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Artificial Intelligence Technique for Optimal Allocation of Renewable Energy Based DGs in Distribution Networks

  • Zia UllahEmail author
  • M. R. Elkadeem
  • Shaorong Wang
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 97)

Abstract

This paper proposes the artificial intelligence technique based on hybrid optimization phasor particle swarm optimization and a gravitational search algorithm, called PPSO-GSA for optimal allocation of renewable energy-based distributed generators (OA-RE-DGs), particularly wind and solar power generators, in distribution networks. The main objective is to maximize the techno-economic benefits in the distribution system by optimal allocation and integration of RE-DGs into distribution system. The proposed PPSO-GSA is implemented and validated on 94-bus practical distribution system located in Portuguese considering single and multiple scenarios of RE-DGs installation. The results reveal that optimizing the location and size of RE-DGs results in a substantial reduction in active power loss and yearly economic loss as well as improving system voltage profile and stability. Moreover, the convergence characteristics, computational efficiency and applicability of the proposed artificial intelligence technique is evaluated by comparative analysis and comparison with other optimization techniques.

Keywords

Artificial intelligence technique Renewable energy Distributed generators Phasor particle swarm optimization Gravitational search algorithm 

Notes

Acknowledgments

The authors would like to thank the State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology for providing the essential facilities.

