HOW TO EFFICIENTLY SUPPORT RENEWABLE ELECTRICITY THE FUTURE TASK IN EUROPE Christian PANZER, Energy Economics Group Vienna University of Technology, Phone +43-1-58801-37360, E-mail: panzer@eeg.tuwien.ac.at Gustav Resch, Energy Economics Group Vienna University of Technology, Phone +43-1-58801-37354, E-mail: resch@eeg.tuwien.ac.at Reinhard Haas, Energy Economics Group Vienna University of Technology, Phone +43-1-58801-37354, E-mail: haas@eeg.tuwien.ac.at Thomas Faber, Elektrizitäts-Gesellschaft Laufenburg Austria GmbH (EGL), Phone +43-1-5850909-89, E-mail: Thomas.Faber@egl.ch OVERVIEW Energy policy is the main driver for the enhanced deployment of electricity from renewable energy sources (RES-E) as observed in several countries worldwide. Now, to the first time in Europe, binding targets for renewable energy sources (RES), regardless the energy sector, have been set 20% RES up to 2020 indicates a huge future challenge for upcoming years. Despite, efforts have to be taken in all three energy sectors, the electricity sector will play a major role in achieving the overall target. Hereby, efficient and effective support measures have to be implemented in order to accompany a strong increase in the share of RES-E with low transfer costs for the society. Several policy options will be discussed with respect to their effectiveness the development of RES-E and their efficiency the associated costs to the development of RES-E1. Besides the Feed-In Tariffs and the quota systems based on Tradable Green Certificates (TGC), some flexibility mechanism are needed in order to support Member States with moderate RES potentials achieving their RES targets up to 2020. Since all these promotion schemes show different reaction in terms of RES deployment as well as the associated costs, the core objective of this paper is to depict the pros and cons of these policy design options with respect to their impact on future growth of RES and the corresponding costs, and finally draw recommendations for policy makers. METHODS The issue of effectiveness and efficiency of support schemes is discussed mainly based on the results of scenarios using the model Green-X funded by the European Commission (EC). It allows analyses for both, the EU as a whole as well as for every single member state. Within the model all relevant RES-E technologies e.g. biomass, wind, geothermal, PV, solar thermal...) technologies as well as demand-side conservation measures are described for every EU country by means of static (and further-on dynamic) cost-resource curves. A static cost curve provides for a point-of-time a relationship between (categories of) technical potentials (of e.g. wind energy, hydro, biogas..) and the corresponding (full) costs of utilisation of this potential at this point-of-time. To analyse various scenarios different policy schemes can be selected, (e.g. feed-in tariffs, tendering systems, investment subsidies, tax incentives, quotas, tradable certificates) and modelled in a dynamic framework. All the instruments can be applied to all RES technologies separately for the various energy sectors. In addition, general taxes can be adjusted and the effects simulated. These include energy taxes (to be applied to all primary energy carriers as well as to electricity and heat) and environmental taxes on CO 2 -emissions as well as policies supporting demand-side measures. The corresponding costs and benefits for companies and consumers are an output. RESULTS Investigations have been carried out, that strengthening the national RES-E support schemes would allow on the one hand to meet the target of 20% RES by 2020 and on the other hand keep the annual consumer expenditures on a moderate level (see Figure 1). Comparatively and relatively high transfer costs appear by introducing a common quota system based on a uniform tradable green certificate scheme although in this case only the most cost-efficient technologies would be installed, the hereby most expensive power plant determines the common 1 This assessment was conducted for the European Commission, DG TREN within the European research project OPTRES (www.optres.fhg.de) and futures-e (www.futures-e.org).
