Optimal Location and Sizing of Distributed Generation for Minimizing Power Loss Using Simulated Annealing Algorithm

Salah Kamal EL-Sayed

doi:doi:10.11648/j.jeee.20170503.14

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Optimal Location and Sizing of Distributed Generation for Minimizing Power Loss Using Simulated Annealing Algorithm

Salah Kamal EL-Sayed

Published in Journal of Electrical and Electronic Engineering (Volume 5, Issue 3)

Received: 15 May 2017 Accepted: 23 May 2017 Published: 21 June 2017

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Abstract

Distributed Generation integration in electric power system is one of the options which give many benefits such that loss Reduction, peak saving, voltage profile improvement, stability and reliability improvement. The installation of DG units at non-optimal location can result in an increase in system losses, damaging voltage state. In this paper, simulated annealing Algorithm (SAA) techniqueis designed for optimally determining the location, sizing and numbers of distributed generations depending on power loss reduction and voltage profile improvement. The proposed technique is tested on IEEE 57- bus system to demonstrate the performance of the network after inserting the distributed generation in selected optimal location with optimal sizing. Results show the efficiency of the proposed algorithm in reducing power losses, improving voltage profile.

Published in	Journal of Electrical and Electronic Engineering (Volume 5, Issue 3)
DOI	10.11648/j.jeee.20170503.14
Page(s)	104-110
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Distributed Generation, Power Loss, Voltage Profile, Simulated Annealing Algorithm (SAA)

References

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[2]	G. V. Nagesh Kumar, R. SR Krishnam Naidu, SaradaBusam,SnighaHota “Multi objective Optimization of Radial Distribution System with multiple Distributed Generation Units using Genetic Algorithm” International conference on Electrical, Electronics, Signels,Communication and Optimization (EESCO)-2015.
[3]	S. Kumar Injeti, Dr.Navuri P Kumar” Optimal Planning of Distributed Generation for Improved Voltage Stability and Loss Reduction” International Journal of Computer Applications (0975 – 8887) Volume 15– No.1, February 2011.
[4]	Gopiya Naik. S 1, D. K. Khatod 2, M. P. Sharma “Optimal Allocation of Distributed Generation in Distribution System for Loss Reduction” IPCSIT vol. 28 © IACSIT Press, Singapore 2012.
[5]	ParthaKayal and Chandan Kumar Chanda “A simple and fast approach for allocation and size evaluation of distributed generation” International Journal of Energy and Environmental Engineering 2013.
[6]	Minnan Wang and JinZhong “A Novel Method for Distributed Generation and Capacitor Optimal Placement considering Voltage Profiles” IEEE Transactions On Power and Energy Society General Meeting, 978-1-4577-1002-5/11, 2011.
[7]	VahidRashtchi, Mohsen Darabian “A New BFA-Based Approach for Optimal Sitting and Sizing of Distributed Generation in Distribution System” International Journal of Automation and Control Engineering Vol. 1 Issue 1, November 2012.
[8]	Ram Singh1, Gursewak Singh Brar2 and Navdeep Kaur3 “Optimal Placement of DG in Radial Distribution Network for Minimization of Losses” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 1, Issue 2, August 2012.
[9]	Duong Quoc Hung, NadarajahMithulananthan, R. C. Bansal “Analytical Expressions for DG Allocation in Primary Distribution Networks” IEEE Transactions On Energy Conversion, Vol. 25, No. 3, September 2010.
[10]	D. Sharma, R. Bartels, Distributed electricity generation in competitive energy markets: a case study in Australia, in: The Energy Journal Special issue: Distributed Resources: Toward a New Paradigm of the Electricity Business, The International Association for Energy Economics, Clevland, Ohio, USA, pp. 17–40,1998.
[11]	CIGRE, International Council on Large Electricity Systems, http://www.cigre.org.
[12]	G. Pepermans, J. Driesen, D. Haeseldonckx, R. Belmans, and W.D'Haeseleer, "Distributed generation: definition, benefits and issues", Energy Policy, vol. 33, pp. 787-798, 2005.
[13]	Elgerd IO. Electric energy system theory: an introduction. New York: McGraw-Hill; 1971.
[14]	Ingber, L.,, "Simulated annealing: practice versus theory", Mathl. Comput. Modelling 18, 11, 29-57, 1993.
[15]	Engineering optimization: An introduction with metaheuristic applications. By Xin-she Yang. Copyright © John Wiley & Sons, Inc, 2010.

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APA Style

Salah Kamal EL-Sayed. (2017). Optimal Location and Sizing of Distributed Generation for Minimizing Power Loss Using Simulated Annealing Algorithm. Journal of Electrical and Electronic Engineering, 5(3), 104-110. https://doi.org/10.11648/j.jeee.20170503.14

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ACS Style

Salah Kamal EL-Sayed. Optimal Location and Sizing of Distributed Generation for Minimizing Power Loss Using Simulated Annealing Algorithm. J. Electr. Electron. Eng. 2017, 5(3), 104-110. doi: 10.11648/j.jeee.20170503.14

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AMA Style

Salah Kamal EL-Sayed. Optimal Location and Sizing of Distributed Generation for Minimizing Power Loss Using Simulated Annealing Algorithm. J Electr Electron Eng. 2017;5(3):104-110. doi: 10.11648/j.jeee.20170503.14

