Impact of wind and solar production on electricity prices: Quantile regression approach

References

• AG Energiebilanzen. (2015). Bruttostromerzeugung in Deutschland von 1990 bis 2014 nach Energieträgern. Retrieved from http://www.ag-energiebilanzen.de/.
   Google Scholar
• Bello, A., Bunn, D. W., Reneses, J., & Muñoz, A. (2017). Medium-term probabilistic forecasting of electricity prices: A hybrid approach. IEEE Transactions on Power Systems, 32(1), 334–343. doi: 10.1109/TPWRS.2016.2552983
   Web of Science ®Google Scholar
• Bondell, H. D., Reich, B. J., & Wang, H. (2010). Noncrossing quantile regression curve estimation. Biometrika, 97(4), 825–838. doi: 10.1093/biomet/asq048
   PubMed Web of Science ®Google Scholar
• Bunn, D., Andresen, A., Chen, D., & Westgaard, S. (2016). Analysis and forecasting of electricty price risks with quantile factor models. The Energy Journal, 37(1), 101–122 .doi: 10.5547/01956574.37.1.dbun
   Web of Science ®Google Scholar
• Bunn, D. W., & Chen, D. (2013). The forward premium in electricity futures. Journal of Empirical Finance, 23, 173–186. doi: 10.1016/j.jempfin.2013.06.002
   Web of Science ®Google Scholar
• Canova, F., & Hansen, B. E. (1995). Are seasonal patterns constant over time? A test for seasonal stability. Journal of Business & Economic Statistics, 13(3), 237–252. doi: 10.1080/07350015.1995.10524598
   Web of Science ®Google Scholar
• Chen, D., & Bunn, D. W. (2010). Analysis of the nonlinear response of electricity prices to fundamental and strategic factors. IEEE Transactions on Power Systems, 25(2), 595–606. doi: 10.1109/TPWRS.2009.2035129
   Web of Science ®Google Scholar
• Clò, S., Cataldi, A., & Zoppoli, P. (2015). The merit-order effect in the Italian power market: The impact of solar and wind generation on national wholesale electricity prices. Energy Policy, 77, 79–88. doi: 10.1016/j.enpol.2014.11.038
   Web of Science ®Google Scholar
• Conejo, A. J., Contreras, J., Espinola, R., & Plazas, M. A. (2005). Forecasting electricity prices for a day-ahead pool-based electric energy market. International Journal of Forecasting, 21(3), 435–462. doi: 10.1016/j.ijforecast.2004.12.005
   Web of Science ®Google Scholar
• Dillig, M., Jung, M., & Karl, J. (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, 57, 7–15. doi: 10.1016/j.rser.2015.12.003
   Web of Science ®Google Scholar
• Do, L. P. C., Hagfors, L. I., Lin, K. H., & Molnár, P. (2016). Demand and residual demand modelling using quantile regression. Renewable Energy and Environmental Sustainability, 1, 41. doi: 10.1051/rees/2016045
   Google Scholar
• Do, L. P. C., Lin, K. H., & Molnár, P. (2016). Electricity consumption modelling: A case of Germany. Economic Modelling, 55, 92–101. doi: 10.1016/j.econmod.2016.02.010
   Web of Science ®Google Scholar
• ENTSO-E. (2018). Transparency platform. Retrieved from https://transparency.entsoe.eu.
