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Characterization of Iran electricity market indices with pay-as-bid payment mechanism

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  • Bigdeli, N.
  • Afshar, K.

Abstract

Market data analysis in Iran’s electricity market as a market with a pay-as-bid payment mechanism has been considered in this paper. The analysis procedure includes both predictability and correlation analysis of the most important load and price indices. The experimental data from Iran’s electricity market has been employed in its real size which is long enough to take properties such as non-stationarity of the market into account. For predictability, the characteristics of the hourly accepted Weighted Average Price (WAP) as the topmost price index of this market is analyzed. The analysis tools are time series analysis methods such as power spectral density analysis, phase space reconstruction and test of surrogates, the fractional dimension and the slope of integral sums and the recurrence plots. The results indicate a deterministic, un-stationary and seasonal behavior in addition to unstable periodic orbits and even chaotic behavior in WAP time series. These observations imply just short-term predictability of WAP behavior. The interactive behavior of WAP with the hourly required load (RL) is also considered. For this interaction analysis, in addition to the common correlation methods, cross and joint recurrence plot are also employed. The joint behavioral analysis represents an un-stationary mimic correlation between WAP and RL.

Suggested Citation

  • Bigdeli, N. & Afshar, K., 2009. "Characterization of Iran electricity market indices with pay-as-bid payment mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1577-1592.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:8:p:1577-1592
    DOI: 10.1016/j.physa.2009.01.003
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    References listed on IDEAS

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    1. Josep Perello & Miquel Montero & Luigi Palatella & Ingve Simonsen & Jaume Masoliver, 2006. "Entropy of the Nordic electricity market: anomalous scaling, spikes, and mean-reversion," Papers physics/0609066, arXiv.org.
    2. Tung, Wen-wen & Qi, Yan & Gao, J.B. & Cao, Yinhe & Billings, Lora, 2005. "Direct characterization of chaotic and stochastic dynamics in a population model with strong periodicity," Chaos, Solitons & Fractals, Elsevier, vol. 24(2), pages 645-652.
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    Citations

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    Cited by:

    1. Hajar Nasrazadani & Maria Pilar Mu oz Gracia, 2017. "Comparing Iranian and Spanish Electricity Markets with Nonlinear Time Series," International Journal of Energy Economics and Policy, Econjournals, vol. 7(2), pages 262-286.
    2. Asgari, Mohammad Hossein & Monsef, Hassan, 2010. "Market power analysis for the Iranian electricity market," Energy Policy, Elsevier, vol. 38(10), pages 5582-5599, October.
    3. Tokár, T. & Horváth, D., 2012. "Market inefficiency identified by both single and multiple currency trends," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5620-5627.
    4. He, Kaijian & Xu, Yang & Zou, Yingchao & Tang, Ling, 2015. "Electricity price forecasts using a Curvelet denoising based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 425(C), pages 1-9.
    5. Elena Olmedo, 2014. "Forecasting Spanish Unemployment Using Near Neighbour and Neural Net Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 43(2), pages 183-197, February.
    6. Bigdeli, Nooshin & Afshar, Karim & Gazafroudi, Amin Shokri & Ramandi, Mostafa Yousefi, 2013. "A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 20-29.

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