Table of Contents Author Guidelines Submit a Manuscript
Complexity
Volume 2017, Article ID 2158926, 22 pages
https://doi.org/10.1155/2017/2158926
Research Article

Smart Microgrid Energy Management Using a Novel Artificial Shark Optimization

1School of Informatics, Hawassa University Institute of Technology, Hawassa, Ethiopia
2School of Electrical & Computer Engineering, Hawassa University Institute of Technology, Hawassa, Ethiopia

Correspondence should be addressed to Baseem Khan; moc.liamg@40nahk.meesab

Received 2 April 2017; Revised 17 June 2017; Accepted 27 June 2017; Published 8 October 2017

Academic Editor: Roberto Natella

Copyright © 2017 Pawan Singh and Baseem Khan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. X. Tan, Q. Li, and H. Wang, “Advances and trends of energy storage technology in Microgrid,” International Journal of Electrical Power & Energy Systems, vol. 44, no. 1, pp. 179–191, 2013. View at Google Scholar
  2. J. Corrêa, F. Farret, L. Canha, and M. Simões, IEEE Trans.Ind. Elects, vol. 51, article 1103, 2004.
  3. F. Jurado, “Fuzzy Logic Applied to the Inverter of a SOFC Power Plant,” Fuel Cells, vol. 4, no. 4, pp. 378–387, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. F. Marechal, F. Palazzi, J. Godat, and D. Favrat, “Thermo-economic modelling and optimisation of fuel cell systems,” Fuel Cells, vol. 5, no. 1, pp. 5–24, 2005. View at Google Scholar
  5. G. Masters, Renewable and Efficient Electric Power Systems, John Wiley & Sons Inc, Hoboken, NY, USA, 3rd edition, 2004.
  6. J. Mitra, “Reliability-based sizing of backup storage,” IEEE Transactions on Power Systems, vol. 25, no. 2, pp. 1198-1199, 2010. View at Google Scholar
  7. O. Ekren and Y. Ekren Banu, “Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing,” Applied Energy, vol. 87, no. 2, pp. 592–598, 2010. View at Google Scholar
  8. M. Mohammadi, S. H. Hosseinian, and G. B. Gharehpetian, “GA-based optimal sizing of microgrid and DG units under pool and hybrid electricity markets,” International Journal of Electrical Power & Energy Systems, vol. 35, no. 1, pp. 83–92, 2012. View at Google Scholar
  9. B. Bahmani-Firouzi and R. Azizipanah-Abarghooee, “Optimal sizing of battery energy storage for Micro-Grid operation management using a new improved bat algorithm,” Electr. Power Energy Syst, vol. 56, no. 1, pp. 42–54, 2014. View at Google Scholar
  10. S. Chakraborty, M. G. Weiss, M. D. Weiss, and M. G. Simoes, “Distributed intelligent energy management system for a single-phase high frequency AC microgrid,” IEEE Transactions on Industrial Electronics, vol. 54, no. 1, pp. 97–109, 2007. View at Google Scholar
  11. E. Sortomme and M. A. El-Sharkawi, “Optimal power flow for a system of microgrids with controllable loads and battery storage,” in proceedings of the IEEE/PES Power Systems Conf. and Exposition, pp. 1–5, 2009.
  12. T. Niknam, A. Kavousifard, S. Tabatabaei et al., “Optimal operation management of fuel cell /wind /photovoltaic power sources connected to distribution networks,” Journal of Power Sources, vol. 196, no. 20, pp. 8881–8896, 2011. View at Google Scholar
  13. S. Sharma, S. Bhattacharjee, and A. Bhattacharya, “Grey wolf optimisation for optimal sizing of battery energy storage device to minimise operation cost of microgrid,” in IET Generation, Transmission & Distribution, vol. 10, pp. 625–637, 2016. View at Google Scholar
  14. F. Laureri, L. Puliga, M. Robba, F. Delfino, and G. O. Bultò, “An optimization model for the integration of electric vehicles and smart grids: Problem definition and experimental validation,” in proceedings of the IEEE International Smart Cities Conference (ISC2), pp. 1–6, Trento, Italy, 2016.
  15. D. Bai, H. Cao, and L. Wang, “Research and simulation of V2G technology in micro grid,” in proceedings of the China International Conference on Electricity Distribution (CICED), pp. 1–5, Xi'an, China, 2016.
