IDEAS home Printed from https://ideas.repec.org/a/aen/journl/ej40-si1-gianfreda.html
   My bibliography  Save this article

The RES-Induced Switching Effect Across Fossil Fuels: An Analysis of Day-Ahead and Balancing Prices

Author

Listed:
  • Angelica Gianfreda, Lucia Parisio, and Matteo Pelagatti

Abstract

The empirical literature on electricity markets has highlighted a strong cointegrating relationship governing the dynamics of electricity and fuel prices. More recently the massive introduction of RES in electricity generation, fostered by generous supporting schemes, has influenced the shape and position of the supply function and consequently the equilibrium prices. We believe that the new competitive scenario may have influenced the fuel-electricity nexus with a different impact in day-ahead and balancing markets. Empirical evidence of the evolving fuels-electricity nexus is shown looking at one Italian zone across two samples characterized by a significant change in the level of RES penetration. We conduct the analysis taking into account both day-ahead and, for the first time, balancing market sessions. Results indicate that fuel prices are much less relevant in determining the dynamics of electricity prices in recent years characterized by high RES penetration. On the contrary, taking into account flexible thermal sources, we show that in the second sample balancing and fuel prices (especially gas) are in a long run equilibrium.

Suggested Citation

  • Angelica Gianfreda, Lucia Parisio, and Matteo Pelagatti, 2019. "The RES-Induced Switching Effect Across Fossil Fuels: An Analysis of Day-Ahead and Balancing Prices," The Energy Journal, International Association for Energy Economics, vol. 0(The New E).
    • Handle: RePEc:aen:journl:ej40-si1-gianfreda
    as

    Download full text from publisher

    File URL: http://www.iaee.org/en/publications/ejarticle.aspx?id=3316
    Download Restriction: Access to full text is restricted to IAEE members and subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Simona Bigerna, Carlo Andrea Bollino and Paolo Polinori, 2016. "Renewable Energy and Market Power in the Italian Electricity Market," The Energy Journal, International Association for Energy Economics, vol. 0(Bollino-M).
    2. Gianfreda, Angelica & Grossi, Luigi, 2012. "Forecasting Italian electricity zonal prices with exogenous variables," Energy Economics, Elsevier, vol. 34(6), pages 2228-2239.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gianfreda, Angelica & Ravazzolo, Francesco & Rossini, Luca, 2020. "Comparing the forecasting performances of linear models for electricity prices with high RES penetration," International Journal of Forecasting, Elsevier, vol. 36(3), pages 974-986.
    2. Hauzenberger, Niko & Pfarrhofer, Michael & Rossini, Luca, 2025. "Sparse time-varying parameter VECMs with an application to modeling electricity prices," International Journal of Forecasting, Elsevier, vol. 41(1), pages 361-376.
    3. Gianfreda, Angelica & Maranzano, Paolo & Parisio, Lucia & Pelagatti, Matteo, 2023. "Testing for integration and cointegration when time series are observed with noise," Economic Modelling, Elsevier, vol. 125(C).
    4. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    5. Mosquera-López, Stephania & Uribe, Jorge M. & Joaqui-Barandica, Orlando, 2024. "Weather conditions, climate change, and the price of electricity," Energy Economics, Elsevier, vol. 137(C).
    6. Nazifi, Fatemeh & Trück, Stefan & Zhu, Liangxu, 2021. "Carbon pass-through rates on spot electricity prices in Australia," Energy Economics, Elsevier, vol. 96(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Angelica, Gianfreda & Lucia, Parisio & Matteo, Pelagatti, 2017. "The RES-induced Switching Effect Across Fossil Fuels: An Analysis of the Italian Day-Ahead and Balancing Prices and Their Connected Costs," Working Papers 360, University of Milano-Bicocca, Department of Economics, revised 03 Feb 2017.
    2. Beltrami, Filippo & Fontini, Fulvio & Grossi, Luigi, 2021. "The value of carbon emission reduction induced by Renewable Energy Sources in the Italian power market," Ecological Economics, Elsevier, vol. 189(C).
    3. Avesani, Diego & Zanfei, Ariele & Di Marco, Nicola & Galletti, Andrea & Ravazzolo, Francesco & Righetti, Maurizio & Majone, Bruno, 2022. "Short-term hydropower optimization driven by innovative time-adapting econometric model," Applied Energy, Elsevier, vol. 310(C).
    4. Andreolli, Francesca & D’Alpaos, Chiara & Moretto, Michele, 2022. "Valuing investments in domestic PV-Battery Systems under uncertainty," Energy Economics, Elsevier, vol. 106(C).
    5. Bigerna, Simona, 2018. "Estimating temperature effects on the Italian electricity market," Energy Policy, Elsevier, vol. 118(C), pages 257-269.
    6. F. Durante & A. Gatto & F. Ravazzolo, 2024. "Understanding relationships with the Aggregate Zonal Imbalance using copulas," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(2), pages 513-554, April.
    7. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
    8. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    9. 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.
    10. Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafał, 2013. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," Energy Economics, Elsevier, vol. 39(C), pages 13-27.
    11. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    12. Qazi, Usama & Jahanzaib, Mirza & Ahmad, Wasim & Hussain, Salman, 2017. "An institutional framework for the development of sustainable and competitive power market in Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 83-95.
    13. Gianfreda, Angelica & Ravazzolo, Francesco & Rossini, Luca, 2020. "Comparing the forecasting performances of linear models for electricity prices with high RES penetration," International Journal of Forecasting, Elsevier, vol. 36(3), pages 974-986.
    14. Bartosz Uniejewski & Jakub Nowotarski & Rafał Weron, 2016. "Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 9(8), pages 1-22, August.
    15. Brown, David P. & Eckert, Andrew, 2021. "Analyzing firm behavior in restructured electricity markets: Empirical challenges with a residual demand analysis," International Journal of Industrial Organization, Elsevier, vol. 74(C).
    16. Mauro Bernardi & Francesco Lisi, 2020. "Point and Interval Forecasting of Zonal Electricity Prices and Demand Using Heteroscedastic Models: The IPEX Case," Energies, MDPI, vol. 13(23), pages 1-34, November.
    17. Simona Bigerna & Carlo Andrea Bollino & Maria Chiara D’Errico & Paolo Polinori, 2023. "A new design for market power monitoring in the electricity market. A simulation for Italy," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 40(1), pages 285-317, April.
    18. Bartosz Uniejewski & Rafał Weron, 2018. "Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models," Energies, MDPI, vol. 11(8), pages 1-26, August.
    19. Zachmann, Georg, 2013. "A stochastic fuel switching model for electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 5-13.
    20. Weron, Rafał & Zator, Michał, 2015. "A note on using the Hodrick–Prescott filter in electricity markets," Energy Economics, Elsevier, vol. 48(C), pages 1-6.

    More about this item

    JEL classification:

    • F0 - International Economics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aen:journl:ej40-si1-gianfreda. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: David Williams (email available below). General contact details of provider: https://edirc.repec.org/data/iaeeeea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.