SunBurn Test

© 2018 Fadi Maalouf
The SunBurn Test™ Page 1 of 10
Trending! The SunBurn Test™
How to Integrate Climate Change Risks in Capital Budgeting for Solar PV Plants

Trending! The SunBurn Test™
Me speak no good english, that’s unpossible! Such a statement is hilarious because it proves the contrary
of its intended claim. An equally hilarious statement is a one that still alleges that climate change
phenomenon is a three-scenario hypothesis: hoax, plausible, or confirmed.
Long story short, it is confirmed, and the scientific evidence is plentiful.
So, the question is now how to deal with climate change related risks and opportunities? A growing market
trend is evolving for better climate change related risk and opportunity transparency and it may soon
become a regulatory or at least a market enforced good practice requirement where corporations will be
required to report climate change related impacts in their financial reporting and disclosure. The
disclosure shall cover both current short-term and forward-looking long-term impacts projections.
This article sheds some light on climate change risks in the context of solar PV power plant project
financial feasibility health-check and how climate change risks impacts can be modelled through a stress
test: The SunBurn Test™, or SBT™ in short.
The proposed test approach is not a scientific research paper but is rather based on a risk management
approach that makes use of available or reasonably forecasted scientific data coupled with scenario
analysis stress test for a baseline case.
Skin sun burns result from the excessive unprotected exposure to direct sunlight. Such
burns can be very painful. In contrast, solar PV power plant welcome such excessive
exposure! However, if that sun exposure is reduced or altered from design levels, the
solar PV power plant will turn pale along with its Independent Power Producer (IPP)
investors! The project forecasted “Internal Rate of Return” IRR will evaporate, i.e. fall
drastically below forecast level, and the project forecasted “Debt Service Coverage Ratio” DSCR will
condensate, i.e. breach contracts covenant levels to the point of default. Unfortunately, such a scenario
may be possible with climate change risks. The SunBurn Test™ may help understand such misfortunes
of pain or financial loss and thus guide us to mitigate them. One could call it The SunPale Test™, but
since climate change risks cause equivalent or even more pain thank skin sun burns, the sun burn
analogy fits better!
Simply and concisely put, Solar PV power plant financial feasibility is modelled based on its forecasted
Levelized Cost of Electricity (LCOE). Such model is based on the principles of Capital Budgeting in
Financial Management. The model uses various specific inputs, generates a cashflow waterfall, discounts
it at an appropriate discount factor, and outputs the LCOE and other financial covenants such as Equity
IRR, DSCR, PLCR, LLCR, DSRA, MMRA, and many others. Simplified basic financial model for a
baseline case is shown in Figure 1.
Furthermore, it is important to understand the impacts of the variance of any input parameter on the
resulting output (LCOE). This is done through one-dimensional sensitivity analysis were one input
parameter is varied a certain percentage and the resultant LCOE change is plotted in a chart. This is
shown in Figure 2.
As expected, the Figure 2 chart helps us visualize the result of an input parameter variance and its
material impact on forecasted LCOE. The material impact may be become a dangerously compounding
effect when multiple parameters vary simultaneously and whose net variance increase the LCOE.
In other words, if parameters vary and the result increases the forecasted LCOE and if we would like to
keep the LCOE constant since we have already signed power purchase agreement and built the project.
then we must adjust other input parameters that counter the increase with an equivalent decrease back
to baseline LCOE. Post project commercial operation date (PCOD), that counter measure will be the
project WACC and thus its Equity IRR, as well as a definite secondary effect of reduced measured DSCR.
That is no good!