   Google Scholar
• Fell, H. (2010). EU-ETS and Nordic electricity: A CVAR analysis. The Energy Journal, 31(2), 1–25. https://www.jstor.org/stable/41323280
   Web of Science ®Google Scholar
• Ferkingstad, E., Løland, A., & Wilhelmsen, M. (2011). Causal modeling and inference for electricity markets. Energy Economics, 33(3), 404–412. doi: 10.5547/ISSN0195-6574-EJ-Vol31-No2-1
   Web of Science ®Google Scholar
• Green, R., & Vasilakos, N. (2010). Market behaviour with large amounts of intermittent generation. Energy Policy, 38(7), 3211–3220. doi: 10.1016/j.enpol.2009.07.038
   Web of Science ®Google Scholar
• Hagfors, L. I., Bunn, D., Kristoffersen, E., Staver, T. T., & Westgaard, S. (2016a). Modeling the UK electricity price distributions using quantile regression. Energy, 102, 231–243. doi: 10.1016/j.energy.2016.02.025
   Web of Science ®Google Scholar
• Hagfors, L. I., Kamperud, H. H., Paraschiv, F., Prokopczuk, M., Sator, A., & Westgaard, S. (2016b). Prediction of extreme price occurrences in the German day-ahead electricity market. Quantitative Finance, 16(12), 1929–1948. doi: 10.1080/14697688.2016.1211794
   Web of Science ®Google Scholar
• Hagfors, L. I., Paraschiv, F., Molnar, P., & Westgaard, S. (2016c). Using quantile regression to analyze the effect of renewables on EEX price formation. Renewable Energy and Environmental Sustainability, 1, 32. doi: 10.1051/rees/2016036
   Google Scholar
• Hammoudeh, S., Nguyen, D. K., & Sousa, R. M. (2014). Energy prices and CO2 emission allowance prices: A quantile regression approach. Energy Policy, 70, 201–206. doi: 10.1016/j.enpol.2014.03.026
   Web of Science ®Google Scholar
• He, Y., Qin, Y., Wang, S., Wang, X., & Wang, C. (2019). Electricity consumption probability density forecasting method based on LASSO-quantile regression neural network. Applied Energy, 233, 565–575. doi: 10.1016/j.apenergy.2018.10.061
   Web of Science ®Google Scholar
• Hong, T., & Fan, S. (2016). Probabilistic electric load forecasting: A tutorial review. International Journal of Forecasting, 32(3), 914–938. doi: 10.1016/j.ijforecast.2015.11.011
   Web of Science ®Google Scholar
• Hylleberg, S., Engle, R. F., Granger, C. W., & Yoo, B. S. (1990). Seasonal integration and cointegration. Journal of Econometrics, 44(1–2), 215–238. doi: 10.1016/0304-4076(90)90080-D
   Web of Science ®Google Scholar
• Hyndman, R., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O'Hara-Wild, M., … Yasmeen, F. (2018). Forecast: Forecasting functions for time series and linear models. R package version 8.4. Retrieved from http://pkg.robjhyndman.com/forecast.
   Google Scholar
• International Atomic Energy Agency. (1999). Modern instrumentation and control for nuclear power plants: A guidebook. Technical Report Series N°387.
   Google Scholar
• Jacobsen, H. K., & Zvingilaite, E. (2010). Reducing the market impact of large shares of intermittent energy in Denmark. Energy Policy, 38(7), 3403–3413. doi: 10.1016/j.enpol.2010.02.014
   Web of Science ®Google Scholar
• Janssen, M., & Wobben, M. (2009). Electricity pricing and market power—Evidence from Germany. European Transactions on Electrical Power, 19(4), 591–611. doi: 10.1002/etep.348
   Google Scholar
• Johnson, N. L. (1949a). Systems of frequency curves generated by method of translation. Biometrika, 36(Pt. 1–2), 149–176.
   PubMed Web of Science ®Google Scholar
• Johnson, N. L. (1949b). Bivariate distributions based on simple translation systems. Biometrika, 36(3–4), 297–304. doi: 10.2307/2332669
   PubMed Web of Science ®Google Scholar
• Jónsson, T., Pinson, P., & Madsen, H. (2010). On the market impact of wind energy forecasts. Energy Economics, 32(2), 313–320. doi: 10.1016/j.eneco.2009.10.018
   Web of Science ®Google Scholar
• Jónsson, T., Pinson, P., Madsen, H., & Nielsen, H. A. (2014). Predictive densities for day-ahead electricity prices using time-adaptive quantile regression. Energies, 7(9), 5523–5547. doi: 10.3390/en7095523
   Web of Science ®Google Scholar
• Juban, R., Ohlsson, H., Maasoumy, M., Poirier, L., & Kolter, J. Z. (2016). A multiple quantile regression approach to the wind, solar, and price tracks of GEFCom2014. International Journal of Forecasting, 32(3), 1094–1102. doi: 10.1016/j.ijforecast.2015.12.002
