IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

Modelling Germany´s Energy Transition and its Potential Effect on European Electricity Spot Markets

Listed author(s):
  • Lilian de Menezes
  • Melanie A. Houllier
Registered author(s):

    The German Energiekonzept (Energy Concept) was proposed in 2010 with the goal of making the country one of the world’s most energy efficient and environmentally friendly economies (Bundesregierung, 2011). One year later, as a reaction to the multiple reactor meltdowns in Fukushima, this strategy was reinforced with a broad consensus within the German government to implement its Atomaustiegsgestz (Nuclear Phase-Out Act), by closing immediately eight nuclear power plants and then the remaining nine until 2022 (Bundesregierung, 2011). Subsequently, the Renewable Energy Source Act 2012 (RESA, 2012) aims to increase electricity generated from renewable sources to at least 35% by 2020 and to at least 80% by the year 2050. The RESA 2012 reaffirmed the basic principles of the feed-in tariff policy, which prioritizes renewable energy sources, the pledge to connect all renewable producers to the grid and the guarantee of a favourable unit price. This paper examines the potential impact of wind generated electricity produced in Germany on other European electricity markets, by employing MGARCH (multivariate generalized autoregressive conditional heteroscedasticity) models with constant and time-varying correlations for daily data. The interrelationship of electricity spot prices of APX-ENDEX (UK and Netherlands), Belpex (Belgium), EPEX (Germany and Switzerland), OMEL (Spain and Portugal), Nord Pool (Finland, Denmark and Norway) and Powernext (France) with wind penetration induced by the German system is studied from November 2009 to October 2012, thus covering the period before and after the closures of eight nuclear power plants. Literature Studies, such as Gross et al. (2006), Holttinen at al. (2009) and Smith et al. (2007) have highlighted the challenges associated with increased penetration levels of renewable energy sources as planned by the German government. There is, for example, a significant risk that a system with high wind power capacity will suffer electricity shortages and even blackouts. Other studies have shown that electricity spot market prices decrease to varying extents with the in-feed of wind generated electricity (see for example: Bode and Groscurt, 2006; Gil et al., 2012; Jacobsen and Zvingilaite, 2010; Neubarth et al. ,2006; Saenz de Miera et al., 2008; Sensfuß et al ,2008). The reduction of electricity spot prices is attributed to the cheaper wind generated electricity displacing offers from generators whose technologies have higher marginal costs (Sensfuß et al., 2008; Woo et al., 2011). Nevertheless, this positive effect may come at the cost of an overall increase in spot price volatility, due to the combined effect of non-storability of electricity and the high volatility of wind power (Woo et al., 2011; Milstein and Tishler, 2011; Green and Vasilakos, 2010). Despite integrated electricity markets being a promising instrument when managing intermittent sources of energy, the few studies that assess the volatility interrelationships among electricity markets have largely neglected the potential impact of renewables. Indeed, Bosco et al. (2007) remark that ‘[...] post-reform European price series have generally been studied in isolation and the issue of the interdependency in the price dynamics of neighbouring markets has largely been ignored.’ (p. 2). To date, a small body of literature applied a multivariate framework to electricity price volatilities (Worthington et al. (2005), Higgs (2009), Le Pen and Sévi (2010), Veka et al. (2012)). In this context, the present study aims to assess the potential effects of Germany´s energy transition on level and volatility of electricity spot prices in Germany and in other European countries. Germany serves as a statuary example, because of its increasing reliance on and investments in wind generated electricity as well as the size and importance of the German electricity market in Europe. Constant Conditional Correlation Bollerslev (1990) proposed a Constant Conditional Correlation MGARCH model (CCC), which has been preferred in empirical research over the BEKK specification because of its computational simplicity. This model is based on the decomposition of the conditional covariance matrix into conditional standard deviations and correlations. The conditional correlation matrix is time invariant and the conditional covariance matrix can be written for each time, t, as follows: H_t=D_t ΓD_t=ρ_ij (h_iit h_jjt )^(1/2) (1) Where 1≤i≤j≤K,t=1,…,N; K is the number of variables in the model and N is the number of observations in the estimation period; D_t=diag(h_11t^(1/2)…h_KKt^(1/2)), (2) Γ=ρ_ij (3) h_iit is the conditional variance of a univariate GARCH model and Γ is the symmetric positive definite constant conditional correlation matrix, with ρ_ii=1 ,∀i. Dynamic Conditional Correlation Although the CCC model overcomes the shortcomings of the BEKK and VEC models, the assumption of constant correlations may be too restrictive (Minović, 2009). Tse and Tsui (2002) and Engle (2002) therefore extended the CCC models to dynamic conditional correlation models (DCC), by including a time dependent conditional correlation matrix (Γ_t) and thus the conditional covariance matrix becomes: H_t=D_t Γ_t D_t (4) Where D_(t ) and h_iit are as defined in equation (2). Following, Tse and Tsui (2002) the conditional correlation matrix is given by: Γ_t=(1-θ_1-θ_2 )Γ+θ_2 Γ_(t-1)+θ_1 Ψ_(t-1) , (5) where 1≤i≤j≤K and θ_1 and θ_2 are non-negative constants such thatθ_1+θ_2<1 and. Γ, is the KxK symmetric positive definite constant parameter matrix with ρ_ii=1 for all i. Ψ_(t-1) is a function of the lagged standardized residuals ξ_it, and its ijth element can be denoted as: Ψ_(t-1,ji)=(∑_(m=1)^M▒ξ_(i,t-m) ξ_(j,t-m))/√((∑_(m=1)^M▒ξ_(i,t-m)^2 )(∑_(m=1)^M▒〖ξ_(j,t-m)^2)〗) where ξ_it=e_it/h_iit^(1/2) ` (6) Engle (2002) proposed the following alternative formulation: Γ_t=diag (q_11t^(-1/2)…q_KKt^(-1/2) )((1-θ_1-θ_2 ) Q ̅+θ_1 ξ_(t-1) ξ_(t-1)^'+θ_2 Q_(t-1) )diag(q_11t^(-1/2)…q_KKt^(-1/2) ), (7) where Q ̅ is the KxK unconditional correlation matrix of ξ_t, and θ_1 and θ_2 are non-negative parameters satisfying θ_1+θ_2<1 (Higgs 2009). The results of the MGARCH models indicate positive cross-market and lagged spillovers, as well as significant reduction in electricity spot prices with increasing wind penetration. Positive time-varying correlations between spot market volatilities are found for markets with substantial shared interconnector capacity, and wind penetration volatility is negatively associated with electricity spot price fluctuations. All in all, this study provides evidence that decisions made by one state in the European Union regarding its electricity sector can impact on neighbouring electricity markets.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL:
    Download Restriction: no

