Real Options on power generation systems

There are many examples of applications of real options to electricity production. Section 6.3.4 provides an overview and classification of real options approaches that have been used to aid decision making in the power industry and in particular to those in CCS.

The first part of the literature review covers relevant papers on real options for investment in power generation. As the field is wide, with many papers published on the subject, papers with a focus investment are concentrated on as this is the area of the research question.

Insley, (Insley, 2003) valued the option for a firm to invest in equipment to retrofit a plant with emission abatement technology (to reduce SOx) in light of an uncertain emissions penalty, which varied stochastically as a GBM. The options valued included that of optimal investment price i.e. the trigger price of emissions permits required for the option to be exercised. In addition, the construction process was staged so that the owner had the option to abandon construction at discrete points in the build process. Once the facility had been built, the owners also had the option to cease production if the emissions price was too low. The problem was set up using partial differential equations that were solved using the Crank-Nicholson method. The option to halt construction was valued using contingent claims analysis. The authors find that increasing volatility increases the critical trigger price to retrofit pollution abatement and that including the time to build a facility and modelling the option to halt construction adds significant realism to the model. The authors do not consider the option to switch to low sulphur coal (as is happening in the UK at present due to the LCPD- interestingly, this is why the UK imports large quantities of Russian coal), which could be a more economic way of dealing with uncertainty in emissions prices as no large sunk costs in new structures would need to be made, rather an increased variable cost might be seen (as the fuel is more expensive).

Cavus, (Cavus, 2001) evaluates the investment in a new CCGT plant compared to running or retiring an existing OCGT plant. Plant dispatch is simulated on an hourly basis i.e. the option to run the plant or not is assessed at each hour, with the NPV being the discounted sum of net operating costs. The problem is first modelled as a series of European call options with one stochastic parameter before being extended to two correlated stochastic parameters (electricity price and fuel price). The method of solution for the first problem is the standard Black-Scholes for a European option. The second option is evaluated using the

Margrabe formula, allowing the 2D problem to be valued as a 1D spread option. The author finds that the NPV is sensitive to the correlation between electricity and gas prices. The influence of start up and no load costs is not included in the analysis, nor is the impact of fixed costs e.g. loan repayments, which make up a substantial proportion of CCS costs.

Yang et al (Yang et al., 2007) and Blyth et al (Blyth et al., 2007) present a framework to evaluate investment in generation assets under multiple input price uncertainties. The approach coupled IEA software for generating the levelised cost of electricity with commercial real options software (real option calculator). The stochastic parameters were fuel price and carbon price. Three plants were investigated- a nuclear plant, a coal plant and a CCGT plant. The decision to invest in one of the technologies under uncertain climate policy was set up as an American call option and evaluated using dynamic programming. Revenues are determined by the plant operating on the margin i.e. the plant with the highest cost of generation. To reflect uncertain climate policy, the price of carbon was modelled as a jump process. The authors found that the timing of the policy was important when there was a short time period between the decision to invest and the resolution of policy uncertainty- i.e. the authors showed that long term emissions frameworks that remove uncertainty result in more environmental technologies being adopted. The paper also showed that the effect of CO2 price risk was higher when the marginal plant was coal due to the carbon intensive nature of emissions. The study assumed plant operated as base load and did not consider uncertain costs of generation. In addition, no CCS plants were included in the analysis.

Roques et al (Roques et al., 2006) use a Monte Carlo approach with stochastic optimisation to maximise NPV when a generator considers building 5 new plants over 20 years. The Monte Carlo approach allows stochastic electricity, carbon and gas prices to be modelled as well as correlations between the price processes and a sensitivity analysis on new build costs. The authors find that the correlation of gas and carbon prices leads to a decrease in the NPV of nuclear plant, suggesting that there is little incentive for generators to diversify away from natural gas plants to hedge fuel prices and therefore it is likely that new nuclear plants will require government support.

Laurikka (Laurikka, 2006) used real options analysis to evaluate the option value associated with operations of a new CHP plant or a modification to an existing plant using three stochastic variables;

electricity, fuel and emissions to calculate the expected NPV. The stochastic model was compared to a normal discounted cash flow model- it was found that the DCF model could cause bias results where a number of uncertainties could make quantitative appraisal complex.

Gollier (Gollier et al., 2005) analyse the value associated with building a series of modular nuclear plants compared to one large capacity nuclear plant. In effect, the authors value a series of options to invest compared to a single option to invest, known as a sequential investment. The authors model revenue as being stochastic using geometric Brownian motion to evaluate the American option of the optimal investment rule. The authors find that significant value is attached to the flexibility associated if investment takes place as a series of sequential options.

The following paragraphs contain the second part of the literature review which examines papers assessing investment in CCS using options analysis.

Abadie (Abadie and Chamorro, 2008a) evaluated the option to retrofit a PC plant with CCS based on uncertain electricity prices and carbon prices. The process for electricity was mean reversion, while carbon prices were assumed to follow a GBM. The principal aim was to find the trigger price of carbon allowances that would cause a generator to install the capture facility. The option was evaluated using a 2D binomial lattice. It was found that the trigger price to justify immediate retrofit of CCS to a PC plant was 55€/tonne carbon. In addition, sensitivity analysis showed that increasing the price of electricity increased the trigger price as did increasing the cost of the capture technology. Meanwhile, plants with a remaining economic life of less than 8 years would not adopt the technology due to their inability to recoup the investment cost. In order to reduce the trigger price, the volatility of the emissions allowances must be reduced- reducing the volatility of emissions from 49% to 20% reduces the trigger price to 32€/tonne. The model does not evaluate new build CCS plants or “capture ready” plants i.e. plants that are designed to be easily retrofitted, therefore the analysis was applicable to existing PC plant, which in the case of the UK have relatively short remaining life times. In addition, it was assumed that the plant operated as base load and was not capable of flexible operations i.e. the ability to switch the capture process on and off depending on the external constraints (e.g. fuel price, electricity price or carbon price)

Abadie (Abadie and Chamorro, 2008b) also investigated the value of two technologies; a flexible IGCC plant and an inflexible CCGT plant. The value of the option of the IGCC plant was associated with the option to switch inputs i.e. the plant could switch from coal to gas or vice versa. In addition, the optimal investment rules were derived for CCGT plant and IGCC plant before a comparison of the two was made (on the basis of the maximum NPV). The modelling approach used involved a 1D binomial lattice for evaluation of the (inflexible) CCGT plant and a 2D binomial lattice to value the (flexible) IGCC plant.

Electricity prices were considered to be deterministic and carbon prices were not included in the analysis.

In addition flexibility in output (the decision whether to run or not) was not considered i.e. the model assumed the plants would run base load.

Reinelt, (Reinelt and Keith, 2007) investigated the decision of whether to replace an ageing PC plant with either; a PC plant, a CCGT plant, a (retrofitted) IGCC plant or an IGCC plant with CCS. The model uses two stochastic variables- the price of natural gas and carbon prices which are invoked with a probabilistic timing. However once a carbon tax is invoked, it persists at the same value throughout the simulation.

The methodology used to solve the model is stochastic dynamic programming set to minimise the expected present value (cost) of generation over a finite time horizon. The authors examined the impact of policy on the optimal investment timing and (through the carbon price) on the value attached to retrofitting IGCC plant with CCS. It is found that a lack of retrofit flexibility results in delaying the retirement of the existing PC plant, as uncertainty regarding future regulations (in the form of a carbon tax). In terms of assumptions, base load operation was assumed and uncertainty regarding electricity prices was excluded from the analysis. In addition, PC plant with CCS was not considered in the analysis.

