A Novel Control Strategy for Autonomous Operation of Isolated Microgrid with Prioritized Loads

  • R. Hari KumarEmail author
  • S. Ushakumari
Original Contribution


Maintenance of power balance between generation and demand is one of the most critical requirements for the stable operation of a power system network. To mitigate the power imbalance during the occurrence of any disturbance in the system, fast acting algorithms are inevitable. This paper proposes a novel algorithm for load shedding and network reconfiguration in an isolated microgrid with prioritized loads and multiple islands, which will help to quickly restore the system in the event of a fault. The performance of the proposed algorithm is enhanced using genetic algorithm and its effectiveness is illustrated with simulation results on modified Consortium for Electric Reliability Technology Solutions (CERTS) microgrid.


Microgrid Load shedding Reconfiguration Genetic Algorithm Prioritized Loads 


  1. 1.
    Y. Hu, N. Hua, C. Wang, J. Gong, X. Li, Research on distribution network reconfiguration, in Proceedings of the International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE), vol. 1 (2010), pp. 176–180Google Scholar
  2. 2.
    H. Kim, Y. Ko, K.H. Jung, Artificial neural-network based feeder reconfiguration for loss reduction in distribution systems. IEEE Trans. Power Delivery 8(3), 1356–1366 (1993)CrossRefGoogle Scholar
  3. 3.
    P. Kayal, S. Chanda, C. K. Chanda, An ANN based network reconfiguration approach for voltage stability improvement of distribution network, in Proceedings of the International Conference on Power and Energy Systems (2011), pp. 1–7Google Scholar
  4. 4.
    K. Nara, A. Shiose, M. Kitagawa, T. Ishihara, Implementation of genetic algorithm for distribution systems loss minimum reconfiguration. IEEE Trans. Power Syst. 7(3), 1044–1051 (1992)CrossRefGoogle Scholar
  5. 5.
    Y.S. Jun, Y. Zhi, W. Yan, Y.Y. Xin, S.X. Yan, Distribution network reconfiguration with distributed power based on genetic algorithm, in Proceedings of 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (2011), pp. 811–815Google Scholar
  6. 6.
    V. Farahani, B. Vahidi, H.A. Abyaneh, Reconfiguration and capacitor placement simultaneously for energy loss reduction based on an improved reconfiguration method. IEEE Trans. Power Syst. 27(2), 587–595 (2012)CrossRefGoogle Scholar
  7. 7.
    Y.J. Jeon, J.C. Kim, Network reconfiguration in radial distribution system using simulated annealing and tabu search. Proceedings of Power Engineering Society Winter Meeting 4, 2329–2333 (2000)Google Scholar
  8. 8.
    Y.J. Jeon, J.C. Kim, J.O. Kim, J.R. Shin, K. Lee, An efficient simulated annealing algorithm for network reconfiguration in large-scale distribution systems. IEEE Trans. Power Delivery 17(4), 1070–1078 (2002)CrossRefGoogle Scholar
  9. 9.
    B. Amanulla, S. Chakrabarti, S.N. Singh, Reconfiguration of power distribution systems considering reliability and power loss. IEEE Trans. Power Delivery 27(2), 918–926 (2012)CrossRefGoogle Scholar
  10. 10.
    W.C. Wu, M.S. Tsai, F.Y. Hsu, A new binary coding particle swarm optimization for feeder reconfiguration, in Proceedings of the International Conference on Intelligent Systems Applications to Power Systems (2007), pp. 1–6Google Scholar
  11. 11.
    Y.T. Hsiao, Multiobjective evolution programming method for feeder reconfiguration. IEEE Trans. Power Syst. 19(1), 594–599 (2004)CrossRefGoogle Scholar
  12. 12.
    M.S. Tsai, C.C. Chu, Applications of hybrid EP-ACO for power distribution system loss minimization under load variations, in Proceedings of the 16th International Conference on Intelligent System Application to Power Systems (2011), pp. 1–7Google Scholar
  13. 13.
