Deriving Future Support Schemes of RES, by Considering the Cost Evolution of RES Technologies

Efficient but sufficient support of all RES technologies in times of volatile raw energy prices Christian Panzer1, Gustav Resch1, Ric Hoefnagels², Martin Junginger² 1Energy Economics Group (EEG), Vienna University of Technology Gusshausstrasse 25-29/373-2, A-1040 Vienna, Austria Tel +43-1-58801-37360, Fax +43-1-58801-37397 Email [email protected] Web http://eeg.tuwien.ac.at ² Dept. Science, Technology and Society Copernicus Institute, Utrecht University Keywords: Renewable energy, modeling, efficient support, volatile raw material price Overview and Motivation In an 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. Thus, an important parameter for efficient RES support schemes is the incorporation of expected evolution of generation costs of RES technologies. Since this development is influenced by several different input factors, showing a historical volatile development, this paper puts special emphasis on the approach of determining the future development of overall RES investment costs and consequently deriving more cost-efficient support schemes for RES. In context, not only too high levels of support result in a low efficiency of the overall support scheme, but also too low levels of support schemes are dangerous since only limited installations of RES will be realized due to the missing incentive for investors. Therefore, this paper focuses on bridging the path between too high financial incentives and consequently increased consumer expenditures and too low incentives with only moderate additional RES installations. Consequently it is the final aim of this research to reshape support schemes of RES technologies by taking into account besides the technological learning effects also the influence of raw material prices in order to allow adjusting the incentives level of RES technologies in an appropriate manner. Methodology / practical implementation approach Building on the current status of the renewable energy policy simulation tool Green-X (Huber et al, 2004) – a simulation tool in order to derive optimal support strategies for RES in a dynamic energy market – it is the task of this work to integrate the impacts of raw oil price, as well as silicon, steel or concrete prices on the different renewable energy technologies and their related costs. Therefore the following approach is applied: Two factor learning curve, considering the impact of energy and raw material prices: Overall, the one factor learning curve approach, considering certain cost reductions with each doubling of cumulative installations, is extended to a two factor learning curve, additionally considering the influence of raw material prices. Depending on the specific energy technology, the most important materials will be considered in the model, according to Eq(1), in order to calculate future cost developments of the energy technologies. Since the definition of the specific raw material price is an exogenous parameter for the simulation model, the reference source has to be decided carefully, especially caused by the fact that recent observations have shown volatile energy and raw material prices. b  x   t  LCP Eq(1) cxt   cx0   CP  x0  In Eq(1) the product of the first two terms represents a certain cost reduction based on technological learning with each doubling of cumulative installations and the last term indicates the positive or negative impact of raw material prices on RES technology costs, depending on the raw material price. In this context, arguments exist to link the development of the raw material prices to the development of the oil price, which showed a high correlation in the recent past, but on the other hand the increase of i.e. the real steel price was mainly driven by the tremendous demand increase in China and India and only to a minor extend influenced by the oil price. Therefore, an opportunity to incorporate the influence of raw material prices in a first stage is to consider two exogenous raw material price scenarios, one with a moderate development and the other with a strong increase of raw material prices showing very volatile dynamics. In order to allow the modeling of the impact of raw energy prices on RES technology costs, the impact of a specific raw material price on the overall investment costs of a certain energy technology needs to be indentified. This share is determined based on empirical studies and forms an exogenous parameter for the model. In addition, strong increase of raw material prices might lead to material substitutions in the development of the energy technology and consequently reduces the impact of the material price. Since material substitutions based on R&D experience are impossible to model, the material substitution caused by material price increases are only considered by defining thresholds where a material substitution is very likely to take place. Results and conclusion In a first stage the new model-integrated approach allows the simulation tool Green-X for an endogenous and more precise prediction of the cost evolution of all RES technologies due to the additional impact of raw- material prices. Amongst other, Yu et al, 2010 have exemplarily approved this fact in the case of the historic module price developments of Photovoltaics, shown in Figure 1. Similar results are expected at cost development predictions of other RES technologies and their main impact parameters like wind energy converter and the influence of the steel price.

Figure 1 Development of historic PV module price in doted line compared to approximations based on a one factor learning curve (red line) and a two factor learning curve considering the silicon price as well (black line); Source: Yu et al, 2010 Based on the results mentioned above, in a second step, this new approach allows increasing the efficiency of RES support schemes by adjusting the support level according to market price and consequently investment cost developments of RES technologies. Reducing the level of support in times of low raw material prices but increasing the support level again in times of rising raw material prices guarantees for a constant, strong and desired increase of the share of renewable energy source due to constant incentives for investors. Additionally, this approach also keeps consumer expenditures at a moderate level since financial support schemes are adjusted in times of lower investment costs of RES technologies. References Club of Rome – Donella H. Meadows, Dennis l. Meadows, Jorgen Randers, William W. Behrens III: “The limits to growth”; Universe Books, ISBN: 0-87663-165-0, 1972 Francesco Ferioli, K Schoots, B.C.C van der Zwaan: “Use and limitation of learning curves for energy technology policy: A component learning hypothesis”, Energy policy 37(2009) page 2525-2535, 2009 Huber C., Faber T., Haas R., Resch G.; Green J., Ölz S., White S., Cleijne H., Ruigrok W., Morthorst P.E., Skytte K., Gual M., del Rio P., Hernandes F., Tacsir A., Ragwitz M., Schleich J., Orasch W., Bokemann M., Lins C.; “Green-X – Deriving optimal promotion stratgegies for increasing the share of RES-E in a dynamic European electricity market”, FP5 (ENG2-CT-2002-00607), EC-DG Research, Vienna, Austria 2004 Martin Junginger: “Learning in renewable energy technology development” PhD thesis, p.216, Copernicus Institute, Utrecht University, The Netherlands, 2005 Yu, C F, van Sark, W G J H M, Alsema, E.A.: “How to incorporate input price changes and scale effects into the learning curve? A photovoltaics case study”, submitted to Energy Economics, 2010.