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PV Penetration under Market Environment and with System Constraints

Author

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    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. Ventosa, Mariano & Baillo, Alvaro & Ramos, Andres & Rivier, Michel, 2005. "Electricity market modeling trends," Energy Policy, Elsevier, vol. 33(7), pages 897-913, May.
    6. 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.
    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.
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