1. Energy Technology Investment Decision-Making under Multi-Dimensional Price Risk: The Case of CCS and Carbon Capture Readiness
Reinhard Madlener1 and Wilko Rohlfs2
1 Institute for Future Energy Consumer Needs and Behavior (FCN), School of Business and Economics / E.ON Energy Research Center, RWTH Aachen University
2 Institute of Heat and Mass Transfer, Faculty of Mechanical Engineering, RWTH Aachen University
International Exergy Economics University of Sussex
July 14-15, 2016, Brighton, UK

2. Presentation outline Need for CCS and CCS-readiness Modeling Results
Conclusion

3. Need for CCS The IEA Blue Map Scenario IEA:
Annual rates of investment in many low-carbon electricity generating technologies must be massively increased compared to today’s levels
Carbon capture and storage (CCS) is an important part of the lowest-cost greenhouse gas (GHG) mitigation portfolio. IEA analysis suggests that without CCS, overall costs to reduce emissions to 2005 levels by 2050 increase by 70%. This roadmap includes an ambitious CCS growth path in order to achieve this GHG mitigation potential, envisioning 100 projects globally by 2020 and over projects by 2050.
CO2 capture technology is commercially available today, but the associated costs need to be lowered and the technology still needs to be demonstrated at commercial scale. Additional research and development is also needed, particularly to address different CO2streams from industrial sources and to test biomass and hydrogen production with CCS.
Source:

4. Research Motivation
Decision-making concerning long-lived irreversible energy technology investments under uncertainty calls for multi-dimensional models, thus accounting for the unknown price trajectories of all underlying commodities’ prices
. For such scenario-based analysis, deterministic approaches are commonly used that assume constant price trends, which are then varied for checking the robustness of the model outcomes.
More sophisticated approaches for dealing with price uncertainty make use of stochastic models, which can be classified into those that
(1) use stochastic processes for electricity prices, commodity prices, and other uncertain parameters (e.g. hydro inflows, solar irradiation, or wind distributions);
(2) enable scenario generation and reduction;
(3) allow the stochastic optimization of investment decisions, including short- and mid-term electricity generation planning and long-term system optimization.
In recent years, the use of real options (RO) models to decision-making processes in the energy sector (especially investments in new power generation), has soared.
We present a generalized RO model that accounts for multiple commodities by correlated stochastic price paths, with a combined evaluation of an arbitrary no. of technologies.

5. Need and requirements for capture-ready plants
Capture-readiness aims to avoid the lock-in of non-CCS plants
CCS is not commercially available today for large-scale power plants (only after 2020)
Long operational lifetimes of 40 years or more (risky investment decision, technological lock-in)
New No-CCS plants will emit large amounts of CO2 during their lifetime (add to global warming)
Key technical issues of capture-ready plants
Sufficient space and access for additional capture facilities
Identification of reasonable options for carbon storage (choice of the power plant‘s location)
Modifications in the initial design of the steam turbine
Additional foundations, cable trays, and pipe racks
Disadvantages of capture-readiness
Retrofit is not reasonable if the remaining lifetime of a CCS-ready power plant is low
Technical requirements are based on today‘s knowledge, thus new build CCS power plants might use other capturing methods which cannot easily be applied to an existing power plant
Source:

6. Assessing the value of capture-readiness
Conditions that determine the economic value of CCS-readiness
Price of electricity, carbon dioxide and fuel
Cost of the CCS retrofit
Optimal timing of the actual retrofit
What determines the (optimal) time of the CCS retrofit?
Need for lowering CO2 emissions  results from the carbon policy in place
Availability of many other low carbon technologies:
Renewables
Nuclear or
New CCS power plants
If the value of new power plants higher compared to the value of retrofitting, investors will first build new CCS plants which again reduces the need for retrofitting in a cap and trade policy.
Motivation of this study: Compare the value of retrofitting capture-ready power plants with the value of new build CCS power plants

7. Alternative pathways to reduce carbon dioxide emissions
CCS-Retrofit
Construction of capture-ready power plant
Expected lifetime
Retrofit
Extended lifetime
Premature shut-down of an old power plant
Expected lifetime
New-built CCS power plant
Early shut-down
Different age (and efficiency)
Premature shut-down of a new power plant
Expected lifetime
Early shut-down
New-built CCS power plant
Time of decision-making
Time

8. Presentation outline Need for CCS and CCS-readiness Modeling Results
Conclusion

9. Modeling: Overview Expenditures for construction
Revenues for
electricity
Coal-fired power plant
(with or without CCS)
Fuel costs
Expenditures for
CO2 certificates
O&M costs

10. Modeling: Plant specifications
Expenditures for
construction
Basic power plant specifications:
Lifetime
Operating hours
Efficiency
results in:
Electricity output
Fuel demand
CO2 emissions
O&M requirements (all p.a.)
Revenues for
electricity
Fuel costs
Expenditures for
CO2 certificates
O&M costs

11. Modeling: From price paths to internal rate of return
Price path development of correlated stochastic processes
Inputs/outputs result together with the prices in cash flows
Market growth
Net present value calculation
Correlated
Internal rate of return

