Understanding the Determinants of Electricity Prices and the Impact of the German Nuclear Moratorium in 2011.

AuthorThoenes, Stefan
  1. INTRODUCTION

    Electricity is a homogeneous good that cannot be stored at reasonable economic costs. However, the demand is highly seasonal and needs to be satisfied at all times. Hence, it is most efficient to generate electricity with a mixture of various technologies with different properties regarding capital costs and marginal costs. These technologies also differ in terms of input fuels and carbon emissions.

    Therefore, how input price variations affect the electricity price critically depends on the marginal technology used; and the marginal technology used depends on the level of the residual demand. (1) The present paper tries to investigate exactly this effect. To illustrate the point, consider the "merit order," i.e., an ordering of fossil power plants from those with low marginal cost (like lignite or hard coal) to high marginal cost (natural gas). If the residual demand is low (e.g. because electricity demand is low in the night; or because there is a lot of wind feed-in), the marginal power plant will be a coal fired power plant, and we expect that changes in the gas price will not affect the electricity price. This will be the case only if demand is high. The approach in the present paper allows to identify how the fuel price effects vary with the size of the residual demand.

    This is analyzed empirically using data from the German electricity market and applying a semiparametric cointegration model. In order to measure how the fuel price sensitivity changes throughout the merit order, it is necessary to use a model that allows the parameters of the fuel price sensitivity to vary freely. The semiparametric varying smooth coefficient model, which was introduced by Hastie and Tibshirani (1993), allows for straightforward analysis of the relationship between fuel price sensitivity and load. The main advantage of the model is that the nature of the varying effect is directly derived from the data, which means that there is no need for ad-hoc assumptions or restrictive functional specifications. Recent work by Cai et al. (2009) and Xiao (2009) shows that such a model can be used to estimate the nonlinear functional coefficients of a cointegration relationship. The application of this estimator is novel for modeling the dynamics of electricity markets. This method indicates a technology switch from coal to gas fueled power plants at around 60 gigawatt (GW) average non-wind daily peak generation. The estimated input price sensitivities are used to simulate the merit order for different natural gas and carbon price scenarios. (2)

    The usefulness of this approach can be illustrated by analyzing a specific policy intervention like the German nuclear power suspension in March 2011. After the incident in Japan's Fukushima nuclear power plant, the German government decided to put the so called "Nuclear Moratorium" in place. Seven nuclear power plants, all built before 1980, had to be switched off from 03/15/2011 to 06/15/2011 to examine the security of these plants. After the announcement, the market reacted with immediate price increases of electricity, gas and carbon emission allowance futures. Using only these futures prices, the proposed model is able to split the electricity price increase into a fuel price component and a capacity effect. It is also possible to measure the expectations of the market for the period after the end of the moratorium. The results of the event study show that the market accounts for most of the capacity effect during the period of the moratorium and expects that several nuclear power plants remain closed. This expectation proved to be correct as all affected nuclear power plants were permanently decommissioned after the end of the moratorium.

    The approach in this paper relates to two distinct strands of the literature on empirical modeling of energy prices. The first strand focuses solely on the electricity market and tries to resemble the stochastic characteristics of the typical price patterns. Driven by capacity constraints, hourly and daily prices have a high volatility and spikes. There are also hourly, daily and monthly seasonalities that reflect demand patterns of consumers and industry. The two most prominent approaches are the "Mean Reverting Jump Diffusion Model" and the "Markov Regime Switch Model," which are both described by Weron et al. (2004). These models can also be extended by additionally accounting for fundamental factors like load (see Mount et al. (2006), Kanamura and Ohashi (2007)). However, this class of models has the drawback that the relationship between the electricity price and input fuel prices is not analyzed.