Figure 1 – LCOE Financial Model – Baseline Case
Figure 2 – LCOE Sensitivity Analysis – Baseline Case for 25-Year Term
INPUTS
General LCOE Component Component $ ¢/kWh Component Percentage
Analysis Period (years) 25 & 20 Capex Component 2.073064501 86.20%
Finance Structure Opex Component 0.331940496 13.80%
Debt Percentage 76.00% Total Percentage Check
Equity Percentage 24.00% 100.00%
Debt Interest Rate 3.00%
Return on Equity Rate 7.00% LCOE ($ ¢/kWh) 2.405004997
WACC / Nominal Discount Rate 3.96%
Capital Expenditure
Overnight EPC Cost ($/kWp) $700.00
Overnight Development Cost ($/kWp) $10.00
Total Overnight CAPEX Cost ($/kWp) $710.00 LCOE Component Component $ ¢/kWh Component Percentage
O&M Expenditure Capex Component 2.359810857 88.80%
Fixed Annual O&M ($/kWp/year) $8.50 Opex Component 0.297589947 11.20%
O&M Annual Escalation (%) 1.20% Total Percentage Check
System 100.00%
Power Plant Installed Size (kWp) 1.00
Estimated Annual Specific Yield P50 (kWh/kWp) 2,325.88 LCOE ($ ¢/kWh) 2.657400804
Installed Annual Energy Output (kWh) 2,325.88
Annual Energy Degradation Year 1 (%/year) 0.00%
Annual Energy Degradation Year 2 to 25 (%/year) 0.60%
Power Plant Annual Availability (%) 99.60%
Net Annual Energy Output Year 1 (kWh) 2,316.58
Residual Value at End of Service Life
Salvage % of EPC at Year 25 14%
PV POWER PLANT PROJECT LCOE
PRE-FEASIBILITY ECONOMIC ANALYSIS
OUTPUTS - 20 Years
OUTPUTS - 25 Years

But, where is the climate change in all of that scary scenario? Climate change results in risks related to
the assumed validity (or partial invalidity or accuracy) of the P50 TMY weather file where such historically
based data sets file may not represent the true P50 baseline for the next 25 years. Therefore, our
estimated P50 energy yield may consistently fall short of forecasts. Additionally, the cousin of our P50
weather file, the P99, may also fall short of estimates. Moreover, climate change has the potential of
impacting our forecasted O&M costs significantly, as well as impacting our other LCOE financial model
input parameters. All these impacts may compound negatively!
A qualitative and quantitative risk management approach can help us understand climate changes risks
and their impacts on our solar PV project forecasted LCOE and hence the Equity IRR and other covenants
like DSCR. Climate change related risks may exhibit attributes as shown in Table 1.
SunBurn Test™
Scenario Analysis Model
Climate Change Risk Register (Extract)
S.N. Qualitative Quantitative
1 Year 1
Air pollution (PM2.5/10, smog/haze)
Not accounted for or fully accounted for in historical
P50 TMY weather file and P50 forecasted energy
yield report
Decreased solar irradiance, decreased annual
energy yield, whilst noting that different PV
module technologies get impacted differently
according to their light spectrum range
2 Forward looking: Year 2 onwards till plant end of life
(PPA term). Continuous percentage increase YoY in
air pollution
Continuous percentage decrease YoY in solar
irradiance, continuous percentage decrease YoY
in annual energy yield
3 Year 1
Higher annual average ambient temperature than
forecasted in historical P50 TMY weather file (global
warming due to GHG, frequent heat wave events)
and hence impacting P50 forecasted energy yield
report
Decreased annual energy yield, decrease is
proportional to solar module power temperature
coefficient
(PPA term). Continuous percentage increase YoY in
annual average ambient temperature, net 2 °C
increase in the next 30 years
(straight line, slope +0.0666 °C / year)
Continuous percentage decrease YoY in annual
energy yield, decrease is proportional to solar
module power temperature coefficient
5 Year 1
Extreme weather events
Increased frequency of sand storms and/or muddy
rain and/or acid rain
Increase in solar modules cleaning frequency
(dry and wet), Increase in OPEX Cost (parts &
labor & water consumption rate as well as water
unit cost rate due to scarcity)
(PPA term). Consistent extreme and harsh weather
events
Accelerated solar module power degradation,
higher percentage rate per annum, Decreased
energy yield
7 Extreme weather events that result in
increased frequency of preventive and corrective
maintenance events
Increase in OPEX Cost due to increase MTBF
(parts & labor costs)
8 Extreme weather events that result in
equipment being out-of-operating-range (high wind
speed events for tracking systems, ambient
temperature Tmax & Tmin, etc.), and hence plant on
temporary curtailment or shutdown
Decrease in power plant annual availability
percentage
9 Adverse weather events that result in
increased frequency of preventive and corrective
maintenance events (hurricanes, floods, landslides,
wild fires, etc.) requiring partial or complete power
plant shutdown events
Decrease in power plant annual availability
percentage
10 Consistent adverse weather events YoY
that result in insurance claims (hurricanes, floods)
Increase in OPEX Cost due to increase in
insurance costs
11 Catastrophic climate change phenomenon (rising
sea levels) that necessitate remedial measures
Increase in CAPEX and OPEX Costs due to
protection and fortification measures
12 New & emerging risks attributable to climate change Ongoing proactive analysis
Table 1 – Climate Change Risk Register (Extract)

