otra vez el miedo otra vez el miedo otra vez el miedo otra vez el miedo

The supply chain optimisation model’s solution for the storage sites in the Central North Sea scenario and the Storage life cycle cost model were used to carry out an analysis of the storage cash flows in various

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scenarios which mimic the financial constraints of real leasing options discussed with the Crown Estate [22]. Three scenarios were devised each emulating a leasing option; Open season leasing was considered through a scenario allowing the full utilisation of the optimal CNS multi-storage capacity for a fixed CO2 price (£25) and royalty rate (15% of the CO2 price) to determine each site’s rate of return. Auctioning with a reserve price was considered through a scenario where a target IRR (10%) is set for all sites and the royalty fee that can be afforded per site is calculated as a guide, considering a fixed CO2 price (£30). Finally, the effect of market conditions on project finances is investigated for a scenario of fixed royalty rate (15% of the CO2 price) and IRR (15%). As shown in table 4.12 both saline aquifers and depleted oil and gas fields may differ significantly in terms of economic performance. It is also shown that a multi-store portfolio as a whole stabilises the economic performance. For example it is shown that in the last scenario, as soon as the expected CO2 storage price reaches £26.33 per tonne, all of the seven storage sites can be leased as a pack- age to meet the target IRR (15%) and royalty rate (15% of the CO2 price) [22].

Table 4-12 Storage sites’ performance under alternative leasing conditions [22]

Storage site Open sea-

son IRR (%)

Auctioning with reserve price

Royalty rate (% of CO2 price)

Dependence on market condi- tions

CO2 price (£/tonne)

Britannia aquifer 17.91 43.18 23.03

Captain aquifer block 17 30.77 57.67 17.01

Captain aquifer block 18 25.87 47.94 20.26

Goldeneye gas condensate field

6.85 22.87 31.08

Britannia condensate field 3.89 17.88 33.19

Scapa oil field 34.25 62.91 15.18

Blake oil field 12.18 33.34 26.99

Multi-storage portfolio 17.08 39.65 26.33

Figure I-1 of the appendix demonstrates the cash flows and leasing royalty incomes of the storage sites during the planning horizon (2014-2050) for alternative leasing scenarios. This analysis can be used for pro- ject finance budgeting and for the identification of expenditure outliers [22].

We also carried out a cash flow analysis of the whole transportation network for the multi-store scenario. A target IRR of 15% and a royalty rate of 15% of the transport price were assumed for the transportation network. Table I-5 of the appendix contains the amounts of CO2 transported through each route at each time period. It also contains the net present value of the accumulated operational cost of transport. Table I-

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4 contains the time of construction and the net present value of the accumulated annual transport capital costs. In order to obtain the transport network cash flows we transformed these values back to the equiva- lent nominal cash flows. On the other hand, the annual revenues are defined as the product of the trans- ported mass and the price. The royalty is set at 15% of the annual revenue. A price is then calculated for every unit of CO2 transported at which the internal rate of return is 15%. This price was calculated to be £8.51 or $13.61 per tonne. Figure 4.10 shows the cash flow of the transportation network for the Central North Sea multi-storage scenario based on the assumptions above. The relevant figures can be found in table I-6.

Figure 4.10 CO2 transportation network cash flow with a 15% IRR (2011-2050)- CNS multi-store scenario [22]

4.4

Conclusion

This preliminary study demonstrates to TCE the background methodology that will be used to implement the real option analysis of complex CCS supply chains. The results illustrate that our network optimisation tool provides cost optimal solutions for all components of an evolving CCS chain. Through the scenarios, it is also demonstrated that the model is a generic tool that can be adapted to any user defined CCS supply chain boundaries or analyse the sensitivity of the system’s techno-economic performance to any of the operational, design or market dependent parameters.

On the other hand, the combined CO2 transport and storage cost modelling of the anchor case reinforced that to ensure financial viability of CCS projects, whole system evaluation of their technical and economic performance of the supply chain has to be carried d out. A multi–period, multi-storage scenario connecting the Scottish emitters to several Central North Sea storage sites showed that it is not wise to estimate costs for a single CO2 storage value chain as this neglects the opportunity to reduce costs through transport and storage network sharing and optimisation. In addition, the scenario analysis and the results illustrate that our network optimisation and life cycle cost model for CCS value chains can sensibly capture the effects of technical and market constraints on individual storage site costs, as well as complex multi-storage scenarios

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[22]. It is shown the models can be used to make cost optimal decisions in managing storage sites’ leasing alternatives under user-defined financial constraints or targets.

This work successfully illustrated to TCE that the risks that may be imposed on site owners and operators can be reduced by optimisation during pre-project planning [98]. However considering the technical and market uncertainties, rigid strategies could undermine the solution’s viability. As per the objectives of this thesis to address the issue of deterministic optimisation, the multi-period supply chain optimisation model of chapter 3 is improved to become a stochastic optimisation tool in chapter 5. The stochastic model of chapter 5 is used (discussed in chapter 6) in quantitative assessment of the choice between the storage sites for different realisations of uncertainties around injection strategies and also considering the uncer- tainties in the evolution of market conditions, so as to maximise value for TCE and the operators. In the next stage of the real options project, Imperial College’s models will also be used to assess the value of additional data collection for individual storage sites in the decision making context.

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Chapter 5 Multi-stage stochastic optimisa-

tion of an integrated CO

2

capture, transporta-

tion and storage supply chain

A deterministic method was adapted earlier in this thesis to optimise the future evolution of a CCS net- work. This method could also be viewed as “most probable scenario optimisation”. The optimal expansion plan is determined based on the best available data for the uncertainties of the future system. Although widely used due to computational simplicity, it does not include uncertainty or any risk analysis and the lack of consideration of other plausible scenarios could result in substantial unexpected system costs. Different techniques and approaches have been deployed to solve problems arising in supply chain management under uncertainty. A Non-flexible probabilistic optimisation approach can be used to incorporate uncer- tainty into the multi-period CCS model. This method is not selected because although all alternative scenar- ios are represented, the investment strategy is only on average an optimal strategy given any of the possi- ble scenarios [107]. It is risk-neutral and does not allow for flexibility or future optionality either. In that sense it is similar to deterministic optimisation, although the entire scenario tree is considered as opposed to the condensed deterministic equivalent. Section 5.1 reviews the optimisation methods in the literature that deal with uncertainty and identifies which approaches are most suitable to the objectives of this chap- ter (also discussed in detail in chapter 2 as part of the main thesis objectives).

This chapter extends the multi-period CCS model to consider uncertainty and allow for flexibility in decision making. A mathematical programming approach is used for stochastic optimisation of a multi-stage CCS network. In section 5.2, the mathematical formulation of the stochastic model is developed. In section 5.3 a case study is developed to examine the optimal strategy for CCS investment and operation in the UK under carbon price uncertainties. The case study entails four stages, eighteen biggest emitters in the UK and ten largest sinks in the surrounding seas. In a report published in January 2014, The Climate Economics Chair [108] analysed the simulations from the ZEPHYR model [109] to determine the combined effect of 2030 GHG targets [110], amendments to the EU ETS directive such as back-loading and Market Stability Reserve mechanisms on future price trajectories. This analysis together with the European Commission’s latest as-

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sessment of the economic impacts of 2030 decarbonisation scenarios [110] have been used to develop a scenario tree for the potential carbon price evolution paths from 2014 to 2040. The simplified scenario tree contains six potential paths for the evolution of the price of carbon throughout the planning horizon. The stochastic model then outputs the optimal strategy in terms of CCS investment and operation according to the system stage changes i.e. the price of carbon at every stage.

The results are discussed in detail in section 5.3. In summary, the results show that at a current price of 12Eur per tonne, CCS is not part of the portfolio for all scenarios. Investment in CCS begins at stage 2 at a carbon price of 40Eur per tonne. At stage 3, for the scenarios where the price of carbon drops to 27Eur per tonne, the role of CCS drops from 78% to 53% of the target whereas in the scenarios which exhibit a price increase to 53Eur per tonne, the model invests further in a vast CCS infrastructure which is responsible for mitigating 96% of the target. At stage 4, neither of the two carbon prices; 100Eur per tonne and 152Eur per tonne offers a cheaper solution than carbon capture. Hence the CCS infrastructure is developed enough to handle 100% of the mitigation target which is 52% of the annual emissions considered or 59Mt per year.