Characterizing and Managing
Weather-Related Financial Risk for
Algal Biofuels
Rachel M Kleiman1,2, Gregory W Characklis1,2, Jordan Kern3,
Robin Gerlach4
Algae Biomass Summit 2019
(1) Department of Environmental Sciences and Engineering, (2) Center on Financial Risk in
Environmental Systems, Gillings School of Global Public Health and UNC Institute for the
Environment, University of North Carolina at Chapel Hill, (3) Department of Forestry and
Environmental Resources, North Carolina State University, (4) Department of Chemical and
Biological Engineering, Center for Biofilm Engineering, Montana State University
Unpredictable risks at Cyanotech
“The production of our algae products involves
complex agricultural systems with inherent risks
including weather, disease, and contamination. These
risks are unpredictable.”
–Cyanotech Annual Report
Revenues from algae production
Stable revenues come from stable algae production
In reality, algae production is highly variable and unpredictable
• How much of this variety is from weather?
300
ATP3 data:
Florida Algae
What an algae
producer wants
250
produced
Biomass
Revenues
(g AFDW)
($)
•
•
200
150
100
50
Reality
May 2014
Jul 2014
Sep 2014
Nov 2014
Date
Jan 2015
Mar 2015
Financial risk
• Financial risk: the possibility that a firm may
not have sufficient cash flow to meet financial
obligations
• Impacts of financial risk:
• Default/bankruptcy
• Lower valuation of firm
• Higher costs of financing
• Variable, unpredictable cash flow results in
financial risk, preventing investment in and
growth of new technologies
Financial risk in wind and solar energy
Solar power production (MW)
• The variability of wind and solar prevent investment and
growth for the industry
• Tools used for managing risk include: panel and turbine
innovation, battery storage, electricity futures, and
weather-based insurance contracts
Extreme fluctuations
Figure adapted from: Hand et al., 2012 and Ela et al., 2013
2018 Farm Bill: Success for Algae
• As of December 2018, algae is considered an
agricultural commodity
• Algae now eligible for crop insurance
Research questions
• How much of the variation in algae production is
due to weather variability?
• Can a weather-based insurance tool mitigate
financial risk?
Model Schematic
Environmental
outcomes
Biophysical Algal
Growth Model
Financial
outcomes
Cash flow
Impact
Pond Temperature
Model
Frequency
Frequency
Improved outcomes
ATP3 data
(validation & fitting)
ATP3 data
(validation)
Risk management
scenarios:
-Structural
adaptations
-Non-structural
adaptations
-Financial tools
Frequency
Stochastic
Vector-Auto
Regression model
Combined life cycle
analysis (LCA)/ Technoeconomic analysis (TEA)
Frequency
Meteorological
Data
Stochastic Ornsteinuhlenbeck model
Price( ($/GGE)
Fuel price
Data
Impact
Cash flow
Stochastic Weather Modelling
Air temperature
Temperature (°C)
30
25
20 Interested in these
15
10
extreme deviations
Average
Historic
2001
2002
2004
2005
Historic – Average
Probability density function
Count
5
Residuals (°C)
2003
0
-5
• Captures randomness
(noise) in data to model
real-world situations
• Useful for situations,
such as weather, that
cannot be modelled
deterministically
2001
2002
2003
2004
2005
Residual
Stochastic Model: Seasonality
Solar Loss (W m-2)
Air Temperature (°C)
Historic
Modelled
Wind Speed (m s-2)
Month
Relative Humidity
Month
Stochastic Model: Autocorrelation
Historic
Modelled
Stochastic Model: Cross-Correlation
Solar-air
Solar-wind
Solar-rel. humidity
Historic
Modelled
Air-rel. humidity
Wind-rel. humidity
Lag (days)
Lag (days)
