Abstract
In Germany, substantial drops in wholesale power prices have become a regular phenomenon. While such price drops have far-reaching implications for the functioning of the power market, their underlying determinants remain poorly understood. To fill this gap, we propose a Markov regime-switching model to investigate low-price events at the European Power Exchange. Our analysis focuses on the role of energy policies that promote renewable energies and have led to significant reductions of nuclear capacities after the Fukushima accident. We find that high electricity infeed from renewable sources increases negative price spike probabilities, while the decommissioning of nuclear plants under the Nuclear Moratorium had an opposing effect. Simulations of market outcomes under different energy policies indicate that reaching ambitious renewable energy targets increases the frequency of low-price events and compromises the financial viability of conventional generation units, while a nuclear phase-out or an increase in storage capacities mitigates these effects.
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Notes
Data on the coal prices (API 2 index) was obtained using Thomson Reuters Datastream. As the API 2 index is not given for weekends, the values for weekend days are calculated as the average of the preceeding Friday and the succeeding Monday.
As initial values for the filter inferences we assume \((0.5 , 0.5)'\) for period 1. For the starting value needed to approximate the latent \(p_{t-1}^b\), we set \(E(p_{1}^b)= \mu _1\). To avoid non-defined functional values, we reparameterize the parameters \(\theta \), \(\sigma ^b\) and \(\sigma ^l\). As the log-likelihood function turns out to have multiple local maxima, we randomly draw 50 starting values for the estimation routine and choose the coefficient estimates that lead to the highest log-likelihood.
Using the functional form as displayed in Eq. (1), such changes in residual load can be calculated as: \(\Delta resload_t = (\hat{c}_b/\hat{b}_b) \cdot (nuclear_{af}-nuclear_{be})\), where \(nuclear_{af}\) (\(nuclear_{be}\)) corresponds to the average nuclear capacities after (before) the Nuclear Moratorium and \(\hat{c}_b\) and \(\hat{b}_b\) correspond to the estimates of the respective model parameters.
Variable cost are composed of fuel cost, \(\text {CO}_2\) emission cost and operations and maintenance cost (O&M). For hard coal-fired power plants with 1970 (2010) technology we assume (Klaus et al. 2009; IFEU 2007): heat rates of 36% (46%), specific \(\text {CO}_2\) emission rates of 0.939 t/MWh (0.735 t/MWh), O&M cost of 1 EUR/MWh as well as coal prices (API 2) as introduced in Table 1. For lignite-fired power plants with 1970 (2010) technology we assume (Klaus et al. 2009; BKartA 2011): heat rates of 36% (46%), specific \(\text {CO}_2\) emission rates of 1.263 t/MWh (0.940 t/MWh), combined fuel and O&M cost of 10 EUR/MWh (4 EUR/MWh). As the \(\text {CO}_2\) emission price, we use the price of carbon emission futures due in December 2016 (CFI2Z6).
References
Andor, M., Flinkerbusch, K., Janssen, M., Libeau, B., & Wobben, M. (2010). Negative Strompreise und der Vorrang Erneuerbarer Energien. Zeitschrift für Energiewirtschaft, 34, 91–99.
Andor, M., & Voss, A. (2016). Optimal renewable-energy promotion: Capacity subsidies vs. generation subsidies. Resource and Energy Economics, 45, 144–158.
Bierbrauer, M., Menn, C., Rachev, S. T., & Trück, S. (2007). Spot and derivative pricing in the EEX power market. Journal of Banking & Finance, 31(11), 3462–3485.
BKartA. (2011). Sektoruntersuchung Stromerzeugung Großhandel. Bundeskartellamt. http://www.bundeskartellamt.de.
BNetzA. (2016). Kraftwerksliste. Bundesnetzagentur. http://www.bundesnetzagentur.de.
BRD. (2010). Energiekonzept. Bundesregierung. http://www.bmu.de/files/pdfs/allgemein/application/pdf/energiekonzept_bundesregierung.pdf.
Diebold, F. X., Lee, J.-H., & Weinbach, G. (1994). Regime switching with time-varying transition probabilities. In C. Hargreaves (Ed.), Nonstationary time series analysis and cointegration (pp. 283–302). Oxford: Oxford University Press.
