Post on 12-Sep-2021
Oleg Kucher and Jerald J. Fletcher
West Virginia University
30th USAEE/IAEE North American Conference,
October 9-12, 2011, Washington, DC
Economic Feasibility and Investment Decisions of Coal and Biomass to Liquids
Acknowledgment: “This material is based upon work supported by the Department of Energy under Award Number DE-FC26-06NT42804. Disclaimer: “This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.”
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We provide micro-economic analysis of commercial
CBTL 50,000 bpd projected plant with 7.7% by weight biomass
based on NETL(2009)* techno-economic design
Conclusions: Despite the technical feasibility of CBTL
processes, there is little evidence of strong commercial
viability of CBTL development in the U.S. under present
energy prices and projected costs. In the presence of
uncertainty over the payoff from investing, the high capital
cost of CBTL plant is the main barrier to the construction
of a large-scale CBTL plant in the U.S.
Upfront conclusion
Source: *National Energy Technology Laboratory. 2009. "Affordable Low-Carbon Diesel Fuel from Domestic Coal and Biomass." DOE/NETL-2009/1349.
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Oil
Alternatives:
Costs and
Emissions Vary
Widely
High and low range of
costs and emissions
Assumed range of
crude oil prices
Midpoint of cost and
emission range
PERCENT OF GREENHOUSE GAS EMISSIONS RELATIVE TO CONVENTIONAL OIL
PR
OD
UC
TIO
N C
OS
T (
US
DO
LL
AR
S P
ER
BA
RR
EL
OF
OIL
EQ
UIV
AL
EN
T)
Figure 1: Cost and emissions data for 2008–2009
1. Introduction 2. Research methods and model 3. CBTL project evaluation 4. Summary and policy implications
Oil Alternatives Overview: Costs and Emissions
Coal-Biomass to
Liquids, NETL(2009)
$
$
$
$
$
$
$
$
-20%
Source: Adapted from Darmstadter, Joel. 2010. "The Prospective Role of Unconventional Liquid Fuels." Resources for the Future, 1-31 based on Newell. 2006. “What’s the Big Deal about Oil?” Resources.
NETL study*: CBTL could produce“affordable & low-carbon diesel”
Feasibility of CBTL 50k bpd plant with 8%wt at $80-$100 bbl
In U.S. 3 CBTL projects with capacity over 110k bpd:
American Clean Coal Fuels, IL; Baard Energy, Wellsville, OH;
Rentech, Natchez, MS
None of the CBTL projects have been completed
Reasons:
Financing issues, Environmental concern, Costs, Uncertainty
Feasible Solution to the Carbon Problem?
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Source: *National Energy Technology Laboratory. 2009. "Affordable Low-Carbon Diesel Fuel from Domestic Coal and Biomass." DOE/NETL-2009/1349.
1. Introduction 2. Research methods and model 3. CBTL project evaluation 4. Summary and policy implications
Motivation and Objectives
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CBTL economic feasibility and investment decisions:
• Is the CBTL project commercially viable in the U.S.?
• If yes, than why are CBTL projects delayed so far? Risks?
Research objectives:
• economic assessment of the CBTL cash flows and NPV;
• assess the value option to invest into the CBTL plant
• draw insights on investments for the CBTL plant in the U.S.
1. Introduction 2. Research methods and model 3. CBTL project evaluation 4. Summary and policy implications
• Valuing NPV • Uncertainty estimation • Valuing investment opportunity
• Valuation of cash flows discounted at the end of the year
• Sensitivity analysis
• The probability occurrence technique
• Monte Carlo simulation
• Dynamic programming
• NPV, IRR
• Sensitivity results
• Distribution parameters
• Volatility estimates
• Value of options to invest
1. DCF 2. Risk analysis 3. Real options
Research Methods and Model O
bje
ctiv
es
Tech
niq
ues
R
esu
lts
Figure 2: Research framework for the CBTL evaluation
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1. Introduction 2. Research methods and model 3. CBTL project evaluation 4. Summary and policy implications
Real Options Model
𝐹 𝑉 = max 𝜀 (VT − 𝐼)𝑒−𝜌𝑇
𝑑𝑉 = 𝛼 𝑉 𝑑𝑡 + 𝜎𝑉𝑑𝑧
The basic continuous-time model after Dixit and Pindyck (1994)
Investor’s problem:
Maximize a payoff 𝑉𝑇−𝐼, discounted at 𝜌
s.t. change in NCF, 𝑑𝑉 that follows GBM,
with 𝛼 growth rate, 𝜌-discount, 𝜎- st.dev.
