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Stochastic modeling of intraday photovoltaic power generation

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  • Lingohr, Daniel
  • Müller, Gernot

Abstract

Renewable energies play an increasing role in power generation worldwide. Electricity generated by photovoltaic power plants is an important factor here. The fact that no solar power is generated at night makes modeling for high resolution difficult. Previous work has therefore been limited to daily variation. However, this obviously leads to a lack in description of the data, a gap which we will fill in this work. To do this, first we filter a cloud cover component from the infeed data by using physical relationships. This variable incorporates the complete stochastic and can be modeled as a non-linear continuous-time autoregression as defined by Brockwell and Hyndman (1992). We fit our model to infeed data in Germany and show that it describes the data better than other comparable approaches. The model enables pricing of derivatives, which is illustrated by a new future contract. This product allows the volume risk of photovoltaic power plants to be hedged.

Suggested Citation

  • Lingohr, Daniel & Müller, Gernot, 2019. "Stochastic modeling of intraday photovoltaic power generation," Energy Economics, Elsevier, vol. 81(C), pages 175-186.
    • Handle: RePEc:eee:eneeco:v:81:y:2019:i:c:p:175-186
    DOI: 10.1016/j.eneco.2019.03.007
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    Citations

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    Cited by:

    1. Lingohr, Daniel & Müller, Gernot, 2021. "Conditionally independent increment processes for modeling electricity prices with regard to renewable power generation," Energy Economics, Elsevier, vol. 103(C).
    2. Kraft, Emil & Russo, Marianna & Keles, Dogan & Bertsch, Valentin, 2023. "Stochastic optimization of trading strategies in sequential electricity markets," European Journal of Operational Research, Elsevier, vol. 308(1), pages 400-421.
    3. Rowińska, Paulina A. & Veraart, Almut E.D. & Gruet, Pierre, 2021. "A multi-factor approach to modelling the impact of wind energy on electricity spot prices," Energy Economics, Elsevier, vol. 104(C).
    4. Nikodinoska, Dragana & Käso, Mathias & Müsgens, Felix, 2022. "Solar and wind power generation forecasts using elastic net in time-varying forecast combinations," Applied Energy, Elsevier, vol. 306(PA).
    5. Larsson, Karl & Green, Rikard & Benth, Fred Espen, 2023. "A stochastic time-series model for solar irradiation," Energy Economics, Elsevier, vol. 117(C).
    6. Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2022. "Short-term risk management of electricity retailers under rising shares of decentralized solar generation," Energy Economics, Elsevier, vol. 109(C).
    7. Russo, Marianna & Bertsch, Valentin, 2020. "A looming revolution: Implications of self-generation for the risk exposure of retailers," Energy Economics, Elsevier, vol. 92(C).
    8. Laura Casula & Guglielmo D’Amico & Giovanni Masala & Filippo Petroni, 2020. "Performance estimation of photovoltaic energy production," Letters in Spatial and Resource Sciences, Springer, vol. 13(3), pages 267-285, December.

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    More about this item

    Keywords

    Clear sky; Cloud cover; Beer-Lambert; CTAR; Power future; Volume risk;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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