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Simplified Electricity Market Models with Significant Intermittent Renewable Capacity: Evidence from Italy

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  • Christoph Graf
  • Federico Quaglia
  • Frank A. Wolak

Abstract

Using hourly offer curves from the Italian day-ahead market and the real-time re-dispatch market for the period January 1, 2017 to December 31, 2018, we show how thermal generation unit owners are able to profit from differences between a simplified day-ahead market design that ignores system security constraints as well as generation unit operating constraints, and real-time system operation where these constraints must be respected. We find that thermal generation unit owners increase or decrease their day-ahead offer prices depending on the probability that their final output will be increased or decreased relative to their day-ahead schedules because of real-time operating constraints. First, we estimate generation unit-level models of the probability of each of these outcomes conditional on forecast demand and renewable production in Italy and neighboring countries. Our most conservative estimate of the impact of a change in the probability a unit owner will have its day-ahead schedule increased in the real-time re-dispatch market implies a day-ahead offer price increase of 5 EUR/MWh if this probability changes by 0.1. If the probability of a day-ahead schedule decrease rises by 0.1 the unit owner's offer price is predicted to be 6 EUR/MWh less. Over our sample period, we find that the economic re-dispatch cost averaged approximately 15% of the total cost of energy consumption valued at the day-ahead price.

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  • Christoph Graf & Federico Quaglia & Frank A. Wolak, 2020. "Simplified Electricity Market Models with Significant Intermittent Renewable Capacity: Evidence from Italy," NBER Working Papers 27262, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:27262
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    1. Roger E. Bohn & Michael C. Caramanis & Fred C. Schweppe, 1984. "Optimal Pricing in Electrical Networks over Space and Time," RAND Journal of Economics, The RAND Corporation, vol. 15(3), pages 360-376, Autumn.
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    1. Marzena Czarnecka & Katarzyna Chudy–Laskowska & Grzegorz Kinelski & Grzegorz Lew & Beata Sadowska & Magdalena Wójcik-Jurkiewicz & Borys Budka, 2022. "Grants and Funding for the Processes of Decarbonization in the Scope of Sustainability Development—The Case from Poland," Energies, MDPI, vol. 15(20), pages 1-20, October.
    2. Triolo, Ryan C. & Wolak, Frank A., 2022. "Quantifying the benefits of a nodal market design in the Texas electricity market," Energy Economics, Elsevier, vol. 112(C).
    3. Abate, Arega Getaneh & Riccardi, Rossana & Ruiz, Carlos, 2022. "Contract design in electricity markets with high penetration of renewables: A two-stage approach," Omega, Elsevier, vol. 111(C).
    4. Fianu, Emmanuel Senyo & Ahelegbey, Daniel Felix & Grossi, Luigi, 2022. "Modeling risk contagion in the Italian zonal electricity market," European Journal of Operational Research, Elsevier, vol. 298(2), pages 656-679.
    5. Katzen, Matthew & Leslie, Gordon W., 2024. "Siting and operating incentives in electrical networks: A study of mispricing in zonal markets," International Journal of Industrial Organization, Elsevier, vol. 94(C).
    6. Arega Getaneh Abate & Rossana Riccardi & Carlos Ruiz, 2022. "Contract design in electricity markets with high penetration of renewables: A two-stage approach," Papers 2201.09927, arXiv.org, revised Jun 2022.
    7. Lisi, Francesco & Grossi, Luigi & Quaglia, Federico, 2023. "Evaluation of Cost-at-Risk related to the procurement of resources in the ancillary services market. The case of the Italian electricity market," Energy Economics, Elsevier, vol. 121(C).
    8. 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).
    9. Eicke, Anselm & Schittekatte, Tim, 2022. "Fighting the wrong battle? A critical assessment of arguments against nodal electricity prices in the European debate," Energy Policy, Elsevier, vol. 170(C).
    10. Graf, Christoph & Quaglia, Federico & Wolak, Frank A., 2021. "(Machine) learning from the COVID-19 lockdown about electricity market performance with a large share of renewables," Journal of Environmental Economics and Management, Elsevier, vol. 105(C).
    11. Michael G. Pollitt, 2023. "Locational Marginal Prices (LMPs) for electricity in Europe? The untold story," Working Papers EPRG2318, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    12. Beltrami, Filippo, 2024. "The impact of hydroelectric storage in Northern Italy’s power market," Energy Policy, Elsevier, vol. 191(C).
    13. Jahns, Christopher & Stein, Tobias & Höckner, Jonas & Weber, Christoph, 2023. "Prevention of strategic behaviour in local flexibility markets using market monitoring – Concept, application example and limitations," Energy Policy, Elsevier, vol. 174(C).
    14. Beltrami, Filippo & Fontini, Fulvio & Grossi, Luigi, 2021. "The value of carbon emission reduction induced by Renewable Energy Sources in the Italian power market," Ecological Economics, Elsevier, vol. 189(C).
    15. Christoph Graf & Viktor Zobernig & Johannes Schmidt & Claude Klockl, 2021. "Computational Performance of Deep Reinforcement Learning to find Nash Equilibria," Papers 2104.12895, arXiv.org.

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    JEL classification:

    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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