DISCOUNTED CASH FLOW AND OPTIONS THINKING APPROACHES APPLIED TO GREEN ENERGY INVESTMENT THE CASE OF CCS. David Reiner Kong Chyong Danny Ralph

Transcription

1 DISCOUNTED CASH FLOW AND OPTIONS THINKING APPROACHES APPLIED TO GREEN ENERGY INVESTMENT THE CASE OF CCS David Reiner Kong Chyong Danny Ralph

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:

4 Why Options Analysis? Probability NPV without optionalities (i.e. DCF value) Expanded NPV with optionalities Value of optionality 0 Profit

5 How to Use Options Analysis Qualitative asset/project(s) screening & analysis Input data & assumptions Discounted Cash Flow Analysis Traditional valuation approach Distribution of NPVs and Expected NPVs Sensitivity analysis Net Present Value Options thinking approach Identification of strategic optionalities Options valuation Expanded NPV (NPV + Options Value) Reporting & results presentation

6 When to Use Options Analysis? Valuation methodology Conditions Degree and nature of uncertainty around future market conditions Shape of probability distribution of future market conditions Management flexibility to change strategy in response to new information Use traditional valuation methodology (eg DCF) Uncertainty is limited and can be credibly quantified Close to symmetric Management flexibility is low; investment problem does not have optionality embedded Enhance valuation methodology with an options approach Uncertainty is significant and cannot be credibly quantified Asymmetric, with the possibility of high-impact, low-probability events Management does have flexibility; investment problem has optionality embedded

7 Evidence of Asymmetries in Key Variables (Henry Hub Gas Price ) -2σ 2σ

8 More Evidence of the Dangers of Forecasting 8 7 Actual price in 2010 US$/MBTU AEO09 AEO10 AEO11 Sources: US Energy Information Administration (EIA), Annual Energy Outlook (AEO) for 2009, 2010 and

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? CCR NGCC? Non-CCR (Baseline) NGCC? Capture plant retrofit (for CCR & non-ccr options): every two years Uncertainties (stochastic): Gas price & Electricity price CCS Learning rate Timeframe:

12 CCS Decision Tree

13 Carbon Capture and Storage for Natural Gas Power Plant in the UK: Inputs & Assumptions (2) Pipeline CAPEX, mn 139 OPEX, mn/year 12.6 Storage CAPEX, mn 74.2 OPEX, mn/year 12.8

14 Valuing Carbon Capture Readiness (CCR) Option Definition & Assumptions Baseline CCGT 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 CCGT), 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 = enpvccr enpvbaseline InvCostCCR

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% Beta 0.51 Market Risk Premium 6.00% Cost of Equity 5.03% Mid-year factor % Tax, Inflation and Depreciation Inflation rate 2.40% Tax rate 23% Depreciation method straight line Annual Asset Depreciation 5% Carry Forward if no income to depreciate yes

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 ( )

17 CCS technological learning: Improvement in capture efficiency Scenarios for the CCS case study CCS technological learning 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 Min Max Most likely High 11% 18% 15% Base* 4% 6% 5% Low 2% 3% 3% * Based on survey of literature Learning rate scenarios: CAPEX of Capture Min Max Most likely High 18% 51% 33% Base* 6% 17% 11% Low 3% 9% 6% * Based on survey of literature Learning rate scenarios: OPEX of Capture Min Max Most likely High 30% 90% 66% Base* 10% 30% 22% Low 5% 15% 11% * Based on survey of literature Global CCS deployment rate (% pa): High 40.00% Base 10.00% No CCS 0.00% 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 Investment in natural gas power plants with postcombustion carbon capture technology in the UK Modest capital investment in CCR now would serve as a valuable hedge against future market conditions involving a high carbon price: In a low carbon price world, the payoff from CCR investment is similar to the payoff from investing in the conventional (baseline) gas-fired power plant. In a high carbon price world, the payoff yielded by the CCR optionality is higher than the payoff from investing in a baseline gas plant. If global deployment of the CCS technology is high (i.e. there is a high learning rate) then the payoff from CCR optionality is significantly higher than the payoff from the investment in the conventional plant.