support level, increasing the transfer costs for the society dramatically (see Figure 1). However, a quota system based on a technology specific support measure almost equals the strengthened national policy system with respect to both, the transfer costs for the society and the achieved overall RES target. Strengthening national policy schemes implies on the one hand to adjust the support level appropriate and on the other hand to overcome non-economic market barriers (as grid connection issue, planning bureaucracy, etc ). Average yearly consumer expenditures (due to RES support) for NEW RES plant (2006 to 2020) [Bill. ] 40 35 30 25 20 15 10 5 0 12.5% 15.0% 17.5% 20.0% RES deplyoment (in terms of (gross) final energy) [%] BAU BAU (removed barriers, DSM) National action 2011 Quota (uniform) 2011 Quota (banding) 2011 Feed-in premium 2011 BAU + Quota (uniform) 2015 BAU + Quota (banding) 2015 BAU + Feed-in premium 2015 National action 2011 + Quota (uniform) 2015 National action 2011 + Quota (banding) 2015 National action 2011 + Feed-in premium 2015 Figure 1: Comparison of average yearly transfer cost / consumer expenditure for new RES plants in relation to the achieved RES deployment in terms of gross final energy within the European Union (EU27) CONCLUSIONS The key criterion for achieving an enhanced future deployment of RES-E in an effective and efficient manner, besides the continuity and long-term stability of any implemented policy, is the technology specification of the necessary support. Concentrating on only the currently most cost-competitive technologies would exclude the more innovative technologies needed in the long run. In other words technology neutrality may be cost-efficient in the short term, but is more expensive in the long term. The major part of possible efficiency gains can already be exploited by optimising RES-E support measures at the national level about two thirds of the overall cost reduction potential can be attributed to optimising national support schemes. Further efficiency improvements are possible through guaranteed but strictly limited duration of support as well as that support schemes are targeting solely new RES installations. Introducing a harmonized RES policy can only be favourable if it is designed technology-specific and, that a common European power market exists. REFERENCES Resch Gustav, C. Panzer, R. Haas, T. Faber, M. Ragwitz, A. Held, M. Rathmann, G. Reece, C. Huber: 20% RES by 2020 Scenarios on future European policies for RES-E. Project report of futures-e (www.futurese.org), conducted for the European Commission EACI European Agency for Competitiveness and Innovations. TU Vienna, Energy Economics Group in cooperation with Fraunhofer ISI, Ecofys, EGL. Vienna, Austria, 2009.
HOW TO EFFICIENTLY SUPPORT RENEWABLE ELECTRICITY THE FUTURE TASK IN EUROPE Authors: Christian Panzer, Gustav Resch, Reinhard Haas, Thomas Faber* Energy Economics Group, Vienna University of Technology *EGL Austria Contact Web: http://eeg.tuwien.ac.at Email: panzer@eeg.tuwien.ac.at based on calculations. made with the help of the. computer model Green-X. www.green-x.at European IAEE conference Vienna - September 8 th, 2009 Slide 1
Content 1. Introduction European RES Directive 2020 targets 2. Background information Short characterisation of the Green-X model, scenario assumptions and future pathways 3. Results on the future deployment of RES in Europe Results on RES deployment, investment needs, costs & benefits 4. The policy context Assessment of future RES policy options Concluding remarks European IAEE conference Vienna - September 8 th, 2009 Slide 2
(1) Introduction National RES targets for 2020 the binding goal! RES in terms of final energy [% of demand] 80% 70% 60% 50% 40% 30% 20% 10% 0% RES potential 2020 - share on current (2005) demand Proposed RES target for 2020 RES share 2005 AT BE BG CY CZ DK EE FI FR DE GR HU IE IT LA LT LU MT NL PL PT RO SK SI ES SE UK EU27 Note: Additional potentials do not include biofuel imports How the European Commission set the targets FLAT RATE & GDP-Variation European i.e.: RES-target IAEE conference 2020 = RES Vienna 2005% - September + 50% *RES 8 th, 2009 NEW % + Slide 50%* RES 3 NEW % GDP-weighting - first the future task mover in bonus Europe
