Short characterisation of the Green-X model

Transcription

1 Short characterisation of the Authors: Gustav Resch, Andre Ortner, Sebastian Busch, Thomas Faber, Reinhard Haas all Energy Economics Group, Vienna University of Technology Contact Web: developed initially in the period 2002 to 2004 within the research project Green-X (5th framework programme of the European Commission, DG RESEARCH) General overview Slide 1

2 Energy Economics Group at TU Wien The Energy Economics Group (EEG) is within the Institute of Energy Systems and Electric Drives at Technische Universität Wien (TU WIEN). EEG s key research areas are: Dissemination and integration strategies for renewable and new energy systems Energy modelling, forecasting and analysis of energy policy strategies Competition in energy markets (liberalisation vs. regulation) Sustainable energy systems and climate change EEG, employing a permanent scientific staff of about thirty people with expertise across all disciplines necessary to assess the impact of energy policy initiatives at the European level, has managed and carried out many international and national research projects funded by the European Commission, national governments, public and private clients. General overview Slide 2

3 Energy Economics Group at TU Wien EEG is at the forefront of discussion on energy policy instruments for the enhanced deployment of renewable energies at the European level as well as at global scale. It substantially contributed to the assessment of the effectiveness and efficiency of support schemes for renewable energies either by conducting ex-post or ex-ante evaluations. Consequently, to facilitate prospective assessments and scenario elaboration, a broad set of in-door models have been developed by EEG. One of the most prominent tools in this respect is the. This model allows performing a detailed quantitative assessment of the RES deployment until 2030 in a real-world policy context at national and European level. It has been successfully applied for the European Commission within several tenders and research projects to assess the feasibility of 20% RES by 2020 and for assessments of RES developments beyond that time horizon (up to 2050). General overview Slide 3

4 Content 1. Introduction Objective 2. Basic principles referring to modelling aspects 3. The simulation model Green-X model 4. Background data General overview Slide 4

5 (1) Introduction 1. Introduction: Objective The core objective of the project Green-X was to develop a computer model allowing an assessment of the future deployment of RES-E in the real world. Derived objectives are: to describe the & the accompanying cost of the various RES-E options in a brief and suitable manner for model implementation; to model the impact of policy instruments; to address dynamic aspects in a proper way, including: Future technological changes e.g. a reduction of investment costs or efficiency improvements due to technological learning Technology diffusion i.e. the impact of non-economic barriers for RES-E to derive a picture of a likely future as close as possible to reality General overview Slide 5

6 (2) Basic principles 2. Basic principles: Static cost-resource curves Combines information on the and the according costs (of electricity/heat/fuel for a specific energy source). For limited resources (as RES-E) costs rise with increased utilization. All costs/s-bands are sorted in a least cost way continuous function costs costs = f (); t = constant stepped (discrete) function costs band 1 band 2 band 3 every location is slightly different General overview Slide 6 Practical approach: Sites with similar characteristics described by one band

7 US(1990)$/kW (2) Basic principles 2. Basic principles: Experience curves describe how costs decline with cumulative production. Empirical observations costs decline by a constant percentage with each doubling of the units produced or applied. ECHNOLOGY LEARNING CURV ES C CUM C 0 CUM C CUM Costs per unit C 0 Costs of the first unit CUM Cumulative production b Experience index LR Learning rate (LR=1-2 b ) b Costs per unit 100 U SA J apan Photovoltaics (learning rate ~ 20%) Windmills (USA) (learning rate ~ 20%) 1963 linear scale Cumulative production of units General overview Slide 7 Cumulative MW installed 1987 Gas turbines (USA) (learning rate ~ 20%, ~10%) R D &D phas e Commerc ializ ation phas e 1980 Costs per unit log-log scale e.g. Learning rate LR = 15% Cumulative production of units

