IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i19p14448-d1252917.html
   My bibliography  Save this article

Study of Potential Impact of Wind Energy on Electricity Price Using Regression Techniques

Author

Listed:
  • Neeraj Kumar

    (Electrical and Electronics Engineering Department, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India)

  • Madan Mohan Tripathi

    (Electrical Engineering Department, Delhi Technological University, New Delhi 110042, India)

  • Saket Gupta

    (Instrumentation and Control Engineering Department, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India)

  • Majed A. Alotaibi

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Hasmat Malik

    (Department of Electrical Power Engineering, Faculty of Electrical Engineering, University Technology Malaysia (UTM), Johor Bahru 81310, Malaysia
    Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun 248002, India)

  • Asyraf Afthanorhan

    (Faculty of Business and Management, Universiti Sultan Zainal Abidin (UniSZA), Gong Badak, Kuala Terengganu 21300, Malaysia)

Abstract

This paper seeks to investigate the impact analysis of wind energy on electricity prices in an integrated renewable energy market, using regression models. This is especially important as wind energy is hard to predict and its integration into electricity markets is still in an early stage. Price forecasting has been performed with consideration of wind energy generation to optimize energy portfolio investment and create an efficient energy-trading landscape. It provides an insight into future market trends which allow traders to price their products competitively and manage their risks within the volatile market. Through the analysis of an available dataset from the Austrian electricity market, it was found that the Decision Tree (DT) regression model performed better than the Linear Regression (LR), Random Forest (RF), and Least Absolute Shrinkage Selector Operator (LASSO) models. The accuracy of the model was evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The MAE values considering wind energy generation and without wind energy generation for the Decision Tree model were found to be lowest (2.08 and 2.20, respectively) among all proposed models for the available dataset. The increasing deployment of wind energy in the European grid has led to a drop in prices and helped in achieving energy security and sustainability.

