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The impact of Clean Spark Spread expectations on storage hydropower generation

Author

Listed:
  • Claudia Condemi

    (Roma Tre University)

  • Loretta Mastroeni

    (Roma Tre University)

  • Pierluigi Vellucci

    (Roma Tre University)

Abstract

Storage hydropower generation plays a crucial role in the electric power system and energy transition because it is the most widespread power generation with low greenhouse gas emissions and, moreover, it is relatively cheap to ramp up and down. As a result, it provides flexibility to the grid and helps mitigate the short-term production uncertainty that affects most green energy technologies. However, using water in reservoirs represents an opportunity cost, which is related to the evolution of plant production capacity and production profitability. As the latter is related to a wide range of types of variables, in order to incorporate it in a large-scale prediction model it is important to select the variables that impact most on storage hydropower generation. In this paper, we investigate the impact of the variables influencing the choices of price maker producers, and, in particular we study the impact of Clean Spark Spread expectations on storage hydroelectric generation. In this connection, using entropy and machine learning tools, we present a method for embedding this expectations in a model to predict storage hydropower generation, showing that, for some time horizon, expectations on CSS have a greater impact than expectations on power prices. It is shown that, if the right mix of power price and CSS expectations is considered, the prediction error of the model is drastically reduced. This implies that it is important to incorporate CSS expectations into the storage hydropower model.

Suggested Citation

  • Claudia Condemi & Loretta Mastroeni & Pierluigi Vellucci, 2021. "The impact of Clean Spark Spread expectations on storage hydropower generation," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1111-1146, December.
    • Handle: RePEc:spr:decfin:v:44:y:2021:i:2:d:10.1007_s10203-021-00355-6
    DOI: 10.1007/s10203-021-00355-6
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    References listed on IDEAS

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    1. Benedetto, Francesco & Mastroeni, Loretta & Quaresima, Greta & Vellucci, Pierluigi, 2020. "Does OVX affect WTI and Brent oil spot variance? Evidence from an entropy analysis," Energy Economics, Elsevier, vol. 89(C).
    2. He, Jiayi & Shang, Pengjian, 2017. "Comparison of transfer entropy methods for financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 772-785.
    3. Jahns, Christopher & Podewski, Caroline & Weber, Christoph, 2020. "Supply curves for hydro reservoirs – Estimation and usage in large-scale electricity market models," Energy Economics, Elsevier, vol. 87(C).
    4. 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.
    5. Banerjee, Anindya & Dolado, Juan J. & Galbraith, John W. & Hendry, David, 1993. "Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data," OUP Catalogue, Oxford University Press, number 9780198288107.
    6. Behrendt, Simon & Prange, Philipp, 2021. "What are you searching for? On the equivalence of proxies for online investor attention," Finance Research Letters, Elsevier, vol. 38(C).
    7. Dimpfl, Thomas & Peter, Franziska J., 2018. "Analyzing volatility transmission using group transfer entropy," Energy Economics, Elsevier, vol. 75(C), pages 368-376.
    8. Park, Sangjin & Jang, Kwahngsoo & Yang, Jae-Suk, 2021. "Information flow between bitcoin and other financial assets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    9. Ghoddusi, Hamed & Creamer, Germán G. & Rafizadeh, Nima, 2019. "Machine learning in energy economics and finance: A review," Energy Economics, Elsevier, vol. 81(C), pages 709-727.
    10. Uzlu, Ergun & Akpınar, Adem & Özturk, Hasan Tahsin & Nacar, Sinan & Kankal, Murat, 2014. "Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey," Energy, Elsevier, vol. 69(C), pages 638-647.
    11. Hirth, Lion, 2016. "The benefits of flexibility: The value of wind energy with hydropower," Applied Energy, Elsevier, vol. 181(C), pages 210-223.
    12. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
    13. Gregory Steeger & Steffen Rebennack, 2015. "Strategic bidding for multiple price-maker hydroelectric producers," IISE Transactions, Taylor & Francis Journals, vol. 47(9), pages 1013-1031, September.
    14. Komiyama, Ryoichi & Fujii, Yasumasa, 2014. "Assessment of massive integration of photovoltaic system considering rechargeable battery in Japan with high time-resolution optimal power generation mix model," Energy Policy, Elsevier, vol. 66(C), pages 73-89.
    15. Singh, Vineet Kumar & Singal, S.K., 2017. "Operation of hydro power plants-a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 610-619.
    16. Moreno, Julián, 2009. "Hydraulic plant generation forecasting in Colombian power market using ANFIS," Energy Economics, Elsevier, vol. 31(3), pages 450-455, May.
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    More about this item

    Keywords

    Storage hydropower prediction; Clean Spark Spread; Energy market expectations; Entropy methods; Machine learning;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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