Green-X model The Green (2) Background Simulation model for energy policy instruments in the European energy market RES-E, RES-H, RES-T and CHP, conventional power Based on the concept of dynamic cost-resource curves Allowing forecasts up to 2020/2030 on national / EU-27 level Base input information Country selection Technology selection Power generation (Access Database) Electricity demand reduction (Access Database) Economic market and policy assessment potential, costs, offer prices Simulation of market interactions RES-E, RES, CHP, DSM power market, EUAs Scenario Information Policy strategies selection Social behaviour Investor/consumer Externalities Framework Conditions (Access Database) Results Costs and Benefitson a yearly basis (2005-2020 (2006-2030) ) Reference clients: European Commission (DG RESEARCH, DG TREN, DG ENV), Sustainable Energy Ireland, German Ministry for Environment, European Environmental Agency, Consultation to Ministries in Serbia, Luxembourg, European Morocco, etc. IAEE conference Vienna - September 8 th, 2009 Slide 5
(2) Background Penetration (as share of longterm potential) [%] Technology diffusion in accordance with general diffusion theory, penetration of a market by any new commodity typically follows an S-curve pattern applied within the model to describe the impact of non-economic barriers on RES-E deployment 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Historical record Market barrier - Model implementation e.g. development of wind power (onshore) in Germany Model implementation of P Mn P d F ( 1 F) dynamic non-economic market barriers Deployment Realisable potential due to non-economic Markt barriers 1990 1995 2000 2005 2010 European IAEE Year [t] conference Vienna - September 8 th, 2009 Slide 6 Yearly realisable potential (as share of long-term potential) [%] 14% 12% 10% 8% 6% 4% 2% 0% F d ΔP Mn P F General diffusion theory 1 e d ( t t0 ) 0% How 20% to efficiently 40% 60% support 80% RES-E 100% Penetration (as share of long-term potential) [%] Markt penetration Diffusion rate Yearly realisble potential (according to market barrier) Long-term realisable potential 1
(2) Background The Green-X approach: Dynamic cost-resource cost-resource curves curves Potentials Costs of electricity by RES-E technology (by band) by RES-E technology (by band) by country by country DYNAMIC COST-RESOURCE CURVES by RES-E technology by country by year Dynamic aspects Costs: Dynamic cost assessment Potentials: Dynamic restrictions costs potential European IAEE conference Vienna - September 8 th, 2009 Slide 7
Energy generation Historical deployment (2) Background: Potentials and cost for RES Definition of the (additional) realisable mid-term potential (up to 2020) Theoretical potential Technical potential Maximal time-path for penetration (Realisable Potential) Barriers (non-economic) Economic Potential (without additional support) 2000 2005 2010 2015 Policy, Society 2020 European IAEE conference Vienna - September 8 th, 2009 Slide 11 R&D Mid-term potential Definition of potential terms Theoretical potential... based on the determination of the energy flow. Technical potential based on technical boundary conditions (i.e. efficiencies of conversion technologies, overall technical limitations as e.g. the available land area to install wind turbines) Long-term potential Realisable potential Additional realisable mid-term potential (up to 2020) Achieved potential (2005) The realisable potential represents the maximal achievable potential assuming that all existing barriers can be overcome and all driving forces are active. Thereby, general parameters as e.g. market growth rates, planning constraints are taken into account in a dynamic context i.e. the realisable potential has to refer to a How to efficiently support certain RES-E year.
