Impacts of the German support for renewable energy on electricity prices, emissions, and firms.

1. INTRODUCTION

Today, in most industrialized countries, renewable energy is supported by policy schemes to bring this favorable option to the market. When compared with fossil sources, major advantages attributed to renewable energies include low carbon emissions, sustainability, and enhanced security of supply. Unfortunately, with the exception of long-established large hydropower, renewable energies come at a high price. The hope of the industrial policy makers is that the renewable energy technologies will break even once they are more developed and the external effects of C[O.sub.2] emissions are priced in. Therefore, Germany and many other European countries grant a so-called feed-in tariff (FIT) to certain renewable energy technologies. The FIT obliges the established electricity sector to accept any amount of electricity provided by renewable energy producers at the politically predetermined tariff, while its burden is distributed among suppliers. In addition, the European Emission Trading System (ETS) creates a price for carbon emissions.

The ETS and the FIT do not act independently of each other. On the one hand, the ETS decreases the cost disadvantage of renewable energy supply (RES) and, thus, reduces the costs of renewable support. On the other hand, the support of RES substitutes conventional electricity production and subsequent emissions, leading to a reduction of the emission permit price. Furthermore, these interactions take place on a market that is suspect to market power which influences the channels of the submission of the different price effects. Therefore, our paper elaborates in a quantitative setting the feed backs of the ETS and the influence of market power on the functioning of the FIT in Germany.

Two strands of the literature are of special relevance to our study. The first stream of publications analyzes the compatibility of multiple policy instruments on energy markets. This literature can be separated into analytical and numerical investigations, of which the majority uses analytical models. Amundsen (2001) uses a partial equilibrium model to investigate the interaction of the ETS with green certificates, which create a certain renewable energy quota by means of a market system. He derives comparative static results and shows that trade in electricity matters for the effects of a tightening of the ETS's emission cap on green certificate prices. Morthorst (2001) develops a framework in which he analyzes the interaction of an ETS with the effects of internationally tradable green certificates. He finds that in the absence of an ETS, international trade in green certificates will be biased towards domestic capacity expansion, if a national value is attributed to the induced emission reduction. In a similar three country model, Morthorst (2003) analyzes the promotion of renewable energy usage by alternative instruments and derives results which suggest that renewable energy support schemes are questionable climate policy instruments when an ETS is present. He suspects that a coordinated policy would be more efficient, i.e. the ETS should be tightened if more renewable electricity is induced by other policies. Jensen and Skytte use static models to analyze the impact of green certificates on electricity prices (2002) and the combination of green certificates with an ETS when an emission goal and a renewable energy goal are simultaneously targeted (2003). They find that the effect of a simple green certificate market on electricity prices is ambiguous and that the optimal combination of instruments to reach two goals simultaneously depends on the cost structures. In contrast to analytical treatments, little work has utilized numerical models. One example is provided by Rathmann (2007), who analyzes the support for renewable energy created by the German feed-in tariff by using a model in which he applies assumptions on the cost structure based on real world data. He shows that renewable energy support can reduce electricity prices for certain parameter values.

The second stream of related literature applies numerical models to the analysis of electricity markets under the suspicion of market power. Models that focus on the problem of the presence of market power naturally apply ex post analysis and compare competitive benchmark results with observed market outcomes. An early example is the study of the British electricity spot market of Wolfram (1999) which found significant mark-ups priced on top of marginal costs in the years 1992, 1993, and 1994. However, the full mark-up that a static Cournot model predicted was not reached. Similar results have been found by Borenstein et al. (2002), and Joskow and Kahn (2002) who applied competitive benchmark methods for the Californian electricity market in the year 2000. Again, the observed prices could not be explained only by costs. The paper by von Hirschhausen et al. (2007) provides a comprehensive literature survey on further studies that find market power on electricity markets with a focus on recent developments on the German market. In addition, the authors show with several methods that the German electricity prices still indicate imperfections ex post. The methodology applied in these studies is, however, subject to criticism, e.g. by Swider et al. (2007). One major line of attack points to the lack of detail, especially in regard to start-up costs in the construction of the marginal costs curves. The analysis conducted by Weigt et al. (2008), however, explicitly accounts for the costs induced by start-up processes with a model of high time resolution and still finds significant deviations from the observed market outcomes, amounting on average to mark-ups of eleven percent in baseload, and to thirty percent in peak load periods. These findings suggest that market power is still an important feature, at least of the German electricity market. Therefore, we develop a model that calculates an oligopolistic benchmark together with results that would occur under perfect competition. This procedure allows us to assess the "space of possible static, non cooperative outcomes" as Bushnell et al. (2008) put it.

Many studies of electricity markets take oligopolistic behavior into account. A ground-breaking work is the paper of Green and Newberry (1992) who use a model of duopolistic firms without transmission restrictions which is calibrated to reproduce historic seasonal market outcomes as close as possible. They find that market power has been an important market determinant of the British electricity market in the years 1988 and 1989, and that a division of dominant firms would most likely result in preferable market outcomes.

When one focuses on the electricity grid properties in the presence of market power, a high time resolution and a complex regional resolution may become necessary. An early example of such analysis is provided by Jing-Yuan and Smeers (1999) who develop spatial oligopolistic electricity models with Cournot competition on the producer side and regulated transmission prices. They find that the restrictions of the international transmission lines between France, Italy and Germany, an increased competition is unlikely to emerge from the electricity market liberalization in these countries. In a similar vein, Hobbs (2001) compares market designs with and without arbitrageurs between supply and demand hubs in a Cournot-Nash framework that takes into account both Kirchhoff's laws in a complex nodal structure. He computes unique solutions under the simplifying assumption of price-taking behavior of producers in regard to transmission prices. In a small example for the UK he finds ex ante a welfare enhancing effect of the presence of arbitrageurs in the case of assumed Cournot behavior in regard to output.

In a model Comparison study, Neuhoff et. al (2005) show the variety of outcomes that Cournot models of electricity generation and transmission can yield if transmission is itself subject to market power. Although under perfect competition the model results almost match each other, largely different results are obtained which are contingent on minor differences in specification, e.g. the timing of the game. Since our paper focuses on the potential influence of market power on the producer side of the market, we follow the approach of Hobbs (2001) and assume price taking behavior in regard to transmission prices.

A similar specification of transmission pricing has been applied in Amundsen and Bergman (2002). They investigate the impact of cross-ownership on the Nordic power market in a Cournot framework with competitive fringe firms, and cross-border transmission constraints. They find that increased cross-ownership might re-establish market power. A more recent example for market power analysis in electricity markets is provided by Bushnell et al. (2008), who investigate ex post the impact of vertical structures in a hourly resolved model for different markets in the US and find that the presence of vertical arrangements are an important feature for the evaluation of market conduct. They stress the importance of thorough market analysis not only in regard to horizontal market structure, but also in regard to vertical structures prior to ex post policy recommendations. Clearly, this caveat applies also to our analysis.

Few numerical models have so far addressed questions related to emission policies in oligopolistic frameworks. Among these, the model used in Lise et al. (2006) is of special relevance for our study. This model is developed on the basis of the original model documented in Kemfert (2007) that has been applied to the investigation of the liberalization of the German electricity market. In comparison with the original model, Lise et al. introduce several refinements. They extend the country coverage of the model to the Northwestern European electricity market, including Belgium, Denmark, Finland, France, Germany, the Netherlands, Norway and Sweden. In addition, an emission cap for the electricity sector's C[O.sub.2] has been implemented. Moreover, the technological richness in the power plant representation has been enhanced, and emissions of different pollutants are considered. Finally, the model incorporates peak and baseload demand.

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