IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i6p1460-d150820.html
   My bibliography  Save this article

Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence

Author

Listed:
  • Simon Pezzutto

    (Institute for Renewable Energy, European Academy of Bozen/Bolzano (EURAC Research), Viale Druso 1, 39100 Bolzano, Italy)

  • Gianluca Grilli

    (Economic and Social Research Institute, Sir John Rogerson’s Quay, Dublin Dublin 2, Ireland
    Trinity College Dublin, the University of Dublin, Dublin Dublin 2, Ireland)

  • Stefano Zambotti

    (Institute for Renewable Energy, European Academy of Bozen/Bolzano (EURAC Research), Viale Druso 1, 39100 Bolzano, Italy)

  • Stefan Dunjic

    (Joule Assets Europe Group SRL, Via Cesare Battisti 56, 41121 Modena, Italy)

Abstract

The scope of the present investigation is to provide a description of final electricity prices development in the context of deregulated electricity markets in EU28, up to 2020. We introduce a new methodology to predict long-term electricity market prices consisting of two parts: (1) a self-developed form of Porter’s five forces analysis (PFFA) determining that electricity markets are characterized by a fairly steady price increase. Dominant driving factors come out to be: (i) uncertainty of future electricity prices; (ii) regulatory complexity; and (iii) generation overcapacities. Similar conclusions derive from (2) a self-developed form of multiple-criteria decision analysis (MCDA). In this case, we find that the electricity market particularly depends on (i) market liberalization and (ii) the European Union (EU)’s economy growth. The applied methodologies provide a novel contribution in forecasting electricity price trends, by analyzing the sentiments, expectations, and knowledge of industry experts, through an assessment of factors influencing the market price and goals of key market participants. An extensive survey was conducted, interviewing experts all over Europe showed that the electricity market is subject to a future slight price increase.

Suggested Citation

  • Simon Pezzutto & Gianluca Grilli & Stefano Zambotti & Stefan Dunjic, 2018. "Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence," Energies, MDPI, vol. 11(6), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1460-:d:150820
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/6/1460/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/6/1460/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Crespo Cuaresma, Jesús & Hlouskova, Jaroslava & Kossmeier, Stephan & Obersteiner, Michael, 2004. "Forecasting electricity spot-prices using linear univariate time-series models," Applied Energy, Elsevier, vol. 77(1), pages 87-106, January.
    2. Bowden, Nicholas & Payne, James E., 2008. "Short term forecasting of electricity prices for MISO hubs: Evidence from ARIMA-EGARCH models," Energy Economics, Elsevier, vol. 30(6), pages 3186-3197, November.
    3. Rob J Hyndman & Shu Fan, 2008. "Density forecasting for long-term peak electricity demand," Monash Econometrics and Business Statistics Working Papers 6/08, Monash University, Department of Econometrics and Business Statistics.
    4. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    5. Kok, M. & Lootsma, F. A., 1985. "Pairwise-comparison methods in multiple objective programming, with applications in a long-term energy-planning model," European Journal of Operational Research, Elsevier, vol. 22(1), pages 44-55, October.
    6. Rubin, Ofir D. & Babcock, Bruce A., 2013. "The impact of expansion of wind power capacity and pricing methods on the efficiency of deregulated electricity markets," Energy, Elsevier, vol. 59(C), pages 676-688.
    7. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    8. Lijesen, Mark G., 2007. "The real-time price elasticity of electricity," Energy Economics, Elsevier, vol. 29(2), pages 249-258, March.
    9. Agustín A. Sánchez de la Nieta & Virginia González & Javier Contreras, 2016. "Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming," Energies, MDPI, vol. 9(12), pages 1-19, December.
    10. 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.
    11. Tan, Zhongfu & Zhang, Jinliang & Wang, Jianhui & Xu, Jun, 2010. "Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models," Applied Energy, Elsevier, vol. 87(11), pages 3606-3610, November.
    12. Terry Robinson, 2000. "Electricity pool prices: a case study in nonlinear time-series modelling," Applied Economics, Taylor & Francis Journals, vol. 32(5), pages 527-532.
    13. Robert S. Pindyck, 1999. "The Long-Run Evolutions of Energy Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-27.
    14. Sailor, D.J & Pavlova, A.A, 2003. "Air conditioning market saturation and long-term response of residential cooling energy demand to climate change," Energy, Elsevier, vol. 28(9), pages 941-951.
    15. Simon Pezzutto & Reza Fazeli & Matteo De Felice & Wolfram Sparber, 2016. "Future development of the air-conditioning market in Europe: an outlook until 2020," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 5(6), pages 649-669, November.
    16. Chuntian Cheng & Bin Luo & Shumin Miao & Xinyu Wu, 2016. "Mid-Term Electricity Market Clearing Price Forecasting with Sparse Data: A Case in Newly-Reformed Yunnan Electricity Market," Energies, MDPI, vol. 9(10), pages 1-22, October.
    17. Marin Cerjan & Marin Matijaš & Marko Delimar, 2014. "Dynamic Hybrid Model for Short-Term Electricity Price Forecasting," Energies, MDPI, vol. 7(5), pages 1-15, May.
    18. E R Larsen & D W Bunn, 1999. "Deregulation in electricity: understanding strategic and regulatory risk," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(4), pages 337-344, April.
    19. Keles, Dogan & Scelle, Jonathan & Paraschiv, Florentina & Fichtner, Wolf, 2016. "Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks," Applied Energy, Elsevier, vol. 162(C), pages 218-230.
    20. Papadimitriou, Theophilos & Gogas, Periklis & Stathakis, Efthimios, 2014. "Forecasting energy markets using support vector machines," Energy Economics, Elsevier, vol. 44(C), pages 135-142.
    21. Rossana Galli, 1998. "The Relationship Between Energy Intensity and Income Levels: Forecasting Long Term Energy Demand in Asian Emerging Countries," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 85-105.
    22. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number hsbook0601, December.
    23. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
    24. Antonio Bello & Javier Reneses & Antonio Muñoz, 2016. "Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case," Energies, MDPI, vol. 9(3), pages 1-27, March.
    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. Skalyga, Mikhail & Amelin, Mikael & Wu, Qiuwei & Söder, Lennart, 2023. "Distributionally robust day-ahead combined heat and power plants scheduling with Wasserstein Metric," Energy, Elsevier, vol. 269(C).
    2. Pavel Atănăsoae, 2018. "The Operating Strategies of Small-Scale Combined Heat and Power Plants in Liberalized Power Markets," Energies, MDPI, vol. 11(11), pages 1-16, November.
    3. Simon Pezzutto & Giulio Quaglini & Philippe Riviere & Lukas Kranzl & Antonio Novelli & Andrea Zambito & Luigi Bottecchia & Eric Wilczynski, 2022. "Space Cooling Market in Europe: Assessment of the Final Energy Consumption for the Year 2016," Sustainability, MDPI, vol. 14(5), pages 1-23, February.
    4. Tayebeh Sadat Tabatabaei & Pedram Asef, 2021. "Evaluation of Energy Price Liberalization in Electricity Industry: A Data-Driven Study on Energy Economics," Energies, MDPI, vol. 14(22), pages 1-19, November.
    5. Tomasz Rokicki & Piotr Bórawski & Barbara Gradziuk & Piotr Gradziuk & Aldona Mrówczyńska-Kamińska & Joanna Kozak & Danuta Jolanta Guzal-Dec & Kamil Wojtczuk, 2021. "Differentiation and Changes of Household Electricity Prices in EU Countries," Energies, MDPI, vol. 14(21), pages 1-21, October.
    6. Tadeusz A. Grzeszczyk & Michal K. Grzeszczyk, 2022. "Justifying Short-Term Load Forecasts Obtained with the Use of Neural Models," Energies, MDPI, vol. 15(5), pages 1-20, March.
    7. Wang, Junqi & Cao, Hongjun, 2022. "Improving competitive strategic decisions of Chinese coal companies toward green transformation: A hybrid multi-criteria decision-making model," Resources Policy, Elsevier, vol. 75(C).
    8. Eng Tseng Lau & Kok Keong Chai & Yue Chen & Jonathan Loo, 2018. "Efficient Economic and Resilience-Based Optimization for Disaster Recovery Management of Critical Infrastructures," Energies, MDPI, vol. 11(12), pages 1-20, December.
    9. Shengyuan Liu & Fangbin Ye & Zhenzhi Lin & Jia Yang & Haigang Liu & Yinghe Lin & Haiwei Xie, 2019. "Comprehensive Quality Assessment Algorithm for Smart Meters," Energies, MDPI, vol. 12(19), pages 1-20, September.
    10. Mohammad Shakeri & Jagadeesh Pasupuleti & Nowshad Amin & Md. Rokonuzzaman & Foo Wah Low & Chong Tak Yaw & Nilofar Asim & Nurul Asma Samsudin & Sieh Kiong Tiong & Chong Kok Hen & Chin Wei Lai, 2020. "An Overview of the Building Energy Management System Considering the Demand Response Programs, Smart Strategies and Smart Grid," Energies, MDPI, vol. 13(13), pages 1-15, June.
    11. Simon Pezzutto & Giulio Quaglini & Andrea Zambito & Antonio Novelli & Philippe Riviere & Lukas Kranzl & Eric Wilczynski, 2022. "Potential Evolution of the Cooling Market in the EU27+UK: An Outlook until 2030," Sustainability, MDPI, vol. 14(8), pages 1-25, April.
    12. Shadi Tehrani & Jesús Juan & Eduardo Caro, 2022. "Electricity Spot Price Modeling and Forecasting in European Markets," Energies, MDPI, vol. 15(16), pages 1-23, August.
    13. Costa, C.M. & Barbosa, J.C. & Castro, H. & Gonçalves, R. & Lanceros-Méndez, S., 2021. "Electric vehicles: To what extent are environmentally friendly and cost effective? – Comparative study by european countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    14. Christoph Mazur & Stephen Hall & Jeffrey Hardy & Mark Workman, 2019. "Technology is not a Barrier: A Survey of Energy System Technologies Required for Innovative Electricity Business Models Driving the Low Carbon Energy Revolution," Energies, MDPI, vol. 12(3), pages 1-13, January.