  16. G. Chen, Q. Cheng, H. Wang, M. Li, C. Xu, and L. Deng, “Study on bi-directional energy transfer of EV charging station on micro-grid operation,” in Proceeding of the 11th World Congress on Intelligent Control and Automation, pp. 5517–5522, Shenyang, China, 2014.
  17. S. J. Gunter, K. K. Afridi, and D. J. Perreault, “Optimal design of grid-connected pev charging systems with integrated distributed resources,” IEEE Transactions on Smart Grid, vol. 4, no. 2, pp. 956–967, 2013. View at Google Scholar
  18. S. X. Chen, H. B. Gooi, and M. Q. Wang, “Sizing of energy storage for microgrids,” IEEE Trans Smart Grid, vol. 3, pp. 142–151, 2012. View at Google Scholar
  19. M. Basu, “Hybridization of bee colony optimization and sequential quadratic programming for dynamic economic dispatch,” International Journal of Electrical Power & Energy Systems, vol. 44, pp. 591–596, 2013. View at Google Scholar
  20. R. Azizipanah-Abarghooee, “A new hybrid bacterial foraging and simplified swarm optimization algorithm for practical optimal dynamic load dispatch,” Int J Electr Power Energy Syst, vol. 49, pp. 414–429, 2013. View at Google Scholar
  21. E. Bunting, Sea World Book of Sharks, Harcourt, Brace, Janovich, NY, USA, 1989.
  22. Y. Wang, B. Li, T. Weise, J. Wang, B. Yuan, and Q. Tian, “Self-adaptive learning based particle swarm optimization,” Information Sciences, vol. 181, pp. 4515–4538, 2011. View at Google Scholar
  23. F. Van den Bergh and A. Engelbrecht, “A study of particle swarm optimization particle trajectories,” Information Sciences, vol. 176, pp. 937–971, 2006. View at Google Scholar
  24. A. Anvari Moghaddam, A. Seifi, T. Niknam, and M. R. Alizadeh Pahlavani, “Multiobjective operation management of a renewable MG (micro-grid) with backup micro-turbine/fuel cell/battery hybrid power source,” Energy, vol. 36, pp. 6490–6507, 2011. View at Google Scholar
  25. T. Niknam and F. Golestaneh, “Probabilistic multiobjective operation management of microgrids with hydrogen storage and polymer exchange fuel cell power plants,” Fuel Cells, vol. 12, pp. 809–826, 2012. View at Google Scholar
  26. X. Tan, Q. Li, and H. Wang, “Advances and trends of energy storage technology in Microgrid,” International Journal of Electrical Power & Energy Systems, vol. 44, pp. 179–191, 2013. View at Google Scholar
  27. W. Al-Saedi, S. W. Lachowicz, D. Habibi, and O. Bass, “Power quality enhancement in autonomous microgrid operation using particle swarm optimization,” International Journal of Electrical Power & Energy Systems, vol. 42, pp. 139–149, 2013. View at Google Scholar
  28. W. Gu, Z. Wu, R. Bo, W. Liu, G. Zhou, W. Chen et al., “Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: a review,” Int J Electr Power Energy Syst, vol. 54, pp. 26–37, 2014. View at Google Scholar
  29. R. Azizipanah-Abarghooee, T. Niknam, A. Roosta, A. R. Malkpour, and M. Zare, “Probabilistic multiobjective wind-thermal economic emission dispatch based on point estimated method,” Energy, vol. 37, pp. 322–335, 2012. View at Google Scholar
  30. T. Niknam, R. Azizipanah-Abarghooee, and M. R. Narimani, “An efficient scenario based stochastic programming framework for multi-objective optimal microgrid operation,” Applied Energy, vol. 99, pp. 455–470, 2012. View at Google Scholar
  31. R. Ashok Bakkiyaraj and N. Kumarappan, “Optimal reliability planning for a composite electric power system based on Monte Carlo simulation using particle swarm optimization,” International Journal of Electrical Power & Energy Systems, vol. 47, pp. 109–116, 2013. View at Google Scholar
  32. T. Niknam, R. Azizipanah-Abarghooee, M. Zare, and B. Bahmani-Firouzi, “Reserve constrained dynamic environmental/economic dispatch: a new multiobjective self-adaptive learning bat algorithm,” IEEE Systems Journal, vol. 7, pp. 763–776, 2013. View at Google Scholar