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Chaotic behavior of price in the power markets with pay-as-bid payment mechanism

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  • Bigdeli, N.
  • Afshar, K.

Abstract

Price forecasting in the current deregulated power markets is an important requirement for deriving proper bidding strategy and profit maximization of producers. On the other hand, the energy price in the power market experiences lots of fluctuations which may affect the accuracy of the price forecasting seriously. Seeking for predictability, in this paper, the characteristics of these fluctuations are investigated through time series analysis methods. The results of analyses are representative of the existence of a deterministic chaos in the system with a mimic predictability. Besides, it is observed that because of existing the seasonality and non-stationarity in the system dynamics, a fixed model cannot perform properly even in case of normalized input data, but the developed models should be updated regularly.

Suggested Citation

  • Bigdeli, N. & Afshar, K., 2009. "Chaotic behavior of price in the power markets with pay-as-bid payment mechanism," Chaos, Solitons & Fractals, Elsevier, vol. 42(4), pages 2560-2569.
    • Handle: RePEc:eee:chsofr:v:42:y:2009:i:4:p:2560-2569
    DOI: 10.1016/j.chaos.2009.03.193
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    1. Facchini, Angelo & Rubino, Alessandro & Caldarelli, Guido & Di Liddo, Giuseppe, 2019. "Changes to Gate Closure and its impact on wholesale electricity prices: The case of the UK," Energy Policy, Elsevier, vol. 125(C), pages 110-121.
    2. 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.
    3. Lahmiri, Salim & Bekiros, Stelios, 2018. "Chaos, randomness and multi-fractality in Bitcoin market," Chaos, Solitons & Fractals, Elsevier, vol. 106(C), pages 28-34.
    4. 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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