Optimum capacity allocation of distributed generation units using parallel pso using message passing interface

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 02 Issue: 09 | Sep-2013, Available @ http://www.ijret.org 215
OPTIMUM CAPACITY ALLOCATION OF DISTRIBUTED GENERATION
UNITS USING PARALLEL PSO USING MESSAGE PASSING INTERFACE
Rosamma Thomas1
, Jino M Pattery2
, Surumi Hassainar3
1
M.Tech Student, Electrical and Electronics, FISAT, Kerala, India, rosamma.tk@gmail.com
2
Senior Engineer, Product Division, Kalkitech, Kerala, India, jino.pattery@kalkitech.in
3
Assistant Professor, Electrical and Electronics, FISAT, Kerala, India, surumishf@gmail.com
Abstract
This paper proposes the application of Parallel Particle Swarm Optimization (PPSO) technique to find the optimal sizing of multiple
DG(Distributed Generation) units in the radial distribution network by reduction in real power losses and enhancement in voltage
profile. Message passing interface (MPI) is used for the parallelization of PSO. The initial population of PSO algorithm has been
divided between the processors at run time. The proposed technique is tested on standard 123-bus test system and the obtained results
show that the simulation time is significantly reduced and is concluded that parallelization helps in enhancing the performance of
basic PSO. The procedure has been implemented in an environment in which OpenDSS (Open Distribution System Simulator) is
driven from MATLAB. An adaptive weight particle swarm optimization algorithm has been developed in MATLAB , parallelization is
achieved using MATLABMPI and the unbalanced three-phase distribution load flow (DLF) has been performed using Electric Power
Research Institute’s (EPRI) open source tool OpenDSS.
Index Terms: Distributed Generation, Message Passing Interface, Optimal Placement, Parallel Particle Swarm
Optimisation
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1. INTRODUCTION
Distribution network characteristics include radial structure,
unbalanced operation, high R/X ratios, and nowadays
distributed generation(DG) too . Distribution systems were
designed to operate under unidirectional power flow
conditions, but with distributed generation in the network the
power flow is no longer unidirectional [1]. Some benefits of
installation of distributed generation to the distribution system
are improving voltage profile, reduction in losses, and
providing backup power [2]. Reverse power flow may occur
and can cause voltage rise problem , if penetration level is too
high and also the power loss in the network may be more due
to inappropriate sizing of DG than that of the case without any
DG [3] , [4]. Deep and detailed studies are needed for the
analysis of the distribution network with DG. For these
studies to be possible an appropriate power flow algorithm is
necessary. So the distribution load flow is carried out using
OpenDSS (Open Distribution System Simulator).
The global minimum is easily found out by particle swarm
optimization (PSO) algorithm. It can be used to optimize
objective functions which are not differentiable and irregular.
So PSO based unbalanced three-phase power flow can be
used to solve DG capacity problem in a distribution network
[5]. But it has the disadvantage that, since the algorithm
simulates the movement of a swarm of particle solutions over
a very large number of iterations, it takes a significant
processing time. So a parallel implementation of the PSO is
proposed to reduce the computation time. In this approach the
basic PSO with a very large number of particles and iterations
are simulated in a reduced time[6].
The paper has been organised as follows: In section 2, PSO
algorithm is described briefly. Section 3 describes parallel
PSO using MPI. The objective function formulation of DG
generation capacity is explained in section 4.1.
Implementation of the procedure is given in section 4.2 . Test
system and results are given in section 5.
2. PARTICLE SWARM OPTIMIZATION
Kennedy and Eberhart proposed Particle swarm optimization
(PSO) algorithm which is a population-based optimization
method inspired by social behavior of bird flocking or fish
schooling[7] . In this method individuals called particles
change their position during the optimization process. Each
particle adjusts its position according to its own experience
(pbest), and according to the experience of a neighboring
particle (gbest). The velocity at which the particles move is
given by the equation (1).
𝑣𝑖
𝑘+1
= 𝑤𝑣𝑖
𝑘
+ 𝑐1 𝑟1 𝑝𝑏𝑒𝑠𝑡𝑖 − 𝑠𝑖
𝑘
+ 𝑐2 𝑟2 𝑔𝑏𝑒𝑠𝑡𝑖 − 𝑠𝑖
𝑘
(1)
The particle‟s new position in the search space is given by

__________________________________________________________________________________________
the equation(2 ).
