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Quantifying the economic efficiency impact of inaccurate renewable energy price forecasts

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  • Croonenbroeck, Carsten
  • Hüttel, Silke

Abstract

We demonstrate a simple approach to quantify economic efficiency losses due to inaccurate energy price forecasts. We show that empirically, forecasting errors have their impact on efficiency losses. There are hints that suggest a time-dependent pattern of deadweight losses. Solar infeed, which is considered to be a strongly volatile form of energy, increases the efficiency loss of energy allocations. For wind power, however, this effect is not significant. After all, we provide yet another argument for the requirement of even more accurate forecasts in order to decrease unwanted losses of economic efficiency, even though we show that at their current state, forecasts are fairly accurate already.

Suggested Citation

  • Croonenbroeck, Carsten & Hüttel, Silke, 2017. "Quantifying the economic efficiency impact of inaccurate renewable energy price forecasts," Energy, Elsevier, vol. 134(C), pages 767-774.
  • Handle: RePEc:eee:energy:v:134:y:2017:i:c:p:767-774
    DOI: 10.1016/j.energy.2017.06.077
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    1. Würzburg, Klaas & Labandeira, Xavier & Linares, Pedro, 2013. "Renewable generation and electricity prices: Taking stock and new evidence for Germany and Austria," Energy Economics, Elsevier, vol. 40(S1), pages 159-171.
    2. Widén, Joakim & Wäckelgård, Ewa, 2010. "A high-resolution stochastic model of domestic activity patterns and electricity demand," Applied Energy, Elsevier, vol. 87(6), pages 1880-1892, June.
    3. Catalão, J.P.S. & Pousinho, H.M.I. & Mendes, V.M.F., 2011. "Short-term wind power forecasting in Portugal by neural networks and wavelet transform," Renewable Energy, Elsevier, vol. 36(4), pages 1245-1251.
    4. Croonenbroeck, Carsten & Stadtmann, Georg, 2015. "Minimizing asymmetric loss in medium-term wind power forecasting," Renewable Energy, Elsevier, vol. 81(C), pages 197-208.
    5. Ketterer, Janina C., 2014. "The impact of wind power generation on the electricity price in Germany," Energy Economics, Elsevier, vol. 44(C), pages 270-280.
    6. Kiesel, Rüdiger & Paraschiv, Florentina, 2017. "Econometric analysis of 15-minute intraday electricity prices," Energy Economics, Elsevier, vol. 64(C), pages 77-90.
    7. Kim, Soo-Hyun & Shin, Hyung-Ki & Joo, Young-Chul & Kim, Keon-Hoon, 2015. "A study of the wake effects on the wind characteristics and fatigue loads for the turbines in a wind farm," Renewable Energy, Elsevier, vol. 74(C), pages 536-543.
    8. Croonenbroeck, Carsten & Møller Dahl, Christian, 2014. "Accurate medium-term wind power forecasting in a censored classification framework," Discussion Papers 351, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
    9. Hong, Ying-Yi & Chang, Huei-Lin & Chiu, Ching-Sheng, 2010. "Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs," Energy, Elsevier, vol. 35(9), pages 3870-3876.
    10. Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(8), pages 1352-1362, August.
    11. Hirth, Lion & Ziegenhagen, Inka, 2015. "Balancing power and variable renewables: Three links," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1035-1051.
    12. Huisman, Ronald & Mahieu, Ronald, 2003. "Regime jumps in electricity prices," Energy Economics, Elsevier, vol. 25(5), pages 425-434, September.
    13. Jursa, René & Rohrig, Kurt, 2008. "Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 694-709.
    14. Kümmel, Reiner & Lindenberger, Dietmar & Weiser, Florian, 2015. "The economic power of energy and the need to integrate it with energy policy," Energy Policy, Elsevier, vol. 86(C), pages 833-843.
    15. Croonenbroeck, Carsten & Dahl, Christian Møller, 2014. "Accurate medium-term wind power forecasting in a censored classification framework," Energy, Elsevier, vol. 73(C), pages 221-232.
    16. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
    17. Paul L. Anderson & Mark M. Meerschaert & Kai Zhang, 2013. "Forecasting with prediction intervals for periodic autoregressive moving average models," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(2), pages 187-193, March.
    18. Elberg, Christina & Hagspiel, Simeon, 2015. "Spatial dependencies of wind power and interrelations with spot price dynamics," European Journal of Operational Research, Elsevier, vol. 241(1), pages 260-272.
    19. Spiecker, Stephan & Vogel, Philip & Weber, Christoph, 2013. "Evaluating interconnector investments in the north European electricity system considering fluctuating wind power penetration," Energy Economics, Elsevier, vol. 37(C), pages 114-127.
    20. Jónsson, Tryggvi & Pinson, Pierre & Madsen, Henrik, 2010. "On the market impact of wind energy forecasts," Energy Economics, Elsevier, vol. 32(2), pages 313-320, March.
    21. Croonenbroeck, Carsten & Ambach, Daniel, 2015. "Censored spatial wind power prediction with random effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 613-622.
    22. Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 524-524, March.
    23. Dillig, Marius & Jung, Manuel & Karl, Jürgen, 2016. "The impact of renewables on electricity prices in Germany – An estimation based on historic spot prices in the years 2011–2013," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 7-15.
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    Cited by:

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    4. Hugo Algarvio & Fernando Lopes & António Couto & João Santana & Ana Estanqueiro, 2019. "Effects of regulating the European Internal Market on the integration of variable renewable energy," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(6), November.

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    More about this item

    Keywords

    Economic efficiency; Electricity prices; Forecasts; Deadweight loss; Electricity supply and demand;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D61 - Microeconomics - - Welfare Economics - - - Allocative Efficiency; Cost-Benefit Analysis
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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