Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Distributed generation integration optimization using fuzzy logic controller

Rajasthan Technical University, India

Topical Section: Smart Grids and Networks

Distributed generators (DGs) are connected near load centers and integrated to grid for optimum power system operations. The challenging task for integration of DGs is the proper size and location, due to random character of generation and energy consumption. The objective of the research is to provide the proper size and location of DGs to optimize the voltage profile and reduction in active and reactive power losses. Optimization of distributed generators is performed through fuzzy logic controllers at Ramchandrapura 33/11 kV, 69 bus radial distribution substation, Kota, India. Fuzzy logic controller provides possible solution on selection of distributed generators on the basis of voltage profile of the bus and the impedance of the branch connected DGs at consumer’s end. Forward or backward sweep methodology is used to optimize the load flow analysis for calculation of real and reactive power losses and voltage profile with and without implementation of distributed generators. Proposed approach provides a possible solution for efficient integration of DGs.
  Article Metrics


1. Government of India, Ministry of Power Central Electricity Authority, New Delhi. Available from: http://www.cea.nic.in/reports/monthly/executivesummary/2018/exe_summary-05.pdf .

2. Singh P, Kothari DP, Singh M (2014) Interconnected distribution network for the integration of distributed energy resources. Res J Appl Sci, Eng Technol 7: 240–250.    

3. Abu-Mouti FS, El-Hawary ME (2011) Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony algorithm. IEEE Trans Power Delivery 26: 2090–2101.    

4. Akash T, Santosh KS, Pushpendra S, et al. (2018) Smart grid development in India with challenges and opportunities: Execution with game theory. Int J Tech Res Sci 3: 122–128.

5. Akash T, Santosh KS, Pushpendra S, et al. (2018) Game theory: demand side management with DG's and storage units. Int J Tech Res Sci 3: 129–133.

6. Mahari A, Babaei E (2012) Optimal DG placement and sizing in distribution systems using imperialistic competition algorithm. In 2012 IEEE 5th India international conference on power electronics (IICPE).

7. Syahputra R, Soesanti I, Ashari, M (2016) Performance enhancement of distribution network with DG integration using modified PSO algorithm. J Electr Syst 12: 1–19.

8. Talwariya A, Sharma D, Pandey AK, et al. (2016) An execution of smart grid with game theory. In 2016 International Conference on Recent Advances and Innovations in Engineering (ICRAIE) 1–4.

9. Sharma D, Talwariya A, Pandey A, et al. (2017) Shrouded problems of solar power plant and recommendations. Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore 1–5.

10. Prabhu JAX, Sharma S, Nataraj M, et al. (2016) Design of electrical system based on load flow analysis using ETAP for IEC projects. In Power Systems (ICPS), 2016 IEEE 6th International Conference 1–6.

11. Talwariya A, Singh P, Kolhe M (2019) A stepwise power tariff model with game theory based on Monte-Carlo simulation and its applications for household, agricultural, commercial and industrial consumers. Int J Electr Power Energy Syst 111: 14–24.    

12. Talwariya A, Sharma SK, Singh P, et al. (2018) A game theory approach for energy tariff and demand side management. International Conference on Recent Advancement and Innovations in Engineering (ICRAIE), Jaipur, India, 1–4.

13. Rupa JM, Ganesh S (2014) Power flow analysis for radial distribution system using backward/forward sweep method. Inter J Electr, Comput, Electron Commun Eng 8: 1540–1544.

14. Abdel-Akher M (2013) Voltage stability analysis of unbalanced distribution systems using backward/forward sweep load-flow analysis method with secant predictor. IET gener, transm distrib 7: 309–317.    

15. Hasanien HM, Matar M (2015) A fuzzy logic controller for autonomous operation of a voltage source converter-based distributed generation system. IEEE Trans Smart grid 6: 158–165.    

16. Huang SJ, Gu PH, Su WF, et al. (2015) Application of flower pollination algorithm for placement of distribution transformers in a low-voltage grid. In 2015 IEEE international conference on industrial technology (ICIT) 1280–1285.

17. Freitas W, Vieira JCM, Morelato A, et al. (2006) Comparative analysis between synchronous and induction machines for distributed generation applications. IEEE Trans Power Syst 21: 301–311.    

18. Alajmi BN, Ahmed KH, Finney SJ, et al. (2011) Fuzzy-logic-control approach of a modified hill-climbing method for maximum power point in micro-grid stand-alone photovoltaic system. IEEE trans power Electron 26: 1022–1030.    

© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved