1. DISCOUNTED CASH FLOW AND OPTIONS THINKING APPROACHES APPLIED TO CLEAN ENERGY INVESTMENT – THE CASE OF CCS Chi Kong CHYONG David REINER Danny RALPH CUEN Seminar Series – 25 Feb 2013

2. Background and Overview Valuation methodologies like Discounted Cash Flow (DCF) do not, on their own, offer an explicit way to incorporate uncertain future market or policy conditions that could have asymmetric impacts on investment performance into valuations. Nor do they account for managerial flexibility to respond as uncertainties are resolved. Clean energy investments are particularly exposed to this set of conditions, so such tools could lead to suboptimal investment decisions if not used appropriately. We conclude that where these conditions of uncertainty exist, enhancing valuation methodologies with approaches that explicitly value embedded optionality to respond should become standard practice. This would formalise some existing market practices.

3. Background and Overview II Options analysis explicitly treats and acknowledges uncertainties and strategic investment decisions contingent on evolution of key uncertainties Therefore, our research objective was to examine: “under what circumstances traditional DCF analysis understates the option value of clean energy investment decisions” Three clean energy investment case studies were analysed: Natural gas power generation plant with post-combustion carbon capture technology Offshore wind farm investment in the UK North Sea Onshore wind farm investment in the US Midwest region We present the CCS case here, but the full analysis is at: http://www.cpsl.cam.ac.uk/bei#fragment-4

4. Why Options Analysis?

5. How to Use Options Analysis

6. When to Use Options Analysis?

7. Evidence of Asymmetries in Key Variables (Henry Hub Gas Price 2003-2012)

8. More Evidence of the Dangers of Forecasting Sources: US Energy Information Administration (EIA), Annual Energy Outlook (AEO) for 2009, 2010 and 2011

9. Key Principles Traditional discounted cash flow analysis is unable to reveal strategic project value embedded in optionality in light of great uncertainties Traditional valuation (such as DCF): present value of Benefits less Costs Highly sensitive to weighted average cost of capital (discounting): WACC depends on many factors, such as default premium, inflation premium, CEO’s strategic decision-making ability (options thinking), etc. Issue: projects with high uncertainties, such as clean energy investment, are penalised with much higher WACC because of high uncertainties; but uncertainties ≠ risks. Therefore, capital markets put an unjustifiably high premium on clean energy investment. However…

10. Key Principles …Not all uncertainties are risk, and not all risk is bad Uncertainties have both downsides as well as upsides; thus, uncertainties are both risks and opportunities Given ability to make strategic investment decisions contingent on evolution of these uncertainties, we can capitalise opportunities and minimize risks embedded in uncertainties Thus, options have strategic value when there are uncertainties - -> Higher uncertainties increase the options value Options analysis adds another analytical layer on top of DCF to better value and reflect strategic nature of decision making under uncertainties

11. Carbon Capture and Storage for Natural Gas Power Plant in the UK: Inputs & Assumptions (1) Decisions: Power plant investment CCS (post-combustion) NGCC? Carbon Capture ready (CCR) NGCC? Non-CCR (Baseline) NGCC? Capture plant retrofit (for CCR & non-CCR options): every two years Uncertainties: Gas price & Electricity price (stochastic) CCS Learning rate (stochastic) Carbon price (scenarios) Timeframe: 2013-2033

12. CCS Decision Tree

13. Carbon Capture and Storage for Natural Gas Power Plant in the UK: Inputs & Assumptions (2)

14. Valuing Carbon Capture Readiness (CCR) Option Definition & Assumptions Baseline NGCC is required by the UK law to demonstrate capture readiness, but in essence: Demonstrate technical ability to retrofit (i.e. engineering report) & enough physical space CCR option costs £3mn (on top of the baseline NGCC), includes: Space and foundations reinforcement for turbines Engineering design to accommodate new solvents & ability to export additional power (from reduced power requirement for solvent regen & CO2 compression) Thus, CCR can be viewed as an option which gives a power generator the right (but not the obligation) to retrofit the power plant with CO2 capture on or before a future date (the exercise date or expiration). Given engineering design of the CCR option, its value depends on expectations of CCS deployment in the future, carbon prices & uncertain CCS technology learning. Thus, CCR options value = eNPV CCR – eNPV baseline – InvCost CCR

15. Carbon Capture and Storage for Natural Gas Power Plant in the UK Inputs & Assumptions (3) Parameters for modelling gas prices (stochastic process): Estimated price volatility: 4% Assumed growth rate : 0.06% Parameters for modelling power prices (stochastic process): Estimated price volatility: 7.6% Assumed growth rate : 0.3% Cost of Equity Risk-free rate (10Y UK Government Bond)4.00% Beta0.51 Market Risk Premium6.00% Cost of Equity5.03% Mid-year factor102.48% Tax, Inflation and Depreciation Inflation rate2.40% Tax rate23% Depreciation methodstraight line Annual Asset Depreciation5% Carry Forward if no income to depreciateyes

16. Scenarios for the CCS case study Carbon prices Carbon price paths: Base case: corresponds to the UK carbon price floor Low C-price case High C-price case (‘Katrina-type’ of hurricanes are more often by 2020) Carbon price effects on wholesale electricity price (‘pass through’ effect): 0% (no effect); 23% - DECC’s average assumption (i.e., £1 increase in C-price increases wholesale price by £0.23) 50% (i.e., £1 increase in C-price increases wholesale price by £0.5); 100% (£1 increase in C-price increases wholesale price by £1)  this is possible if we believe that fossil fuel generation will dominate the electricity system in the UK (no renewables) DECC’s projection of Carbon and wholesale electricity prices (2012-2030)

17. Scenarios for the CCS case study CCS technological learning CCS technological learning: Improvement in capture efficiency Reductions in CAPEX & OPEX of a capture plant Technological learning depends on: Learning rate (tables on the right); modelled stochastically Global CCS deployment; deterministic scenarios according to the following deployment scenarios (tables on the right) Central Case for the analysis includes: Base case C-price DECC’s assumption regarding the effect of C- price on wholesale electricity price (23% pass through) Base case CCS technological learning: Base case rate of global CCS deployment & Base case learning rates for capture efficiency improvements and cost reductions Learning rate scenarios: Efficiency of Capture MinMaxMost likely High11%18%15% Base*4%6%5% Low2%3% * Based on survey of literature Learning rate scenarios: CAPEX of Capture MinMaxMost likely High18%51%33% Base*6%17%11% Low3%9%6% * Based on survey of literature Learning rate scenarios: OPEX of Capture MinMaxMost likely High30%90%66% Base*10%30%22% Low5%15%11% * Based on survey of literature Global CCS deployment rate (% pa): 2013-2033 High40.00% Base10.00% No CCS0.00% IEA (2010) assumes global deployment of 470 GW of powergen with CCS by 2035 (70% of all coal generation), or 17% p.a., in its most ‘optimistic’ CCS scenario

18. CCR Investment as a Hedge under Different Carbon Prices and Learning Rates

19. Conclusions CCS Case study In general, gas power plants (and gas with CCS) in the UK seem to be priced out of the market by the UK’s carbon price floor unless carbon costs can be passed through to consumers (which partly depends on the deployment of renewables in the UK). CCR optionality has minimal impact in the high profit (low carbon price) scenario, but provides a significant benefit for the lower profit (base and high carbon price) scenarios A natural next step is to conduct a portfolio analysis where we would, for example, value CCS and wind and other low- C generation technologies (e.g., nuclear)

20. THANK YOU FOR YOUR ATTENTION!