Efficient but sufficient support of all RES technologies in times of volatile raw energy prices

Increasing penetrations of renewable energy sources (RES) require both effective and efficient support schemes in order to keep additional consumer expenditures at a moderate level. A necessary precondition for the design of efficient RES support options is a precise forecast tool of future investment costs of RES technologies. Recent market observations have shown that not only technological learning influences RES technology costs but much more also volatile raw material prices hold a significant impact. Hence, this paper discusses the multi-factor learning curve approach for incorporation of steel price impacts on wind energy technologies, in particular wind onshore, into energy models. First results show a high correlation of the historic development of primary energy prices, especially coal prices, to the steel price and in last consequence to wind onshore investment costs. However, the historic development of wind energy technology prices can not only be described by the impact of volatile steel prices, as other parameters as market structure or power influence the investment price strongly, as well. Based on this result a qualitative discussion addresses the potential adjustments of RES support options in order to guarantee efficient but sufficient support of RES technology installations for investors and the society. Introduction and background In a European context, significantly increasing the share of renewable energy sources (RES) of gross final energy demand up to 2020 in order to meet the target (Directive 2009/28/EC) of 20% RES by 2020 is currently high on the agenda of European policy makers. This implies effective and efficient policy support measures whereas especially efficiency is determined by the real generation costs of renewable energy technologies versus the eligible total level of income from selling the produced energy [1]. Thus, an important parameter for efficient RES support schemes is the incorporation of expected evolution of generation costs of RES technologies. In this respect, historic energy models have only considered a dynamic development of technology investment costs, respectively generation costs, by taking into account technological learning based on cumulative production [2, 3]. However, recent market observations have shown volatile investment costs of several energy technologies in general, and RES technologies in particular. At the same time energy and raw material prices showed a comparatively high level of fluctuations. Consequently, it is the aim of this paper to identify and elaborate on key parameters (besides those associated with the well-established concept of technological learning) that influence the evolution of investment cost for RES technologies. Therefore, the approach of determining the future development of overall RES investment costs is discussed in detail and derived results are depicted, serving as a basis for further research in this field. Christian Panzer, Vienna University of Technology USAEE conference, Calgary Page 2 of 8 In general, this paper focus exemplarily on wind onshore technology although it is only one among many which shows the discussed impact of energy and raw material prices on RES investment costs (see Yu et al, 2010 [4] for Photovoltaics). Besides energy and raw material prices, several other parameters, as market power and strategic pricing, have had important impacts on technology investment costs but are beyond the scope of this research. The key driver of raw material prices with respect to wind onshore investment costs in the recent past has been the steel price. Again, steam coal and coking coal prices have steadily been influencing the steel price development to certain extent. However, especially coking coal recently showed strong price increases due to mining capacity shortages which has not been reflected in the same extend in the steel price development. Figure 1 depicts above mentioned relations between historic observations of the coal-, steeland wind onshore investment costs. Thus, in 2001 the decreasing steel price was also noted in decreasing wind investment costs whereas an increasing steel price in 2005 has led to higher wind investment costs. However, in certain years as 2008, a strongly increasing steel price did not affect the wind investment costs similarly, mainly caused by two events. The increase in the steel price was driven by the strong global economic growth before the crises whereas wind investment costs have peaked in the years before due to a high demand on wind onshore technologies, caused by favorable RES support schemes, accompanied by manufacturing shortages. Hence, not every change of investment costs can solely be explained by volatile raw material prices but certainly there is an empirical evidence of a strong impact of raw material prices on RES technology investment costs. Moreover, linkages between the historic coal price and the historic steel price development (in Euro 2006 values) become obvious from Figure 1, since steam coal and coking coal are the main input parameters for steel-making processes, besides iron ore [5]. Figure 1 Relative, historic deployment of wind onshore investment costs (Source: EWEA, 2010), steel price (Source: Steel Business Briefing) and coal price development (Source: European Commission); Index year 2000 = 100%; Methodology / practical implementation approach The application of the simulation tool Green-X [6] allows deriving efficient and effective support schemes of RES technologies. The model Green-X has been developed by the Energy Economics Group (EEG) at Vienna University of Technology in the research project “Green-X – Deriving optimal promotion strategies for increasing the share of RES-E in a dynamic European electricity market”, a joint European research project funded within the 5th framework program of the European Commission, DG Research (Contract No. ENG2-CT-2002-00607). Initially focused on the electricity sector, this tool and its database 0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200% 220% 240% 260% 280% 200

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