IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i22p8673-d977254.html
   My bibliography  Save this article

PV Penetration under Market Environment and with System Constraints

Author

Listed:
  • Aris Dimeas

    (Power System Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens 9, Iroon Polytechniou St, 15780 Athens, Greece)

  • George Kiokes

    (Laboratory of Electrical Machines and Installations, Division of Electrical, Electronics and Informatics, School of Engineering, Merchant Marine Academy of Aspropyrgos, 19300 Aspropyrgos, Greece)

Abstract

The installed capacity of PVs in the distribution grid is affected not only by network constraints, but also by the economic viability of the related investments. Depending on the market participation models, this is determined critically by the Day Ahead Market (DAM) prices. Increasing RES installations in a country usually results in a long term drop in the market prices and, as a consequence, a reduction in the income of the PVs investors and possible market cannibalization. This paper models the effect of large-scale penetration of PVs on the market prices and identifies the optimal penetration level for the viability of PV projects. The optimal penetration is highly related to the installation of new PVs and this is a parameter for the analysis. Therefore, the paper identifies different penetration costs for the different installation cost. Furthermore, the PV network hosing capacity can be increased by distribution network reinforcements. Therefore, in the paper, the investments for enhancement of the distribution grid are assessed with respect to market prices and are analyzed at the macroscopic level. Again, the analysis considers different costs for network reinforcements.

Suggested Citation

  • Aris Dimeas & George Kiokes, 2022. "PV Penetration under Market Environment and with System Constraints," Energies, MDPI, vol. 15(22), pages 1-11, November.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8673-:d:977254
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/22/8673/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/22/8673/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jesus Lago & Grzegorz Marcjasz & Bart De Schutter & Rafal Weron, 2021. "Erratum to 'Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark' [Appl. Energy 293 (2021) 116983]," WORking papers in Management Science (WORMS) WORMS/21/12, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    2. 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).
    3. Ventosa, Mariano & Baillo, Alvaro & Ramos, Andres & Rivier, Michel, 2005. "Electricity market modeling trends," Energy Policy, Elsevier, vol. 33(7), pages 897-913, May.
    4. Ali Hortaçsu & Steven L. Puller, 2008. "Understanding strategic bidding in multi‐unit auctions: a case study of the Texas electricity spot market," RAND Journal of Economics, RAND Corporation, vol. 39(1), pages 86-114, March.
    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. Clò, Stefano & Cataldi, Alessandra & Zoppoli, Pietro, 2015. "The merit-order effect in the Italian power market: The impact of solar and wind generation on national wholesale electricity prices," Energy Policy, Elsevier, vol. 77(C), pages 79-88.
    7. Gianfreda, Angelica & Parisio, Lucia & Pelagatti, Matteo, 2016. "Revisiting long-run relations in power markets with high RES penetration," Energy Policy, Elsevier, vol. 94(C), pages 432-445.
    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. 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.
    2. Tomasz Zema & Adam Sulich, 2022. "Models of Electricity Price Forecasting: Bibliometric Research," Energies, MDPI, vol. 15(15), pages 1-18, August.
    3. Stefano Frizzo Stefenon & Laio Oriel Seman & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2023. "Aggregating Prophet and Seasonal Trend Decomposition for Time Series Forecasting of Italian Electricity Spot Prices," Energies, MDPI, vol. 16(3), pages 1-18, January.
    4. Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023. "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, vol. 125(C).
    5. Mira Watermeyer & Thomas Mobius & Oliver Grothe & Felix Musgens, 2023. "A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling," Papers 2304.09336, arXiv.org.
    6. Ziel, Florian & Steinert, Rick, 2016. "Electricity price forecasting using sale and purchase curves: The X-Model," Energy Economics, Elsevier, vol. 59(C), pages 435-454.
    7. Olivares, Kin G. & Challu, Cristian & Marcjasz, Grzegorz & Weron, Rafał & Dubrawski, Artur, 2023. "Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx," International Journal of Forecasting, Elsevier, vol. 39(2), pages 884-900.
    8. Joanna Janczura & Andrzej Puć, 2023. "ARX-GARCH Probabilistic Price Forecasts for Diversification of Trade in Electricity Markets—Variance Stabilizing Transformation and Financial Risk-Minimizing Portfolio Allocation," Energies, MDPI, vol. 16(2), pages 1-28, January.
    9. Daron Acemoglu, Ali Kakhbod, and Asuman Ozdaglar, 2017. "Competition in Electricity Markets with Renewable Energy Sources," The Energy Journal, International Association for Energy Economics, vol. 0(KAPSARC S).
    10. Arkadiusz Jędrzejewski & Grzegorz Marcjasz & Rafał Weron, 2021. "Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO," Energies, MDPI, vol. 14(11), pages 1-17, June.
    11. Diankai Wang & Inna Gryshova & Mykola Kyzym & Tetiana Salashenko & Viktoriia Khaustova & Maryna Shcherbata, 2022. "Electricity Price Instability over Time: Time Series Analysis and Forecasting," Sustainability, MDPI, vol. 14(15), pages 1-24, July.
    12. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
    13. Michał Narajewski, 2022. "Probabilistic Forecasting of German Electricity Imbalance Prices," Energies, MDPI, vol. 15(14), pages 1-17, July.
    14. Bartosz Uniejewski, 2023. "Electricity price forecasting with Smoothing Quantile Regression Averaging: Quantifying economic benefits of probabilistic forecasts," Papers 2302.00411, arXiv.org, revised Jan 2024.
    15. Ivan Borisov Todorov & Fernando Sánchez Lasheras, 2022. "Forecasting Applied to the Electricity, Energy, Gas and Oil Industries: A Systematic Review," Mathematics, MDPI, vol. 10(21), pages 1-15, October.
    16. Stephen Haben & Julien Caudron & Jake Verma, 2021. "Probabilistic Day-Ahead Wholesale Price Forecast: A Case Study in Great Britain," Forecasting, MDPI, vol. 3(3), pages 1-37, August.
    17. Micha{l} Narajewski, 2022. "Probabilistic forecasting of German electricity imbalance prices," Papers 2205.11439, arXiv.org.
    18. Goodarzi, Shadi & Perera, H. Niles & Bunn, Derek, 2019. "The impact of renewable energy forecast errors on imbalance volumes and electricity spot prices," Energy Policy, Elsevier, vol. 134(C).
    19. Wilkinson, Sam & Maticka, Martin J. & Liu, Yue & John, Michele, 2021. "The duck curve in a drying pond: The impact of rooftop PV on the Western Australian electricity market transition," Utilities Policy, Elsevier, vol. 71(C).
    20. Bartosz Uniejewski, 2024. "Regularization for electricity price forecasting," Papers 2404.03968, arXiv.org.

    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:15:y:2022:i:22:p:8673-:d:977254. 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.