(2) Background realisable RES potentials How far can we go with the renewable energy sources within the electricity sector as applicable in the years up to 2020? RES-E - Electricity generation potential [% of gross electricity demand] 120% 110% 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% RES share 2005 RES potential 2020 - share on 2020 demand (baseline case) RES potential 2020 - share on 2020 demand (energy efficiency case) RES potential 2020 - share on current (2005) demand AT BE BG CY CZ DK EE FI FR DE GR HU IE IT LA LT LU MT NL PL PT RO SK SI ES SE UK EU27 Achieved potential 2005 und additional realisable potential (up to 2020) for RES-E (in terms of final electricity demand) in the EU-27 by country European IAEE conference Vienna - September 8 th, 2009 Slide 12
RES cost Cost reduction - share of initial investment costs (as in the year 2006) [%] 120% 115% 110% 105% 100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% 45% 40% 35% (2) Background RES cost 2006 RES-Electricity technologies 2008 2010 2012 European IAEE conference Vienna - September 8 th, 2009 Slide 14 2014 Assumptions on expected future technological progress (technological learning) 2016 2018 Moderate learning High energy prices changed the overall situation Prior learning expectations will not be met with a continuation of high energy prices (i.e. an increase of investment cost could be observed for almost all energy technologies in 2006 to 2008 caused by increasing energy and raw material prices) 2020 2022 2024 2026 2028 2030 Hydropower Geothermal electricity Solid biomass - cofiring & large-scale plant Solid biomass - smallscale CHP Gaseous biomass Gaseous biomass CHP Wind energy Tidal & wave Solar thermal electricity Photovoltaics Resulting (investment) cost reduction due to technological progress (learning) How to efficiently (according support RES-E to the the future policytask scenario) in Europe
(2) Background Overview on RE scenarios (Green-X) Geographical scope: EU27 Member States Time horizon: 2006 to 2020 BAU case: RES policies are applied as currently implemented (without any adaptation) business as usual (BAU) forecast and a baseline energy (electricity) demand scenario. Strengthened national policies: Accelerated RES deployment, assuming that the national RES policy framework will be improved with respect to its efficiency & effectiveness. These changes will become effective by 2011 in order to meet the agreed national targets of 20% RES by 2020. Improvements refer to both the financial support conditions (if necessary) as well as to non-financial barriers (i. e. administrative deficiencies etc.) where a rapid removal is also preconditioned. Additional cases for the policy assessment are carried out: Harmonized technology-specific or uniform RES support European IAEE conference Vienna - September 8 th, 2009 Slide 18
(2) Objective / Policy Assessment Core Objective - Method of approach Support instruments have to be effective for increasing the penetration of RES-E and efficient with respect to minimising the resulting public costs over time. Public costs or transfer cost for consumer / society (due to the promotion of RES-E) are defined as direct premium financial transfer costs from the consumer to the producer due to the RES-E policy compared to the case that consumers would purchase conventional electricity from the power market. This means that these costs do not consider any indirect costs / benefits or externalities (environmental benefits, change of employment, etc.). The criteria used for the evaluation of various instruments are based on: Minimise generation costs Lower producer profits p MC Market clearing price = price for certificate p C price, costs [ /MWh] Producer surplus (PS) Transfer cost for consumer / society Transfer costs for consumer (additional costs for society) Generation Costs (GC) Quota Q = PS + GC p C * Q MC quantity [GWh/year] p C... market price for (conventional) European IAEE conference Vienna - September 8 th, 2009 electricity Slide 19 p MC... marginal price for MC... marginal How RES-E to (due efficiently to support generation RES-E costs quota obligation)