𝑠𝑖
𝑘+1
= 𝑠𝑖
𝑘
+ 𝑣𝑖
𝑘+1
𝑖 = 1,2, … , 𝑛 (2)
where,
𝑠 𝑘
is current searching point,
𝑠 𝑘+1
is modified searching point,
𝑣 𝑘+1
is modified velocity of agent i,
𝑛 is number of particles in a group,
𝑝𝑏𝑒𝑠𝑡𝑖 is pbest of agent i,
𝑔𝑏𝑒𝑠𝑡𝑖 is gbest of the group,
𝑤 is weight function for velocity of agent i,
𝑟1and 𝑟2are random numbers between 0 and 1
𝑐𝑖is weight coefficients for each term.
Inertia weight can be calculated by equation (3) .
𝑤𝑖 = 𝑤 𝑚𝑎𝑥 − 𝑤 𝑚𝑎𝑥 − 𝑤 𝑚𝑖𝑛 𝑘 𝑘 𝑚𝑎𝑥 (3)
where,
𝑤 𝑚𝑖𝑛 and 𝑤 𝑚𝑎𝑥 are the minimum and maximum weights
respectively. 𝑘 and 𝑘 𝑚𝑎𝑥 are the current and maximum
iteration. Appropriate value ranges for 𝑐1 and 𝑐2 are between 1
and 2.
3. PARALLEL PSO USING MPI
To analyze distribution systems of realistic size, some
refinements are needed to significantly reduce the simulation
time of basic PSO. The efficiency of PSO algorithm can be
improved largely by parallel computing technique. Multiple
processors are used to implement the algorithm which reduces
the simulation time. Hence complex functions can be
optimized using parallel PSO [11]. MPI is used to parallelise
PSO so easily. In this paper the swarm particles of basic PSO
are distributed equally among processors such that all
processors have equal load.
4. OPTIMUM CAPACITY ALLOCATION OF DG
UNITS BASED ON PARALLEL PSO
4.1 Objective Function Formulation
The objective of the paper is to determine the optimum
generation capacity of multiple DG units in an unbalanced
distribution network so that efficiency is enhanced and
stability is ensured. The utility‟s operating cost can be reduced
and efficiency can be enhanced by reducing the power losses
of the distribution area. So, objective function is considered as
follow:
𝑓𝑜𝑏𝑗 = 𝑃𝐿 𝑘 (4)𝑛
𝑘=1
where, PL is the power loss in power delivery lines and n is
the total number of distribution lines[5].
Reverse power flow may occur if DG penetration is too high
and this causes over voltage at the buses[3] . Therefore,
steady-state voltage stability limits have been considered as
inequality constraint. If 𝑣 𝑚𝑖𝑛 and 𝑣 𝑚𝑎𝑥 are the minimum and
maximum voltage limit and N is the total number of nodes,
then voltage stability limit can be written as:
𝑣 𝑚𝑖𝑛 < 𝑣𝑖 < 𝑣 𝑚𝑎𝑥 , 𝑖 ∀ 𝑁 (5)
In this analysis, the inequality constraint is added to the
objective function using penalty factors[6]. Therefore, overall
objective function can be written as:
min 𝑓(𝑥, 𝑢) (6)
where
𝑓 𝑥, 𝑢 = 𝑓𝑜𝑏𝑗 𝑥, 𝑢 + 𝑓𝑝 𝑥, 𝑢 (7)
subject to 𝑔 𝑥, 𝑢 = 0 (8)
with 𝑥 ∈ 𝑅 𝑛
𝑎𝑛𝑑 𝑢 ∈ 𝑅 𝑚
Three different types of DG technologies have been
considered for optimum capacity allocation[5].
They are:
• Type-1 DG: which delivers real power only; therefore,
power factor is 1. e.g. PV cell, fuel cells etc.
• Type-2 DG: which delivers both real and reactive power,
therefore, power factor is positive (In this analysis pf=0.88 has
been considered). e.g. synchronous generators.
• Type-3 DG: which delivers real power but consumes
reactive power, therefore, power factor is negative (In this
analysis pf=-0.88 has been considered).e.g. induction
generators
4.2 Implementation of the Procedure
Optimum generation capacity of multiple DG units are
found out using PSO algorithm implemented in Matlab.