    The second strand of literature consists of studies that broadly analyze the interdependencies between different energy commodities, but fail to account for the aforementioned specific fundamentals of the electricity market. Mohammadi (2009) uses a vector error correction model (VECM) to analyze the long-term relationship between fuel prices and electricity prices in the United States. Mjelde and Bessler (2009) indicate that fossil fuels are weakly exogenous and electricity prices adapt to re-establish the equilibrium. Similar results hold for the European electricity markets. Bosco et al. (2010) employ a set of robust tests to show that European electricity time series have a unit root and are cointegrated. Electricity prices seem to share a common trend with gas prices, but not with oil prices. Ferkingstad et al. (2011) also find that gas prices have strong instantaneous and lagged causal effects on electricity prices, while coal and oil prices are less important. Furthermore, coal, oil and gas prices are weakly exogenous. Fell (2010) finds evidence that the effect of fuel prices varies with the level of demand. The author estimates a VECM for the Scandinavian electricity spot market and several inputs. The short-term impact of the carbon price on the electricity price is higher in off-peak hours than in peak hours. Coulon and Howison (2009) account for this effect by directly modeling different parts of the supply stack. The actual bids are split into clusters, which are governed by different fuels.

    The present paper advances the current literature by showing how exactly the natural gas and carbon price sensitivities vary with load. It fills the gap between models that focus on idiosyncratic effects of the electricity market and models that focus broadly on interdependencies between energy markets. The remainder of this paper is organized as follows. Section 2 describes the data sets that are used for the analysis. Section 3 outlines the semiparametric varying coefficient cointegration model and discusses the empirical results. This part includes the semiparametric estimates of the gas and carbon price sensitivity functions as well as the predicted merit order simulation for different input price scenarios. In Section 4, the proposed semiparametric model is used to analyze the market impact of the German nuclear moratorium in March 2011. The conclusion is given in the final section.

  2. DATA

    This study focuses on electricity, natural gas and carbon prices in Germany. The data consists of daily observations from 2008/04/01 to 2010/09/29. All price time series were obtained from the European Energy Exchange (EEX). This analysis uses day-ahead base, peak and off-peak electricity prices on weekdays. The peak block covers the hours from 8 am to 8 pm, while the off-peak block covers the remaining time. The base block is the daily average price. Daily day-ahead EEX gas prices are quoted from July 2007 onwards. Both Gaspool and NetConnect Germany (NCG) contracts are traded, but I choose NCG because of the higher liquidity in this market. NCG gas prices are denominated in Euro/MWh and will be used as an indicator for the gas market as a whole. For carbon prices, the EEX Carbix index of the EU Emission Trading Scheme phase II is used. (3) All prices are transformed into their natural logarithms.

    The choice of price time series used for the analysis is driven by the consistency of both the geography of the German market and the exchange itself. However, the liquidity of the natural gas and carbon market at the EEX remains an issue for this study, which could potentially affect the results. While the Belgian natural gas hub Zeebrugge or the Dutch Title Transfer Facility (TTF) are sometimes considered more important markets with a higher liquidity, it is a priori not clear whether they are better proxies for the German natural gas price. Furthermore, due to a high convergence of European natural gas prices, as shown by Renou-Maissant (2012) or Asche et al. (2013), the choice of the natural gas price time series does not have a relevant impact on the results.

    Lignite, coal and oil prices are not included for several reasons. First, the oil fueled electricity generation capacity in Germany is rather small, as it is shown in Table 1. Moreover, the trading and transportation properties of the coal market do not match the daily frequency setup of this study. Lignite is not actively traded and is usually not the marginal technology, which also holds for nuclear power. Adjustments for electricity ex- and imports as well as reservoir power stations can be neglected, because the observed relationship between load, input prices and electricity prices implicitly accounts for their influence. Several comparable studies, including Fezzi and Bunn (2009) and Zachmann and von Hirschhausen (2008), choose a similar approach and focus on the cointegration relationship between electricity, gas and EU emission allowance prices. The analysis of detailed cross-commodity relationships for a system of all different energy commodities is not the aim of this study, but can be found in Ferkingstad et al. (2011) and Mjelde and Bessler (2009).

    Despite the fact that coal markets are biased towards over-the-counter trading and long-term contracts, there are some...

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