12. Modeling: From price paths to internal rate of return
Correlated
Internal rate of return
Market growth
multiple
simulation
runs
CAPM
Required rate of return
Results of one simulated price path
𝜙:market price of risk
Decision rule
Optimal decision for only one constellation of initial prices:
Pel. Pfuel, PCO2, and PMarket

13. Modeling: From price paths to internal rate of return
Multi-dimensional binomial tree
The initial price for the investment in future periods is defined by a multi-dimensional binomial tree.
Each node defines the commodity prices at time t, P(t).
Based on these “initial” prices, we calculate the rate of return of all different technologies, assuming the investment decision takes place at this point in time.
P(tstart) t1 t2

14. Presentation outline Need for CCS and CCS-readiness Modeling Results
Conclusion

15. Baseline Scenario Key findings
No investment prior to 2020, because CCS is assumed to be not commercially available.
Immediate replacement of the old hard coal plant with h = 35% intended to operate until
High probability to replace the second oldest power plant (h = 40%, expected lifetime 2040).
Strongly growing probability to replace the new power plant.
Retrofit probability is only 40% for the capture- ready power plant.
Nearly no chance of retrofitting a non-capture- ready power plant.

16. Parameter variation 1: Influence of the existing power plants‘ net efficiencies
-2%
+2%
Key findings:
Increasing the new power plant‘s efficiency strongly reduces the probability of retrofitting.
Increasing the older power plant’s efficiency lowers the benefit of retrofitting marginally due to the low lifetime which remains.

17. Parameter variation 2: Influence of the existing power plants‘ lifetimes
Baseline
+10 years
Key findings:
Increasing the lifetime of the existing power plants reduces the probability of premature shutdown while increasing the probability for retrofits.
High sensitivity of the lifetime on the retrofit.

18. Parameter variation 3: Influence of initial price levels
CO2
Hard coal
Electricity
High influence on retrofit options
Higher price levels increase the value of CCS
Generally only a low influence
Higher price levels decrease the value of CCS
High influence on the early replacement of old power plants
Coal-fired power plants become, overall, unattractive
Increasing price level

19. Conclusion
The value of capture-readiness was examined by finding the merit order of various clean-coal pathways.
An enhanced NPV model for multi-dimensional price risk was developed with technology- and path-dependent discounting based on the CAPM.
Application of the model yields the following key findings:
The option of replacing older power plants, including a premature shut-down, with new CCS plants is found to be the preferred choice.
Replacing new conventional power plants (built in 2015) with new CCS power plants is much more likely than retrofitting non-capture-ready or even capture-ready power plants.
The chances of retrofitting capture-ready power plants is low if other pathways do exist.
Expenditures for capture-readiness should be well-deliberated.

20. Thank you for your kind attention!
Any questions?
Reinhard Madlener Institute for Future Energy
Consumer Needs and
Behavior (FCN), RWTH Aachen
Wilko Rohlfs Institute of Heat and Mass
Transfer (WSA), RWTH Aachen
References:
Rohlfs R., Madlener R. (2014). Optimal Investment Strategies in Power Generation Assets: The Role of Technological Choice and Existing Portfolios in the Deployment of Low-carbon Technologies, International Journal of Greenhouse Gas Control, 28(September):
Rohlfs, W. and Madlener, R. (2014): Multi-commodity real options analysis of power plant investments: Discounting endogenous risk structures. Energy Systems, 5(3):
Rohlfs, W. and Madlener, R. (2013): Assessment of clean-coal strategies: The questionable merits of carbon capture-readiness. Energy, 52,
Rohlfs, W. and Madlener, R. (2013): Investment decisions under uncertainty: CCS competing with green energy technologies, Energy Procedia , 37,
Rohlfs, W. and Madlener, R. (2011): Valuation of CCS-ready coal-fired power plants A multi-dimensional real options approach, Energy Systems, 2 (3-4),

21. Comparison between NPV and RO analysis
Classical NPV model: Investment decision without value of waiting
Value:
€54 mio.
Postponed
investment
Increased value
Reduced probability
of losses
Different
technologies
Main results
The additional option to wait increases the value of the investment decision
Next to the increase, a strong reduction in the probability of losses occurs
 Raises the question about the correct way of discounting
Real option model: Investment decision including the value of waiting
NPV:
€71 mio.

22. Influence of existing power plants
Slight increase of HC
 Only small diversification effects for HC due to onshore wind
Increase of HC due to COGAS
 Advantage of diversification
Dashed line: Reference case without existing power plants

23. Backup slides: Data, parameterization price processes
Need for CCS and CCS-readiness
Modeling
Results
Conclusion

24. Binomial approximation of the multi-dimensional Brownian motion
Pathway and log-normal distribution
Binomial approximation

25. Data for existing and new coal-fired power plants
Existing coal-fired power plants
Options for clean-coal strategies:
Data are taken from a German pilot study (“Leitstudie 2010“, Nitsch et al. 2010), providing projections for the required power plant specifications until 2050

26. Parameterization of the price processes
Projections for the prices are required up to 2075 because of the long lifetime and the latest decision being made in 2050.
Growth rates and volatilities are adopted from the price projections given in a German pilot study (“Leitstudie 2010”, Nitsch et al., 2010)
Correlation coefficients are calculated based on historical price paths