Lag (days)
Cross-Correlation
Air-wind
Model Schematic
Environmental
outcomes
Biophysical Algal
Growth Model
Financial
outcomes
Cash flow
Impact
Pond Temperature
Model
Frequency
Frequency
Improved outcomes
ATP3 data
(validation & fitting)
ATP3 data
(validation)
Risk management
scenarios:
-Structural
adaptations
-Non-structural
adaptations
-Financial tools
Frequency
Stochastic
Vector-Auto
Regression model
Combined life cycle
analysis (LCA)/ Technoeconomic analysis (TEA)
Frequency
Meteorological
Data
Stochastic Ornsteinuhlenbeck model
Price( ($/GGE)
Fuel price
Data
Impact
Cash flow
Biophysical Model & Validation
𝑃𝑚𝑎𝑠𝑠 = 𝑓(𝑆, 𝜀𝑆 , 𝜀𝑡 )
Pmass= algal biomass growth
S = GHI solar radiation
εS = the effect of light saturation
εt = the effect of suboptimal water
temperature
0.8
0.7
εs
0.6
0.5
0.4
0.3
0.2
0.1
0
0
100 200 300 400 500 600 700 800 900 1000
Solar radiation, S (W/m2)
1
0.9
0.8
18
16
14
12
10
8
6
4
2
0.7
0.6
εt
20
Productivity (g AFDW/m2/d)
1
0.9
Seasonality in Productivity
0
0.5
0.4
1
0.3
2
3
Quarter
0.2
0.1
0
0
5
10
15
20
25
30
35
Pond temperature (°C)
40
Wigmosta, M. S., Coleman, A. M., Skaggs, R. J., Huesemann, M. H., &
Lane, L. J. (2011). National microalgae biofuel production potential
and resource demand. Water Resources Research,47(3).
https://doi.org/10.1029/2010WR009966
4
Biophysical Model: Stochastic Results
Model Schematic
Environmental
outcomes
Biophysical Algal
Growth Model
Financial
outcomes
Cash flow
Impact
Pond Temperature
Model
Frequency
Frequency
Improved outcomes
ATP3 data
(validation & fitting)
ATP3 data
(validation)
Risk management
scenarios:
-Structural
adaptations
-Non-structural
adaptations
-Financial tools
Frequency
Stochastic
Vector-Auto
Regression model
Combined life cycle
analysis (LCA)/ Technoeconomic analysis (TEA)
Frequency
Meteorological
Data
Stochastic Ornsteinuhlenbeck model
Price( ($/GGE)
Fuel price
Data
Impact
LCA/TEA adapted from: Hise, A. M., Characklis, G. W., Kern, J., Gerlach, R., Viamajala, S., Gardner, R.
D., & Vadlamani, A. (2016). Evaluating the relative impacts of operational and financial factors on the
competitiveness of an algal biofuel production facility. Bioresource Technology, 220, 271–281.
doi:10.1016/j.biortech.2016.08.050
Cash flow
Characterization of financial risk
•
•
•
Capital costs
Operational costs
Financing assumptions
Discounted Cash
flow analysis
Worst-case
scenario
Animal feed price*
*scaled up to make average return on
investment 12%
Modelled net revenues
30
Run 1
Run 2
Run 3
Net revenues (million $/year)
25
20
15
10
5
0
-5
Interested in managing
this risk
-10
-15
0
2
4
6
8
10
Simulated year
12
14
16
18
20
Model Schematic
Environmental
outcomes
Biophysical Algal
Growth Model
Financial
outcomes
Cash flow
Impact
Pond Temperature
Model
Frequency
Frequency
Improved outcomes
ATP3 data
(validation & fitting)
ATP3 data
(validation)
Risk management
scenarios:
-Structural
adaptations
-Non-structural
adaptations
-Financial tools
Frequency
Stochastic
Vector-Auto
Regression model
Combined life cycle
analysis (LCA)/ Technoeconomic analysis (TEA)
Frequency
Meteorological
Data
Stochastic Ornsteinuhlenbeck model
Price( ($/GGE)
Fuel price
Data
Impact
Cash flow
Strategies for managing financial risk
Managing Financial
Risk
Structural
adaptations
Co-locate with
power plant
Financial
Non-structural
adaptations
instruments
Photobioreactors
Strain selection/
engineering
Index-based
insurance
Cultivation
techniques
Processing
techniques
Co-products
Reserve fund
Diesel
future/forwards
Effective risk management
30
Net revenues (million $/year)
25
Run 3
Run 3 w/
risk mgmt.