The above risks are associated with climate change. This analysis will not be complete without evaluating
and noting opportunities that may be associated with climate change. Such opportunities may create an
“upside” potential in certain locations. It is important to note that the above risk list is generic. In addition,
risks are site specific and not all sites will experience all and the same the risks, but all sites may
experience at least a number or risks.
To run the SunBurn Test™, a risk must have Net Value that will impact an input parameter of our LCOE
financial model. The Net Risk Value calculation is a two-step approach: First we need to calculate the
Gross Risk Value and then the Net Risk Value.
Gross Risk Value = GRV = Risk Value x Probability of Occurrence = RV x PO
Net Risk Value = NRV = Gross Risk Value x Post-Mitigation Correction Factor = GRV x PMCF
NRV = RV x PO x PMCF
PO = 0% to 100%
PMCF = 0 to 1
PMCF examples as follows:
PMCF = 0 is for fully mitigated Gross Risk and hence no residual risk remains (no Net Risk Value)
PMCF = 1 is for fully unmitigated Gross Risk and hence remaining residual risk (NRV) equals GRV
PMCF = 0.7 is for 30% mitigated Gross and hence 70% residual risk remains (NRV)
PMCF = 0.25 is for 75% mitigated Gross Risk and hence 25% residual risk remains (NRV)
Mini Case: Hypothetical Example of SunBurn Test™
Risk A Description: Air pollution resulting in 4% decrease in annual energy yield
RV = P50 Annual Energy Yield Baseline Value x (-0.04)
PO = 75%
PMCF = 1
NRV = P50 Annual Energy Yield Baseline Value x (-0.04x0.75x1)
Risk B Description: Year 2 onwards till plant end of life (PPA term), continuous percentage
increase YoY in annual average ambient temperature, net 2.5 °C increase in the next 30 years
(straight line, slope +0.0833 °C / year) which result in annual energy yield decrease of 0.0375%
RV = P50 Annual Energy Yield Baseline Value x (-0.000375xN) ; N = Year number -1
PO = 100%
PMCF = 1
NRV = P50 Annual Energy Yield Baseline Value x (-0.000375xN1x1)
Risk C Description: Extreme weather events causing increased frequency of preventive and
corrective maintenance events which result in additional annual OPEX of 25%
RV = Annual OPEX Baseline Value x 0.25
PO = 50%
PMCF = 1
NRV = Annual OPEX Baseline Value x (0.25x0.50x1)
Risk D Description: Adverse weather events causing increased frequency of preventive and
corrective maintenance events and/or plant-out of-operating-range requiring partial power plant
shutdown events, hence plant’s overall annual availability baseline value is reduced by 2%
RV = Plant’s Annual Availability Baseline Value x (-0.02)
PO = 50%
PMCF = 1
NRV = Plant’s Annual Availability Baseline Value x (-0.02x0.5x1)

