Optimal allocation of distributed generator in a radial distribution system using genetic algorithm and particle swarm optimization

##plugins.themes.academic_pro.article.sidebar##

Published Dec 2, 2014
Download
PDF
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Voumel 10, Issue 4, December 2014

##plugins.themes.academic_pro.article.main##

N. Ashwin
Department of Electrical Engineering, University Visvesvaraya College of Engineering, K.R. Circle, Bangalore – 560 001
H. S. Veena
Department of Electrical Engineering, University Visvesvaraya College of Engineering, K.R. Circle, Bangalore – 560001

Abstract

Many areas in power systems require solving one or more nonlinear optimization problems. Optimal allocation of distributed generators (DGs) is one of them. This paper proposes two optimization methods to determine the optimal allocation of distributed generators in radial distribution systems (RDS), for the purpose of maximizing power loss reduction and improving voltage profile. The proposed methodology uses Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to optimize the objective function and is used to compare it with the analytical method of optimization. The Genetic Algorithm and Particle Swarm Optimization methods are tested on a standard radial distribution test systems, which is, IEEE 33 bus RDS using MATLAB R2008b. The results indicate that the optimal location of the DG is at bus number 6, with a power loss reduction of 83.3 kW.

##plugins.themes.academic_pro.article.details##

How to Cite
Ashwin, N., & Veena, H. S. (2014). Optimal allocation of distributed generator in a radial distribution system using genetic algorithm and particle swarm optimization. Power Research - A Journal of CPRI, 799–808. Retrieved from https://node6473.myfcloud.com/~geosocin/CPRI/index.php/pr/article/view/780

References

  1. S Joshi, A Mathur, A Jain, S Gupta, N Jani and B Chhabra “Generation of Electricity using Wind Energy Produced due to the Motion of Trains” Journal of Energy Technologies and Policy Vol. 2, No. 7, pp. 19-23, 2002
  2. G Koeppel “Distributed generationliterature review and outline of the Swiss station”, 2003: Internal Report, ETH Zurich, N Hadjsaid, JF Canard and F Dumas. Dispersed generation impact on distribution networks. IEEE Computer Application to Power systems 1999; pp. 12:22–8
  3. K Tuitemwong and S Premrudeepreechacharn. “Expert system for protection coordination of distribution system with distributed generators”. International Journal on Electric Power; 33 pp. 466–71, 2011
  4. T Griffin, K Tomosovic, D Secrest and A Law. “Placement of dispersed generation systems for reduced losses”. In: 33rd International conference on sciences. Hawaii, 2000.
  5. M P Lalitha, V C V Reddy and V Usha, “Optimal DG placement for minimum real power loss in radial distribution systems using PSO”, Journal of Theoretical and Applied Information Technology, pp. 107116, 2010.
  6. P Chiradeja and R Ramkumar “An approach to quantify the technical benefits of distributed generation”.IEEE Transaction on Energy Conversion,19 (4): pp. 764-773, 2004.
  7. H Khan and M A Choudhry “Implementation of distributed generation algorithm for performance enhancement of distribution feeder under extreme load growth”. International Journal of Electrical Power and Energy Systems, 32 (9): pp. 985-997, 2010.
  8. D Q Hung, N Mithulanathan and R C Bansal “Multiple distributed generators placement in primary distribution networks for loss reduction”. IEEE Transactions on Industrial Electronics. (In Press).
  9. R M Kamel and B Karmanshahi “Optimal size and location of DGs for minimizing power losses in a primary distribution network”. Transaction on Computer Science and Electrical and Electronics Engineering, 16 (2): pp. 137-144, 2009.
  10. Y del Valle, G K Venayagamoorthy, S. Mohagheghi, Jean-Carlos Hernandez and R G Harley,” Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 2, April 2008.
  11. J H Teng, “A Direct Approach for Distribution System Load Flow Solutions”, IEEE Transactions on Power Delivery, vol. 18, no. 3, pp. 882-887, 2003.
  12. T Ananthapadmanabha, H A Maruthi Prasanna., A G Veeresha and M V Likith Kumar, “A new simplified approach for optimum allocation of a DG unit in distribution network for voltage improvement and loss minimization”, International Journal of Electrical Engineering and Technology-IJEET, ISSN 0976-6553, pp. 165-178, Volume 4, Issue 2, March-April 2013.
  13. R R Sharapov, Genetic Algorithms: Basic Ideas, Variants and Analysis, Vision Systems: Segmentation and Pattern Recognition, Goro Obinata and Ashish Dutta (Ed.), ISBN: 978-3-902613-05-9, InTech, 2007.