References

  1. 1.
    IRENA. Global Energy, a Roadmap to 2050 by Irena 2018. http://www.irena.org
  2. 2.
    Dawoud, S.M., Lin, X., Okba, M.I.: Hybrid renewable microgrid optimization techniques: a review. Renew. Sustain. Energy Rev. 82, 2039–2052 (2018)CrossRefGoogle Scholar
  3. 3.
    Djelailia, O., Kelaiaia, M.S., Labar, H., Necaibia, S., Merad, F.: Energy hybridization photovoltaic/diesel generator/pump storage hydroelectric management based on online optimal fuel consumption per kWh. Sustain. Cities Soc. 44, 1–15 (2019)CrossRefGoogle Scholar
  4. 4.
    Quek, T.Y.A., Ee, W.L.A., Chen, W., Ng, T.S.A.: Environmental impacts of transitioning to renewable electricity for Singapore and the surrounding region: a life cycle assessment. J. Clean. Prod. 214, 1–11 (2019)CrossRefGoogle Scholar
  5. 5.
    Poornazaryan, B., Karimyan, P., Gharehpetian, G.B., Abedi, M.: Optimal allocation and sizing of DG units considering voltage stability, losses and load variations. Int. J. Electr. Power Energy Syst. 79, 42–52 (2016)CrossRefGoogle Scholar
  6. 6.
    Kosai, S.: Dynamic vulnerability in standalone hybrid renewable energy system. Energy Convers. Manag. 180, 258–268 (2019)CrossRefGoogle Scholar
  7. 7.
    Xia, S., Bu, S., Wan, C., Lu, X., Chan, K.W., Zhou, B.: A fully distributed hierarchical control framework for coordinated operation of DERs in active distribution power networks. IEEE Trans. Power Syst. 8950 (2018)Google Scholar
  8. 8.
    Wang, L., Yuan, M., Zhang, F., Wang, X., Dai, L., Zhao, F.: Risk assessment of distribution networks integrating large-scale distributed photovoltaics. IEEE Access 7, 59653–59664 (2019)CrossRefGoogle Scholar
  9. 9.
    Xiang, Y., et al.: Optimal active distribution network planning: a review. Electr. Power Compon. Syst. 44(10), 1075–1094 (2016)CrossRefGoogle Scholar
  10. 10.
    Qazi, A., Hussain, F., Rahim, N.A.B.D., Member, S.: Towards sustainable energy: a systematic review of renewable energy sources, technologies, and public opinions. IEEE Access 7, 63837–63851 (2019)CrossRefGoogle Scholar
  11. 11.
    Razavi, S.E., et al.: Impact of distributed generation on protection and voltage regulation of distribution systems: a review. Renew. Sustain. Energy Rev. 105, 157–167 (2019)CrossRefGoogle Scholar
  12. 12.
    Mararakanye, N., Bekker, B.: Renewable energy integration impacts within the context of generator type, penetration level and grid characteristics. Renew. Sustain. Energy Rev. 108, 441–451 (2019)CrossRefGoogle Scholar
  13. 13.
    Ghadi, M.J., Ghavidel, S., Rajabi, A., Azizivahed, A., Li, L., Zhang, J.: A review on economic and technical operation of active distribution systems. Renew. Sustain. Energy 104, 38–53 (2019)CrossRefGoogle Scholar
  14. 14.
    Carvalho, P.C.M., Costa, R.M.: Implementation and evaluation of the first renewable energy systems technical course in Brazil. IEEE Access 7, 46538–46549 (2019)CrossRefGoogle Scholar
  15. 15.
    Mahmoud, P.H.A., Huy, P.D., Ramachandaramurthy, V.K.: A review of the optimal allocation of distributed generation: objectives, constraints, methods, and algorithms. Renew. Sustain. Energy Rev. 75, 293–312 (2017)CrossRefGoogle Scholar
  16. 16.
    Nduka, O.S., Pal, B.C.: Quantitative evaluation of actual loss reduction benefits of a renewable heavy DG distribution network. IEEE Trans. Sustain. Energy 9(3), 1384–1396 (2018)CrossRefGoogle Scholar
  17. 17.
    Sultana, U., Khairuddin, A.B., Aman, M.M., Mokhtar, A.S., Zareen, N.: A review of optimum DG placement based on minimization of power losses and voltage stability enhancement of distribution system. Renew. Sustain. Energy Rev. 63, 363–378 (2016)CrossRefGoogle Scholar
  18. 18.
    Ehsan, A., Yang, Q.: Optimal integration and planning of renewable distributed generation in the power distribution networks: a review of analytical techniques. Appl. Energy 210, 44–59 (2018)CrossRefGoogle Scholar
  19. 19.
    Mithulananthan, N.: Optimal allocation of distributed generation using hybrid grey wolf optimizer. IEEE Access 5, 14807–14818 (2017)CrossRefGoogle Scholar
  20. 20.
    El-Fergany, A.: Multi-objective allocation of multi-type distributed generators along distribution networks using backtracking search algorithm and fuzzy expert rules. Electr. Power Compon. Syst. 44(3), 252–267 (2016)CrossRefGoogle Scholar
  21. 21.
    Kumar, S., Mandal, K.K., Chakraborty, N.: Optimal DG placement by multi-objective opposition based chaotic differential evolution for techno-economic analysis. Appl. Soft Comput. J. 78, 70–83 (2019)CrossRefGoogle Scholar
  22. 22.
    Ullah, Z., Wang, S., Radosavljevic, J., Lai, J.: A solution to the optimal power flow problem considering WT and PV generation. IEEE Access 7, 46763–46772 (2019)CrossRefGoogle Scholar
  23. 23.
    Mirjalili, S., Zaiton, S., Hashim, M.: A new hybrid PSOGSA algorithm for function optimization, no. 1, pp. 374–377 (2010)Google Scholar
  24. 24.
    Ghasemi, M., Akbari, E., Rahimnejad, A., Ehsan, S., Sahand, R., Li, G.: Phasor particle swarm optimization: a simple and efficient variant of PSO (2018)Google Scholar
  25. 25.
    Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. (NY) 179(13), 2232–2248 (2009)CrossRefGoogle Scholar
  26. 26.
    Maleki, A., Khajeh, M.G., Ameri, M.: Optimal sizing of a grid independent hybrid renewable energy system incorporating resource uncertainty, and load uncertainty. Int. J. Electr. Power Energy Syst. 83, 514–524 (2016)CrossRefGoogle Scholar
  27. 27.
    Radosavljevic, J.: Metaheuristic optimization in power engineering. Institution of Engineering and Technology (2018)Google Scholar
  28. 28.
    El-Fergany, A.: Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm. Int. J. Electr. Power Energy Syst. 64, 1197–1205 (2015)CrossRefGoogle Scholar
  29. 29.
    Pires, D.F., Antunes, C.H., Martins, A.G.: NSGA-II with local search for a multi-objective reactive power compensation problem. Int. J. Electr. Power Energy Syst. 43(1), 313–324 (2012)CrossRefGoogle Scholar
  30. 30.
    El-Fergany, A.: Study impact of various load models on DG placement and sizing using backtracking search algorithm. Appl. Soft Comput. J. 30, 803–811 (2015)CrossRefGoogle Scholar
  31. 31.
    ChithraDevi, S.A., Lakshminarasimman, L., Balamurugan, R.: Stud Krill herd algorithm for multiple DG placement and sizing in a radial distribution system. Eng. Sci. Technol. Int. J. 20(2), 748–759 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Key Laboratory of Advanced Electromagnetic Engineering and TechnologyHuazhong University of Science and TechnologyWuhanChina
  2. 2.Electrical Power and Machines Engineering Department, Faculty of EngineeringTanta UniversityTantaEgypt

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