1. Planning of Renewable DGs
for Distribution Network
Considering Load Model: A
Multi-Objective Approach
Tanushree Bhattacharjee
Department of Electrical Engineering, Bengal Engineering and Science
University, Shibpur, Howrah, India
Partha Kayal
Department of Electrical Engineering, Future Institute of Engineering and
Management, Kolkata, India
Chandan Kumar Chanda
Department of Electrical Engineering, Bengal Engineering and Science
University, Shibpur, Howrah, India
12/11/2013
1/25

2. Introduction

Distributed generation(DG)promises to supply eco-friendly, reliable
and cost effective electricity to the customer.

It includes renewable technologies (photovoltaic, biomass and wind
power) and high efficiency non-renewable power (fuel cell, internal
combustion engine, gas turbine and micro turbine).

MNRE report, Govt. of India has set a target to produce 9,000 MW
wind, 1,780 MW biomass and 50 MW solar power with grid
interaction in the 11th plan (2011-2017).

Indiscriminant application of individual DG may cause higher
distribution power losses and poor voltage stability of the network.

So, appropriate location, type and size selection of renewable DGs
in the distribution network is a very crucial aspect of energy system
planning.
This paper presents a multi-objective particle swarm optimization
(MOPSO) based approach to allocate renewable DGs optimally in a
28-bus radial distribution network employing trade-off between
multiple objectives.
12/11/2013
2/25

3. Literature Survey
•
•
•
•
•
•
•
•
Wang et al (2004) [3] have formulated an expression which only
chooses proper location keeping size of DG constant.
Acharya et al (2006) [4] have select appropriate location of DG in
distribution network based on loss sensitivity of buses and appropriate
size of DG was evaluated based on exact loss formula.
Singh et al (2009) [8] have presented multi-objective genetic algorithm
approach for DG sitting and sizing.
In Zangeneh et al (2009) [10], multi-objective genetic algorithm is
applied to produce optimal planning schemes by taking into account
cost and grant functions as objectives.
In Arya et al (2012) [6], voltage stability criterion has been used to find
DG location and differential evaluation optimization technique is used to
determine DG capacity considering objective of power loss minimization.
In Guo et al (2012) [9], optimal generation dispatch by renewable energy
has been calculated using weighted sum multi-objective particle swarm
optimization technique.
Kathod et al (2013) [5] has present evolutionary programming based
optimization technique to place renewable DGs optimally .
In Injeti et al (2013) [7] a simulated annealing optimization technique has
been utilized for optimal placement and sizing of DGs.
12/11/2013
3/2
5

4. OBJECTIVE FUNCTIONS
The researchers choose objective functions from different
technical, economical and environmental aspect and they are not
empirical. While some authors have given concern on economy for
DG selection [13, 14], others emphasized on power loss minimization
[3, 4] or both economical benefit and power loss minimization [6].
Optimal placement and sizing of DG based on system security and
reliability are also reported in the literature [15, 16]. Some authors
have discussed about location and size selection of DG on view point
of voltage stability improvement [7, 9].
This paper studied with objective of benefit to cost ratio
maximization, voltage stability enhancement and network security
improvement satisfying power and voltage constraint criteria.
12/11/2013
4/2
5

5. Benefit to cost ratio

BCR indicates the economical benefit that can be realized with
respect to cost for implementation of the project.
is investment, operating and maintenance cost
I
Icij and OMCij are investment cost; and operating and maintenance
cost of type-j renewable DG at bus-i
ni is the number of DG unit that can be connected at bus-i.
li is location variable at bus-i
P is the power generated by type-j DG at bus-I
N is the number of buses in the network
12/11/2013
5/2
5

6. Cumulative Present Value (CPV) of future cost includes interest
rate, inflation rate, and economic life of equipment .It can be
determined as follows:PV is the present value of cost.
IF and IR are the percentage of inflation
rate and interest rate
Nyr is the number of year in planning
horizon
N
BenefitrenwDG
{(
j type
PrenwDG ,ij * ni * li )
PlossrenwDG }* Chr *8760* CPV
i 2
is the power loss reduction due to allocation of
renewable DGs
Chr is the cost of electricity
12/11/2013
6/2
5

