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Davion M. Hill, Ph.D.
01 November 2010
Quantifying Risk in Energy Systems
Palisade @Risk Conference Las Vegas, Nevada, Nov 4-5 2010
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Energy System Output, Reliability, and ROI Projections01 November 2010
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What does DNV do?
DNV was founded in 1864 to fill a need for an objective
third party to assess sailing ships for their seaworthiness- In the19th century, sailing and shipping was risky
business, but with great rewards possible
- Ship builders needed an objective reviewer to prove
their worth to buyers and insurers
Milestones- 1951: Internal Research
- 1969: Oil in the North Sea
- 2004: Offshore Wind
Buyer Seller
3rd party role
Advisory role
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Predictions
Dont cross a river if it is four feet deep onaverage.
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Risk in Energy Systems
Wind Solar Oil & Gas Nuclear
PricesandSaleof ProductsConvertedfrom CO2
$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
HCOOH CO Methanol Ethylene Methane
Product
$permetricton
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
InvestedEnergyinCreationofProduct(kWh/ton)
MarketPrice $/ton Invested kWh/ton
Energy-PriceGap too high
Energy-PriceGap favorable
CO2Recycling
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WIND AND SOLAR
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Two Case Studies: US Wind Turbine Fleet and 1 MW Solar Farm
Efficiency of
Panel
Cost of
electricity
Output perpanel per day
(kWh/day)
Earnings per
panel per day
($/day)
Energy: Total
Output
(kWh/day)
CO2: Total Offset
Emissions (ton/day)
based on kWh output
(includes avoided
and life cycle
emissions)
Dollars: Product of
Total Output and
cost of electricity
($/day)
First Level
Outputs
Inputs
ROIOutputs
Case 2: Solar Farm
Capacity Factor
Sale price of electricity
Electrical Subsystem
Failure Rates
Blade Failure Rates
Gearbox Failure Rates
Output of
fleet per year
(MWh/year)
Earnings of
fleet per year
($/year)
Energy: Total
Output
(MWh/year)
CO2: Total
Offset Emissions
(ton/year) based
on MWh output
(includes
avoided and lifecycle emissions)
Dollars: Product
of Total Output
and cost of
electricity
($/year)
First Level
Outputs
Inputs
Case 1: Wind Fleet
Risk
Metrics
What effect
would
reliabilityhave on
meeting the
20% Wind by
2030 goal?
How much
does panel
efficiencydegradation
matter for
large scale
solar farms?
Probabilistic Energy ROI Models: Carbon, Energy, and Dollars. ASME Energy Sustainability, 2010.#90408 Phoenix, AZ.
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Life Cycle Estimates Including Materials Failure: Wind Turbines
Production of hypothetical US wind fleet between
present and 2030, including blade, gearbox, and
electronics failures (first generation turbines).
Best case scenario with
minimal failures, improved
capacity factor.
Least favorable scenario
with compounded failures
and poor capacity factor.
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Capacity Factor dominates
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Sensitivity to Return on Investment
Impact on ROI is more visible from failures, but revenue per kWh produced
and capacity factor are again dominant.ROI Parameter Minimum Mean Maximum
Energy ROI 22 30 37CO2 ROI 210 270 370Financial ROI 1.2 3 4.5
Payback projections to 2050
(multiples of original investment).
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Reliability of Solar Photovoltaic Panels
Panel Metrics over Time
$0.00
$0.02
$0.04
$0.06
$0.08
$0.10
$0.12
0 2 4 6 8 10 12 14 16 18 20
Time (years)
COE($/kWh)orPa
nelEarnings
($/day
)
0.000
0.020
0.040
0.060
0.080
0.100
0.120
0.140
0.160
0.180
EfficiencyofPanel
Cost of electricity ($/kWh) Single Panel Earnings Per day ($/d) Efficiency of Panel
Decreasing
efficiency
Rising
electricity prices
Resulting
revenue per
panel
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Financial ROI Sensitivity of 1 MW Farm
Sun exposureand electricity
revenue are
dominant
variables.
Secondary
negative effect
from efficiency
degradation
factors.
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ROI: Multiples of Original Investment
Energy ROI
Carbon ROI
Financial ROI
Factors that make ROI
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Sensitivity for Return on Carbon Investment (ROCI)
Embodied CO2 is a
strong factor for
solar energy.
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Sensitivity for EROEI
Embodied energy
is a strong factor
for solar energy.
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Unexpected Findings - Wind and Solar
Embodied CO2: 0.01 ton/W for the solar panel, and 0.0002 ton/W
for the turbine (2 order of magnitude difference)
Where it is manufactured can matter as much as where it isemployed.