    A. Swarnkar, N. Gupta, K. R. Niazi, Efficient reconfiguration of distribution systems using ant colony optimization adapted by graph theory, in Proceedings of the IEEE Power and Energy Society General Meeting (2011), pp. 1–8Google Scholar
  14. 14.
    F. Scenna, D. Anaut, L.I. Passoni, G.J. Meschino, Reconfiguration of electrical networks by an ant colony optimization algorithm. IEEE Latin America Transactions 11(1), 538–544 (2013)CrossRefGoogle Scholar
  15. 15.
    X. Zhan, T. Xiang, H. Chen, B. Zhou, Z. Yang, Vulnerability assessment and reconfiguration of microgrid through search vector artificial physics optimization algorithm. Int. J. Electr. Power Energy Syst. 62, 679–688 (2014)CrossRefGoogle Scholar
  16. 16.
    V.C. do Nascimento, G. Lambert-Torres, C.I. de Almeida Costa, L.E.B. da Silva, Control model for distributed generation and network automation for microgrids operation. Electr. Power Syst. Res. 127, 151–159 (2015)CrossRefGoogle Scholar
  17. 17.
    H. Nafisi, V. Farahani, H. Askarian Abyaneh, M. Abedi, Optimal daily scheduling of reconfiguration based on minimisation of the cost of energy losses and switching operations in microgrids. IET Gener. Transm. Distrib. 9(6), 513–522 (2015)CrossRefGoogle Scholar
  18. 18.
    A. Mokari-Bolhasan, H. Seyedi, B. Mohammadiivatloo, S. Abapour, S. Ghasemzadeh, Modified centralized ROCOF based load shedding scheme in an islanded distribution network. Int. J. Electr. Power Energy Syst. 62, 806–815 (2014)CrossRefGoogle Scholar
  19. 19.
    A. Ketabi, M.H. Fini, An under frequency load shedding scheme for islanded microgrids. Int. J. Electr. Power Energy Syst. 62, 599–607 (2014)CrossRefGoogle Scholar
  20. 20.
    W. Liu, W. Gu, Y. Xu, S. Xue, M. Chen, B. Zhao, M. Fan, Improved average consensus algorithm based distributed cost optimization for loading shedding of autonomous microgrids. Int. J. Electr. Power Energy Syst. 73, 89–96 (2015)CrossRefGoogle Scholar
  21. 21.
    F. Shariatzadeh, C.B. Vellaithurai, S.S. Biswas, R. Zamora, A.K. Srivastava, Real-time implementation of intelligent reconfiguration algorithm for microgrid. IEEE Transactions on Sustainable Energy 5(2), 598–607 (2014)CrossRefGoogle Scholar
  22. 22.
    N. Kumar, A. K. Srivastava, N. N. Schulz, Shipboard power system restoration using binary particle swarm optimization, in Proceedings of the 39th North American Power Symposium (2007), pp. 164–169Google Scholar
  23. 23.
    P. Mitra, G.K. Venayagamoorthy, Implementation of an intelligent reconfiguration algorithm for an electric ship’s power system. IEEE Trans. Ind. Appl. 47(5), 2292–2300 (2011)CrossRefGoogle Scholar
  24. 24.
    S.H.K. Vuppalapatil, A.K. Srivastava, Application of ant colony optimisation for reconfiguration of shipboard power system. International Journal of Engineering, Science and Technology 2(3), 119–131 (2010)Google Scholar
  25. 25.
    K.R. Padamati, N. Schulz, A.K. Srivastava, Application of genetic algorithm for reconfiguration of shipboard power system, in Proceedings of 39th North American Power Symposium (2007), pp. 159–163Google Scholar
  26. 26.
    D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. (Addison Wesley Longman Publishing Co., Inc., Boston, 1989)zbMATHGoogle Scholar
  27. 27.
    R. Lasseter, A. Akhil, C. Marnay, J. Stephens, J. Dagle, R. Guttromson, M. A. Sakis, R. Yinger, J. Eto, White Paper on Integration of Distributed Energy Resources, The CERTS MicroGrid Concept. Tech. Rep., Consortium for Electric Reliability Technology Solutions, California Energy Commission (2002)Google Scholar

Copyright information

© The Institution of Engineers (India) 2018

Authors and Affiliations

  1. 1.Department of Electrical EngineeringCollege of Engineering TrivandrumThiruvananthapuramIndia

Personalised recommendations