Optimal Location and Sizing of Distributed Generation Unit Using Human Opinion Dynamics Optimization Technique
DOI:
https://doi.org/10.13052/dgaej2156-3306.3322Abstract
The demand of electricity is soaring rapidly. Distributed genera-
tion (DG) is one of the most suitable alternatives to fulfill this swelling
demand of energy. DG is a small scale generation which is directly in-
stalled in the distribution network or at load centre. Optimal allocation
of DG is a vital factor in improving the voltage profile of the system and
in reduction of total power losses. In this article, a detailed study of three
different methods for DG allocation and sizing has been discussed. The
first method is based on Newton Raphson load flow based technique to
deduce the optimal location of DG in two different IEEE bus systems in
MATLAB software. The next methodology is based on particle swarm
optimization (PSO) technique where a multi-objective function is being
minimized. The objective function has been modified and PSO has been
implemented to attain optimal size and location of DG unit. The third
method considered is based on human opinion dynamics evolution-
ary multi-objective optimization technique which is used to obtain the
best possible size and location of DG unit in IEEE 14 and IEEE 30 bus
systems. The human opinion dynamics method shows superiority in
minimizing the size and location muti-objective function, over the other
methods considered herein.
Downloads
References
L. Lai and T.F. Chan, Distributed generation: Induction and permanent magnet
generators, Wiley, 2007.
T. Ackermann, G. Andersson, and L. Soder, "Distributed generation: a definition,"
Electric Power System Research, vol. 57, pp. 195-204, 2001.
W. El-Khattam and M.M.A. Salama, "Distribution generation technologies, defini-tions and benefits," Electric Power Systems Research, vol. 71, pp. 119-128, 2004.
A. Kazemi and M. Sagedhi, "A load flow based method for optimal1ocation of dis-
persed generation units," Power Systems Conference and Exposition, 2009.
N. Acharya, P. Mahat, and N. Mithu1ananthan, "An analytical approach for DG
allocation in primary distribution network," Electric Power Systems Research, vol.
, pp. 669--678, 2006.
P. Chidareja and R. Rarnkumar, "An approach to quantify the technical benefits of
distributed generation/' IEEE Trans. Energy Conversion, vo1. 19, no. 4, pp. 764-773,Dec.2004.
Ashwani Kumar and Wenzhong Gao, "Voltage profile improvement and line loss
reduction with distributed generation in deregulated electricity markets," IEEE
Region 10 Conference TENCON, 2008.
V.K. Shrivastava, O.P. Rahi, V.K. Gupta, and S.K. Singh, "Optimal location of dis-
tributed generation source in power system network," Power India Conference,
S. Ghosh, S.P. Ghoshal, and Saradindu Ghosh, "Optimal sizing and placement of
distributed generation in a network system," Electrical Power and Energy Systems,
val. 32, pp. 849-856, 2010.
C.L.T. Borges and D.M. Falcao, "Optimal distributed generation allocation for reli-
ability, losses and voltage improvement," Electrical Power and Energy Systems, val.
, pp. 413-420, 2006.
M. Sedighizadeh, M. Fallahnejad, M.R. Alerni, M. Ornidvaran, and D. Arzaghi-
haris, "Optimal placement of distributed generation using combination of PSO and
clonal algorithm," IEEE International Conference on Power and Energy, 2010.
https: I I www.ee. washington.edu I research I pstcal
Hadi Sadaat, Power System Analysis, Tata McGraw-Hill Education, 2002.
R. Eberhart and J. Kennedy, "Particle swarm optimization," IEEE International Con-
ference on Neural Networks, val. 4, pp. 1942-1948, 1995.
R.C. Eberhart and Shi Yuhui, "Particle Swarm Optimization: developments, appli-
cations and resources," Congress on Evolutionary Computation, val. 1, pp. 81-86,
R. Kaur, R. Kumar, A.P. Bhondekar, and P. Kumar, "Human opinion dynamics: An
inspiration to solve complex optimization problems," Scientific Reports 3, Nature,
no. 3008, 2013.
M. Macas and L. Lhotska, "Social theory impact based optimizer," Advances in
Artificial life, val. 4648, pp-635-644, 2007.