Optimal Embedded Generation Allocation in Distribution System Employing Real Coded Genetic Algorithm Method

This paper proposes a new methodology for the optimal allocation and sizing of Embedded Generation (EG) employing Real Coded Genetic Algorithm (RCGA) to minimize the total power losses and to improve voltage profiles in the radial distribution networks. RCGA is a method that uses continuous floating numbers as representation which is different from conventional binary numbers. The RCGA is used as solution tool, which can determine the optimal location and size of EG in radial system simultaneously. This method is developed in MATLAB. The effect of EG units- installation and their sizing to the distribution networks are demonstrated using 24 bus system.

Authors:

• Mohd Herwan Sulaiman
• Omar Aliman
• Siti Rafidah Abdul Rahim

Keywords:

• Embedded generation (EG)
• load flow study
• optimal allocation
• real coded genetic algorithm (RCGA).

References:

[1] A guide to embedded generation and the connection to the distribution
network. Available onlinehttp//:www.wscoca.sa.gov.au/061219-GEmbeddedGenerationGuide.
pdf.
[2] F. Sheidaei, M. Shadkam, and M. Zarei, "Optimal Distributed generation
allocation in distribution systems employing ant colony to reduce
losses," in Universities Power Engineering Conference 2008, UPEC
2008, 43rd Interntional, 2008, pp. 1-5.
[3] T. K. A. Rahman, S. R. A. Rahim, and I. Musirin, "Optimal allocation
and sizing of embedded generators, " in Power and Energy Conference,
2004, PECon 2004, Proceeding, National, 2004, pp. 288-294.
[4] M. F. AlHajri, M. R. AlRashidi, and M. E. El-Hawary, "Hybrid Particle
Swarm Optimization Approach for Optimal Distribution Generation
Sizing and Allocation in Distribution Systems," in Electrical and
Computer Engineering, 2007. CCECE 2007. Canadian Conference on,
2007, pp. 1290-1293.
[5] Y. Alinejad-Beromi, M. Sedighizadeh, M. R. Bayat, and M. E.
Khodayar, "Using genetic alghoritm for distributed generation allocation
to reduce losses and improve voltage profile," in Universities Power
Engineering Conference, 2007. UPEC 2007. 42nd International, 2007,
pp. 954-959.
[6] P. Alemi and G. B. Gharehpetian, "DG allocation using an analytical
method to minimize losses and to improve voltage security," in Power
and Energy Conference, 2008. PECon 2008. IEEE 2nd International,
2008, pp. 1575-1580.
[7] N. Acharya, P. Mahat, and N. Mithulananthan, "An analytical approach
for DG allocation in primary distribution network," International
Journal of Electrical Power & Energy Systems, vol. 28, pp. 669-678,
2006.
[8] Pappala VS, Erlich I. Uncertainty Modeling for the Management of
Distributed Generation Units using PSO. Power Tech, 2007 IEEE
Lausanne, 2007. p.497.
[9] Zou K, Agalgaonkar AP, Muttaqi KM, Perera S. Voltage support by
distributed generation units and shunt capacitors in distribution systems.
Power & Energy Society General Meeting, 2009. PES '09. IEEE, 2009.
p.1.
[10] Niknam T, Ranjbar AM, Shirani AR, Mozafari B, Ostadi A. Optimal
operation of distribution system with regard to distributed generation: a
comparison of evolutionary methods. Industry Applications Conference,
2005. Fourtieth IAS Annual Meeting. Conference Record of the 2005,
vol. 4, 2005. p.2690.
[11] J. H. Holland, Adaptation in natural and artificial systems : an
introductory analysis with applications to biology, control, and artificial
intelligence. London: MIT Press, 1992.
[12] D. E. Goldberg, Genetic algorithms in search, optimization, and
machine learning. Reading, Mass.: Addison-Wesley, 1989.
[13] R. L. Haupt and S. E. Haupt, Practical genetic algorithms. New York:
Wiley Pub, 1998.
[14] H. Saadat, Power system analysis, 2Rev ed. Boston: McGraw-Hill,
2004.