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Prediction of Market Power Using SVM as Regressor Under Deregulated Electricity Market

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Proceedings of Fifth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 437))

Abstract

This paper proposes a methodology to utilize support vector machines (SVM) as a regressor tool for predicting market power. Both the companies, i.e., Generation (Gencos) and the Distribution (Discos), can utilize this tool to forecast market power on their perspective. Attributes and criterion are to be chosen properly to classify market power. In this paper, the effectiveness of SVM technique in predicting market power is formulated. Independent system operator (ISO) can also use this tool as regressor and it is discussed elaborately. Both linear and nonlinear kernels are compared. Nodal must run share (NMRS) is used as an index for predicting market power. A sample of three-bus system consisting of two generators and one load/two loads is used to illustrate the study.

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References

  1. Wang, P., Xiao, Y., Ding, Y.: Nodal market power assessment in electricity markets. IEEE Trans. Power Syst. 19(3), 1373–1379 (2004)

    Article  Google Scholar 

  2. Prabhakar Karthikeyan, S., Jacob Raglend, I., Kothari, D.P.: A review on market power in deregulated electricity market. Int. J. Electr. Power Energy Syst. 48, 139–147 (2013)

    Google Scholar 

  3. Correa-Tapasco, E., Perez-Londono, S., Mora-Florez, J.: Setting strategy of a SVM regressor for locating single phase faults in power distribution systems. In: IEEE/PES Transmission and Distribution Conference and Exposition, Latin America, pp. 798–802 (2010)

    Google Scholar 

  4. Wang, Y., Wu, C., Wan, L., Liang, Y.: A study on SVM with feature selection for diagnosis of power systems 2, 173–176 (2010)

    Google Scholar 

  5. Guo, Y., Li, C., Li, Y., Gao, S.: Research on the Power system fault classification based on HHT and SVM using wide-area information. Energy Power Eng. 5, 138–142 (2013)

    Article  Google Scholar 

  6. De Yong, D., Bhowmik, S., Magnago, F.: An effective power quality classifier using wavelet transform and support vector machines. Expert Syst. Appl. 42, 6075–6081 (2015)

    Article  Google Scholar 

  7. Kalyani, S., Shanti Swarup, K.: Classification and assessment of power system security using multiclass SVM. IEEE Trans. Syst. Man Cybernetics-Part C: Appl. Rev. 41(5), 753–758 (2011)

    Google Scholar 

  8. Hua, D., Zhang, Y.T., Wang, Z., Dang, X.: The research of power market patterns incorporation of large-scale wind power. In: IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), pp. 1–4 (2013)

    Google Scholar 

  9. Arunkumaran, B., Raghavendra Rajan, V., Ajin Sekhar, C.S., Tejesvi, N.: Comparison of SVPWM and SVM controlled smart VSI fed PMSG for wind power generation system. In: IEEE National Conference on Emerging Trends in New & Renewable Energy Sources and Energy Management (NCET NRES EM), pp. 221–226 (2014)

    Google Scholar 

  10. Sharma, G., Ibraheem, R.: Application of LS-SVM technique based on robust control strategy to AGC of power system. In: Proceedings of IEEE International Conference on Advances in Engineering & Technology Research (ICAETR-2014) (2014)

    Google Scholar 

  11. Sajan, K.S., Kumar, V., Tyagi, B.: Genetic algorithm based support vector machine for on-line voltage stability monitoring. Electr. Power Energy Syst. 73, 200–208 (2015)

    Article  Google Scholar 

  12. Shawe-Taylor, J., Cristianini, N.: Support Vector Machines and other kernel-based learning methods. Cambridge University Press (2000)

    Google Scholar 

  13. Support Vector Machines Explained—Tristan Fletcher, Tutorial paper-(Ph D 2008). www.cs.ucl.ac.uk/staff/T.Flecher (2009)

  14. Prabhakar Karthikeyan, S., Jacob Raglend, I., Sathish Kumar, K., Sahoo, S.K., Priya Esther, B.: Application of SVM as classifier in estimating market power under deregulated electricity market, Lecture Notes in Electrical Engineering. In: 2015 Proceedings of Power Electronics and Renewable Energy Systems: ICPERES 2014

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electrical Engineering, VIT University, Vellore, 632014, Tamil Nadu, India

    K. Shafeeque Ahmed,  Fini Fathima, Ramesh Ananthavijayan, Prabhakar Karthikeyan Shanmugam, Sarat Kumar Sahoo & Rani Chinnappa Naidu

Authors
  1. K. Shafeeque Ahmed
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  2. Fini Fathima
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  3. Ramesh Ananthavijayan
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  4. Prabhakar Karthikeyan Shanmugam
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  5. Sarat Kumar Sahoo
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  6. Rani Chinnappa Naidu
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Corresponding author

Correspondence to Prabhakar Karthikeyan Shanmugam .

Editor information

Editors and Affiliations

  1. Dept of Applied Sci & Eng, Indian Instit of Tech Roorkee, Roorkee, India

    Millie Pant

  2. Department of Mathematics, Indian Inst of Tech Roorkee, Roorkee, India

    Kusum Deep

  3. Dept of Mathematics, South Asian University New Delhi, New Delhi, India

    Jagdish Chand Bansal

  4. Department of Mathematics and Comp Sci, Liverpool Hope University, LIVERPOOL, United Kingdom

    Atulya Nagar

  5. Department of Mathematics, National Inst of Tech Silchar, Silchar, Assam, India

    Kedar Nath Das

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© 2016 Springer Science+Business Media Singapore

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Shafeeque Ahmed, K., Fini Fathima, Ananthavijayan, R., Shanmugam, P.K., Sahoo, S.K., Naidu, R.C. (2016). Prediction of Market Power Using SVM as Regressor Under Deregulated Electricity Market. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-10-0451-3_54

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  • DOI: https://doi.org/10.1007/978-981-10-0451-3_54

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0450-6

  • Online ISBN: 978-981-10-0451-3

  • eBook Packages: EngineeringEngineering (R0)

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