Skip to main content

Advertisement

SpringerLink
Log in

A Graphical Probabilistic Representation for the Impact Assessment of Wind Power Plants in Power Systems

  • Original Article
  • Published:
Journal of Electrical Engineering & Technology Aims and scope Submit manuscript

Abstract

Traditional methods used for the analysis and design of power systems, like power flow studies (PFS), do not consider any uncertainties. For example, when there is a high penetration of wind power plants (WPPs), whose raw material is intermittent. In this paper is proposed a graphical probabilistic representation (GPR) based on multi-objective performance index (MPI) to assess the impact of the WPPs penetration in power systems. This representation is applied to the southeastern network of Mexico, where there is increasing penetration of WPPs. Besides, a comparative study is presented, with and without a static var compensator (SVC) device connected to the mentioned network, to evaluate the effects of shunt compensation in the point of common coupling (PCC) with WPPs. The results of this comparison are discussed using the GPR proposed. The results show that GPR can be utilized as a useful tool to represent a considerable amount of information in a clear, compact, and single visual representation.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Adefarati T, Bansal RC (2016) Integration of renewable distributed generators into the distribution system: a review. IET Renew Power Gen 10:873–884

    Article  Google Scholar 

  2. Ssekulima EB, Anwar MB, Hinai A, Moursi M (2016) Wind speed and solar irradiance forecasting techniques for enhanced renewable energy integration with the grid: a review. IET Renew Power Gen 10:885–989

    Article  Google Scholar 

  3. Claudia M, Martínez M, Peña R (2014) Scenarios for a hierarchical assessment of the global sustainability of electric power plants in México. Renew Sustain Energy Rev 33:154–160

    Article  Google Scholar 

  4. Ackermann T (2005) Wind power in power systems. Wiley, USA

    Book  Google Scholar 

  5. Saberian A, Farzan P, Nejad MF, Hizam H, Gomes C, Othman MLB, Mohd Radzi MA, Ab Kadir MZA (2013) Role of FACTS devices in improving penetration of renewable energy. In: 2013 IEEE 7th International Power Engineering and Optimization Conference, pp 432–437

  6. El-Shimy M, Badr MAL, Rassem OM (2008) Impact of large scale wind power on power system stability. In: 12th International Middle East Power System Conference, MEPCON 2008, pp 630–636

  7. Slootweg J, Kling W (2003) Aggregated modelling of wind parks in power system dynamics simulations, Power Tech Conference Proceedings, Bologna, Italy

  8. Saadat H (2010) Power system analysis. McGraw-Hill, USA

    Google Scholar 

  9. Papaefthymiou G, Schavemaker P, Sluis L, Kling W et al (2006) Integration of stochastic generation in power systems. Elsevier, Netherlands, pp 655–667

    Google Scholar 

  10. Yürüen NY, Melero JJ (2016) Probability density function selection based on the characteristic of wind speed data. J Phys.: Conf Ser 763:1–10

  11. Ochoa L, Padilha-Feltrin A, Harrison G (2006) Evaluating distributed generation impacts with a multiobjective index. IEEE Trans Power Deliv 21:1452–1458

    Article  Google Scholar 

  12. Cableados Industriales (2010) Parque eólico Bii Nee Stipa II, Comisión Federal de Electricidad, México

  13. Gonzalez M, Rueda L (2014) PowerFactory applications for power system analysis. Springer, Berlin

    Google Scholar 

  14. DIgSILENT (2018) https://www.digsilent.de. Accessed 23 Oct 2018

  15. He P, Wen F, Ledwich G, Xue Y (2016) An investigation on interarea mode oscillations of interconnected power systems with integrated wind farms. Electric Power Syst Res 78:148–157

    Article  Google Scholar 

  16. Beltran O, Peña R, Segundo J, Esparza A et al (2018) Inertia estimation of wind power plants based on the swing equation and phasor measurement units. Appl Sci MDPI 12:1–16

    Google Scholar 

  17. Beltran-Valle O, Peña-Gallardo R, Segundo-Ramirez J, Muljadi E (2016) A comparative study of the application of FACTS devices in wind power plants of the southeast area of the Mexican electric system. In: 2016 IEEE International Autumn Meeting on Power, Electronics and Computing, pp 1–6

  18. Chen P, Chen Z, Bak-Jensen B (2008) Probabilistic load flow: a review, electric utility deregulation and restructuring and power technologies. In: 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, pp 1586–1591

  19. Leite da Silva A, Ribeiro S, Arienti V, Allan R et al (1990) Probabilistic load flow techniques applied to power system expansion planning. IEEE Trans Power Syst 5:1047–1053

    Article  Google Scholar 

  20. Jorgensen P, Christensen J, Tande J (1998) Probabilistic load flow calculation using Monte Carlo techniques for distribution network with wind turbines. Proceedings of 8th international conference on harmonics and quality of power, 2, pp 1146–1151