(3) Results RES deployment Sectoral contributions to achieve 20% RES by 2020? RES-deployment - in relative terms (i.e. as share of corresponding (gross) demand) [%] 40% 35% 30% 25% 20% 15% 10% 5% 35.3% 19.8% 19.6% 17.7% 8.1% RES-electricity RES-heat RES-transport RES primary (EUROSTAT) 0% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 RES final Deployment of RES-E, RES-H, RES-T and RES in total as shares of corresponding gross demands up to 2020 within the European Union (EU27) (according to the strengthened national policy scenario) European IAEE conference Vienna - September 8 th, 2009 Slide 20
(3) Results RES deployment RES contribution to achieve 20% RES by 2020 within the electricity sector 1200 1000 800 600 400 200 0 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 RES-E - energy output [TWh/year] Historical development Future development Wind offshore Wind onshore Tide & wave Solar thermal electricity Photovoltaics Hydro large-scale Hydro small-scale Geothermal electricity Biowaste Solid biomass Biogas Historical and projected future deployment of RES in the electricity sector up to 2020 within the European Union (EU27) (according to the strengthened national policy scenario) European IAEE conference Vienna - September 8 th, 2009 Slide 21
(3) Results RES deployment RES-E E contribution and associated capital expenditures New RES-E installations Breakdown by RES-electricity category BAU (Business as usual) Strengthened national policies [Unit] 2006-2010 2011-2015 2016-2020 2006-2020 2006-2010 2011-2015 2016-2020 2006-2020 Biogas BG MW 1,588 1,700 2,341 5,629 1,803 3,364 7,330 12,498 (Solid) Biomass BM MW 7,612 7,013 6,054 20,679 8,498 11,577 8,803 28,878 Biowaste BW MW 1,479 1,158 1,011 3,648 1,661 1,222 790 3,674 Geothermal electricity GE MW 147 118 60 325 148 128 90 365 Hydro large-scale HY-LS MW 6,876 3,064 1,391 11,331 6,991 2,432 1,378 10,802 Hydro small-scale HY-SS MW 1,424 2,389 958 4,771 1,631 2,745 552 4,928 Photovoltaics SO-PV MW 2,834 1,096 2,580 6,510 2,963 8,366 17,372 28,700 Solar thermal electricity SO-ST MW 367 560 1,348 2,274 390 963 3,498 4,850 Tide & Wave TW MW 404 517 285 1,206 416 564 775 1,755 Wind onshore WI-ON MW 33,951 33,038 39,334 106,324 34,717 56,436 27,000 118,152 Wind offshore WI-OFF MW 1,727 1,735 942 4,404 2,149 12,817 37,851 52,817 RES-E TOTAL RES-E MW 58,409 52,389 56,304 167,101 61,365 100,614 105,440 267,419 Capital expenditure [Mill. ] 250,000 200,000 150,000 100,000 50,000 0 2006-2010 2011-2015 2016-2020 Wind offshore Wind onshore Tide & Wave Solar thermal electricity Photovoltaics Hydro large-scale Hydro small-scale Geothermal electricity Biowaste (Solid) Biomass Biogas Tremendous increase in wind offshore and PV in terms of installed capacity and capital expenditures (according to the strengthened national policy scenario compared to BAU ) Capital expenditure [Mill. ] 250,000 200,000 150,000 100,000 50,000 0 2006-2010 2011-2015 2016-2020 European IAEE conference Vienna - September 8 th, 2009 Slide 22
RES-E deployment (as share on gross electricity demand) [%] 36% 34% 32% 30% 28% 26% 24% 22% 20% 18% 16% 2006 (4) Results / Policy Assessment Total electricity generation from RES (EU27) as share of gross electricity demand Required RES-E deployment 2008 2010 2012 2014 European IAEE conference Vienna - September 8 th, 2009 Slide 26 2016 2018 2020 BAU BAU (removed barriers, DSM) National action 2011 Quota (uniform) 2011 Quota (banding) 2011 Feed-in premium 2011 Overview on assessed policy scenarios MAIN CASES BAU - continuation of current national RES policies BAU with removed barriers and active DSM Strengthened national RES support Harmonised uniform RES support by 2011 (quota) Harmonised technologyspecific RES support by 2011 (quota with banding) Harmonised technologyspecific to efficiently RES support RES-E by How 2011 (feed-in the future premium) task in Europe