Unbalanced three phase DLF is done using OpenDSS. At
every iteration Matlab and OpenDSS interact with each
other. OpenDSS has a COM (Component Object Model)
server DLL (Dynamic-link library) that can be driven from
other software platforms such as Matlab[10]. For
parallelization of PSO MatlabMPI is used. It is set of Matlab
scripts that implement a subset of MPI and allow any Matlab
program to be run on a parallel computer. MatlabMPI will
run on any combination of computers that Matlab
supports[10].The block diagram of the implemented
procedure is shown in Fig.2 and flowchart is given in Fig. 1

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Fig.1 Flowchart of the implemented procedure
Processor1
Swarm 1 to m
Run DLF using
OpenDSS
Calculate objective
function for each
particle of swarm
Processor1
Swarm 1 to m
Processor p
Swarm pm+ 1 to n
Run DLF using
OpenDSS
Run DLF using
OpenDSS
Calculate objective
function for each
particle of swarm
Calculate objective
function for each
particle of swarm
Calculate number of swarm per processor (m)
Randomly initialise swarm particle position and velocity Also
initialise other PSO parameters
Initialize number of swarm particles (n) and number
of processors(p)
Start
Initialise particle’s best known position Pi and swarm’s
best known position Pg
Update velocity and position of each particle
Processor2
Swarm m+1 to 2m
Processor p
Swarm pm+ 1 to n
Run DLF using
OpenDSS
Run DLF using
OpenDSS
Calculate objective
function for each
particle of swarm
Calculate objective
function for each
particle of swarm
Update particle’s best known position Pi and swarm’s best
known position Pg if necessary
Processor2
Swarm m+1 to 2m
Run DLF using
OpenDSS
Calculate objective
function for each
particle of swarm
Check if any stopping
criterion is satisfied
Stop and print results

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Fig. 2 Block diagram of implemented procedure
5. TEST SYSTEM AND RESULTS
The analysis has been carried out on IEEE 123 node
test distribution feeder which is three-phase unbalanced in
nature. The IEEE 123 node test system does not have any DG
in the base case. In this analysis potential sites for DG
installation have been selected prior to the analysis.
In [5] the author has used basic PSO for optimising
multiple DG unit capacities. The data used in [5] are as
follows. In [5] DG units have been installed in the heavily
loaded buses selected which are bus 76 , bus 48 and bus 65 .
Optimal generation capacity of DG units installed in those
buses has been determined. For the optimization process each
DG capacity has been limited by the equation(9).
𝑃𝐷𝐺
𝑚𝑖𝑛
< 𝑃𝐷𝐺
𝑖
< 𝑃𝐷𝐺
𝑚𝑎𝑥
, 𝑖 ∀ 𝑁 (9)
where 𝑃𝐷𝐺
𝑚𝑖𝑛
is 90 kW (around 2.5% penetration) and 𝑃𝐷𝐺
𝑚𝑎𝑥
is
3 MW (around 85% penetration) and 𝑁 represents the number
of DG units. The same case is simulated with parallel PSO in
this paper.
The purpose of this test scenario is to analyze
computational complexity and time and to examine the
impact of multiple optimum sized DG on power loss profile
and voltage profile of the network. As the number of DG units
increases and the network size increases, solution
complexitywill increase to determine the optimum generation
capacity. Completeness and speed are two qualities of the
proposed method.
In this work the computations are performed
on a HPDL980G8 hardware with 4 processor each with 10
cores. The simulation is conducted in a virtual environment
with four operating system with one master node and three
slave nodes.
Fig.3 IEEE node test feeder
Results for optimum DG capacity at bus 76 , 48 and
65 using the proposed method of parallel PSO using MPI for
three different DG technologies are given in Table I.