  21. Leite da Silva A, Arienti V (1990) Probabilistic load flow by a multilinear simulation algorithm. IEE Proc C Gen Transm Distrib 137:276–282

    Article  Google Scholar 

  22. Ahadi A, Reza S, Liang X (2017) Probabilistic reliability evaluation for power systems with high penetration of renewable power generation. 2017 IEEE international conference on industrial technology (ICIT), pp 464–468

  23. Sulaeman S, Alharbi F T, Benidris M, Mitra J (2017) A new method to evaluate the optimal penetration level of wind power, 2017 North American Power Symposium (NAPS), pp 1–6

  24. González-Longatt F (2012) Effects of the synthetic inertia from wind power on the total system inertia: simulation study. 2nd International symposium on environment friendly energies and applications, pp 389–395

  25. Nikkhah S, Rabiee A (2018) Optimal wind power generation investment considering voltage stability of power systems. Renew Energy 115:308–325

    Article  Google Scholar 

  26. Nikolaidis A, González F, Charalambous C (2013) Indices to asses the integration of renewable energy resources on transmission systems. Proceedings of conference papers in energy 1–8

  27. González F, Roldan J, Rueda J, Charalambous C (2012) Evaluation of power flow variability on the Paraguan transmission system due to integration of the first venezuelan wind farm. 2012 IEEE power and energy society general meeting, July, pp 1–8

  28. Borkowska B (1974) Probabilistic load flow. IEEE Transactions on Power Apparatus and Systems, PAS-93, pp 752–759

  29. Anders G (1990) Probability concepts in electric power systems. Wiley-IEEE Press, USA

    Google Scholar 

  30. Mur-Amada J, Bayod-Rujula A (2007) Wind power variability model Part II—probabilistic power flow. 9th International conference on electrical power quality and utilization, October, pp 1–6

  31. Da Allan R, Silva A, Burchett R (1981) Evaluation methods and accuracy in probabilistic load flow solutions. IEEE Trans Power Apparatus Syst 5:2539–2546

    Google Scholar 

  32. Mo-Shing C, Ohba Y, Reynolds L, Donald W (1, 977) Losses in electrical power system, 1, Elsevier Sequoia, Netherlands, pp 9–19

  33. Dokur E, Kurban M (2015) Wind speed potential analysis based on weibull distribution. Balkan J Electrical Comput Eng 3:231–235

  34. Luna E, Perez C, Fernandez E, Tequitlalpa G (2014) Active power control of wind farms in Mexico to mitigate congestion energy problems and contribute to frequency regulation, November, pp 451–456

  35. Milano F (2005) An open source power system analysis toolbox. IEEE Trans Power Syst 20:1199–1206

Download references

Acknowledgements

The authors want to acknowledge the Universidad Autónoma de San Luis Potosí- and the Universidad Michoacana de San Nicolás de Hidalgo for the facilities granted to carry out this research. Rafael Peña Gallardo thanks the financial support received from CONACYT to carry out a sabbatical research stay at the Universidad Michoacana de San Nicolás de Hidalgo. Omar Beltran Valle wants to acknowledge the financial support received from CONACYT through a scholarship to carry out his PhD studies.

Author information

Authors and Affiliations

  1. Facultad de Ingeniería, Universidad Autónoma de San Luis Potosí, Manuel Nava #8, Zona Universitaria, San Luis Potosí, 78290, México

    Omar Beltran Valle, Rafael Peña Gallardo & Juan Segundo Ramirez

  2. Department of Electrical and Computer Engineering, University of Denver, Engineering and Computer Science Building, Room 269, 2155 E. Wesley Ave., Denver, CO, USA

    David Wenzhong

  3. University of Aurbun, Alabama, 36849, USA

    Eduard Muljadi

Authors
  1. Omar Beltran Valle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rafael Peña Gallardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juan Segundo Ramirez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David Wenzhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eduard Muljadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Omar Beltran Valle.

Ethics declarations

Funding

This work was supported by the CONACYT (National Council of Science and Technology of Mexico) [Grant Number 740599].

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix A: Test System Parameters

Appendix A: Test System Parameters

The parameters of the single-line diagram used in this research are given in the Tables 2, 3, 4, 5, 6 and 7 .

Table 2 Parameters of the PSS model used in the hydropower plant
Full size table
Table 3 Parameters of the AVR model used in the hydropower plant
Full size table
Table 4 Parameters of the DFIG
Full size table
Table 5 Parameters of the synchronous generator of the hydropower plant
Full size table
Table 6 Parameters of the governor used in the hydropower plant
Full size table
Table 7 Parameters of the transmission lines
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Beltran Valle, O., Peña Gallardo, R., Segundo Ramirez, J. et al. A Graphical Probabilistic Representation for the Impact Assessment of Wind Power Plants in Power Systems. J. Electr. Eng. Technol. 15, 2033–2043 (2020). https://doi.org/10.1007/s42835-020-00480-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42835-020-00480-z

Keywords

Search

Navigation