IDEAS home Printed from https://ideas.repec.org/p/esr/wpaper/wp511.html
   My bibliography  Save this paper

Forward Price, Renewables and the Electricity Price: The Case of Italy

Author

Listed:
  • Di Cosmo, Valeria

Abstract

This paper investigates the determinants of the Italian electricity price (PUN) in order to determine the major challenges this market is currently facing. The results suggest that the policy maker should be aware that the importance of market expectations is increasing (captured in the model by the forward electricity price) and this may be used to understand and forecast the dynamics of spot prices. Second, the positive link between fuel prices and the Italian electricity price may lead to a greater exposure of the Italian electricity price to fluctuations in the international fuel markets. However the results show that the risks associated with higher fuel prices are partially mitigated by the presence of wind generation installed in the system.

Suggested Citation

  • Di Cosmo, Valeria, 2015. "Forward Price, Renewables and the Electricity Price: The Case of Italy," Papers WP511, Economic and Social Research Institute (ESRI).
    • Handle: RePEc:esr:wpaper:wp511
    as

    Download full text from publisher

    File URL: https://www.esri.ie/pubs/WP511.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christopher F. Baum & Richard Sperling, 2001. "Tests for stationarity of a time series: update," Stata Technical Bulletin, StataCorp LP, vol. 10(58).
    2. Gianfreda, Angelica & Grossi, Luigi, 2012. "Forecasting Italian electricity zonal prices with exogenous variables," Energy Economics, Elsevier, vol. 34(6), pages 2228-2239.
    3. Christopher F. Baum, 2001. "Tests for stationarity of a time series," Stata Technical Bulletin, StataCorp LP, vol. 10(57).
    4. Traber, Thure & Kemfert, Claudia, 2011. "Gone with the wind? -- Electricity market prices and incentives to invest in thermal power plants under increasing wind energy supply," Energy Economics, Elsevier, vol. 33(2), pages 249-256, March.
    5. Bruno Bosco & Lucia Parisio & Matteo Pelagatti, 2007. "Deregulated Wholesale Electricity Prices in Italy: An Empirical Analysis," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 13(4), pages 415-432, November.
    6. Angelica Gianfreda, Lucia Parisio and Matteo Pelagatti, 2016. "The Impact of RES in the Italian DayAhead and Balancing Markets," The Energy Journal, International Association for Energy Economics, vol. 0(Bollino-M).
    7. repec:kap:iaecre:v:13:y:2007:i:4:p:415-432 is not listed on IDEAS
    8. Conejo, Antonio J. & Contreras, Javier & Espinola, Rosa & Plazas, Miguel A., 2005. "Forecasting electricity prices for a day-ahead pool-based electric energy market," International Journal of Forecasting, Elsevier, vol. 21(3), pages 435-462.
    9. Bruno Bosco & Lucia Parisio & Matteo Pelagatti, 2006. "Deregulated Wholesale Electricity Prices in Italy," Working Papers 20060301, Università degli Studi di Milano-Bicocca, Dipartimento di Statistica, revised Apr 2006.
    10. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
    11. Mulder, Machiel & Scholtens, Bert, 2013. "The impact of renewable energy on electricity prices in the Netherlands," Renewable Energy, Elsevier, vol. 57(C), pages 94-100.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. Di Cosmo, Valeria & Malaguzzi Valeri, Laura, 2014. "The incentive to invest in thermal plants in the presence of wind generation," Energy Economics, Elsevier, vol. 43(C), pages 306-315.
    4. Lisi, Francesco & Nan, Fany, 2014. "Component estimation for electricity prices: Procedures and comparisons," Energy Economics, Elsevier, vol. 44(C), pages 143-159.
    5. Gianfreda, Angelica & Grossi, Luigi, 2012. "Forecasting Italian electricity zonal prices with exogenous variables," Energy Economics, Elsevier, vol. 34(6), pages 2228-2239.
    6. 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.
    7. Florian Ziel & Rick Steinert & Sven Husmann, 2014. "Efficient Modeling and Forecasting of the Electricity Spot Price," Papers 1402.7027, arXiv.org, revised Oct 2014.
    8. 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.
    9. 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.
    10. Auer, Benjamin R., 2016. "How does Germany's green energy policy affect electricity market volatility? An application of conditional autoregressive range models," Energy Policy, Elsevier, vol. 98(C), pages 621-628.
    11. 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.
    12. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    13. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    14. Bigerna, Simona & Andrea Bollino, Carlo & Polinori, Paolo, 2015. "Marginal cost and congestion in the Italian electricity market: An indirect estimation approach," Energy Policy, Elsevier, vol. 85(C), pages 445-454.
    15. Andrea Cervone & Ezio Santini & Sabrina Teodori & Donatella Zaccagnini Romito, 2014. "Electricity Price Forecast: a Comparison of Different Models to Evaluate the Single National Price in the Italian Energy Exchange Market," International Journal of Energy Economics and Policy, Econjournals, vol. 4(4), pages 744-758.
    16. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2020. "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 466-479.
    17. Sandro Sapio, 2012. "Modeling the distribution of day-ahead electricity returns: a comparison," Quantitative Finance, Taylor & Francis Journals, vol. 12(12), pages 1935-1949, December.
    18. Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
    19. Lisi, Francesco & Pelagatti, Matteo M., 2018. "Component estimation for electricity market data: Deterministic or stochastic?," Energy Economics, Elsevier, vol. 74(C), pages 13-37.
    20. Florian Ziel & Rafal Weron, 2016. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models," HSC Research Reports HSC/16/08, Hugo Steinhaus Center, Wroclaw University of Technology.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:esr:wpaper:wp511. 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: Sarah Burns (email available below). General contact details of provider: https://edirc.repec.org/data/esriiie.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.