Skip to main content
SpringerLink
Log in

Growing RBFNN-based soft computing approach for congestion management

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In the emerging restructured power system, the congestion management (CM) has become extremely important in order to ensure the security and reliability of the system. In addition to this, lack of CM can impose a hindrance in electricity trading. This paper presents a novel, growing radial basis function neural network (GRBFNN)-based approach for CM. For achieving CM, Nodal congestion price (NCP) forecasting is performed in real time competitive power market. NCP forecasting is an effective way of price-based preventive CM as it directly indicates the presence as well as the severity of the congestion in the system. In present paper, GRBFNN has been developed for NCP forecasting dividing the whole power system into various congestion zones. An unsupervised learning vector quantization (VQ) clustering algorithm is applied as feature selection technique for the developed GRBFNN and for partitioning the power system into different congestion zones. For each congestion zone a separate neural network has been developed to ensure faster training and accurate forecasting results. The proposed approach of CM is implemented on an RTS 24-bus system. The results obtained are compared with a different constructive algorithm-based RBF network called as general regression neural network (GRNN) and two back-propagation algorithms based ANNs. Comparison results show that proposed GRBFNN is more computationally efficient with better predictive ability.

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

Similar content being viewed by others

Abbreviations

\( C_{{{\text{S}}_{{_{i} }} }} \) :

supply bid of unit i in $/MWh

\( C_{{{\text{D}}_{j} }} \) :

demand bid of unit j in $/MWh

\( P_{{{\text{S}}_{i} }} \) :

supply bid volume of unit i in MW

\( P_{{{\text{D}}_{j} }} \) :

demand bid volume of unit j in MW

\( P_{{{\text{S}}_{{\max_{i} }} }} \) :

upper limit of supply bid volume of unit i in MW

\( P_{{{\text{S}}_{{\min_{i} }} }} \) :

lower limit of supply bid volume of unit i in MW

\( P_{{{\text{D}}_{{\max_{j} }} }} \) :

upper limit of demand bid volume of unit j in MW

\( P_{{{\text{D}}_{{\min_{j} }} }} \) :

lower limit of demand bid volume of unit j in MW

\( Q_{{{\text{G}}_{i} }} \) :

reactive power output of unit i in MVAr

\( Q_{{{\text{G}}_{{\min_{i} }} }} \) :

upper limit of reactive power at unit i in MVAr

\( Q_{{{\text{G}}_{{\max_{i} }} }} \) :

lower limit of reactive power at unit i in MVAr

V k :

voltage magnitude at node k in pu

\( V_{{\min_{k} }} \) :

lower limit of voltage magnitude at node k in pu

\( V_{{\max_{k} }} \) :

upper limit of voltage magnitude at node k in pu

I mk :

line currents between nodes m and k in pu

\( I_{{mk_{\max } }} \) :

upper limit of line current between nodes m and k

P mk :

real power flow between nodes m and k

\( P_{{mk_{\max } }} \) :

upper limit of real power flow through line between nodes m and k

P k :

net real power injected at bus k in MW

References

  1. Crause T (2001) Congestion management in liberalized electricity markets—theoretical concepts and international application. EEH Power System Laboratory Swiss Federal Institute of Technology Zurich. http://www.eeh.ee.ethz.ch/downloads/psl/publications/krause_congestion_management.pdf

  2. Talukdar BK, Sinha AK, Mukhopadhyay S, Bose A (2005) A computationally simple method for cost-efficient generation rescheduling and load shedding for congestion management. Int J Electr Power Energy Syst 27:379–388. doi:10.1016/j.ijepes.2005.02.003

    Article  Google Scholar 

  3. Granelli G, Montagna M, Zanellini F, Bresesti P, Vailati R, Innorta M (2006) Optimal network reconfiguration for congestion management by deterministic and genetic algorithms. Electr Power Syst Res 76:549–556. doi:10.1016/j.epsr.2005.09.014

    Article  Google Scholar 

  4. Tuan LA, Bhattacharya K, Daalder J (2005) Transmission congestion management in bilateral markets: an interruptible load auction solution. Electr Power Syst Res 74:379–389. doi:10.1016/j.epsr.2004.11.010

    Article  Google Scholar 

  5. Kristiansen T (2004) Congestion management, transmission pricing and area price hedging in the Nordic region. Int J Electr Power Energy Syst 26:685–695. doi:10.1016/j.ijepes.2004.05.004

    Article  Google Scholar 

  6. Xie K, Song YH, Stonham J, Yu E, Liu G (2000) Decomposition model and interior point methods for optimal spot pricing of electricity in deregulation environments. IEEE Trans Power Syst 15:39–50. doi:10.1109/59.852099

    Article  Google Scholar 

  7. Gil HA, Galiana FD, Da Silva EL (2006) Nodal price control: a mechanism for transmission network cost allocation. IEEE Trans Power Syst 21:3–10. doi:10.1109/TPWRS.2005.861990