20
Earn less in period
of good weather
15
10
5
0
-5
Interested in managing
this
risk against
To
protect
-10
Improves worst
case scenario
periods of bad weather
-15
0
2
4
6
8
10
Simulated year
12
14
16
18
20
Future Work: Index-Based insurance for algae
• Most insurance is “indemnity-based”: payouts happen
after loss
• Index-based: payouts happen according to an index
• Advantages:
• Lower transaction costs
• Fewer “moral hazard” concerns
• Quick resolution of payouts/claims
Algae Contract Structure
Sample “Call”
Index will be a
multivariate function of
weather parameters
such as:
•
Solar insolation
•
Air & pond
temperatures
•
•
•
Wind speed
Relative humidity
Model will be used to
evaluate effectiveness of
index
160
140
Payout 𝐿𝑖 = 𝐴 × Max 𝑆𝐿 − 𝐿𝑖 , 0
120
Payout ($)
•
100
80
“Strike” (SL)
60
40
20
0
0
1000
2000
3000
4000
5000
6000
7000
8000
f(solar insolation, air temp., pond temp.,
wind speed, rel. humidity)
9000
10000
Thanks to:
• DOE PEAK Innovations in Algae biofuel
Technology
• MSU
• Dr. Robin Gerlach
• Dr. Matthew Fields
• Dr. Brent Peyton
• MSU Algae group
• University of Toledo
• Dr. Sridhar Viamajala and colleagues
• UNC
• Dr. Greg Characklis
• Dr. Jordan Kern
• Dr. Jill Stewart
• Adam Hise
• The CoFiRES research team
Growth model: methods (Weyer, Bush, Darzins, & Willson,
2010; Wigmosta et al., 2011; Zemke et al., 2010)
The model considers energy flows, photosynthetic limits, and light and water requirements to predict
algal biomass growth, Pmass (Wigmosta et al., 2011):
𝑃𝑚𝑎𝑠𝑠 =
𝜏𝑝 𝐶𝑃𝐴𝑅 𝜀𝑎 𝐸𝑠
(1)
𝐸𝑎
where τp is the transmission efficiency of incident solar radiation to algae, CPAR is the fraction of
photosynthetically active radiation (PAR) that can be photosynthesized by algae, εa is the efficiency of
photon conversion to biomass, Es is the GHI solar radiation, and Ea is the energy content per unit of
biomass:
𝐸𝑎 = 𝑓𝑙 𝐸𝑙 + 𝑓𝑝𝑟 𝐸𝑝𝑟 + 𝑓𝑐 𝐸𝑐
(2)
where f and E are the fraction and energy content of the lipids (l), proteins (pr), and carbohydrates
(c). The photoconversion efficiency, εa, can be found with equation (3):
𝐸 𝜀 𝜀 𝜀
𝜀𝑎 = 𝑐 𝑆 𝑡 𝑏
(3)
𝑄𝑟 𝐸𝑝
where Ec is the photosynthetic conversion of light to chemical energy, εt is the effect of suboptimal
water temperature, εb is the biomass efficiency, Qr is the quantum requirement, Ep is the photon
energy, and εS is the effect of light saturation, given by equation (4):
𝜀𝑆 =
𝐸𝑆
𝑆𝑜
ln
𝑆𝑜
𝐸𝑠
+1
(4)
where So is the light saturation constant, a strain-specific coefficient that represents photoinhibition
(Chisti, 2007; M. H. Huesemann et al., 2009).
