Optimal Reconfiguration of Intelligent Distribution Net-works in the Presence of DGs using TLBO Algorithm

  • Davar Mirabbasi Department of electrical engineering, Ardabil branch, Islamic Azad University, Ardabil
  • Bahman Taheri iau
  • Keyvan Shirzad Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil
Keywords: Distributed Generation (DG), Optimization, intelligent distribution system, reconfiguration, TLBO


DG is a better alternative to meet power demand near the load centers than centralized power generation. Optimal placement and sizing of DGs plays a crucial role in improving the performance of distribution systems in terms of network loss reduction, voltage profile improvement, reliability of power supply and stability issues. In this paper, a new method is proposed to solve intelligent distribution network reconfiguration in the presence of DGs aiming to minimize active power loss and improve voltage profile. Meta-heuristic optimization algorithm based on training-learning (TLBO) is used to reconfigure distribution network. In order to investigate different operational conditions of distribution systems and efficiency of the proposed method, different scenarios of network reconfiguration are simulated and studied. In order to illustrate performance and effectiveness of the proposed method, IEEE-33 bus radial distribution network is simulated at three different levels. In the proposed scenarios, presence or absence of DGs in different buses and effect of capacity of these sources is studied and simulated.


[1] B. Venkatesh, Rakesh Ranjan, and H. B. Gooi, "Optimal Reconfiguration of Radial Distribu tion Sys-tems to Maximize Loadability," IEEE Trans. on Power Syst.Vol. 19, No.1, Feb. 2004
[2] I. Ahmad Quadri, S.Bhowmick D.Joshi ,, A comprehensive technique for optimal allocation of distributed energy resources in radial distribution systems,, Applied Energy Volume 211, 1 February 2018, Pages 1245-1260
[3] D. Singh, R. K. Misra,"Load type impact on distribution system reconfiguration," Elec. Power and Energy Syst. Vol. 42, No. 5, pp. 583–592, June 2012.
[4] B. Tomoiag, M. Chindris, A. Sumper,R. Villafafila-Robles, A. Sudria-Andreu, "Distribution system recon-figuration using genetic algorithm based onconnected graphs," Elec. Power Syst. Research VOL. 104, pp. 216–225, June 2013.
[5] T. Niknam, R. Azizipanah, M. Rasoul Narimani,, A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems,, Engineering Applications of Ar-tificial Intelligence,25 (2012)-1577-1588
[6] E.S. Ali, S.M. Abd Elazim, A.Y. Abdelaziz Ant Lion Optimization Algorithm for optimal location and siz-ing of renewable distributed generations,, Renewable Energy101(2017)-1311-1324
[7] A. Zidan, E.F.Saadany, "Distribution system reconfiguration for energy loss reduction considering the var-iability of load and local renewable generation," Energy, Vol. 59 , pp. 698-707, July 2013.
[8] R.S. Rao, K. Raindrop, K. Satish, S.V.L. Narasimham, "Power Loss Minimization in Distribution System Using Network Reconfiguration in the Presence of Distributed Generation," IEEE Trans. on Power Syst., Vol. 28, No. 1, pp 317-325, Feb. 2013.
[9] J .C. Cebrian, N. Kagan, "Reconfiguration of distribution networks to minimize loss and disruption costs using genetic algorithms," Elect. Power Syst. Research, Vol. 80, pp. 53–62, 2010.
[10] H. Moarrefi, M. Nematollahi, M. Tadayon, "Reconfiguration and Distributed Generation (DG) Placement Considering Critical System Condition," 22nd International Conference on Elec. Distribution, No. 0732, June 2013.
[11] Rao RV, Savsani VJ, Vakharia DP., "Teaching-learning-based optimization: a novel method for con-strained mechanical design optimization problems," Computer Aided Design 2011; 43:303–15
[12] J. A. M. Garcia, A. J. Gil Mena, "Optimal distributed generation location and size using a modified teach-ing–learning based optimization algorithm," Elec. Power and Energy Sys. 50 (2013) 65–75
[13] Gupta N. Swarnkar A. and Niazi K.R., "A modified branch- exchange heuristic algorithm for large-scale distribution networks reconfiguration," Power and Energy Society General Meeting, 2012 IEEE
[14] Jen-Hao Teng, "A Direct Approach for Distribution System Load Flow Solutions", IEEE Transactions on Power Delivery, Vol. 18, No. 3, JU
[15] D. S. Frankel, Model Driven Architecture: Applying MDA to Enterprise Computing, OMG Press, Wiley Publishing, 2003
[16] M. J. Sannella, Constraint Satisfaction and Debugging for Interactive User Interfaces, Ph.D. Thesis, University of Washington, Seattle, WA, 1994.
[17]. P Farhadi, M Sedaghat, S Sharifi, B Taheri,, Power point tracking in photovoltaic systems by sliding mode control,, Advanced Topics in Electrical Engineering (ATEE), 2017 10th International Symposium on,, 781-785 IEEE
[18]. Sh, Sharifi, M. Sedaghat, P. Farhadi, N. Ghadimi, B. Taheri,,Environmental economic dispatch using im-proved artificial bee colony algorithm,, Evolving Systems, Springer Berlin Heidelberg,8.3. 233-242.2017.
[19]. B. Taheri • Gh.Aghajani • M. Sedaghat , Economic dispatch in a power system considering environmental pollution using a multi-objective particle swarm optimization algorithm based on the Pareto criterion and fuzzy logic,, Int J Energy Environ Eng (2017) 8:99–107
DOI 10.1007/s40095-017-0233-9
[20]. Ebrahimian, H., Taheri, B., Yousefi, N.: Optimal operation of energy at hydrothermal power plants by simultaneous minimization of pollution and costs using improved ABC algorithm. Front. Energy 9(4), 426–432 (2015). doi:10.1007/s11708-015-0376-4
How to Cite
Mirabbasi, D., Taheri, B., & Shirzad, K. (2018). Optimal Reconfiguration of Intelligent Distribution Net-works in the Presence of DGs using TLBO Algorithm. Majlesi Journal of Energy Management, 7(3), 11-25. Retrieved from http://journals.iaumajlesi.ac.ir/em/index/index.php/em/article/view/368