(4) Results / Policy Assessment Transfer costs for consumer (due to the support of RES-E) Transfer costs for consumer / society (sometimes also called additional / premium costs for consumer / society) are defined as direct premium financial transfer costs from the consumer to the producer due to the RES-E policy compared to the case that consumers would purchase conventional electricity from the power market. Overview on assessed policy scenarios MAIN CASES Yearly consumer expenditures due to RES-E support (as premium per MWh gross demand) [ /MWh DEMAND ] 30 25 20 15 10 5 0 2006 2007 accelerating RES-E deployment: higher RES deployment requires higher transfer cost in absolute terms (but with lower specific RES support) 2008 2009 2010 2011 European IAEE conference Vienna - September 8 th, 2009 Slide 28 2012 2013 2014 2015 2016 2017 2018 2019 2020 BAU BAU (removed barriers, DSM) National action 2011 Quota (uniform) 2011 Quota (banding) 2011 Feed-in premium 2011
(4) Results / Policy Assessment Costs of (an enhanced) RES deployment Average yearly additional generation cost for NEW RES plant (2006 to 2020) [Bill. ] 6 5 4 3 2 1 least cost policies lead to lower additional generation cost (~ 2 billion per year)! Average (2006 to 2020) yearly additional cost (generation & policy cost (consumer expenditures) referring to the required new RES deployment (2006 to 2020) (EU27) for the assessed policy scenarios BAU BAU (removed barriers, DSM) National action 2011 Quota (uniform) 2011 Quota (banding) 2011 Feed-in premium 2011 BAU + Quota (uniform) 2015 BAU + Quota (banding) 2015 BAU + Feed-in premium 2015 National action 2011 + Quota (uniform) 2015 National action 2011 + Quota (banding) 2015 0 12.5% 15.0% 17.5% 20.0% RES deplyoment European IAEE (in conference terms of (gross) Vienna final - September energy) 8 [%] th, 2009 Slide 31 National action 2011 + Feed-in premium 2015
(4) Results / Policy Assessment Costs of (an enhanced) RES deployment Average yearly consumer expenditures (due to RES support) for NEW RES plant (2006 to 2020) [Bill. ] 40 35 30 25 20 15 10 5 0 least cost policies lead to higher policy cost (consumer expenditures) (~ 12 15 billion per year)! removal of noneconomic barriers is crucial to reduce cost and speed-up RES diffusion 12.5% 15.0% 17.5% 20.0% RES deplyoment European IAEE(in conference terms of (gross) Vienna - September final energy) 8 th, 2009 [%] Slide 32 Average (2006 to 2020) yearly additional cost (generation & policy cost (consumer expenditures) referring to the required new RES deployment (2006 to 2020) (EU27) for the assessed policy scenarios BAU BAU (removed barriers, DSM) National action 2011 Quota (uniform) 2011 Quota (banding) 2011 Feed-in premium 2011 BAU + Quota (uniform) 2015 BAU + Quota (banding) 2015 BAU + Feed-in premium 2015 National action 2011 + Quota (uniform) 2015 National action 2011 + Quota (banding) 2015 National action 2011 + Feed-in premium 2015
Concluding remarks RES policies should be supported by a strong energy efficiency policy. The RES policy framework needs an integrated perspective on the use of biomass. Biomass is a crucial element of RES policy, used in all three sectors Efforts are needed in all Member States All modelling exams clearly illustrate Each MS has to contribute! A wide range of technologies has to be supported Even in a pure least cost case a broad portfolio of RE technologies is needed to achieve the 20% target! Costs vary over time, but even more between RES technologies Any future policy framework has to address this sufficiently by providing technology specific support to the various RES options. European IAEE conference Vienna - September 8 th, 2009 Slide 34
The discussion on adequate flexibility for target achievement should not lead to quick and too simplistic policy answers that directs us into a wrong policy direction and hinder the move towards effective & efficient RES support Thanks for your attention! In case of questions / remarks Email: panzer@eeg.tuwien.ac.at Phone: +43-1-58801-37360 www.futures-e.org or http://eeg.tuwien.ac.at European IAEE conference Vienna - September 8 th, 2009 Slide 35