Resource utilization dominates all forms of payback for therenewable energy systems studied.
Though materials failures have direct financial consequences, theuptime is dominant.
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TRADITIONAL ENERGY SYSTEMS (OIL, GAS, NUCLEAR)
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Nuclear Waste Storage
Carbon steel ASTM A-516, double-walledtanks
Liquid interior kept at ~pH 13
(12.5 mm) or 5/8 (16 mm) walls
750,000-1,300,00 gallons (2.8M to 5M L)
Diameter: 75-85 ft (23-26 m)
Depth (Height): 24-33 ft (7.5-10.3 m)
Risk Models for Materials Selction and Corrosion Inhibition in Offshore Oil/Gas Risers and Nuclear WasteStorage Tanks. NACE 2010. San Antonio TX.
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Nuclear Waste Storage Tanks: Wall Lifetime
Analysis does not include stress.
Wall thickness
Corrosion rate
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Offshore Oil and Gas Risers
Majority of riser material is conventional
carbon steel such as C1018 or pipesteel.
Near bend and subsea stations, interior
is clad with nickel alloy such as Inconel
625.
Cladding important for stressedsections (like bends)
Corrosion rate profile is affected by
galvanic corrosion.
Desire to add inhibitor to reduce
corrosion rate of carbon steel and
improve lifetime.
Areas that may
contain interior
cladding (Inconel)
Bare carbon
steel
Corrosionrate
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Experimental and Field Data Conditions
150,000 ppm
chloride, 55 ppmbicarbonatesimulated brine
CO2 purged throughsystem
10:1 area ratio of
I625:C1018
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Probability vs. Consequence: Operations Risk for Offshore Operations
Highest probability,
lowest
consequence.
High probability,
medium-high
consequence
Medium probability,
high consequence
Medium probability,
medium consequence
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Calculating the Combined Effect of Corrosion Rate and Pressure
Wall thickness
Corrosion Rate
Pressure
Pipe radius
Effect of Pressure:
rt
P
Corrosion rate reduces wall thickness over
time, but wall thickness is critical to hold
pressure. Pressure reduces lifetime further.
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Lifetime Predictions in each case
Stopped flow and failed
inhibitor flow are high riskconditions.
Lifetime reduced to
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CARBON DIOXIDE VALUE CHAIN
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CO2 Useful Products
Thermochemical
Biochemical
Photochemical
Electrochemical
DNV Strategic Research program:
Investigation of sustainable
technologies for carbon dioxide
management.
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Recoverable Energy Density kWh/ton
0
1000
2000
3000
4000
50006000
7000
8000
9000
10000
NiMH
Battery
Flywheel NaS Battery Li-ion
Battery
HCOOH CO Methanol Ethylene Methane
Energy Storage Medium
RecoverableEnergyDensity
(kWh/ton
)
Recoverable Energy Density of Useful Products
Products
electrochemically
converted fromCO2
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+ O2(combustion)
Coal orpetroleumreformed to coke
CO
Methyl Formate
Methanol
Formic
Acid
Methane
+CO +H2O +Energy
+CO
Coal orMethane
CO2 + Impurites
MEA
CO2
Formic
Acid(ECFORM)
Waste
Product
Useful Product
Methane
Ethylene
Oxide Ammonia
Ethylene
+ O2(combustion)
Conventional Formic Acid Value Chain ECFORM
+H2O +
Energy
+H2O +
Energy
+H2O +
Energy
+H2O +
Energy
+H2O +
Energy
+H2O +
Energy
Lost Costs
Waste
Water
Waste
Product
Generation of Formic Acid without Dedicated Feedstock
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Electrochemical Conversion
Prices and Sale of Products Converted from CO2
$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
HCOOH CO Methanol Ethylene Methane
Product
$permetricto
n
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
InvestedEnergyinCreationof
Product(kWh/ton)
Market Price $/ton Invested kWh/ton
Energy-Price
Gap too high
Energy-Price
Gap favorable
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CO2 Recycling
Energy dominates the profitability of the reaction, but
consumables are minimized with electrolyte selection.
Can it be done
profitably, efficiently,
and net carbon
negative?
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Conclusions
1. Variability Matters: dont cross a river if it is four feet deep on average.
- We can see the deep spots2. Forecast degradation: far future is more uncertain than near future (read
Orwells 1984)
- Still difficult to capture, but we can at least see whyuncertainty exists
3. Misunderstanding randomness: dont underestimate the consequences
of rare events
- Buried within the probability distributions are random and seemingly
unlikely events we can at least acknowledge them and hope for the
best
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01 November 2010
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