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Dynamic analysis of the German day-ahead electricity spot market

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  • Paschen, Marius

Abstract

This paper analyzes the dynamic behavior of day-ahead spot prices in the German electricity spot market due to positive structural shocks in wind and solar power. It uses a dynamic structural vector autoregressive model to estimate the related structural impulse response functions. The estimates suggest that wind power shocks have a more prolonged negative effect on spot prices than solar power shocks. Significant autocorrelations of wind power for larger lags indicate these results. The total negative merit order effect of a solar power shock is larger when using a reasonable model specification. One reason might be that solar power shocks coincide with demand peaks. However, results regarding the relation of the effects of wind and solar should be dealt with carefully because these results do not hold for all specifications. Past static estimates show differences in the total average negative merit order effects. These estimates might be overstated if wind and solar power dynamics are transferred to the merit order effects.

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  • Paschen, Marius, 2016. "Dynamic analysis of the German day-ahead electricity spot market," Energy Economics, Elsevier, vol. 59(C), pages 118-128.
    • Handle: RePEc:eee:eneeco:v:59:y:2016:i:c:p:118-128
    DOI: 10.1016/j.eneco.2016.07.019
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    4. Katarzyna Maciejowska, 2022. "A portfolio management of a small RES utility with a Structural Vector Autoregressive model of German electricity markets," Papers 2205.00975, arXiv.org.
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    6. Marius Paschen, 2016. "The effect of intermittent renewable supply on the forward premium in German electricity markets," Working Papers V-397-16, University of Oldenburg, Department of Economics, revised Nov 2016.
    7. Haxhimusa, Adhurim, 2018. "The Effects of German Wind and Solar Electricity on French Spot Price Volatility: An Empirical Investigation," Department of Economics Working Paper Series 258, WU Vienna University of Economics and Business.
    8. McDonagh, Shane & Deane, Paul & Rajendran, Karthik & Murphy, Jerry D., 2019. "Are electrofuels a sustainable transport fuel? Analysis of the effect of controls on carbon, curtailment, and cost of hydrogen," Applied Energy, Elsevier, vol. 247(C), pages 716-730.
    9. Mosquera-López, Stephanía & Nursimulu, Anjali, 2019. "Drivers of electricity price dynamics: Comparative analysis of spot and futures markets," Energy Policy, Elsevier, vol. 126(C), pages 76-87.
    10. Rainer Baule & Michael Naumann, 2021. "Volatility and Dispersion of Hourly Electricity Contracts on the German Continuous Intraday Market," Energies, MDPI, vol. 14(22), pages 1-24, November.
    11. Kolb, Sebastian & Dillig, Marius & Plankenbühler, Thomas & Karl, Jürgen, 2020. "The impact of renewables on electricity prices in Germany - An update for the years 2014–2018," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    12. Frondel, Manuel & Kaeding, Matthias & Sommer, Stephan, 2022. "Market premia for renewables in Germany: The effect on electricity prices," Energy Economics, Elsevier, vol. 109(C).
    13. Yasir Alsaedi & Gurudeo Anand Tularam & Victor Wong, 2020. "Impact of Solar and Wind Prices on the Integrated Global Electricity Spot and Options Markets: A Time Series Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 337-353.
    14. Lehna, Malte & Scheller, Fabian & Herwartz, Helmut, 2022. "Forecasting day-ahead electricity prices: A comparison of time series and neural network models taking external regressors into account," Energy Economics, Elsevier, vol. 106(C).
    15. Gürtler, Marc & Paulsen, Thomas, 2018. "The effect of wind and solar power forecasts on day-ahead and intraday electricity prices in Germany," Energy Economics, Elsevier, vol. 75(C), pages 150-162.
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    More about this item

    Keywords

    Electricity market; Spot prices; Wind and solar power dynamics; Structural vector autoregressive model; Structural impulse response functions;
    All these keywords.

    JEL classification:

    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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