7. Voltage stability index
VSI is a very fast and effective tool and widely used by the power
engineers for off-line measurement of voltage stability condition of
buses
Where ri and xi are resistance and reactance of line connected
between bus-i and bus-i+1
PD,i +1 and QD,i +1 are active and reactive load at bus-i+1 respectively
Vi is the voltage magnitude at bus-i
Voltage stability condition of the whole network can be realized as
12/11/2013
7/2
5

8. Network security index
To get idea about network security index we need to know line
loading of the network.
Line Loading (LL) is the MVA flow through the line with respect to
maximum MVA capacity of that line
network security is represented as
12/11/2013
8/2
5

9. LOAD MODEL
Consideration of voltage dependent load model is more relevant than
constant power load model for analysis of a system as loads of the
buses in distribution network depend on their respective bus
voltages.So practical voltage dependent load model i.e.
residential, industrial and commercial used have been adopted for
investigation.
PD ,i
QD ,i
P0,i *
Vi
V0,i
Vi
Q0,i *
V0,i
PD,i, , QD,i and Vi are active load, reactive load and voltage magnitude
at bus-i;
P0.i , Q0,i and V0,i are nominal active load, reactive load and voltage
magnitude at bus-i;
α and
are active and reactive power exponents
12/11/2013
9/2
5

10. Active and reactive power exponents are different for different
types of load [15, 18] and shown in Table 1
Table 1: Values of exponents used for different types of
load
Load type
(1)
Constant power
0
0
(2)
Residential
0.92
4.04
(3)
Commercial
1.51
3.40
(4)
Industrial
0.18
6.0
12/11/2013
10/2
5

11. PROBLEM FORMULATION
The problem of this study is to generate potential solution with
BCRrenwDG
NSI and
maximization
of
minimization of VSI renwDGrenwDG
minimization of
Overall objective can be formulated as
Equality constraint:
N
N
PGSS
j type
P renwDG ,ij * ni * li
i 2
PD ,i
PL
0
i 2
N
QGSS
QD ,i QL
0
i 2
12/11/2013
11/2
5

12. Inequality constraints:
1. Generation limit constraint at bus-i
PrenwDG ,ij * ni ,min
PrenwDG ,ij * ni
PrenwDG ,ij * ni ,max
2. Bus voltage tolerance constraint at Bus-i
Vi ,min
Vi
Vi ,max
3. Line capacity constraint of line connecting bus-i and bus-j
Sij
max
Sij
12/11/2013
12/2
5

13. Multi-objective Particle
Swarm Optimization (MOPSO)
Particle swarm optimization (PSO):
PSO is basically a population-based search procedure in which
individual particle adjusts its position according to its own
experience, and the experience of fittest neighboring particle.
•
The first one is best solution it has achieved so far which is called
pbest..
•
Second one is the best solution in the whole swarm and it is called
gbest.
•
In each flight the particles modify their position and velocity and try
to converge in more promising region of solution.
Non-dominated sorting and crowding distance concepts are
incorporated into PSO and extended it to handle multi-objective
optimization problems.
12/11/2013
13/2
5

14. Multi-objective Particle
Swarm Optimization (MOPSO)
In MOPSO, the whole population is sorted into various non-domination
front levels based on non-domination criteria.
A solution is said to be non-dominated if it is impossible to improve one
component of the solution without worsening the value of at least
one other component of the solution.
After complete of set iteration, MOPSO generate a set of nondominated solutions.
Fuzzy rule have the capability to select the optimal non-dominated
solution in better way.
12/11/2013
14/2
5

15. max
min
fi
fi
A linear fuzzy membership function is chosen with
and
corresponds to unacceptable and satisfactory value of objective f
Objective function in MOPSO can be defined as:
0
if
fi max
i
fi
max
if
fi
min
fi
1
i
fi
fi max
if
fi
fi max
fi
fi min
fi min
is the membership value ofth
i
objective
Overall membership value of any non-dominated solution k is
N obj
N obj is the number of objective functions
k
i
k
M
i 1
N obj
k
i
M is number of non-dominated solution
k 1 i 1
12/11/2013
15/25