    Paper provided by EcoMod in its series EcoMod2013 with number 5395.

    in new window

    Date of creation: 21 Jun 2013
    Handle: RePEc:ekd:004912:5395
    Contact details of provider: Postal:
    351 Pleasant Street, #357, Northampton MA 01060-3900 USA

    Phone: +1 413 586 3203
    Fax: +1 413 517 0900
    Web page:

    More information through EDIRC

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    in new window

    1. Frank A. Wolak, 2000. "Market Design and Price Behavior in Restructured Electricity Markets: An International Comparison," NBER Chapters,in: Deregulation and Interdependence in the Asia-Pacific Region, NBER-EASE Volume 8, pages 79-137 National Bureau of Economic Research, Inc.
    2. T Robinson, 2008. "The Evolution of Electricity Prices in The EU since the Single European Act," Economic Issues Journal Articles, Economic Issues, vol. 13(2), pages 59-70, September.
    3. Koopman, Siem Jan & Ooms, Marius & Carnero, M. Angeles, 2007. "Periodic Seasonal Reg-ARFIMAGARCH Models for Daily Electricity Spot Prices," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 16-27, March.
    4. Green, Richard & Vasilakos, Nicholas, 2010. "Market behaviour with large amounts of intermittent generation," Energy Policy, Elsevier, vol. 38(7), pages 3211-3220, July.
    5. Klinge Jacobsen, Henrik & Zvingilaite, Erika, 2010. "Reducing the market impact of large shares of intermittent energy in Denmark," Energy Policy, Elsevier, vol. 38(7), pages 3403-3413, July.
    6. Okimoto, Tatsuyoshi & Shimotsu, Katsumi, 2010. "Decline in the persistence of real exchange rates, but not sufficient for purchasing power parity," Journal of the Japanese and International Economies, Elsevier, vol. 24(3), pages 395-411, September.
    7. Bruno Bosco & Lucia Parisio & Matteo Pelagatti & Fabio Baldi, 2010. "Long-run relations in european electricity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 805-832.
    8. Terry Robinson, 2000. "Electricity pool prices: a case study in nonlinear time-series modelling," Applied Economics, Taylor & Francis Journals, vol. 32(5), pages 527-532.
    9. Higgs, Helen, 2009. "Modelling price and volatility inter-relationships in the Australian wholesale spot electricity markets," Energy Economics, Elsevier, vol. 31(5), pages 748-756, September.
    10. Worthington, Andrew & Kay-Spratley, Adam & Higgs, Helen, 2005. "Transmission of prices and price volatility in Australian electricity spot markets: a multivariate GARCH analysis," Energy Economics, Elsevier, vol. 27(2), pages 337-350, March.
    11. Klessmann, Corinna & Nabe, Christian & Burges, Karsten, 2008. "Pros and cons of exposing renewables to electricity market risks--A comparison of the market integration approaches in Germany, Spain, and the UK," Energy Policy, Elsevier, vol. 36(10), pages 3646-3661, October.
    12. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    13. Hooper, Elizabeth & Medvedev, Andrei, 2009. "Electrifying integration: Electricity production and the South East Europe regional energy market," Utilities Policy, Elsevier, vol. 17(1), pages 24-33, March.
    14. Gil, Hugo A. & Gomez-Quiles, Catalina & Riquelme, Jesus, 2012. "Large-scale wind power integration and wholesale electricity trading benefits: Estimation via an ex post approach," Energy Policy, Elsevier, vol. 41(C), pages 849-859.
    15. Matteo Pelagatti & Bruno Bosco & Lucia Parisio & Fabio Baldi, 2007. "A Robust Multivariate Long Run Analysis of European Electricity Prices," Working Papers 2007.103, Fondazione Eni Enrico Mattei.
