9091
22 0.9568 45 0.9978 68 0.9088
23 0.9567 46 0.9978 69 0.9088

Table 8: Variation of TENVDI with DG Placement
Bus TENVDI Bus TENVDI Bus TENVDI
no (x10-3) no (x10-3) no (x10-3)
1 0.3982 24 0.3137 47 0.3973
2 0.3980 25 0.3343 48 0.3969
3 0.3978 26 0.3434 49 0.3974
4 0.3973 27 0.3486 50 0.3980
5 0.3918 28 0.3978 51 0.2580
6 0.3298 29 0.3978 52 0.1536
7 0.2716 30 0.3986 53 0.2305
8 0.2583 31 0.3988 54 0.2084
9 0.2517 32 0.4004 55 0.1796
10 0.2443 33 0.4072 56 0.1533
11 0.2433 34 0.4328 57 0.0537
12 0.2416 35 0.4663 58 0.0263
13 0.2450 36 0.3978 59 0.0194
14 0.2546 37 0.3977 60 0.0138
15 0.2702 38 0.3978 61 0.0113
16 0.2737 39 0.3979 62 0.0115
17 0.2809 40 0.3979 63 0.0123
18 0.2810 41 0.4028 64 0.0221
19 0.2879 42 0.4070 65 0.0530
20 0.2925 43 0.4076 66 0.2206
21 0.3007 44 0.4078 67 0.2203
22 0.3011 45 0.4096 68 0.2101
23 0.3049 46 0.4096 69 0.2100

174


Table 9 : Comparison of Complex Power Losses for
Optimal sizing of DG at Optimal location: Bus 61

Optimal Complex Power Loss (Sloss) in
Location KVA
= Bus 61
DG Case1 Case2 Case3
Rating in (Unity (0.9Pf lag) (0.8Pf
MVA Pf) lag)
0.5 180.2229 162.6008 161.3606
1.0 128.9543 95.4359 92.9176
1.5 102.0178 53.8736 50.1355
2.0 96.7717 34.8566 30.0237
2.5 111.0474 35.7901 29.9683
3.0 143.0446 54.7633 48.1141
3.5 191.2309 90.1621 82.9125
4.0 254.1131 140.5060 132.8839
Minimum
96.7717 34.8566 29.9683
Loss
Optimal
DG
capacity 2.0 2.0 2.5
(SDGopt) in
MVA

Figure 11: Variation of TENVDI with DG placement Figure 12:Variation of Tail End
Node Voltage with DG Placement

175


Figure 13: Comparison of complex power losses after placement of DG for different cases

Table 10: Improvement of system parameters with optimal allocation of DG
Parameters Base Case Case I Case II CaseIII
Active Power losses in Pu 0.2365 0.0872 0.0300 0.0254
Reactive Power losses in Pu 0.1065 0.0420 0.0174 0.0152
Active Power drawn from Substation in Pu
4.1272 1.9779 2.1206 1.9161
Reactive Power drawn from Substation in Pu
2.8001 2.7356 1.8393 1.2088

Figure 14: Comparison of System Voltage Profile after DG placement (3 cases) with base
case

As per the flowchart of fig.1, the optimal location for DG having rating of 50% of
total complex demand of distribution system found to be Bus No: 61 (corresponding to
minimum TENVDI). At this optimal location the optimum size of DG for loss minimisation
for various cases is given in table9. From fig 14, it is evident the optimal allocation of DG
results in improved voltage profile.

176


5. CONCLUSION

The determination of size and location of DG are two important factors for the planning and
operation of smart electrical distribution systems. This paper presents a simplified approach for
optimum allocation of DG in distribution system in which the optimal location of DG is determined
by TENVD index for improving the tail end node voltages and optimal sizing of DG is determined at
the optimal location for minimising the power losses. The proposed method has been tested on IEEE-
33bus system & IEEE-69bus system using MATLAB 2008. The results of these two systems have
proved the impact of optimal allocation of DG in terms of better voltage profile especially for
consumers connected to tail end node and reduced power losses.

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3(Online)

AUTHORS’

Dr. T. Ananthapadmanabha received the B.E. degree in
r.
Electrical Engineering in 1980, M.Tech degree in Power Systems
(1st Rank) in 1984 and Ph.D. degree (Gold Medal) in 1997 from
and
University of Mysore, Mysore. He is presently working as Professor
in Department of Electrical and Electronics Engineering and
Controller of Examinations at The National Institute of Engineering,
Mysore, Karnataka, India.
His research interest includes Reactive Power Optimization,
Voltage Stability, Distribution Automation and AI applications to
Power Systems.

Maruthi Prasanna. H. A. received the Diploma in Electrical &
Electronics Engineering in 2004 from D.R.R.Government
Polytechnic, Davanagere and B.E. degree in Electrical & Electronics
D
Engineering in 2011 from B.M.S.Evening College of Engineering
Engineering,
Bangalore. He is presently pursuing research work at Department of
Electrical and Electronics Engineering The National Institute of
Engineering,
Engineering, Mysore, Karnataka, India.
ering,
His research interest includes Distribution System
Optimisation, Power System Stability studies, A.I. applications to
power system and Smart Grid.

Veeresha. A. G. received the B.E. degree in Electrical &
Electronics Engineering in 2003 from SJMIT, Chitraduraga. He is
presently pursuing research work at Department of Electrical and
Electronics Engineering, The National Institute of Engineering,
Engineering
,
His research interest includes Wind Energy, Distribution
System Design, Distributed Generation.

Likith Kumar. M. V. received the B.E. degree in Electrical &
Electronics Engineering in 2011 from SKIT, Bangalore. He is
presently pursuing research work at Department of Electrical and
Electronics Engineering, The National Institute of Engineering,
tronics Engineering
,
His research interest includes Smart Grid, Communication
System, Renewable Energy.

178

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