Growth model: methods cont’d
The effect of pond temperature (εt) is considered in the piece-wise function shown in
Equation 5 (Wigmosta et al., 2011):
0
(T-Tmin)/(Topt_low-Tmin)
1.0
(Tmax-T)/(Tmax-Topt_high)
0
when T < Tmin
when Tmin < T <Topt_low
when Topt_low < T <Topt_high
when Topt_high < T <Tmax
when Tmax < T
where Tmin = 10°C, Topt_low = 20°C, Topt_high = 30°C, and Tmax = 35°C
Pond temperature model: schematic of heat fluxes
(Bechet et al., 2011)
Temperature model: methods (Bechet et al., 2011)
𝑑𝑇
Changes in temperature ( 𝑝) simulated in a thoroughly mixed open raceway pond by
𝑑𝑡
considering net heat fluxes (W) from solar radiation (Qs), pond radiation (Qp), air radiation (Qa),
evaporation/condensation (Qe), and convection (Qc):
𝑑𝑇𝑝
𝜌𝑤 𝑉𝐶𝑝𝑤
= 𝑄𝑠 + 𝑄𝑝 + 𝑄𝑎 + 𝑄𝑒 + 𝑄𝑐
(1)
𝑑𝑡
where pw and Cpw are the density (kg m-3) and specific heat capacity (J kg-1 K-1) of water, and V
is the pond volume (m3). Heat fluxes from water inflows, precipitation, and conduction between
the pond bottom and soil are negligible and therefore not modelled. Qs is found using the Global
Horizontal Insolation from the NSRDB, Es (W m-2), algal absorption fraction (fa), and pond
surface area, S (m2):
𝑄𝑠 = 1 − 𝑓𝑎 𝐸𝑠 𝑆
(2)
Qp and Qa are found using the Stefan-Boltzmann fourth power law:
𝑄𝑝 = −𝜀𝑤 𝜎𝑇𝑝4 𝑆
(3)
𝑄𝑎 = 𝜀𝑤 𝜀𝑎 𝜎𝑇𝑎4 𝑆
(4)
where εw and εa are the emissivities of water and air, σ is the Stefan-Boltzmann constant (W m-2
K-4), and Ta is air temperature (K) from the NSRDB. Qe and Qc are found with the Buckingham
theorem:
𝑄𝑒 = −𝑚𝑒 𝐿𝑤 𝑆
(5)
𝑄𝑐 = ℎ𝑐𝑜𝑛𝑣 𝑇𝑎 − 𝑇𝑝 𝑆
(6)
-1
-2
-1
where me is the rate of evaporation (kg s m ), Lw is the latent heat of water (J kg ), and hconv is
a convection coefficient (W m-2 K-1).
Biophysical Model: Stochastic Results
Solar Radiation
Air temperature
Validation: ATP3 Dataset
• ATP3 testbed: controls for everything
besides weather across 5 sites
• Six 1,000L ponds at each site
Strain
Name
Media
Season
KA32
Nannochloropsi Saline
s maritima
LRB AZ
1201
Chlorella
vulgaris
Freshwate Year-round
r
C046
Desmodesmus
sp.
Saline
Year-round
Summer only
Biophysical Model: validation results
Florida Algae in Vero Beach, FL
20
Experimental
Productivity (g AFDW/m2/d)
18
Modelled
16
14
12
10
8
6
4
2
0
Apr 2014
Jul 2014
Oct 2014
Jan 2015
Date
Apr 2015
Jul 2015
Oct 2015
Model Schematic
Risk management
scenarios:
-Structural
adaptations
-Non-structural
adaptations
-Financial tools
Frequency
Goals:
• To isolate
the role of weather risk in driving
Biophysical Algal
Growth Modelin algal biomass productivity
fluctuations
Cash flow
Impact
• To assess the effectiveness of instruments
designed to protect against periods of
Improved outcomes
unexpectedly low productivity
ATP3 data
(validation & fitting)
ATP3 data
(validation)
Financial
outcomes
Frequency
Pond Temperature
Model
Environmental
outcomes
Frequency
Stochastic
Vector-Auto
Regression model
Combined life cycle
analysis (LCA)/ Technoeconomic analysis (TEA)
Frequency
Meteorological
Data
Stochastic Ornsteinuhlenbeck model
Price( ($/GGE)
Fuel price
Data
Impact
Cash flow
Error residuals
Seasonality in Productivity
20
18
16
14
12
10
8
6
4
2
0
1
2
3
Quarter
4