Renewable energy carriers in Austria Scenario assessment focusing on uncertainties

Transcription

1 Renewable energy carriers in Austria Scenario assessment focusing on uncertainties Andreas Müller Energy Economics Group (EEG) Vienna University of Technology

2 . Introduction Motivation Objectives Outline 2. Methodology Approach Underlying assumptions and data 3. Model results Value of flexibility Present Value of subsidies Sensitivity analysis 5. Conclusions, Outlook 2

3 . Introduction Motivation Objectives 2. Methodology Approach Underlying assumptions and data 3. Model results Value of flexibility Present Value of subsidies Sensitivity analysis 5. Conclusions, Outlook 3

4 Motivation Long-term deployment and competiveness of renewable energy technologies in Austria Literature provides us a wide range of potentials, competiveness highly uncertain as it strongly depends on future energy-, CO 2 prices and technology cost reductions Objectives: Design a methodology to: Determine the option values of having the flexibility to abandon future subsidies in case technologies doesn t become competitive. Determine the present values of deploying different renewable energy technologies under future uncertainties from the perspective of a Renewable Support system manager / society. 4

5 . Introduction Motivation Objectives 2. Methodology Approach Underlying assumptions and data 3. Model results Value of flexibility Present Value of subsidies Sensitivity analysis 5. Conclusions, Outlook 5

6 Approach 0th IAEE European Conference Model based analysis Simulation model Analysed technologies are compared against a reference system The decisions whether or not to deploy specific technologies in t are triggered by sufficient return on investment and available potentials and restricted by either In general, competiveness depends on Energy prices CO 2 -prices LRMC of technology (and subsidies) c " I #! + c -s var, ref, t i, t i, t var, i, t i,t Reference price Investment object! S n " ges,t new,t = & *# j,t $ r eplace, j,t + ( min_ install _ per _ Year, j % add.potential, j,t $ add.potential, j,t max_ install _ per _ Year, j) ' s i,t j= CAP min ; CA P max CAP ; CAP ;CAP ( ) Subsidies Logistic diffusion 2Δt = 25yr (%-99%) Momentum vector a " CAP! (, 4, ) max new t t 6

7 Approach: Evaluating the effects of uncertainties i. Covering uncertainties in the model (by conducting a Monte Carlo simulation) ii. energy prices CO 2 -prices and learning rates (annual global installations are predefined) Modelling the energy, CO 2 -prices and learning rates max min t pt = pt + xt p min t 0 " x " t+ x = x + a t+ t t+ {,0,} { 0.25, 0.5, 0.75} a )! b) w ( b ) Uniformly distributed set of scenarios Coal, gas and oil are perfectly correlated, CO 2 and energy prices are not correlated (CoV = 0), learning rates for technology clusters are independently as well t, i t+,( a= ) = + b xt, i * + % & t+,( a= 0) p # t $ max % 5, & ' 3 ( # x! 0.5 $ ( 0.5)! / 3 ' 2 ( # 0.5! xt, i $ wt +,( a=! ) = + b( xt, i " 0.5) + ( b / 3) % &! ' 2 ( w =! w( a = )! w( a =! ) Simulation periods Price index(2005=) Depicted a (more or less) uniformly distributed set out of total scenarios derived 7

8 Approach: Evaluating the effects of uncertainties iii. Performing the deterministic simulation tool and by that deriving the deployment and CFs of analysed technologies Investment decisions are taken independently from future uncertainties. A constant WACC (8% in our case) has been used. Subsidies are allocated based on the current status and expected price developments. Yet, in the subsequent ROA, they are calculated on the basis of actual developments iv. Building the binomial tree A specific state/node is defined by: Current state of deployment: low, medium, high Cost comparison of LRMC Technology vs. Reference price (~30 cat) Society / Renewable Support system manager can distinguish between and only between these states Each node faces now an uncertain future, raised by the underlying scenarios 8

9 Approach: Evaluating the effects of uncertainties v. Performing the ROA MAD approach Even though for each technology (and state), the associated risks of uncertain payoffs differ, we did used the same WACC for all technologies to calculate the value of the underlying. -> no CAPM model has been used (something to improve) r f =,5%, WACC soc = 4,5% Flexibility are modelled as compound call options, life time of an option is year If the PV of CFs (c ref c tech ) + value of future options > costs for current subsidies -> execute option To prevent Free Riding (technology learning is exogenously) delaying an investment (subsidies) is not allowed in the current model If an option is not been executed, then the technology is abandoned in all later states 9