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@article{10.11648/j.jeee.20170503.14,
  author = {Salah Kamal EL-Sayed},
  title = {Optimal Location and Sizing of Distributed Generation for Minimizing Power Loss Using Simulated Annealing Algorithm},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {5},
  number = {3},
  pages = {104-110},
  doi = {10.11648/j.jeee.20170503.14},
  url = {https://doi.org/10.11648/j.jeee.20170503.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20170503.14},
  abstract = {Distributed Generation integration in electric power system is one of the options which give many benefits such that loss Reduction, peak saving, voltage profile improvement, stability and reliability improvement. The installation of DG units at non-optimal location can result in an increase in system losses, damaging voltage state. In this paper, simulated annealing Algorithm (SAA) techniqueis designed for optimally determining the location, sizing and numbers of distributed generations depending on power loss reduction and voltage profile improvement. The proposed technique is tested on IEEE 57- bus system to demonstrate the performance of the network after inserting the distributed generation in selected optimal location with optimal sizing. Results show the efficiency of the proposed algorithm in reducing power losses, improving voltage profile.},
 year = {2017}
}

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TY  - JOUR
T1  - Optimal Location and Sizing of Distributed Generation for Minimizing Power Loss Using Simulated Annealing Algorithm
AU  - Salah Kamal EL-Sayed
Y1  - 2017/06/21
PY  - 2017
N1  - https://doi.org/10.11648/j.jeee.20170503.14
DO  - 10.11648/j.jeee.20170503.14
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 104
EP  - 110
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20170503.14
AB  - Distributed Generation integration in electric power system is one of the options which give many benefits such that loss Reduction, peak saving, voltage profile improvement, stability and reliability improvement. The installation of DG units at non-optimal location can result in an increase in system losses, damaging voltage state. In this paper, simulated annealing Algorithm (SAA) techniqueis designed for optimally determining the location, sizing and numbers of distributed generations depending on power loss reduction and voltage profile improvement. The proposed technique is tested on IEEE 57- bus system to demonstrate the performance of the network after inserting the distributed generation in selected optimal location with optimal sizing. Results show the efficiency of the proposed algorithm in reducing power losses, improving voltage profile.
VL  - 5
IS  - 3
ER  -

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Author Information

Salah Kamal EL-Sayed

Department of Electrical Power & Machines, Faculty of Engineering, Al-Azhar University, Cairo, Egypt

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APA Style

Salah Kamal EL-Sayed. (2017). Optimal Location and Sizing of Distributed Generation for Minimizing Power Loss Using Simulated Annealing Algorithm. Journal of Electrical and Electronic Engineering, 5(3), 104-110. https://doi.org/10.11648/j.jeee.20170503.14

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ACS Style

Salah Kamal EL-Sayed. Optimal Location and Sizing of Distributed Generation for Minimizing Power Loss Using Simulated Annealing Algorithm. J. Electr. Electron. Eng. 2017, 5(3), 104-110. doi: 10.11648/j.jeee.20170503.14

Copy | Download

AMA Style

Salah Kamal EL-Sayed. Optimal Location and Sizing of Distributed Generation for Minimizing Power Loss Using Simulated Annealing Algorithm. J Electr Electron Eng. 2017;5(3):104-110. doi: 10.11648/j.jeee.20170503.14

Copy | Download

@article{10.11648/j.jeee.20170503.14,
  author = {Salah Kamal EL-Sayed},
  title = {Optimal Location and Sizing of Distributed Generation for Minimizing Power Loss Using Simulated Annealing Algorithm},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {5},
  number = {3},
  pages = {104-110},
  doi = {10.11648/j.jeee.20170503.14},
  url = {https://doi.org/10.11648/j.jeee.20170503.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20170503.14},
  abstract = {Distributed Generation integration in electric power system is one of the options which give many benefits such that loss Reduction, peak saving, voltage profile improvement, stability and reliability improvement. The installation of DG units at non-optimal location can result in an increase in system losses, damaging voltage state. In this paper, simulated annealing Algorithm (SAA) techniqueis designed for optimally determining the location, sizing and numbers of distributed generations depending on power loss reduction and voltage profile improvement. The proposed technique is tested on IEEE 57- bus system to demonstrate the performance of the network after inserting the distributed generation in selected optimal location with optimal sizing. Results show the efficiency of the proposed algorithm in reducing power losses, improving voltage profile.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Optimal Location and Sizing of Distributed Generation for Minimizing Power Loss Using Simulated Annealing Algorithm
AU  - Salah Kamal EL-Sayed
Y1  - 2017/06/21
PY  - 2017
N1  - https://doi.org/10.11648/j.jeee.20170503.14
DO  - 10.11648/j.jeee.20170503.14
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 104
EP  - 110
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20170503.14
AB  - Distributed Generation integration in electric power system is one of the options which give many benefits such that loss Reduction, peak saving, voltage profile improvement, stability and reliability improvement. The installation of DG units at non-optimal location can result in an increase in system losses, damaging voltage state. In this paper, simulated annealing Algorithm (SAA) techniqueis designed for optimally determining the location, sizing and numbers of distributed generations depending on power loss reduction and voltage profile improvement. The proposed technique is tested on IEEE 57- bus system to demonstrate the performance of the network after inserting the distributed generation in selected optimal location with optimal sizing. Results show the efficiency of the proposed algorithm in reducing power losses, improving voltage profile.
VL  - 5
IS  - 3
ER  -

Copy | Download