Power quality based optimal nodal pricing in tradable electricity market

  • Authors

    • Virendra Umale
    • Sanjay Warkad
    2018-03-19
    https://doi.org/10.14419/ijet.v7i2.8.10560
  • Locational marginal pricing, power quality

  • Optimal Power Flow method described the nodal transmission pricing into different related factors, such as congestion,generation, power and electric load limitations. These detailsof each bus transmission prices can be used for to improve the proper usage of transmission congestion and power grid and to get reasonable transmission pricing for power structure. The proposed methodology is demonstrated on IEEE57 bus system and Maharashtra utility electric 400/765kv network.

  • References

    1. [1] contreras, J., Espínola, R., Nogales, F. J., and Conejo, A. J. ARIMA models to predict nextday electricity prices.

      [2] onejo, A. J., Plazas, M. A., Espinola,R. and Molina, A. B. Dayahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Transactions on Power System,20 (2), 2005, 1035 1042.

      [3] Garcia, R. Contreras, J., Akkeren, M. and Garcia, J. B. C. A GARCH forecasting model to predict day ahead electricity prices. IEEE Transactions on Power System,20 (2), 2005, 867–874.

      [4] Fan, S., Mao, C. and Chen L. Next-day electricity-price forecasting using ahybrid network. IET Generation Transmission Distribution,1 (1), 2007, 176-182.

      [5] Li, Guang, Liu, Ching,Mattson, Chris and Lawarrée, J. Day-Ahead Electricity Price Forecasting in a Grid Environment. IEEE Transactions on PowerSystem,22 (1), 2007, 266–274.

      [6] Pavlos S., Georgilakis Artificial Intelligence solution to electricity price forecasting problem. Journal of Applied Artificial Intelligence,21 (8), 2007, 707-727.

      [7] Rodriguez, C. P. and Anders, G. J. Energy price forecasting in the Ontario competitive power system market. IEEE Transactions. on Power System,19(1), 2004, 366–374.

      [8] Zhang, L. and Luh, P. B. Neural network-based market clearing price prediction and confidence interval estimation with an improved extended Kalman filter method. IEEE Transactions on Power System,20 (1), 2005

      [9] Mandal, P., Srivastava, A.K., and Park, Jung Wook. An Effort to Optimize Similar Days Parameters for ANN Based Electricity Price Forecasting. IEEE Transactions on Industry Applications, 45 (5), 2009

      [10] S.b.Warkad ,M.K.Khedkar,AC-DC OPF based Day-Ahead Electricity Nodal Price Prediction using an ANNI.J.C.A Vol.9-N0.10,Nov.2010

      [11] G. Ward, N. R. Watson, C. P. Arnold, A. J. Turner, and B. J. Ring, “Inversion of Real Time Spot Prices in the Direction of Real Power Flow in a Transmission Lineâ€, IEEE Transactions on Power Systems, vol.15, no.4, pp. 1197-1203, November 2000.

      [12] Luonan Chen, Hideki Suzuki, Tsunehisa Wachi, and Yukihiro Shimura, “Components of Spot Prices for Electric Power Systemsâ€, IEEE Transactions on Power Systems, vol. 17, no.1, pp.41-49, February 2002.

      [13] S.B.Warkad, Dr. M.K. Khedkar, Dr. G. M. Dhole, “Optimal Electricity Nodal Pricing in a Restructured Electricity Marketâ€, Journal of The Institution of Engineers (India), Volume 91, pp. 29-35, June 18, 2010.

      [14] S.V.Manikanthan and K.Baskaran “Low Cost VLSI Design Implementation of Sorting Network for ACSFD in Wireless Sensor Networkâ€, CiiT International Journal of Programmable Device Circuits and Systems,Print: ISSN 0974 – 973X & Online: ISSN 0974 – 9624, Issue : November 2011, PDCS112011008.

      [15] T. Padmapriya and V. Saminadan, “Improving Throughput for Downlink Multi user MIMO-LTE Advanced Networks using SINR approximation and Hierarchical CSI feedbackâ€, International Journal of Mobile Design Network and Innovation- Inderscience Publisher, ISSN : 1744-2850 vol. 6, no.1, pp. 14-23, May 2015.

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  • How to Cite

    Umale, V., & Warkad, S. (2018). Power quality based optimal nodal pricing in tradable electricity market. International Journal of Engineering & Technology, 7(2.8), 692-696. https://doi.org/10.14419/ijet.v7i2.8.10560