Dunn, B., Kamath, H., & Tarascon, J.-M. (2011). Electrical energy storage for the grid: A battery of choices. Science, 334(6058), 928–935.
Ethier, R., Mount, T. D. (1998). Estimating the volatility of spot prices in restructured electricity markets and the implications for option values. PSERC working paper series 31. http://www.pserc.wisc.edu/documents/publications/papers/1998_general_publications/pserc_31.pdf.
Hamilton, J. D. (1994). Time series analysis. Cambridge: Cambridge University Press.
Higgs, H., & Worthington, A. (2008). Stochastic price modeling of high volatility, mean-reverting, spike-prone commodities: The Australian wholesale spot electricity market. Energy Economics, 30(6), 3172–3185.
Huisman, R. (2008). The influence of temperature on spike probability in day-ahead power prices. Energy Economics, 30(5), 2697–2704.
Huisman, R., Huurman, C., & Mahieu, R. (2007). Hourly electricity prices in day-ahead markets. Energy Economics, 29(2), 240–248.
Huisman, R., & Jong, C. (2003). Option pricing for power prices with spikes. Energy & Power Risk Management, 7(11), 12–16.
Huisman, R., & Mahieu, R. (2003). Regime jumps in electricity prices. Energy Economics, 25(5), 425–434.
IFEU. (2007). Das Steinkohle-Kraftwerk Hamburg Moorburg und seine Alternativen. Institut für Energie- und Umweltforschung. http://www.ifeu.de.
Janczura, J., & Weron, R. (2010). An empirical comparison of alternate regime-switching models for electricity spot prices. Energy Economics, 32(5), 1059–1073.
Janczura, J., & Weron, R. (2011). Efficient estimation of Markov regime-switching models: An application to electricity spot prices. Advances in Statistical Analysis, 96(3), 385–407.
Jeon, W., Lamadrid, A., Mo, J., & Mount, T. (2015). Using deferrable demand in a smart grid to reduce the cost of electricity for customers. Journal of Regulatory Economics, 47(3), 239–272.
Jong, C. (2006). The nature of power spikes: A regime-switch approach. Studies in Nonlinear Dynamics and Econometrics, 10(3), 1–28.
Kim, C.-J. (1994). Dynamic linear models with Markov-switching. Journal of Econometrics, 60(1–2), 1–22.
Klaus, T., Loreck, C., & Müschen, K. (2009). Klimaschutz und Versorgungssicherheit: Entwicklung einer nachhaltigen Stromversorgung. Climate Change, 9(13), 1–94.
Knittel, C. R., & Roberts, M. R. (2005). An empirical examination of restructured electricity prices. Energy Economics, 27(5), 791–817.
Kosater, P., & Mosler, K. (2006). Can Markov regime-switching models improve power-price forecasts? Evidence from german daily power prices. Applied Energy, 83(9), 943–958.
Mount, T. D., Maneevitjit, S., Lamadrid, A. J., Zimmerman, R. D., & Thomas, R. J. (2012). The hidden system costs of wind generation in a deregulated electricity market. Energy Journal, 33(1), 161–186.
Mount, T. D., Ning, Y., & Cai, X. (2006). Predicting price spikes in electricity markets using a regime-switching model with time-varying parameters. Energy Economics, 28(1), 62–80.
Nicolosi, M. (2010). Wind power integration and power system flexibility—An empirical analysis of extreme events in germany under the new negative price regime. Energy Policy, 38(11), 7257–7268.
Weron, R. (2009). Heavy-tails and regime-switching in electricity prices. Mathematical Methods of Operations Research, 69(3), 457–473.
Weron, R., Bierbrauer, M., & Trück, S. (2004). Modeling electricity prices: Jump diffusion and regime switching. Physica A, 336(1/2), 39.
Zachmann, G. (2013). A stochastic fuel switching model for electricity prices. Energy Economics, 35, 5–13.
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The author thanks Mark Andor, Colin Vance and, in particular, Manuel Frondel as well as two anonymous reviewers for very helpful comments.
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Gerster, A. Negative price spikes at power markets: the role of energy policy. J Regul Econ 50, 271–289 (2016). https://doi.org/10.1007/s11149-016-9311-9
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DOI: https://doi.org/10.1007/s11149-016-9311-9