   Web of Science ®Google Scholar
• Karakatsani, N. V., & Bunn, D. W. (2010). Fundamental and behavioural drivers of electricity price volatility. Studies in Nonlinear Dynamics & Econometrics, 14(4), 1–40. doi: 10.2202/1558-3708.1657
   Web of Science ®Google Scholar
• Kaza, N. (2010). Understanding the spectrum of residential energy consumption: A quantile regression approach. Energy Policy, 38(11), 6574–6585. doi: 10.1016/j.enpol.2010.06.028
   Web of Science ®Google Scholar
• Keles, D., Genoese, M., Möst, D., & Fichtner, W. (2012). Comparison of extended mean-reversion and time series models for electricity spot price simulation considering negative prices. Energy Economics, 34(4), 1012–1032. doi: 10.1016/j.eneco.2011.08.012
   Web of Science ®Google Scholar
• Keles, D., Genoese, M., Möst, D., Ortlieb, S., & Fichtner, W. (2013). A combined modeling approach for wind power feed-in and electricity spot prices. Energy Policy, 59, 213–225. doi: 10.1016/j.enpol.2013.03.028
   Web of Science ®Google Scholar
• Ketterer, J. C. (2014). The impact of wind power generation on the electricity price in Germany. Energy Economics, 44, 270–280. doi: 10.1016/j.eneco.2014.04.003
   Web of Science ®Google Scholar
• Kiesel, R., & Paraschiv, F. (2017). Econometric analysis of 15-minute intraday electricity prices. Energy Economics, 64, 77–90. doi: 10.1016/j.eneco.2017.03.002
   Web of Science ®Google Scholar
• Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54(1–3), 159–178. doi: 10.1016/0304-4076(92)90104-Y
   Web of Science ®Google Scholar
• Lee, C. C., & Zeng, J. H. (2011). The impact of oil price shocks on stock market activities: Asymmetric effect with quantile regression. Mathematics and Computers in Simulation, 81(9), 1910–1920. doi: 10.1016/j.matcom.2011.03.004
   Web of Science ®Google Scholar
• Lin, J. W., & McLeod, A. I. (2006). Improved Peňa–Rodriguez portmanteau test. Computational Statistics & Data Analysis, 51(3), 1731–1738. doi: 10.1016/j.csda.2006.06.010
   Web of Science ®Google Scholar
• Lindström, E., & Regland, F. (2012). Modeling extreme dependence between European electricity markets. Energy Economics, 34(4), 899–904. doi: 10.1016/j.eneco.2012.04.006
   Web of Science ®Google Scholar
• Liu, B., Nowotarski, J., Hong, T., & Weron, R. (2017). Probabilistic load forecasting via quantile regression averaging on sister forecasts. IEEE Transactions on Smart Grid, 8(2), 730–737.
   Web of Science ®Google Scholar
• Maciejowska, K., & Nowotarski, J. (2016). A hybrid model for GEFCom2014 probabilistic electricity price forecasting. International Journal of Forecasting, 32(3), 1051–1056. doi: 10.1016/j.ijforecast.2015.11.008
   Web of Science ®Google Scholar
• Maciejowska, K., Nowotarski, J., & Weron, R. (2016). Probabilistic forecasting of electricity spot prices using factor quantile regression averaging. International Journal of Forecasting, 32(3), 957–965.
   Web of Science ®Google Scholar
• Martinez-Anido, C. B., Brinkman, G., & Hodge, B. M. (2016). The impact of wind power on electricity prices. Renewable Energy, 94, 474–487. doi: 10.1016/j.renene.2016.03.053
   Web of Science ®Google Scholar
• Mjelde, J. W., & Bessler, D. A. (2009). Market integration among electricity markets and their major fuel source markets. Energy Economics, 31(3), 482–491. doi: 10.1016/j.eneco.2009.02.002
   Web of Science ®Google Scholar
• Moreira, R., Bessa, R., & Gama, J. (2016). Probabilistic forecasting of day-ahead electricity prices for the Iberian electricity market. In 2016 13th International Conference on the European Energy Market (EEM) (pp. 1–5). Porto: IEEE. doi: 10.1109/EEM.2016.7521226
   Google Scholar
• Mosquera-López, S., Uribe, J. M., & Manotas-Duque, D. F. (2017). Nonlinear empirical pricing in electricity markets using fundamental weather factors. Energy, 139, 594–605. doi: 10.1016/j.energy.2017.07.181
   Web of Science ®Google Scholar
• Murray, B. (2009). Power markets and economics: Energy costs, trading, emissions. Chichester, UK: Wiley.