    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. Chuntian Cheng & Bin Luo & Shumin Miao & Xinyu Wu, 2016. "Mid-Term Electricity Market Clearing Price Forecasting with Sparse Data: A Case in Newly-Reformed Yunnan Electricity Market," Energies, MDPI, vol. 9(10), pages 1-22, October.
    2. Afanasyev, Dmitriy O. & Fedorova, Elena A., 2019. "On the impact of outlier filtering on the electricity price forecasting accuracy," Applied Energy, Elsevier, vol. 236(C), pages 196-210.
    3. G P Girish & Aviral Kumar Tiwari, 2016. "A comparison of different univariate forecasting models forSpot Electricity Price in India," Economics Bulletin, AccessEcon, vol. 36(2), pages 1039-1057.
    4. 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.
    5. 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.
    6. S. Vijayalakshmi & G. P. Girish, 2015. "Artificial Neural Networks for Spot Electricity Price Forecasting: A Review," International Journal of Energy Economics and Policy, Econjournals, vol. 5(4), pages 1092-1097.
    7. Liu, Luyao & Bai, Feifei & Su, Chenyu & Ma, Cuiping & Yan, Ruifeng & Li, Hailong & Sun, Qie & Wennersten, Ronald, 2022. "Forecasting the occurrence of extreme electricity prices using a multivariate logistic regression model," Energy, Elsevier, vol. 247(C).
    8. Rodrigo A. de Marcos & Antonio Bello & Javier Reneses, 2019. "Short-Term Electricity Price Forecasting with a Composite Fundamental-Econometric Hybrid Methodology," Energies, MDPI, vol. 12(6), pages 1-15, March.
    9. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    10. Grzegorz Marcjasz & Tomasz Serafin & Rafał Weron, 2018. "Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 11(9), pages 1-20, September.
    11. Bento, P.M.R. & Pombo, J.A.N. & Calado, M.R.A. & Mariano, S.J.P.S., 2018. "A bat optimized neural network and wavelet transform approach for short-term price forecasting," Applied Energy, Elsevier, vol. 210(C), pages 88-97.
    12. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    13. Zoran Gligorić & Svetlana Štrbac Savić & Aleksandra Grujić & Milanka Negovanović & Omer Musić, 2018. "Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process," Energies, MDPI, vol. 11(7), pages 1-17, July.
    14. Carlo Fezzi & Luca Mosetti, 2018. "Size matters: Estimation sample length and electricity price forecasting accuracy," DEM Working Papers 2018/10, Department of Economics and Management.
    15. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression," Applied Energy, Elsevier, vol. 239(C), pages 610-625.
    16. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2016. "Day-ahead electricity price forecasting via the application of artificial neural network based models," Applied Energy, Elsevier, vol. 172(C), pages 132-151.
    17. Ismael Ahrazem Dfuf & José Manuel Mira McWilliams & María Camino González Fernández, 2019. "Multi-Output Conditional Inference Trees Applied to the Electricity Market: Variable Importance Analysis," Energies, MDPI, vol. 12(6), pages 1-24, March.
    18. Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019. "Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1533-1547.
    19. Léonard Tschora & Erwan Pierre & Marc Plantevit & Céline Robardet, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Post-Print hal-03621974, HAL.
    20. Qiao, Weibiao & Yang, Zhe, 2020. "Forecast the electricity price of U.S. using a wavelet transform-based hybrid model," Energy, Elsevier, vol. 193(C).

    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:gam:jeners:v:11:y:2018:i:6:p:1460-:d:150820. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.