F V = 𝐴𝑉𝛽1
𝑉 − 𝐼
V ≤ V∗ V > V∗
𝛽1 = 1/2 − ρ − δ 𝜎2 + [ ρ − δ /𝜎2 − 1/2]2+2𝜌/𝜎2
𝐴 = (V∗−I)𝑉𝛽1 = 𝛽1 − 1 𝛽1−1/[𝛽1𝛽1𝐼 𝛽1−1]
V∗ = 𝛽1/(𝛽1 − 1)𝐼
Solution by Dynamic Programming:
Source: Dixit, Avinash K. and Robert S. Pindyck. 1994. Investment under Uncertainty. Princeton University Press.
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Constants
Trigger value
Value of options to wait
Value of options to invest
1. Introduction 2. Research methods and model 3. CBTL project evaluation 4. Summary and policy implications
CBTL Project Evaluation
Parameters Values
Pla
nt
bas
is Plant type designed by NETL(2009)* CBTL 50k bpd, 7.7wt% biomass
Life of project & construction period 30 & 4 years
Operating capacity 0.89 Coal & biomass input 19948 tpd & 1657 tpd
ULSD & naphtha output 34292 bpd & 15708 bpd
Eco
no
mic
s Startup prices & Inflation 2010 year & 2%
Investments (total as-spent capital) $5.6 billion
Discount rate 8% Tax rate 38%
Fin
anci
ng Ownership: debt/equity, % 60%/40%
Debt: senior/subordinated, % 80%/20%
Senior & Subordinated interest rates 5.5% & 9%
Table 1: Main parameters and assumptions in DCF analysis
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Source: *National Energy Technology Laboratory. 2009. "Affordable Low-Carbon Diesel Fuel from Domestic Coal and Biomass." DOE/NETL-2009/1349.
1. Introduction 2. Research methods and model 3. CBTL project evaluation 4. Summary and policy implications
Discount Cash Flow Analysis (DCF)
Free Cash Flow to Firm (FCFF):
FCFF=EBIT(1-Tax Rate)+Depreciation-CapEx Δ Working Capital
$0.0
$0.5
$1.0
$1.5
$2.0
$2.5
$3.0
2012 2019 2026 2033 2040Year
Product revenue
Billions of dollars
$(2.0)
$(1.5)
$(1.0)
$(0.5)
$-
$0.5
$1.0
$1.5
2012 2019 2026 2033 2040Year
Free cash flow to firm
Billions of dollars
Payback 13 years 78% of revenue from ULSD 22% revenue from naphtha
Cash intensive : revenue$1.87 billion, FCFF$191 million per year
Figure 3: Product revenue Figure 4: Free Cash Flow to Firm
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1. Introduction 2. Research methods and model 3. CBTL project evaluation 4. Summary and policy implications
DCF, Cont.
12.9% -$4,000
$0
$4,000
$8,000
$12,000
1.0% 4.2% 8.4% 12.6%
NP
V
Cost of Equity, %
Millions of dollars
IRR on Equity
9.2% -$4,000
$0
$4,000
$8,000
$12,000
1.0% 4.2% 8.4% 12.6%
NP
V
Cost of Capital, %
Millions of dollars
IRR
Free Cash Flow to Equity Free cash flow to Firm
• NPV>0 accept the CBTL project
• But IRRs are low for high risky project
• The cost of capital indicates high sensitivity of NPV
Figure 5: NPV (FCFF) at discount Figure 6: NPV (FCFE) at discount
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1. Introduction 2. Research methods and model 3. CBTL project evaluation 4. Summary and policy implications
Sensitivity Analysis (1) for NPV, FCFF
Top sensitive variables for NPV (FCFF):
Fuel prices, Operating Capacity, Discount, Investments
NPV= $767 million
Millions of dollars
Figure 7: Impacts of major sensitive parameters ( 10 %) on NPV8 (FCFF)
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1. Introduction 2. Research methods and model 3. CBTL project evaluation 4. Summary and policy implications
Sensitivity Analysis (2) for NPV, FCFE
Top sensitive variables for NPV (FCFE):
Investments, Operating Capacity, Discount, Debt ratio, Fuel prices
NPV= $207 million
Millions of dollars
Figure 8: Impacts of major sensitive parameters ( 10 %) on NPV12 (FCFE)
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1. Introduction 2. Research methods and model 3. CBTL project evaluation 4. Summary and policy implications
Risk Assessment
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Fitting distributions of sensitive variables:
Capital expenditures: $5-$6.5 billion range for CBTL 50k bpd plant
Operating capacity: U.S. refinery utilization rate89%; st.dev. 6.3%
ULSD & coal prices st. dev. up to 30%; Oil price st. dev. 45%
Dividend rate: mean 6% & triangular distribution with ±10% change
Correlation of ULSD & oil prices 0.9; Corr. coal & fuel prices 0.6
1. Introduction 2. Research methods and model 3. CBTL project evaluation 4. Summary and policy implications
Monte Carlo Simulation
The payoff of the CBTL project is lowered by 1/3:
Mean NPV8 $497 millions. It falls -$0.85 & $1.7 billion at 90% CI
Mean of NCF $185 millions, St. Dev. $25 millions (13%)
Figure 9: NPV8 and NCF forecast after 4000 simulations NPV NCF
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1. Introduction 2. Research methods and model 3. CBTL project evaluation 4. Summary and policy implications
497
Real Options Application
I
NPV
V
F(V)
V*
$3,468
-$5,000
-$3,000
-$1,000
$1,000
$3,000
$5,000
$0 $1,000 $2,000 $3,000 $4,000 $5,000 $6,000 $7,000 $8,000 $9,000 $10,000
NP
V
Present value of Net Free Cash Flow, millions of dollars
Linear NPV . . . .
Figure 10: Value of the investment opportunity
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𝐹(𝑉)𝑤 𝐹(𝑉)𝑖𝑛𝑣
1. Introduction 2. Research methods and model 3. CBTL project evaluation 4. Summary and policy implications
4.5 times
Summary
A CBTL project is feasible but in the near-term it cannot be
commercially viable under uncertainty in the energy market
• NPV8 from FCFF$767 million
• The biggest impact on NPV: fuel prices, discount, investments
• Uncertainties lower payoff by 1/3 with 25% chance of NPV<0
Real options yields high value to wait:
• The value of waiting could reach 2/3 value of capital costs
• The payoff needs to exceed the traditional NPV over 4 times
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1. Introduction 2. Research methods and model 3. CBTL project evaluation 4. Summary and policy implications
Policy Implications
In order to make the CBTL project viable:
• CBTL technology will need to be substantially more cost
effective, either through:
• reductions in capital costs,
• increased policy incentives, (i.e, carbon legislation),
• better project economics (i.e. optimized configuration),
increase in product demand and government support to
attract investment.
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1. Introduction 2. Research methods and model 3. CBTL project evaluation 4. Summary and policy implications
Backup: Risk Assessment Parameters
Variable Value Distributional Assumptions
U.S. Ultra low sulfur diesel price, $/gallon $2.31 Lognormal 3: St. Dev. 0.61
Coal price, $/t $44.6 Lognormal: St. Dev. 12.5
Crude oil price, $/oil barrel (bbl.)
$79.4
Lognormal: St. Dev. 35
Operating capacity 0.89 Gumbel Minimum: Beta 0.05
Dividend rate,% 6 Triangular: Min –10%, Max. +10%
Capital costs, millions of dollars
$5,595 Triangular: Min –10%, Max. +15%
Table 2: Fitted distribution parameters and distribution assumptions
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1. Introduction 2. Research methods and model 3. CBTL project evaluation 4. Summary and policy implications
Backup: Real Options Application
Parameter Value
δ, dividend (payout) rate 0.06
𝐼, present value of capital costs (TASC), $ millions $4,972.6
𝑉, present value of net cash flow to the firm, $ millions $5,739.6
𝑁𝑃𝑉, 𝑁𝑃𝑉 = 𝑉 − 𝐼, millions of dollars $767.01
NCF, millions of dollars per year $184.9
𝜎, volatility of average present value of net cash flow, % 13.49
Table 3: Real options model parameters
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𝛽1 = 2.434, 𝐴 = 0.00000096486, V∗= $ 8,440.95 million.
1. Introduction 2. Research methods and model 3. CBTL project evaluation 4. Summary and policy implications