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

20 Conclusions 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 together

21 THANK YOU FOR YOUR ATTENTION!

22 BACK UP SLIDES

23 Contents Investment in natural gas power plant with CCS in the UK Offshore wind farm investment in the UK North Sea Onshore wind farm investment in the US

24 Case 2: Investment in offshore wind power technology in the UK North Sea By investing in the wind farm development phases, which is cheap compared to the capital cost of building the farm, the payoff could be substantially higher than if the decision to invest in the wind farm is made at the outset. Having the option to exit the project without committing large upfront construction cost in case of insufficient government support (ROC scheme) and/or if wind resource is not great have a positive value Therefore, for a wind farm developer, investing in these options (i.e., having multiple options at different locations and at different development stages) creates a long-term economic value

25 Case 2 Schematic

26 UK offshore wind case study Input & assumptions Decisions: Invest in each stage of the development phase or not? Wind turbine installations every two years (minimum increment of 100 MW or 20 turbines); Decision timeframe: Uncertainties (stochastic): Electricity & ROC prices & Wind resource Learning rate (lower Capex & Opex)

27 UK offshore wind case study Input & assumptions (2) Timeframe: (development, contruction, operation phases) Scenarios: Carbon prices and effects on wholesale power prices (see CCS case study slides) Renewable Obligation Certificate (ROC) support regime: Low no ROCs Base 1.3 ROCs per MWh generated Base 1.8 ROCs per MWh generated (according to current ROC banding review; eligible for farms in operation by Apr 2017) Country UK North Sea (round 3: Region Dogger Bank project) Wind farm operational lifetime, years 20 Type of turbines RePower 5M -100M Number of Turbines 100 MW/Wind turbine generator 5 Total Maximum Installed Capacity 500 Construction time, days/turbine 5 Duration, Costs years CAPEX, mn/turbine 15 OPEX, mn/mwh 0.07 Pre-FEED stage, mn 13 1 EIA & Data gathering & FEED Examination and Approval by NIP 13 1

28 UK offshore wind case study Input & assumptions (3) Parameters for modelling power prices (stochastic process): Estimated price volatility: 7.6% Assumed growth rate : 0.3% Modelling ROC prices: ROC price= byu-out price + premium Premium modeled as geometric Brownian motion process: Estimated volatility: 60% Cost of Equity Premium Risk-free rate (10Y UK Government Bond) 4.00% Beta 0.51 Market Risk Premium 6.00% Cost of Equity 5.03% Mid-year factor % Tax, Inflation and Depreciation Inflation rate 2.40% Tax rate 23% Depreciation method straight line Annual Asset Depreciation 5% Carry Forward if no income to depreciate yes

29 UK Wind Speed Model Location-specific wind speed (measured at 10m height; Location: Dogger Bank Project, North Sea; source: NASA); Derivation of wind Speed at 100m heights (RePower 5M) Unadjusted power output required to adjust this for differences in temperature and pressure conditions for the location as well as loss factor turbine s adjusted net power output (locationspecific)

30 Contents Investment in natural gas power plant with CCS in the UK Offshore wind farm investment in the UK North Sea Onshore wind farm investment in the US

31 Case 3: Investment in onshore wind power technology in the US Midwest region If prices are either within the base gas price scenario or are higher, the benefit of investment in the development phase is positive and the NPV for full construction is also positive Onshore wind in the US can serve as a valuable hedge against high gas prices, given the positive correlation between the performance of the wind asset and the gas price.

32 Case 3 Schematic

33 US onshore wind case study Input & assumptions Decisions: Invest in each stage of the development phase or not? Wind turbine installations every two years (min. increment of 25 MW/10 turbines) Uncertainties (stochastic): Electricity price & Wind resource Learning rate (lower Capex) Timeframe: (development, contruction, operation phases)

34 Scenarios: Production tax credit: Duration: 10 years Level of the credit (uncertain variable): $/MWh Timing: Production Tax Credit: (11-33 $/MWh) Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: No PTC Reference case assumes a wind farm enjoys PTC in US onshore wind case study Input & assumptions (2) Country US Region Midwest (Wyoming, Rawlins) Wind farm operational lifetime, years 20 Type of turbines GE -2.5 MW Number of Turbines 200 MW/Wind turbine generator 2.5 Total Maximum Installed Capacity 500 Construction time, days/turbine 3 Costs Duration, years CAPEX, $/KW 2000 OPEX, $/MWh 7.9 Pre-Screening stage, $mn Wind analysis, EIS, stakeholder consult Sign PPA, Procure Approvals and Financing

35 US onshore wind case study Input & assumptions (3) Parameters for power price modelling (stochastic process): Estimated power price volatility: 28.8% Assumed growth rate for power price: 2.4% (consistent with the inflation rate) Cost of Equity Risk-free rate (10Y US Government Bond) 1.75% Beta 0.51 Market Risk Premium 6.00% Cost of Equity 3.93% Mid-year factor % Tax, Inflation and Depreciation Inflation rate 2.40% Tax rate 35% Depreciation method straight line Annual Asset Depreciation 5% Carry Forward if no income to depreciate yes

36 US Wind Speed Model Location-specific wind speed (measured at 10m height; Location: Wyoming, Rawlins; source: NASA); Derivation of wind Speed at 100m heights (GE 2.5xl) Unadjusted power output required to adjust this for differences in temperature and pressure conditions for the location as well as loss factor turbine s adjusted net power output (location-specific)