16. Simulation algorithm of multi-objective problem :
1. Initialize number of particle in MOPSO and dimension of each particle. Select dime
nsion of particle with location variable, type variable and size variable (number of insta
lled renewable DG units at load bus). Generate initial position and velocity of each par
ticle randomly within specified range.
2. Run power flow algorithm and calculate the values of objective functions.
3. Do non-dominated sorting based on domination of objectives for different particles.
4. Sort particles in ascending order according to their occupancy of front levels and ca
lculate crowding distance of each particle.
5. Select Pbest of particle as the best position it has occupied so far.
6. Eliminate solutions that are not satisfying constrain criteria.
7. Choose Gbest randomly from top 5% of non-dominated list.
8. Select maximum size of archive equal to number of particle.
9. For iteration number one, store all particles in an archive. Otherwise compare the fr
ont level and crowding distance of current particles with previous particle stored in arc
hive. Particles with better front level and crowding distances are stored by replacing pr
evious particles.
10. Update position and velocity of each particle.
11. Repeat step 2 to 10 up to maximum iteration.
12. Select particle with highest fuzzy membership value from archive as optimal non-d
ominated solution.
13. Display value of each objective function and variables of particle for optimal non-d
ominated solution. Show the values of location variable, size variable and type variabl
16/2
e correspond to best non-dominated solution.
12/11/2013
5

17. Simulation results

Simulation is carried out on 11kV, 28-bus rural Indian distribution network.

Simulation is done in MATLAB 7.10 software and Newton-Raphson power
flow algorithm is used.

Base values are taken for the network as 1MVA and 11 kV .

For planning horizon of 10 years, IF, IR and Chr are taken 5%, 3% and Rs
5.8/kW-hr .

Required system data and maximum capacity of lines are collected from
Investme Operating and mai
[13, 20].
20
Wind turbine
125
Biomass
200
15,330
20
(3)
11,826
25
(2)
Single line diagram of 28-bus Indian
distribution network
Solar photovoltai
c (PV)
4,818
100,000
(1)
ntenance cost (Rs/k
W-year)
75,000
Plant f
actor (
%)
nt cost (
Rs/kW)
350,000
DG technology
Comme
rcial siz
e (kW)
60
Technical and economical data of wind , biomass
and solar based renewable DGs
12/11/2013
17/2
5

18. Case of constant power load
Solution generated by MOPSO compromising
three conflicting objectives
Location , type and size of renewable DGs in the network for best trade-off between
objective functions
12/11/2013
18/25

19. Comparative graphical representation of voltage magnitudes for different buses with
and without renewable DGs in the network
Comparison of substation (S/S) power consumption, total power loss, VSI and
NSI of DG installed network with before DG installation case
12/11/2013
19/2
5

20. Case of voltage dependent
load
Solution generated by MOPSO by trade-off between three conflicting
objectives
Location, type and size of renewable DGs in the network for best trade-off
between objective functions
12/11/2013
20/2
5

21. Comparative graphical representation of voltage magnitudes for different buses
with and without renewable DGs in the network
Comparison of substation (S/S) power consumption, total power loss, VSF and
NSI of DG installed network with before DG installation case
12/11/2013
21/2
5

22. Conclusion

The article presents a novel multi-objective formulation of renewable DG
planning for distribution network incorporating load model.

A set of non-dominated solution is generated by trade-off between three
objective functions viz. benefit to cost ratio, voltage stability index and ne
twork security index.

In this study satisfactory economical benefit, voltage stability improveme
nt and network security enhancement is obtained for best non-dominated
solution.

This investigations show the importance of load models on choice of DG
s.

It has shown that proper allocation of renewable based DGs have potenti
al to improve voltage profile at distribution buses.

Considerable reductions in consumption of grid power and network
wer losses are also obtained.

The method can be used to allocate renewable DGs in any radial
stribution system and find automatically the best solution.
12/11/2013
po
di
22/2
5