Suggested Citation

  • Neeraj Kumar & Madan Mohan Tripathi & Saket Gupta & Majed A. Alotaibi & Hasmat Malik & Asyraf Afthanorhan, 2023. "Study of Potential Impact of Wind Energy on Electricity Price Using Regression Techniques," Sustainability, MDPI, vol. 15(19), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14448-:d:1252917
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/19/14448/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/19/14448/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Green, Richard & Vasilakos, Nicholas, 2010. "Market behaviour with large amounts of intermittent generation," Energy Policy, Elsevier, vol. 38(7), pages 3211-3220, July.
    2. Diego Aineto & Javier Iranzo-Sánchez & Lenin G. Lemus-Zúñiga & Eva Onaindia & Javier F. Urchueguía, 2019. "On the Influence of Renewable Energy Sources in Electricity Price Forecasting in the Iberian Market," Energies, MDPI, vol. 12(11), pages 1-20, May.
    3. Moreno, Blanca & López, Ana J. & García-Álvarez, María Teresa, 2012. "The electricity prices in the European Union. The role of renewable energies and regulatory electric market reforms," Energy, Elsevier, vol. 48(1), pages 307-313.
    4. Maciejowska, Katarzyna, 2020. "Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach," Energy Economics, Elsevier, vol. 85(C).
    5. Trujillo-Baute, Elisa & del Río, Pablo & Mir-Artigues, Pere, 2018. "Analysing the impact of renewable energy regulation on retail electricity prices," Energy Policy, Elsevier, vol. 114(C), pages 153-164.
    6. Ballester, Cristina & Furió, Dolores, 2015. "Effects of renewables on the stylized facts of electricity prices," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1596-1609.
    7. Yang, Haolin & Schell, Kristen R., 2021. "Real-time electricity price forecasting of wind farms with deep neural network transfer learning and hybrid datasets," Applied Energy, Elsevier, vol. 299(C).
    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. Oosthuizen, Anna Maria & Inglesi-Lotz, Roula & Thopil, George Alex, 2022. "The relationship between renewable energy and retail electricity prices: Panel evidence from OECD countries," Energy, Elsevier, vol. 238(PB).
    2. Adom, Philip Kofi & Insaidoo, Michael & Minlah, Michael Kaku & Abdallah, Abdul-Mumuni, 2017. "Does renewable energy concentration increase the variance/uncertainty in electricity prices in Africa?," Renewable Energy, Elsevier, vol. 107(C), pages 81-100.
    3. Hakan Acaroğlu & Fausto Pedro García Márquez, 2021. "Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy," Energies, MDPI, vol. 14(22), pages 1-23, November.
    4. Lago, Jesus & Marcjasz, Grzegorz & De Schutter, Bart & Weron, Rafał, 2021. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Applied Energy, Elsevier, vol. 293(C).
    5. Matheus Henrique Dal Molin Ribeiro & Stéfano Frizzo Stefenon & José Donizetti de Lima & Ademir Nied & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2020. "Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning," Energies, MDPI, vol. 13(19), pages 1-22, October.
    6. Cristina Ballester & Dolores Furió, 2017. "Impact of Wind Electricity Forecasts on Bidding Strategies," Sustainability, MDPI, vol. 9(8), pages 1-17, August.
    7. Sirin, Selahattin Murat & Uz, Dilek & Sevindik, Irem, 2022. "How do variable renewable energy technologies affect firm-level day-ahead output decisions: Evidence from the Turkish wholesale electricity market," Energy Economics, Elsevier, vol. 112(C).
    8. Macedo, Daniela Pereira & Marques, António Cardoso & Damette, Olivier, 2021. "The Merit-Order Effect on the Swedish bidding zone with the highest electricity flow in the Elspot market," Energy Economics, Elsevier, vol. 102(C).
    9. 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.
    10. Maniatis, Georgios I. & Milonas, Nikolaos T., 2022. "The impact of wind and solar power generation on the level and volatility of wholesale electricity prices in Greece," Energy Policy, Elsevier, vol. 170(C).
    11. Sirin, Selahattin Murat & Yilmaz, Berna N., 2020. "Variable renewable energy technologies in the Turkish electricity market: Quantile regression analysis of the merit-order effect," Energy Policy, Elsevier, vol. 144(C).
    12. Sirin, Selahattin Murat & Yilmaz, Berna N., 2021. "The impact of variable renewable energy technologies on electricity markets: An analysis of the Turkish balancing market," Energy Policy, Elsevier, vol. 151(C).
    13. Hannesson, Rögnvaldur, 2019. "How much do European households pay for green energy?," Energy Policy, Elsevier, vol. 131(C), pages 235-239.
    14. Ben-Moshe, Ori & Rubin, Ofir D., 2015. "Does wind energy mitigate market power in deregulated electricity markets?," Energy, Elsevier, vol. 85(C), pages 511-521.
    15. da Silva, Patrícia Pereira & Cerqueira, Pedro A., 2017. "Assessing the determinants of household electricity prices in the EU: a system-GMM panel data approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1131-1137.
    16. Blazquez, Jorge & Fuentes, Rolando & Manzano, Baltasar, 2020. "On some economic principles of the energy transition," Energy Policy, Elsevier, vol. 147(C).
    17. Peña, Juan Ignacio & Rodríguez, Rosa & Mayoral, Silvia, 2022. "Cannibalization, depredation, and market remuneration of power plants," Energy Policy, Elsevier, vol. 167(C).
    18. John Dorrell & Keunjae Lee, 2021. "The Price of Wind: An Empirical Analysis of the Relationship between Wind Energy and Electricity Price across the Residential, Commercial, and Industrial Sectors," Energies, MDPI, vol. 14(12), pages 1-21, June.
    19. Carmen Ramos Carvajal & Ana Salomé García-Muñiz & Blanca Moreno Cuartas, 2019. "Assessing Socioeconomic Impacts of Integrating Distributed Energy Resources in Electricity Markets through Input-Output Models," Energies, MDPI, vol. 12(23), pages 1-21, November.
    20. Faramarz Saghi & Mustafa Jahangoshai Rezaee, 2023. "Integrating Wavelet Decomposition and Fuzzy Transformation for Improving the Accuracy of Forecasting Crude Oil Price," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 559-591, February.

    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:jsusta:v:15:y:2023:i:19:p:14448-:d:1252917. 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.