Reedman (Reedman et al., 2006) examines the impact of regulatory uncertainty in the form of uncertain carbon tax (magnitude and timing) on investments in a CCGT plant, standard PC and IGCC plants and PC and IGCC plants with CCS. Fuel and electricity prices are treated as exogenous as they are taken from the CSIRO partial equilibrium model; however, when a carbon tax is introduced it is seen as a jump in the electricity price. The analysis uses the expected NPV with dynamic programming to value the option associated with delaying the investment by comparing the NPV in the base case with perfect foresight to the expected NPV in the case involving imperfect foresight and a CO2 tax of uncertain magnitude and timing. It was found that while the standard PC plant was the optimal choice in the no tax scenario (presumably due to gas-coal price relativity), the imposition of a certain carbon tax pushes investment towards CCGT and CCS plants. In comparison, the impact of an uncertain carbon price on an uncertain date neither of the CCS plants was adopted- instead PC plant was chosen to be invested in immediately while IGCC plant was put on hold. Limits to the model include the modelling of carbon taxes instead of permits (and the value associated with settling permits yearly), the exclusion of the option to retrofit plant and the possibility to introduce a different probability distribution regarding the implementation of a carbon tax. Flexible output (the option to curtail production) was not considered.

Laughton, (Laughton et al., 2003) uses real options analysis (decision trees and market based valuation (aka modern asset pricing)) to evaluate the several options faced by a company extracting natural gas from a field and compare this result to that obtained using probabilistic DCF. As CO2 must be removed from the gas prior to selling, the company faces the option to vent or sequester the stripped CO2, using the natural gas as the energy source. In addition, the authors derive the option value of investing in the sequestration facility now or never and finally the value of the investment to decrease the future cost of a sequestration plant. The authors compare two regimes: a tax regime and a cap and trade regime. A 2D lattice is used to model the future trajectories of prices. The authors also consider correlation between gas and carbon prices. The authors find that DCF undervalues the constructed sequestration project compared to ROA. They also find that DCF assigns less value to the investment to decrease future sequestration costs. The model does not consider the effect of delaying investment to obtain information on future CO2

regulation.

Sekar (Sekar, 2005) reported the impact of uncertain CO2 price on the option to retrofit an IGCC plant with CCS compared to a PC and IGCC plant where retrofit was not possible. The problem was approached so that CO2 price was the only type of uncertainty involved. Market based valuation (modern asset pricing) was used to value cash flow uncertainty. The option to retrofit CCS to the flexible IGCC plant was modelled as an American call option and solved using dynamic programming (binomial tree method).The author found that under the fuel price assumptions made, PC plant has the most likelihood of being the optimal investment. In addition, the value of the option to retrofit increased with increasing uncertainty in carbon prices. Limitations to the study include the CCGT being excluded from the analysis and a static fuel price.

Liang et al (Liang et al., 2007) value the option to retrofit a PC plant in China with CCS as an American option. The authors use Monte Carlo analysis to derive the option value by subtracting the mean NPV of a capture ready plant from the mean NPV of a standard plant and find that the gross value of the capture ready option varies between $0.1m (worst case) and $107m (best case), when a range of different input parameters are used. Moreover, the range of gross values is highly dependent on the discount rate used.

The Monte Carlo approach allows multiple parameters to be altered including stochastic carbon and electricity prices and correlations between coal, carbon and electricity to be taken into account. Although CCGT plant is excluded from the analysis, given the amount of coal plants expected to be constructed in China in the future, this assumption is reasonable, however the same does not hold for the UK.

In addition to the papers above Kemp (Kemp and Swierzbinski, 2007) finds the value associated with the UK government issuing long term capture options to finance CCS, in this way long term risk associated with emissions is reduced by guaranteeing investors a minimum price of carbon. The authors acknowledge possible difficulties associated with establishing a baseline for emissions and issues surrounding the transferability of the options.

While most of the studies derive findings related to the optimal decision conditions under which to invest in CCS, none are specific to investment in the full CCS chain in the UK and none have extended the ROA analysis to evaluate both the optimal decision conditions and characterise the (time-dependent) profile over which the optimal investment decision could take place. While the time to exercise an investment option using ROA is necessarily stochastic; the current status of the UK generation system (as presented in Chapter 2) suggests that such analysis would be useful for both government and industry. The following methodology will focus on the option value to wait before making an irreversible investment in coal fired plant investment and to characterise the time dependent probability profile of the investment decision being exercised.