   Google Scholar
• Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 59(2), 347–370. doi: 10.2307/2938260
   Web of Science ®Google Scholar
• Newey, W. K., & West, K. D. (1994). Automatic lag selection in covariance matrix estimation. The Review of Economic Studies, 61(4), 631–653. doi: 10.2307/2297912
   Web of Science ®Google Scholar
• Nowotarski, J., & Weron, R. (2014). Merging quantile regression with forecast averaging to obtain more accurate interval forecasts of Nord Pool spot prices. In 11th International Conference on the European Energy Market (EEM14) (pp. 1–5). Krakow: IEEE. doi: 10.1109/EEM.2014.6861285
   Google Scholar
• Nowotarski, J., & Weron, R. (2015). Computing electricity spot price prediction intervals using quantile regression and forecast averaging. Computational Statistics, 30(3), 791–803. https://link.springer.com/article/10.1007%2Fs00180-014-0523-0
   Web of Science ®Google Scholar
• Nowotarski, J., & Weron, R. (2018). Recent advances in electricity price forecasting: A review of probabilistic forecasting. Renewable and Sustainable Energy Reviews, 81, 1548–1568. doi: 10.1016/j.rser.2017.05.234
   Web of Science ®Google Scholar
• Osborn, D. R., Chui, A. P., Smith, J. P., & Birchenhall, C. R. (1988). Seasonality and the order of integration for consumption. Oxford Bulletin of Economics and Statistics, 50(4), 361–377. doi: 10.1111/j.1468-0084.1988.mp50004002.x
   Web of Science ®Google Scholar
• Paraschiv, F., Erni, D., & Pietsch, R. (2014). The impact of renewable energies on EEX day-ahead electricity prices. Energy Policy, 73, 196–210. doi: 10.1016/j.enpol.2014.05.004
   Web of Science ®Google Scholar
• Peña, D., & Rodríguez, J. (2002). A powerful portmanteau test of lack of fit for time series. Journal of the American Statistical Association, 97(458), 601–610. doi: 10.1198/016214502760047122
   Web of Science ®Google Scholar
• Portnoy, S., & Koenker, R. (1997). The Gaussian hare and the Laplacian tortoise: Computability of squared-error versus absolute-error estimators. Statistical Science, 12(4), 279–300. doi: 10.1214/ss/1030037960
   Web of Science ®Google Scholar
• Sapio, A. (2019). Greener, more integrated, and less volatile? A quantile regression analysis of Italian wholesale electricity prices. Energy Policy, 126, 452–469. doi: 10.1016/j.enpol.2018.10.017
   Web of Science ®Google Scholar
• Sensfuß, F., Ragwitz, M., & Genoese, M. (2008). The merit-order effect: A detailed analysis of the price effect of renewable electricity generation on spot market prices in Germany. Energy Policy, 36(8), 3086–3094. doi: 10.1016/j.enpol.2008.03.035
   Web of Science ®Google Scholar
• Sul, D., Phillips, P. C., & Choi, C. Y. (2005). Prewhitening bias in HAC estimation. Oxford Bulletin of Economics and Statistics, 67(4), 517–546. doi: 10.1111/j.1468-0084.2005.00130.x
   Web of Science ®Google Scholar
• Uniejewski, B., Marcjasz, G., & Weron, R. (2018). On the importance of the long-term seasonal component in day-ahead electricity price forecasting: Part II—Probabilistic forecasting. Energy Economics, 79, 171–182. doi: 10.1016/j.eneco.2018.02.007.
   Web of Science ®Google Scholar
• Uniejewski, B., Nowotarski, J., & Weron, R. (2016). Automated variable selection and shrinkage for day-ahead electricity price forecasting. Energies, 9(8), 621. doi: 10.3390/en9080621
   Web of Science ®Google Scholar
• Weron, R. (2014). Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4), 1030–1081. doi: 10.1016/j.ijforecast.2014.08.008
   Web of Science ®Google Scholar
• Woo, C. K., Horowitz, I., Moore, J., & Pacheco, A. (2011). The impact of wind generation on the electricity spot-market price level and variance: The Texas experience. Energy Policy, 39(7), 3939–3944. doi: 10.1016/j.enpol.2011.03.084
   Web of Science ®Google Scholar
• Worthington, A. C., & Higgs, H. (2017). The impact of generation mix on Australian wholesale electricity prices. Energy Sources, Part B: Economics, Planning, and Policy, 12(3), 223–230. doi: 10.1080/15567249.2015.1060548
   Web of Science ®Google Scholar
• Würzburg, K., Labandeira, X., & Linares, P. (2013). Renewable generation and electricity prices: Taking stock and new evidence for Germany and Austria. Energy Economics, 40, S159–S171. doi: 10.1016/j.eneco.2013.09.011
   Web of Science ®Google Scholar
• Zachmann, G. (2013). A stochastic fuel switching model for electricity prices. Energy Economics, 35, 5–13. doi: 10.1016/j.eneco.2012.06.019
   Web of Science ®Google Scholar
• Ziel, F., & Steinert, R. (2018). Probabilistic mid-and long-term electricity price forecasting. Renewable and Sustainable Energy Reviews, 94, 251–266. doi: 10.1016/j.rser.2018.05.038
   Web of Science ®Google Scholar
• Ziel, F., Steinert, R., & Husmann, S. (2015). Efficient modeling and forecasting of electricity spot prices. Energy Economics, 47, 98–111. doi: 10.1016/j.eneco.2014.10.012
   Web of Science ®Google Scholar