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 Workshop @ University of Sussex July 14-15, 2016, Brighton, UK

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

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 3 000 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: www.iea.org

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.

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: www.iea.org

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

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

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

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

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

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    

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

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

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

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 2030. 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.

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.

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.

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

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.

Thank you for your kind attention! Any questions? Reinhard Madlener Institute for Future Energy email: RMadlener@eonerc.rwth-aachen.de Consumer Needs and Behavior (FCN), RWTH Aachen Wilko Rohlfs Institute of Heat and Mass email: wilko.rohlfs@rwth-aachen.de 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): 114-125. Rohlfs, W. and Madlener, R. (2014): Multi-commodity real options analysis of power plant investments: Discounting endogenous risk structures. Energy Systems, 5(3): 423-447. Rohlfs, W. and Madlener, R. (2013): Assessment of clean-coal strategies: The questionable merits of carbon capture-readiness. Energy, 52, 27-36. Rohlfs, W. and Madlener, R. (2013): Investment decisions under uncertainty: CCS competing with green energy technologies, Energy Procedia , 37, 7029-7038. 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), 243-261.

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.

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

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

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

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

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