Skip to main content
SpringerLink
Log in

Optimal Power Flow with Uncertain Renewable Energy Sources Using Flower Pollination Algorithm

  • Conference paper
  • First Online:
Book cover Advanced Information Networking and Applications (AINA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 926))

Abstract

Optimal power flow (OPF) problem has become more significant for operation and planning of electrical power systems because of the increasing energy demand. OPF is very important for system operators to fulfill the electricity demand of the consumers efficiently and for the reliable operation of the power system. The key objective in OPF is to reduce the total generating cost while assuring the system limitations. Due to environmental emission, depletion of fossil fuels and its higher prices, integration of renewable energy sources into the grid is essential. Classical OPF, which consider only thermal generators is a non-convex, non-linear optimization problem. However, incorporating the uncertain renewable sources adds complexity to the problem. A metaheuristic algorithm which solves the OPF problem with renewable energy sources is to be implemented on a modified IEEE 30-bus system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mohamed, A.-A.A., et al.: Optimal power flow using moth swarm algorithm. Electr. Power Syst. Res. 142, 190–206 (2017)

    Article  Google Scholar 

  2. Duman, S.: A modified moth swarm algorithm based on an arithmetic crossover for constrained optimization and optimal power flow problems. IEEE Access 6, 45394 (2018)

    Article  Google Scholar 

  3. Reddy, S.S.: Optimal power flow using hybrid differential evolution and harmony search algorithm. Int. J. Mach. Learn. Cybern., 1–15 (2018)

    Google Scholar 

  4. Attia, A.-F., El Sehiemy, R.A., Hasanien, H.M.: Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. Int. J. Electr. Power Energy Syst. 99, 331–343 (2018)

    Article  Google Scholar 

  5. Bai, W., Eke, I., Lee, K.Y.: An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem. Control Eng. Pract. 61, 163–172 (2017)

    Article  Google Scholar 

  6. Javaid, N., et al.: Energy efficient integration of renewable energy sources in the smart grid for demand side management. IEEE Access 6, 77077–77096 (2018)

    Article  Google Scholar 

  7. Javaid, N., et al.: An intelligent load management system with renewable energy integration for smart homes. IEEE Access 5, 13587–13600 (2017)

    Article  Google Scholar 

  8. Khan, M., et al.: Game theoretical demand response management and short-term load forecasting by knowledge based systems on the basis of priority index. Electronics 7(12), 431 (2018)

    Article  Google Scholar 

  9. Awais, M., et al.: Towards effective and efficient energy management of single home and a smart community exploiting heuristic optimization algorithms with critical peak and real-time pricing tariffs in smart grids. Energies 11(11), 3125 (2018)

    Article  Google Scholar 

  10. Ahmad, A., et al.: An optimized home energy management system with integrated renewable energy and storage resources. Energies 10(4), 549 (2017)

    Article  Google Scholar 

  11. Bouchekara, H.R., Abido, M.A., Chaib, A.E., et al.: Optimal power flow using the league championship algorithm: a case study of the Algerian power system. Energy Convers. Manage. 87, 58–70 (2014)

    Article  Google Scholar 

  12. Biswas, P.P., Suganthan, P.N., Amaratunga, G.A.: Optimal power flow solutions incorporating stochastic wind and solar power. Energy Convers. Manage. 148, 1194–1207 (2017)

    Article  Google Scholar 

  13. Roy, R., Jadhav, H.T.: Optimal power flow solution of power system incorporating stochastic wind power using Gbest guided artificial bee colony algorithm. Int. J. Electr. Power Energy Syst. 64, 562–578 (2015)

    Article  Google Scholar 

  14. Panda, A., Tripathy, M.: Optimal power flow solution of wind integrated power system using modified bacteria foraging algorithm. Int. J. Electr. Power Energy Syst. 54, 306–314 (2014)

    Article  Google Scholar 

  15. Panda, A., Tripathy, M.: Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm. Energy 93, 816–827 (2015)

    Article  Google Scholar 

  16. Shi, L., et al.: Optimal power flow solution incorporating wind power. IEEE Syst. J. 6(2), 233–241 (2012)

    Article  Google Scholar 

  17. Chang, T.P.: Investigation on frequency distribution of global radiation using different probability density functions. Int. J. Appl. Sci. Eng. 8(2), 99–107 (2010)