Risk E Description: Year 2 onwards till plant end of life (PPA term), consistent extreme and harsh
weather events causing accelerated solar module power degradation, annual degradation rate
increases by 20%
RV = Solar Module Annual Power Degradation Baseline Value x (0.20)
PO = 75%
PMCF = 1
NRV = Solar Module Annual Power Degradation Baseline Value x (0.20x0.75x1)
To stress test our earlier calculated 25 Years LCOE Baseline Case of 2.40 $ ₵/kWh, we apply the five
calculated NRV values to our financial model inputs. The resulting Climate Change Risk Weighted LCOE
is shown in Figure 3.
Figure 3 – Climate Change Risk Weighted LCOE
The 25 Years Baseline Case LCOE of 2.40 $ ₵/kWh increased by 7.85%, whilst holding all baseline case
inputs constant expect for the five climate change risks adjustments. Hence, climate change risks have
a significant impact in this specific case modelling.
INPUTS
Capital Expenditure
System 100.00%
4%
75%
0.0375%
100%
25%
50%
2%
50%
20%
75%
25 Years LCOE Increase from Baseline Case 7.852%
OUTPUTS - 20 Years
The SunBurn Test™ - Stress Test Scenario Analysis Model
Climate Change Risks
Probability of Occurrence x Post-Mitigation Correction Factor
Adverse Weather Events - Decrease Annual Availability
Extreme & Harsh Weather - Increase Annual Module Degradation, Yr2+
OUTPUTS - 25 Years
Air Pollution - Decrease in Energy Yield
Ambient Temperature Increase - Decrease Energy Yield Annually, Yr2+
Extreme Weather Events - Increase OPEX

Assuming that we have already signed the 25-year power purchase agreement (PPA) and built the
project, then we need to hold our 25 Years Baseline Case baseline LCOE value constant at 2.40 $ ₵/kWh.
Keeping the stress test risks in effect, then we calculate resultant Return on Equity (Equity IRR). This is
done via iteration, Excel’s Goal Seek function, sensitivity analysis (similar to Figure 2), a fancy macro, or
an advanced financial model with built-in functionality. The resultant Return on Equity (Equity IRR). is
shown in Figure 4 where baseline case LCOE is maintained at 2.40 $ ₵/kWh. A very minor error is noted,
0.037% variance in 25 Years LCOE. This is due to rounding and it can be safely ignored.
Additionally, it is noted that 20 Years LCOE does not set back to baseline case when we optimize for the
25 Years LCOE and this is due to their different cash flow term and common input parameters. If 20
Years is our baseline case term, then solving for Return of Equity can be performed via the same
aforementioned techniques on the basis of 20 Years LCOE. The result shown in Figure 5.
Figure 4 – Climate Change Risk Weighted Return on Equity (Equity IRR) – 25 Years
Under the SunBurn Test™ scenario analysis model for a 25-year term, the Return on Equity dropped
from 7.00% to 3.61%, a 48.42% decrease. This is a very significant change that will cause considerable
financial loss and some painful “evaporation” of IRR value. This is, of course, under the assumed scenario
parameters.
INPUTS
Capital Expenditure
System 100.00%
4%
75%
0.0375%
100%
25%
50%
2%
50%
20%
75%
OUTPUTS - 20 Years
OUTPUTS - 25 Years

Figure 5 – Climate Change Risk Weighted Return of Equity (Equity IRR) – 20 Years
Under the SunBurn Test ™ scenario analysis model for a 20-year term, the Return on Equity dropped
from 7.00% to 3.37%, a 51.85% decrease. Again, this is a very significant change that will cause
considerable financial loss and some painful “evaporation” of IRR value. Once again, this is under the
assumed scenario parameters.
Good financial modelling practice calls for model integrity checks functionality. In other words, what if
these seemingly complex adjustments of integrating climate change net risk values (NRV) to baseline
financial model have compromised the integrity of the baseline model calculations and formulae? This is
a truly valid question.
Therefore, an integrity check for our financial model is necessary.
Advanced financial models shall have built-in or automated integrity checks functionality. The model used
in our analysis is a simplified one. Nonetheless, a simple and quick integrity check can be performed by
setting the “Probability of Occurrence x Post-Mitigation Correction Factor” of all climate change risks to
zero. With all other input parameters held constant, the model LCOE output shall revert to baseline case.
This is shown is Figure 6.
INPUTS
Capital Expenditure
System 100.00%
4%
75%
0.0375%
100%
25%
50%
2%
50%
20%
75%
25 Years LCOE Increase from Baseline Case -0.500%
OUTPUTS - 20 Years
OUTPUTS - 25 Years