    Article  Google Scholar 

  8. Schweppe FC, Cramanis M, Taboras R, Bohn R (1988) Spot pricing of electricity, Appendix D. Kluwer, Norwell

    Google Scholar 

  9. Pandey SN, Tapaswi S, Srivastava L (2008) Nodal congestion price estimation in spot power market using artificial neural network. IET Proc Gener Trans Distrib 2:280–290. doi:10.1049/iet-gtd:20070309

    Article  Google Scholar 

  10. Szkuta R, Sanabria LA, Dillon TS (1999) Electricity price short-term forecasting using artificial neural networks. IEEE Trans Power Syst 14:851–857. doi:10.1109/59.780895

    Article  Google Scholar 

  11. Yang H, Zhou R, Liu J (2005) A RBFN hierarchical clustering-based network partitioning method for zonal pricing. In: Proceedings of the 2nd International Conference on Electrical and Elex Engineering. Mexico, pp 282–285

  12. Zhang B, Zeng C, Wang S, Xie P (2004) Forecasting market-clearing price in day-ahead market, using SOM-ANN. In: Proceedings of 39th International Universities Power Engineering Conference (UPEC), pp 390–393

  13. Karayiannis NB (2002) Soft learning vector quantization and clustering algorithms based on mean type aggregation operators. Int J Fuzzy Syst 4(3)

  14. Rajasekaran S, Pai GAV (2006) Neural networks, fuzzy logic and genetic algorithms–synthesis and applications. Prentice-Hall of India Private Limited, New Delhi

    Google Scholar 

  15. Platt J (1991) A resource allocating network for function interpolation. Neural Comput 3:213–225

    Article  MathSciNet  Google Scholar 

  16. Fritzke B (1993) Growing cell structures—a self-organizing network for unsupervised and supervised learning. International Computer Science Institute, University of California at Berkeley, Technical Report TR-93-026

  17. Berthold MR, Diamond J (1995) Boosting the performance of RBF networks with dynamic decay adjustment. Adv Neural Inf Process 7

  18. Karayinnis NB, Glenn WM (1997) Growing radial basis neural networks: merging supervised and unsupervised learning with network growth techniques. IEEE Trans Neural Netw 8:1492–1506

    Article  Google Scholar 

  19. Specht DF (1990) Probabilistic neural networks. Neural Netw 3:109–118. doi:10.1016/0893-6080(90)90049-Q

    Article  Google Scholar 

  20. Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2:568–576

    Article  Google Scholar 

  21. Grigg C, Wong P, Albrecht P, Allan R, Bhavaraju M et al (1996) Reliability test system task force: ‘The IEEE reliability test system 1996’. IEEE Trans Power Syst 14(3):1010–1020

    Article  Google Scholar 

  22. Milano F, Canizares A, Conejo AJ (2005) Sensitivity-based security-constrained OPF market clearing model. IEEE Trans Power Syst 20:2051–2060. doi:10.1109/TPWRS.2005.856985

    Article  Google Scholar 

  23. Canizares CA, Chen H, Milano F, Singh A (2004) Transmission congestion management and pricing in simple auction electricity markets. Int J Emerg Electr Power Syst 1:1–28

    Google Scholar 

  24. Srivastava L, Singh SN, Sharma J (2000) Comparison of feature selection techniques for ANN based voltage estimation. Electr Power Syst Res 53:187–195

    Article  Google Scholar 

  25. Hong YY, Chien CNC, Wu KL, Yang MS (2002) Determination of congestion zones in deregulated electricity markets using fuzzy clustering. In: Proceedigns on 14th PSCC, pp 1–7

  26. California ISO (2000) Comprehensive congestion management reform zonal-forward market. White paper. http://www.caiso.com/docs/2000/04/13/2000041317243312125.pdf

  27. Demuth H, Beale M (2002) Neural network toolbox user’s guide. Version 4, release 13. The mathworks, Inc., Natick, pp 1–7

    Google Scholar 

Download references

Acknowledgments

The authors sincerely acknowledge the financial support provided by AICTE, New Delhi, India, under Research grant no. 8023/BOR/RPS-60/2006-07, dated 26-2-07 and Director, ABV-IIITM, Gwalior, India for providing facilities to carry out this research work. Seema N. Pandey thanks the Ministry of Technical Education, Madhya Pradesh, Bhopal, for sponsoring her to carry out her doctoral work under QIP (Poly), AICTE, Govt. of India.

Author information

Authors and Affiliations

  1. IT Department, ABV-IIITM, Gwalior, India

    Seema N. Pandey & Shashikala Tapaswi

  2. Electrical Engineering Department, MITS, Gwalior, India

    Laxmi Srivastava

Authors
  1. Seema N. Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shashikala Tapaswi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laxmi Srivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seema N. Pandey.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pandey, S.N., Tapaswi, S. & Srivastava, L. Growing RBFNN-based soft computing approach for congestion management. Neural Comput & Applic 18, 945–955 (2009). https://doi.org/10.1007/s00521-008-0205-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-008-0205-3

Keywords

Search

Navigation