    16. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
    17. Zachmann, Georg, 2008. "Electricity wholesale market prices in Europe: Convergence?," Energy Economics, Elsevier, vol. 30(4), pages 1659-1671, July.
    18. Creti, Anna & Fumagalli, Eileen & Fumagalli, Elena, 2010. "Integration of electricity markets in Europe: Relevant issues for Italy," Energy Policy, Elsevier, vol. 38(11), pages 6966-6976, November.
    19. Ralf Becker & Stan Hurn & Vlad Pavlov, 2007. "Modelling Spikes in Electricity Prices," The Economic Record, The Economic Society of Australia, vol. 83(263), pages 371-382, December.
    20. Ochoa, Patricia & van Ackere, Ann, 2009. "Policy changes and the dynamics of capacity expansion in the Swiss electricity market," Energy Policy, Elsevier, vol. 37(5), pages 1983-1998, May.
    21. Helen Higgs, 2009. "Modelling price and volatility inter-relationships in the Australian wholesale spot electricity markets," Discussion Papers in Economics economics:200904, Griffith University, Department of Accounting, Finance and Economics.
    22. De Vany, Arthur S. & Walls, W. David, 1999. "Cointegration analysis of spot electricity prices: insights on transmission efficiency in the western US," Energy Economics, Elsevier, vol. 21(5), pages 435-448, October.
    23. Sensfuß, Frank & Ragwitz, Mario & Genoese, Massimo, 2008. "The merit-order effect: A detailed analysis of the price effect of renewable electricity generation on spot market prices in Germany," Energy Policy, Elsevier, vol. 36(8), pages 3076-3084, August.
    24. Le Pen, Yannick & Sévi, Benoît, 2010. "Volatility transmission and volatility impulse response functions in European electricity forward markets," Energy Economics, Elsevier, vol. 32(4), pages 758-770, July.
    25. repec:dau:papers:123456789/5450 is not listed on IDEAS
    26. Fong Chan, Kam & Gray, Philip, 2006. "Using extreme value theory to measure value-at-risk for daily electricity spot prices," International Journal of Forecasting, Elsevier, vol. 22(2), pages 283-300.
    27. 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, Elsevier, vol. 39(7), pages 3939-3944, July.
    28. Shao, Xiaofeng & Wu, Wei Biao, 2007. "Local Whittle Estimation Of Fractional Integration For Nonlinear Processes," Econometric Theory, Cambridge University Press, vol. 23(05), pages 899-929, October.
    29. Teusch, Jonas, 2012. "Renewables and the EU Internal Electricity Market: The case for an arranged marriage," CEPS Papers 6733, Centre for European Policy Studies.
    30. Bunn, Derek W. & Gianfreda, Angelica, 2010. "Integration and shock transmissions across European electricity forward markets," Energy Economics, Elsevier, vol. 32(2), pages 278-291, March.
    31. Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-362, July.
    32. Milstein, Irena & Tishler, Asher, 2011. "Intermittently renewable energy, optimal capacity mix and prices in a deregulated electricity market," Energy Policy, Elsevier, vol. 39(7), pages 3922-3927, July.
    33. Helen Higgs & Andrew C. Worthington, 2005. "Systematic Features of High-Frequency Volatility in Australian Electricity Markets: Intraday Patterns, Information Arrival and Calendar Effects," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 23-42.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:ekd:004912:5395. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Theresa Leary)

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.