Table 1
Comparison Of Solution Time With Basic
PSO And Parallel PSO
Installation of multiple DG units with optimum capacity
will decrease the network power loss significantly. Power loss
of the network becomes 20.17 kW, 19.3 kW and 51.4 kW
respectively for type-1, type-2 and type-3 optimum sized DG
units. Power loss of 123 node test feeder without any DG is
95.8kW. Power loss reduction(PLR) by the DG units can be
calculated using (10) [5]. Power loss reduction is less for
type-3 DG units as they consume reactive power from the
DG Technology
Type-1
DG
Type-2
DG
Type-3
DG
Optimum
DG
capacity
(MW)
at bus 76 1.58 1.22 1.21
at bus 48 0.99 1.02 0.52
at bus 65 0.39 0.39 0.20
Solution time with
basic PSO (s)
20.10 19.90 21.30
Solution time with
parallel PSO (s)
6.91 6.72 7.21
Power Loss (kW) 20.17 19.30 51.40
Power Loss
Reduction (PLR)(%)
78.94 79.80 46.34

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grid; therefore, more power loss occurs compared with the
other two DG technologies[5].
𝑃𝐿𝑅 = 𝑃𝐿𝑂𝑆𝑆 − 𝑃𝐿𝑂𝑆𝑆
𝐷𝐺
𝑃𝐿𝑂𝑆𝑆 ∗ 100% (10)
where,
𝑃𝐿𝑂𝑆𝑆 =Power loss of the system before introducing DG
𝑃𝐿𝑂𝑆𝑆
𝐷𝐺
=Power loss of the system after adding DG
From the table it is clear that with parallelisation of PSO
the simulation time is reduced compared to simulation with
basic PSO used in [5].Following figure shows the location of
three type-1 DG units.
Fig. 4 Location of 3 Type1 DG units in the network
From Fig. 5 it is observed that the steady state voltage
at all nodes are within the permissible limit of 0.94pu and
1.06pu. Thus by optimization of the DG capacities the voltage
profile is also improved.
Fig. 5 Node Voltage considering 3 Type1 DG units
6. CONCLUSION
From the results it can be easily concluded that, using
multiple DG units with optimum generation capacity , power
loss of network is reduced and voltage profile remains within
stability margin. Also without the addition of any extra
parameter PSO can be parallelized which reduces the
simulation time. A major drawback in parallel programming is
communication between processors which cannot be removed
completely. So for getting a considerable reduction in time
with multi processors the problem size should be large.
Distribution system networks are fairly large in size, so this
method can be used to optimize distribution system problems.
7. REFERENCES
[1] V. Janev. Implementation and evaluation of a distribution
load flow algorithm for networks with distributed generation.
Bachelor's Thesis, ETH Zurich, 2009
[2] H. Lee Willis and W.G. Scott, Distributed Power
Generation. Planning and Evaluation, Marcel Dekker, 2000.
[3] P. Carvalho, P. Correia, and L. Ferreira, “Distributed
reactive power generation control for voltage rise mitigation in
distribution networks,” IEEE Transactions on Power Systems,
vol. 23, no. 2, pp. 766–772, May 2008.
[4] V. Quezada, J. Abbad, and T. Roman, “Assessment of
energy distribution losses for increasing penetration of
distributed generation,” IEEE Transactions on Power Systems,
vol. 21, no. 2, pp. 533–540, May 2006.
[5] Adnan Anwar and H. R. Pota, „Optimum Capacity
Allocation of DG Units based on Unbalanced Three-phase
Optimal Power Flow‟ in IEEE PES General Meeting, San
Diego, USA, Jul. 2012.
[6]V. Roberge and M. Tarbouchi”Parallel particle swarm
optimization on graphical processing unit for pose
estimation”. WSEAS Transactions on Computers , 11(6):170
179, June 2012.
[7] J. Kennedy, and R. Eberhart, “Particle swarm
optimization”, IEEE Int. Conf. on Neural Networks IV, p.
1941-1948, Piscataway, NJ, 1995
[8] Eberhart and Y. Shi, “Particle swarm optimization:
developments, applications and resources,” in Proceedings of
the 2001 Congress on Evolutionary Computation.
[9] “OpenDSS manual and reference guide.” [Online].
Available: http://sourceforge.net/projects/electricdss
[10] “MatlamMPI reference guide .”[Online].Available:
http://www.ll.mit.edu/mission/cybersec/softwaretools/matlab
mpi/matlabmpi.html
[11] G.Singal, A. Jain and A. Patnaik , “Parallelization of
Particle Swarm Optimization using Message Passing
Interfaces” in NaBIC 2009 World Congress

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