10 Framework conditions Energy price scenarios 75 $/bar 00 $/bar based on EC, DG TREN, PRIMES Model, 2006 u CO 2 -prices 5 70 /tco 2 in 2050 Coal Gas CO 2 -Prices Oil Gas Coal BIP Unilateral co-integration condition based on electricity production costs c el,coal c el,gas Biomass costs and resources Subsidies Costs-Resource-Curve Marginal costs approach Arable land area per biomass type is exogenously defined (crop rotation), but the simulation model can shift some fraction of unused land area n E = E + $ " E! E # Price coupling of biomass to the oil price: 20% (const. usage) Annual budgets of subsidies given are rather low, in order not to adulterate the results by pushing to fast 0 Mio. p.a.: Wind, PV 50 Mio. p.a.: Biomass 0 Mio. p.a.: Solarthermal, mchp (!! ) p,i,agr p,i,agr,ex p, j,agr,ex,t p, j,agr,ex,tot,t j=! "! 3 2 0

11 20% 8% 6% 4% 2% 0% 8% 6% 4% 2% 0% Output bezogen auf Maximalszenario Anzahl Szenarien 0th IAEE European Conference Evaluation of renewable energy carriers in Austria in 2050 (based on current knowledge) Müller et al. (2008), derived from literature research and the following, uniformly and independently exogenously defined parameters: 60% 50% 40% 30% 20% 0% 0% Biodiesel < 0% 0% - 20% 20% - 30% 30% - 40% 40% - 50% 50% - 60% 60% - 70% 70% - 80% Output bezogen auf Maximalszenario 80% - 90% > 90% Robustness: Ratio of the 40% percentile and the potential in 2050 Energy prices: high, medium, low CO 2 -prices: h, m, l Learning rates: h, m, l Price correlation between biomass and oil price: h, m, l CO 2 -tax for distributed polluters: y/n Subsidies given: h/l 0% 20% 40% 60% 80% 00% Scenarios 00% 80% 60% 40% 20% Heat pump Micro -CHP and ORC Wind Ligno. biomass, electricity Biofuels, st generation Additional hydropower Ligno. biomass, chem. energy carriers Biogas Biomass heat, industry and heat grids PV Biomass space heating, small plants ~35% of electricity consumption in 2006 Solar thermal energy Current electr. generation from hydro power : Electricity generation Heat, biomass Heat driven CHP Heat, solar thermal energy Heat, heat pumps Gaseous biomass Liquid biomass GWh Solarthermal heat 0 0% 20% 40% 60% 80% 00% Scenarios Anzahl Szenarien < 0% 0% - 20% 20% - 30% 30% - 40% 40% - 50% 50% - 60% 60% - 70% 70% - 80% 80% - 90% > 90% 0% Potential: Maximum generation in 2050 (GWh) The size of the circles corresponds to the average of all scenarios 25% of energy demand for space heating in 2006

12 . Introduction Motivation Objectives 2. Methodology Approach Underlying assumptions and data 3. Model results Value of flexibility Present Value of subsidies Sensitivity analysis 5. Conclusions, Outlook 2

13 Back to the job 0th IAEE European Conference Value of flexibility What are the option values of having the flexibility to abandon future subsidies in case technologies doesn t become? Overall, our formulation of the problem results in given, but low option values of having the flexibility of abandoning subsidies for a technology (and by that abandoning the technology ) in later periods In most cases, the number of states where the call options are not executed are limited and lie well ahead in the later simulation periods. Therefore the present values of subsidies saved are limited. The only important exception applies for technologies, which are not or only barely competitive in most scenarios. In those cases, the call option will not be executed in the beginning. 3

14 Value of subsidies 0000 No options Incl. options PV of Subsidies PV CF, without option PV Subsidies, without option PV CF, incl. option PV Subsidies, incl. option NPV PV of CF

15 Sensitivity analysis Only Scenarios with higher CO 2 and energy prices x t : Energy and CO2 prices Scen Scen 2 Scen 3 Scen 4 st period 0 0, 0, 0,2 2nd period 0 0,33 0,5 0,5 3rd period 0 0,33 0,66 0,8 Increasing the risk free interest rate r f :,5-3,5% WACC: 4,5-8,5% Option value increases (if in the money), but value still moderate % +00% +40% % Scenario Scenario 2 00 x0 Scenario 3 Scenario 4 0 NPV increases, Option value decreases (since most underlyings are in the money) 5

16 . Introduction Motivation Objectives 2. Methodology Approach Underlying assumptions and data 3. Model results Value of flexibility Present Value of subsidies Sensitivity analysis 5. Conclusions, Outlook 6

17 Conclusions Introducing uncertainties in such manner as we applied in this study, results in a highly unpredictable development in the long and medium run (5-20 yr). Therefore the value of having the proposed flexibility to abandon subsidies in later periods is low. Taken the uncertainties assumed in this analysis into account, then wind power, solar thermal heating, biomass heating, photovoltaic and biomass gasification processes do have a high net present value. 7

18 Outlook Future work necessary to enhance the quality of the results: Use CAPM to investigate the WACC Integrated the decision on the level of subsidies given in a Real Option Model 8

19 Thank you for your attention Further information / questions: Andreas Müller Energy Economics Group mueller@eeg.tuwien.ac.at tel: web: 9