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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 O.
  • Fedorova, Elena A.
  • Popov, Viktor U.

Abstract

In this research we investigate the problems of dynamic relationship between electricity price and demand over different time scales for two largest price zones of the Russian wholesale electricity market. We use multi-scale correlation analysis based on a modified method of time-dependent intrinsic correlation and the complete ensemble empirical mode decomposition with adaptive noise for this purpose. Three hypotheses on the type and strength of correlations in the short-, medium- and long-runs were tested. It is shown that 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 them. We can 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 O. & Fedorova, Elena A. & Popov, Viktor U., 2015. "Fine structure of the price–demand relationship in the electricity market: Multi-scale correlation analysis," Energy Economics, Elsevier, vol. 51(C), pages 215-226.
    • Handle: RePEc:eee:eneeco:v:51:y:2015:i:c:p:215-226
    DOI: 10.1016/j.eneco.2015.07.011
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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. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    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:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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