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Evaluation of Cost-at-Risk related to the procurement of resources in the ancillary services market. The case of the Italian electricity market

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  • Lisi, Francesco
  • Grossi, Luigi
  • Quaglia, Federico

Abstract

Measuring the risk exposure of TSOs on the dispatching market is a crucial task for the correct management of liberalized electricity markets. To fill a gap in the literature, the notion of Cost-at-Risk (CaR) is defined in the context of the dispatching market. Moreover, we propose a set of semi-parametric and non-parametric models for the estimation of the Cost at Risk (CaR) for the Italian TSO (Terna) and evaluate the corresponding out-of-sample forecasting performance. The empirical analysis relies on a rich hourly dataset provided by Terna, including several costs’ drivers. The results, in terms of 1-day and 30-day ahead predictions, suggest that the model with the globally best performance is the semi-parametric GAM-GARCH model.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:eneeco:v:121:y:2023:i:c:s0140988323001238
    DOI: 10.1016/j.eneco.2023.106625
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    1. Chiu, Yen-Chen & Chuang, I-Yuan & Lai, Jing-Yi, 2010. "The performance of composite forecast models of value-at-risk in the energy market," Energy Economics, Elsevier, vol. 32(2), pages 423-431, March.
    2. Li, Jianglong & Ho, Mun Sing, 2022. "Indirect cost of renewable energy: Insights from dispatching," Energy Economics, Elsevier, vol. 105(C).
    3. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    4. Mauro Caprabianca & Maria Carmen Falvo & Lorenzo Papi & Lucrezia Promutico & Viviana Rossetti & Federico Quaglia, 2020. "Replacement Reserve for the Italian Power System and Electricity Market," Energies, MDPI, vol. 13(11), pages 1-19, June.
    5. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    6. Matteo Fasiolo & Simon N. Wood & Margaux Zaffran & Raphaël Nedellec & Yannig Goude, 2021. "Fast Calibrated Additive Quantile Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(535), pages 1402-1412, July.
    7. Lamadrid, Alberto J. & Mount, Tim, 2012. "Ancillary services in systems with high penetrations of renewable energy sources, the case of ramping," Energy Economics, Elsevier, vol. 34(6), pages 1959-1971.
    8. 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).
    9. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    10. Godoy-González, Diego & Gil, Esteban & Gutiérrez-Alcaraz, Guillermo, 2020. "Ramping ancillary service for cost-based electricity markets with high penetration of variable renewable energy," Energy Economics, Elsevier, vol. 85(C).
    11. Sadeghi, Mehdi & Shavvalpour, Saeed, 2006. "Energy risk management and value at risk modeling," Energy Policy, Elsevier, vol. 34(18), pages 3367-3373, December.
    12. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    13. 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.
    14. Laporta, Alessandro G. & Merlo, Luca & Petrella, Lea, 2018. "Selection of Value at Risk Models for Energy Commodities," Energy Economics, Elsevier, vol. 74(C), pages 628-643.
    15. Emilio Ghiani & Marco Galici & Mario Mureddu & Fabrizio Pilo, 2020. "Impact on Electricity Consumption and Market Pricing of Energy and Ancillary Services during Pandemic of COVID-19 in Italy," Energies, MDPI, vol. 13(13), pages 1-19, July.
    16. Bigerna, Simona & Bollino, Carlo Andrea & Ciferri, Davide & Polinori, Paolo, 2017. "Renewables diffusion and contagion effect in Italian regional electricity markets: Assessment and policy implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 199-211.
    17. Abhimanyu Kaushal & Dirk Van Hertem, 2019. "An Overview of Ancillary Services and HVDC Systems in European Context," Energies, MDPI, vol. 12(18), pages 1-20, September.
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    More about this item

    Keywords

    Cost-at-risk (caR); Electricity market; Ancillary services; GAM models; Q-GAM models; GARCH models; Quantile regression;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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