8 (2) Basic principles 2. Basic principles: Technology diffusion Penetration (as share of longterm ) [%] 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% General overview Slide 8 Historical record Deployment Market barrier - Model implementation e.g. development of wind power (onshore) in Germany Model implementation of dynamic non-economic market barriers Year [t] Yearly realisable (as share of long-term ) [%] Realisable due to non-economic Markt barriers 14% 12% 10% 8% 6% 4% 2% 0% F d ΔP Mn P P Mn F General diffusion theory 1 e d ( t t0 ) 0% 20% 40% 60% 80% 100% Penetration (as share of long-term ) [%] Markt penetration Diffusion rate Yearly realisble (according to market barrier) Long-term realisable P d F ( 1 F) 1

9 (3) The 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 2030(2050) on national / EU level Base input information Country selection Technology selection Power generation (Access Database) Electricity demand reduction (Access Database) Economic market and policy assessment, costs, offer prices Simulation of market interactions RES-E, CHP, DSM power market, EUAs Scenario Information Policy strategies selection Social behaviour Investor/consumer Externalities Framework Conditions (Access Database) Results Costs and Benefits on a yearly basis ( ( ) ) Reference clients: European Commission (DG RESEARCH, DG TREN, DG ENV, DG ENER), Sustainable Energy Ireland, German Ministry for Environment, European Environmental Agency, Consultation to Ministries in Serbia, Luxembourg, Morocco, General overview Slide 9 etc.

10 (3) A broad set of results with respect to RES can be gained on country and technology-level: energy output by sector (RES-E, RES-H, RES-T), by country, by technology installed capacity & corresponding capital expenditures by sector, by country, by technology share on gross domestic electricity / heat / transport fuel demand (average) (additional) generation costs by sector, by country, by technology avoided (fossil) primary energy and CO 2 emissions due to additional RES deployment by sector, by country, by technology impact of selected energy policy instruments on supply portfolio, costs & benefits to the society (consumer) e.g. consumer expenditures due to RES support General overview Slide 10

11 Cost (efficiency) Potential (3) overview & approach Mid-term (up to 2020) realisable s in year n n+1 & corresponding costs for RES at country level by RES technology (subdivided into several bands) costs band 1 band 2 band 3 The Green-X approach: Technology diffusion ( S-curve ) (non-economic barriers by technology/country) Technological change ((global) learning curves by technology) costs Dynamic cost-resource curves Realisable yearly s in year n Energy policy (energy prices, RES support) & e.g. Feed-in tariffs, Investment incentives, Tendering schemes, Quotas with tradable green certificates Deployment in year n and corresponding costs & benefits General overview Slide 11 P FIT costs a detailed energy policy representation

12 (3) Core Objective - Method of approach for the policy assessment Support instruments have to be effective for increasing the penetration of RES-E and efficient with respect to minimising the resulting policy costs over time. Policy 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] General overview Slide 12 p C... market price for (conventional) electricity p MC... marginal price for RES-E (due to quota obligation) MC... marginal generation costs

13 Energy generation (4) Background data: Potentials & cost for RES Definition of the (additional) realisable mid-term (up to 2020/2030/2050) Theoretical Definition of terms Theoretical... based on the determination of the energy flow. Technical 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) Historical deployment Technical Maximal time-path for penetration (Realisable Potential) Barriers (non-economic) Short-term (2020) Economic Potential (without additional support) 2030 R&D Policy, Society Long-term Additional realisable long-term (up to 2050) Achieved (2005) General overview Slide 14

14 (4) Background data: Assumptions on demand & prices Key assumptions To ensure maximum consistency with existing EU scenarios and projections the key input parameters of the Green-X scenarios are (as default) based on PRIMES modelling and the (updates of the) Green-X database. Based on PRIMES* Energy demand by sector Primary energy prices Conventional supply portfolio and conversion efficiencies CO 2 intensity of sectors Defined for this study RES policy framework Reference electricity prices RES cost (Green-X database, incl. biomass) RES (Green-X database) Biomass trade specification Technology diffusion Learning rates Main input sources for scenario parameters *Primes scenario used subsequently: Reference case (as of 2013) General overview Slide 15