    Google Scholar 

  18. Reddy, S.S., Bijwe, P.R., Abhyankar, A.R.: Real-time economic dispatch considering renewable power generation variability and uncertainty over scheduling period. IEEE Syst. J. 9(4), 1440–1451 (2015)

    Article  Google Scholar 

  19. Reddy, S.S.: Optimal scheduling of thermal-wind-solar power system with storage. Renewable Energy 101, 1357–1368 (2017)

    Article  Google Scholar 

  20. Sharma, H., Singh, J.: Run off river plant: status and prospects. Int. J. Innov. Technol. Exploring Eng. (IJITEE) 3(2) (2013)

    Google Scholar 

  21. Pandey, H.K., Dwivedi, S., Kumar, K.: Flood frequency analysis of Betwa river, Madhya Pradesh India. J. Geol. Soc. India 92(3), 286–290 (2018)

    Article  Google Scholar 

  22. Cabus, P.: River flow prediction through rainfall runoff modelling with a probability-distributed model (PDM) in Flanders, Belgium. Agric. Water Manage. 95(7), 859–868 (2008)

    Article  Google Scholar 

  23. Wijesinghe, A., Lai, L.L.: Small hydro power plant analysis and development. In: 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT). IEEE (2011)

    Google Scholar 

  24. Yang, X.-S.: Flower pollination algorithm for global optimization. In: International Conference on Unconventional Computing and Natural Computation. Springer, Heidelberg (2012)

    Google Scholar 

  25. Yang, X.-S., Karamanoglu, M., He, X.: Flower pollination algorithm: a novel approach for multiobjective optimization. Eng. Optim. 46(9), 1222–1237 (2014)

    Article  MathSciNet  Google Scholar 

  26. Abdelaziz, A.Y., Ali, E.S., Abd Elazim, S.M.: Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems. Energy 101, 506–518 (2016)

    Article  Google Scholar 

  27. Velamuri, S., Sreejith, S., Ponnambalam, P.: Static economic dispatch incorporating wind farm using flower pollination algorithm. Perspect. Sci. 8, 260–262 (2016)

    Article  Google Scholar 

  28. Huang, S.-J., et al.: Application of flower pollination algorithm for placement of distribution transformers in a low-voltage grid. In: 2015 IEEE International Conference on Industrial Technology (ICIT). IEEE (2015)

    Google Scholar 

  29. Abdelaziz, A.Y., Ali, E.S., Abd Elazim, S.M.: Flower pollination algorithm and loss sensitivity factors for optimal sizing and placement of capacitors in radial distribution systems. Int. J. Electr. Power Energy Syst. 78, 207–214 (2016)

    Article  Google Scholar 

  30. He, X., et al.: Global convergence analysis of the flower pollination algorithm: a discrete-time Markov chain approach. Procedia Comput. Sci. 108, 1354–1363 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. COMSATS University Islamabad, Islamabad, 44000, Pakistan

    Muhammad Abdullah, Nadeem Javaid & Noman Ahmad

  2. COMSATS University Islamabad, Lahore, 14000, Pakistan

    Inam Ullah Khan

  3. Computer Information Science, Higher Colleges of Technology, Fujairah, 4114, UAE

    Zahoor Ali Khan

  4. COMSATS University Islamabad, Abbottabad, 22010, Pakistan

    Annas Chand

Authors
  1. Muhammad Abdullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nadeem Javaid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Inam Ullah Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zahoor Ali Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Annas Chand
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Noman Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nadeem Javaid .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Advanced Sciences, Hosei University, Koganei-Shi, Tokyo, Japan

    Makoto Takizawa

  3. Department of Computer Science, Technical University of Catalonia, Barcelona, Barcelona, Spain

    Fatos Xhafa

  4. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Abdullah, M., Javaid, N., Khan, I.U., Khan, Z.A., Chand, A., Ahmad, N. (2020). Optimal Power Flow with Uncertain Renewable Energy Sources Using Flower Pollination Algorithm. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2019. Advances in Intelligent Systems and Computing, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-15032-7_8

Download citation

Publish with us

Policies and ethics

Search

Navigation