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Fine structure of the price-demand relationship in the electricity market: multi-scale correlation analysis

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  • Afanasyev, Dmitriy
  • Fedorova, Elena
  • Popov, Viktor

Abstract

The price-demand relationship in the electricity market is a complicated phenomenon. In order to thoroughly investigate the peculiarities of this relationship, a multi-scale correlation analysis of electricity price and demand is carried out in this research. Using a modified method of socalled time-dependent intrinsic correlation (TDIC) (Chen et al., 2010), based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) (Torres et al., 2011), and bootstrapping, we investigate the problems of dynamic interconnection between electricity demand and prices over different time scales (i.e. its fine structure). We formulate and test three hypotheses on the type and strength of correlations between them in the short-, medium- and long-runs. In this research we analyze the data from two largest price zones of Russian wholesale electricity market: Europe-Ural and Siberia. These two zones differ from each other by the structures of electricity generation and consumption. It is shown that these two price zones significantly differ in internal price-demand correlation structure over the comparable time scales, and not each of the theoretically formulated hypotheses is true for each of the price zones. This allows us to conclude that the answer to the question whether it is necessary to take into account the influence of demand-side on electricity spot prices over different time scales, is significantly dependent on the structure of electricity generation and consumption on the corresponding market.

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  • Afanasyev, Dmitriy & Fedorova, Elena & Popov, Viktor, 2014. "Fine structure of the price-demand relationship in the electricity market: multi-scale correlation analysis," MPRA Paper 58827, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:58827
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    Cited by:

    1. Afanasyev, Dmitriy & Fedorova, Elena, 2015. "The long-term trends on Russian electricity market: comparison of empirical mode and wavelet decompositions," MPRA Paper 62391, University Library of Munich, Germany.
    2. Balagula, Yuri, 2020. "Forecasting daily spot prices in the Russian electricity market with the ARFIMA model," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 57, pages 89-101.
    3. Lei Jiang & Ling Bai, 2017. "Revisiting the Granger Causality Relationship between Energy Consumption and Economic Growth in China: A Multi-Timescale Decomposition Approach," Sustainability, MDPI, vol. 9(12), pages 1-17, December.
    4. Wang, Haoyu & Di, Junpeng & Yang, Zhaojun & Han, Qing, 2020. "Assessment of mutual fund performance based on Ensemble Empirical Mode Decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    5. Dmitriy O. Afanasyev & Elena A. Fedorova & Evgeniy V. Gilenko, 2021. "The fundamental drivers of electricity price: a multi-scale adaptive regression analysis," Empirical Economics, Springer, vol. 60(4), pages 1913-1938, April.
    6. Afanasyev, D. & Fedorova, E., 2018. "External and Internal Determinants on the Electricity Market: A Multi-Scale Adaptive Causal Analysis," Journal of the New Economic Association, New Economic Association, vol. 39(3), pages 33-54.
    7. Qunwei Wang & Xingyu Dai & Dequn Zhou, 2020. "Dynamic Correlation and Risk Contagion Between “Black” Futures in China: A Multi-scale Variational Mode Decomposition Approach," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1117-1150, April.
    8. Abdullah, Mohammad & Abakah, Emmanuel Joel Aikins & Wali Ullah, G M & Tiwari, Aviral Kumar & Khan, Isma, 2023. "Tail risk contagion across electricity markets in crisis periods," Energy Economics, Elsevier, vol. 127(PB).
    9. Fang, Guochang & Tian, Lixin & Liu, Menghe & Fu, Min & Sun, Mei, 2018. "How to optimize the development of carbon trading in China—Enlightenment from evolution rules of the EU carbon price," Applied Energy, Elsevier, vol. 211(C), pages 1039-1049.
    10. Qing Peng & Fenghua Wen & Xu Gong, 2021. "Time‐dependent intrinsic correlation analysis of crude oil and the US dollar based on CEEMDAN," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 834-848, January.
    11. Niu, Hongli, 2021. "Correlations between crude oil and stocks prices of renewable energy and technology companies: A multiscale time-dependent analysis," Energy, Elsevier, vol. 221(C).
    12. Afanasyev, Dmitriy O. & Fedorova, Elena A., 2016. "The long-term trends on the electricity markets: Comparison of empirical mode and wavelet decompositions," Energy Economics, Elsevier, vol. 56(C), pages 432-442.

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    More about this item

    Keywords

    electricity spot price; electricity demand; price-demand correlation; empirical mode decomposition; time-dependent intrinsic correlation; trend estimation;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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