23. References
[1] Ackermann, T., Andersson, G., Soder, L. (2001) Distributed generation: a definition, Electric Power
Syst Res, 57(2001), pp. 195–204.
[2] Ministry of New and Renewable Energy report. (2011) Strategic plan for new and renewable energy s
ector for the period 2011-17, MNRE, Govt. of India 2011, pp. 1-85.
[3] Wang, C. and Nehrir, M.H., (2004) Analytical approaches for optimal placement of distributed generati
on sources in power systems, IEEE Trans. on Power Syst, 19(4), pp. 2068-2076.
[4] Acharya, N., Mahat, P., Mithulananthan, N. (2006) An analytical approach for DG allocation in primary
distribution network, Int. J. of Electr. Power Energy Syst, 28(2006), pp. 669-678.
[5] Hedayati, H., Nabaviniaki, S.A., Akbarimajd, A. (2008) A method for placement of DG units in distributi
on networks, IEEE Trans. on Power Deliv, 23(3), pp. 1620-1628.
[6] Ghosh, S., Ghoshal, S.P., Ghosh, S. (2010) Optimal sizing and placement of distributed generation in
a network system, Int. J. of Electr. Power Energy Syst, 32(2011), pp. 849-856.
[7] Abri, R.S.A., El-Saddany, E.F., Atwa, Y.M., (2013) Optimal placement and sizing method to improve th
e voltage stability margin in a distribution system using distributed generation, IEEE Trans. on Power
Syst, 28(1), pp. 326-334.
[8] Kathod, D.K., Pant. V., Sharma, J. (2013) Evolutionary programming based optimal placement of rene
wable distributed generators, IEEE Trans. on Power Syst, 28(2), pp. 683-695.
[9] Arya, L.D., Koshti, A., Choube, S.C. (2012) Distributed generation planning using differential evolution
accounting voltage stability consideration, Int. J. of Electr. Power Energy Syst, 42(2012), pp. 196-20
7.
[10] Injeti, S.K., Kumar, N.P. (2013) A novel approach to identify optimal access point and capacity of mul
tiple DGs in a small, medium and large scale radial distribution systems, Int. J. of Electr. Power Ener
gy Syst, 45(2013), pp. 142-151.
[11] Singh, D., Singh, D., Verma, K.S. (2009) Multiple optimizations for DG planning with load models, IE
EE Trans. on Power Syst, 24(1), pp. 427-436.
12/11/2013
23/25

24. References(continued)
[12] Guo, C.X., Bai. Y.H., Zheng, X., Zhan, J.P., Wu, Q.H. (2012) Optimal generation dispatch with
renewable energy embedded using multiple objectives, Int. J. of Electr. Power Energy Syst, 42
(2012), pp. 440-447.
[13] Zangeneh, A., Jadid, S., Rahimi-Kian, A. (2009) Promotion strategy of clean technologies in di
stributed generation expansion planning, Renewable Energy 34(2009), pp. 2765-2773.
[14] El-Khattam, W., Bhattacharya, K., Hegazy, Y., Salama, M.M.A. (2004) Optimal invest planning
for distributed generation in a competitive electricity market, IEEE Trans. on Power Syst, 19(3)
, pp. 1674-1684.
[15] Singh, R.K., Goswami, S.K. (2011) Multi-objective optimization of distributed generation planni
ng using impact indices and trade-off technique, Electr. Power Comp. and Systems, 39(11), pp
. 1175-1190.
[16] Teng, J.H., Liu, Y.H., Chen, C.Y., Chen, C.F. (2007) Value-based distributed generator placem
ents for service quality improvements, Int. J. of Electr. Power Energy Syst, 29(2007), pp. 268-2
74.
[17] Kayal, P., Chanda, C.K. (2013) A simple and fast approach for allocation and size evaluation o
f distributed generation, Int. J. of Energy and Environmental Engineering, 4(7), pp. 1-9.
[18] IEEE Task Force on Load Representation for Dynamic Performance. (1995) Bibliography on lo
ad models for power flow and dynamic performance simulation, IEEE Trans. on Power Syst, 10
(1), pp. 523-538.
[19] Raquel, C.R., Naval, P.C. (2005) An effective use of crowding distance in multi objective particl
e swarm optimization, Proceedings of Genetic and Evolutionary Computation Conference, US
A, pp. 257-264.
[20] Hazra, J. and Sinha, A.K. (2011) A multi-objective optimal power flow using particle swarm opti
mization. Euro, Trans. Electr. Power, 21(2010), pp. 1028-1045.
[21] Das, D., Nagi, H.S., Kothari, D.P. (1994) Novel method for solving radial distribution network. I
EE Proc. Gener. Transm. Distribution, 141(4) pp. 291-298.
[22] Balamurugan, P., Ashok, S., Joshe, T.L. (2009) Optimal operation of biomass/wind/PV hybrid e
nergy system for rural areas, Int. J. of Green Energy, 6(1), pp. 104-116.
12/11/2013
24/25

25. THANK YOU FOR YOUR
KIND ATTENTION
12/11/2013
25/25