Figure 6 – Model Integrity Check with Zero Climate Change Risks
Thus far, our analysis indicated that the assumed climate change risk scenario has a significant impact
on baseline case LCOE. The Climate Change Risk Weighted. LCOE has increased. Using our basic
financial model, this in turn translated to a significant decrease in the projected Return on Equity Rate
when baseline case LCOE is held constant.
But what about the impacts on Debt Service Coverage Ratio (DSCR) and other project finance term sheet
covenants?
From a qualitative perspective, a decrease in Return on Equity Rate signifies a decrease in project
operating income cashflow (due to operating revenue decrease, everything else held constant) and hence
a decrease in Cashflow Available for Debt Service (CFADS). A decreased CFADS implies in a decreased
DSCR (DSCR = CFADS / Debt Payment). To quantify the decrease in DSCR, an advanced financial
model that factors in the debt structure (type, tenor, T&Cs) will be required. Analysis using advanced
financial modelling will be utilized in a future publication. So, stay tuned for Part 2 of this SunBrun Test™
article!
INPUTS
Capital Expenditure
System 100.00%
4%
0%
0.0375%
0%
25%
0%
2%
0%
20%
0%
OUTPUTS - 20 Years
OUTPUTS - 25 Years

In summary, The SunBurn Test™ (SBT™) key takeaways are:
1. Climate change is a reality. It presents both risks and opportunities, which can be generally
categorized as current short-term impacts and forward-looking long-term impacts.
2. A global mega trend is evolving where corporations will be required to report climate change
related impacts in their financial reporting and disclosure. Hence corporations are integrating
climate related impacts in their corporate strategies.
3. In the context of Independent Power Producers and solar PV power plants, understanding and
accounting for climate change related impacts is paramount.
4. SBT™ is stress test technique in which a scenario analysis is applied to health-check the financial
feasibility of a solar PV power plant. The stress parameters are derived from climate change
related risks.
5. SBT™ is a process that utilizes:
a. Location specific climate change risks from credible scientific research where historical
measured data is modelled to create forward looking climate projections.
b. Risk Management approach to qualify and quantify climate change related risks.
c. Resultant risks values form a scenario and are used to stress test a project baseline case
financial feasibility model.
d. The goal is to determine whether the stressed project remains financially viable. For solar
PV power plant, the focus is equity IRR, DSCR, amongst other covenants.
e. Care of not falling in the trap of GIGO: Garbage In » Garbage Out. Modelling parameters
must neither be artificially low nor doomsday high!
6. A hypothetical stress test with a few selected risks was run. It indicated significant impact on a
solar PV power plant project profitability, especially in very competitively priced LCOE’s with
single digit IRR’s.
7. SBT™ is a useful technique. It may help prevent a nasty sun burn!
Sunny Regards.
Fadi Maalouf
CTO – Director IPP & EPC
Dii Desert Energy
fadi@dii-desertenergy.org
+971 50 624 6126
Disclaimer: This document does not constitute legal, financial, technical advice nor any advice of any
sort. It is issued for general information and research purposes only. All stakeholders should seek their
own in-house and/or external suitably qualified and experienced professional certified advisors. The
author and Dii’s, affiliates, agents, officers, directors, advisors, consultants, advisory board members and
employees do not warrant the correctness, completeness, accuracy of this document nor fitness of
information covered in this document for any purpose, and shall not be held liable for any direct, indirect,
special and consequential liability nor any sort of losses, injury and damages or likewise resulting from
the use of information covered in this document.

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