Importance The paper considers the issues of pricing on the wholesale electricity market under the influence of fundamental factors (demand, fuel prices). Objectives The paper's aim is to identify the factors that significantly affect the wholesale electricity price on different time scales. Methods The study uses the developed multi-scale adaptive approach based on time-dependent internal regression and empirical mode decomposition. I investigate two day-ahead electricity markets: the price areas Europe-Ural and Siberia of the Russian exchange ATS during the period from April 1, 2011 to December 31, 2013. Results The influence of fundamental factors on the electricity price depends on the time scales considered. The impact of fuel prices on the electricity price does not appear in short-term periods and manifests only in the mid-term or long-term time scales. Conclusions and Relevance For the thorough price forecasting, there is a need in rejection of the traditional mono-fractal approach. For instance, in the Europe-Ural price area, it is necessary to focus on demand forecasting and its impact on price, while for Siberia, only long-term changes in demand should be taken into account.
Keywords: price, electric energy, demand, decomposition, empiric mode, internal regression
References:
Howison S., Coulon M. Stochastic Behavior of the Electricity Bid Stack: From Fundamental Drivers to Power Prices. Journal of Energy Markets, 2009, no. 2, pp. 29–69. URL: Link. maths.ox.ac.uk/~howison/papers/CoulonHowison211008.pdf
Carmona R., Coulon M. A Survey of Commodity Markets and Structural Models for Electricity Prices. In: Quantitative Energy Finance. New York, Springer, 2014, pp. 41–83.
Barlow M. A Diffusion Model for Electricity Prices. Mathematical Finance, 2002, vol. 12, iss. 4, pp. 287–289. doi: 10.1111/j.1467-9965.2002.tb00125.x
Pirrong C., Jermakyan M. The Price of Power: The Valuation of Power and Weather Derivatives. Journal of Banking and Finance, 2008, vol. 32, iss. 12, pp. 2520–2529. URL: Link 10.1016/j.jbankfin.2008.04.007
Karakatsani N., Bunn D. Forecasting Electricity Prices: The Impact of Fundamentals and Time-Varying Coefficients. International Journal of Forecasting, 2008, no. 24, pp. 764–785. URL: Link
Uritskaya O.Y., Serletis A. Quantifying Multiscale Inefficiency in Electricity Markets. Energy Economics, 2008, vol. 30, iss. 6, pp. 3109–3117. URL: Link j.eneco.2008.03.009
Alvarez-Ramirez J., Escarela-Perez R. Time-Dependent Correlations in Electricity Markets. Energy Economics, 2010, vol. 32, iss. 2, pp. 269–277. URL: Link j.eneco.2009.05.008
Zhu B., Han D., Chevallier J., Wei Y.-M. Dynamic Multiscale Interactions Between European Carbon and Electricity Markets During 2005–2016. Energy Policy, 2017, vol. 107, pp. 309–322. URL: Link
Huang N., Shen Z. et al. The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 1998, vol. 454, pp. 903–995. doi: 10.1098/rspa.1998.0193
Wu Z., Huang N. Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method. Advances in Adaptive Data Analysis, 2009, vol. 1, iss. 1, pp. 1–41. URL: Link
Torres M., Colominas M., Schlotthauer G., Flandrin P. A Complete Ensemble Empirical Mode Decomposition with Adaptive Noise. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2011, pp. 4144–4147. URL: Link. edu.ar/grupos/ldnlys/metorres/metorres_files/ICASSP2011_Torres.pdf
Colominas M., Schlotthauer G., Torres M., Flandrin P. Noise-Assisted EMD Methods in Action. Advances in Adaptive Data Analysis, 2012, vol. 4, iss. 4. URL: Link. com/doi/abs/10.1142/S1793536912500252
Chen N., Wu Z., Huang N. The Time-Dependent Intrinsic Correlation Based on the Empirical Mode Decomposition. Advances in Adaptive Data Analysis, 2010, vol. 2, iss. 2, pp. 223–265. URL: Link
Ferkingstad E., Løland A., Wilhelmsen M. Causal Modeling and Inference for Electricity Markets. Energy Economics, 2011, vol. 33, iss. 3, pp. 404–412. URL: Link j.eneco.2010.10.006
Moutinho V., Vieira J., Moreira A.C. The Crucial Relationship among Energy Commodity Prices: Evidence from the Spanish Electricity Market. Energy Policy, 2011, vol. 39, iss. 10, pp. 5898–5908. URL: Link
De Menezes L., Houllier M.A., Tamvakis M. Time-Varying Convergence in European Electricity Spot Markets and Their Association with Carbon and Fuel Prices. Energy Policy, 2016, vol. 88, pp. 613–627. URL: Link
Fuss R., Mahringer S., Prokopczuk M. Electricity Derivatives Pricing with Forward-Looking Information. Journal of Economic Dynamics and Control, 2015, vol. 58, pp. 34–57. URL: Link
Chih-Yu K., Shao-Kuan W., Pi-Wen T. Ensemble Empirical Mode Decomposition with Supervised Cluster Analysis. Advances in Adaptive Data Analysis, 2013, vol. 5, iss. 1. URL: Link 10.1142/S1793536913500052