What we do.

Financial risk modeling for large solar and wind renewables.


Next generation investing. Today.

Equipped with advanced credit evaluation skills, we know what lenders & investors look for in transactions & how to stay one step ahead during negotiations.

Our analysis reveal that most models perform either sensitivity analysis &/or scenario testing. But this insight alone will not yield the likelihood of particular financial risks from eventuating.

Inspired by 2 forces of change. 

Shareholders want improved analytics from financial models:

1. What is the probability of achieving a levered equity IRR hurdle rate of 12%-17%?

2. What happens to NPV if load factor & O&M increase rate vary simultaneously over project life?

Basel regulations require lenders to quantify risks with more rigor:

1. What is the probability of the LLCR for a solar project being below a specified level?

2. What is the probability of default on an IPP project if DSCR falls below a specified level of 1.35?
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financial projections.

The bare minimum that every renewable energy project should have - 4 financial statements that tell the project & investor narrative.

Performance.

Revenue, O&M, EBITDA. PROFIT & LOSS

Liquidity.

Operational, Financing, Investment. CASH FLOW

Balance sheet.

Capital, Equity, Liabilities. FINANCIAL POSITION

Economic profit.

APV, CFE, 3 WACC methods. 5 APPROACHES
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valuation analytics.

Valuation is the most misunderstood concept in modern project finance. Purist approaches might seem trivial when dealing with small projects, but for larger marginal projects, tight hurdles may lead to the wrong investment decisions. For this reason, we have revisited the valuation literature extensively, with a view to reconcile theory with real world. The result - algorithms that read like poetry, renewed for an energy future.

Cash flow is the holy grail of RE project finance. Meticulous crafting of project assumptions and framing of inputs allow our model to forecast financials with care. We triangulate different valuation approaches to validate outputs. Three techniques combine the use of APV, CFE, & WACC methods. And finally, our bespoke project finance scorecard effectively communicates the test results of Monte Carlo simulation runs.

A zero tolerance approach to hard coding makes our IP extremely powerful. With the ability to rapidly expand or contract a 2-dimensional simulation routine, multiple vectors are wrapped in Monte Carlo simulations, with as many iterations required to stabilize model outputs. Black Scholes model is used to value the timing of investment cash flows on options. Ener Re is founded on the background - that financial engineering can harness better decisions for RE investments.
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model auditing.

"A representation of relationships among variables, or events using statistical, financial, economic, mathematical, or scientific concepts and equations." - American Academy of Actuaries.
Verify, validate, & assess
Verify correctness.

mathematical formulas & covenants with algorithms
Validate consistencies.

input assumptions & conventions against documentation
Assess adequacy.

hard coding invalidity & incorrect contractual sharing
Check, rate, & advise
Check consistent application.

check proper application of input assumptions in algorithms
Rate model functionality.

sensitivities, scenarios, Monte Carlo simulation, options pricing
Advise of improvements.

opinion on overall design & where it can be improved
Synthesis of audit findings.

report results of model evaluation, tests, & recommendations
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deterministic modeling.

Our deterministic models use logic & calculations to produce a single set of outputs for a single set of inputs. Deterministic methods such as sensitivity analysis & scenario testing are used to measure the risks underlying the RE project financing.
Input assumptions.

We work with sponsors to balance input assumptions
Sensitivity analysis.

Varying individual key input variables using a range of values
Point estimates.

All input variables are static with no inherent uncertainty
Scenario testing.

Varying a collection of input variables simultaneously

From point to sensitivity to scenario. And finally Monte Carlo. In another continent.
SCENARIO TESTING
DESCRIPTION OF TEST

Base case scenario

Conservative assessment of project risks. Set the blended debt amortisation schedule of the finance structure. Lender's case. Proceed with project.

 

 		Lenders  
Worst case scenario

Pessimistic assessment of project risks. Set the level of the reserve accounts. Stress test loan principle repayments & impact of lock-up provisions. Abandon project.


Both        
Best case scenario

Optimistic but likely assessment of project risks. Test pre-payment & refinancing options. Proceed with project.


Sponsors
Arbitrary scenarios

Other scenarios may be tested based upon the results of the sensitivity analysis. Set thresholds to proceed with project.


Both        
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stochastic modeling.

Our stochastic models use logic & iterative computations to produce a range of outputs with associated probabilities from input distributions - per uncertain input variable. Combining multidisciplinary techniques adopted from finance, engineering, actuarial, & computer science fields.

The choice of the input distribution is project specific, & based on specific conditions of how the uncertain input variable behaves. While many very expensive commercial simulation packages deploy a limited number of distribution options with hard coding, the Ener Re proprietary simulation engine has the flexibility to deploy numerous types of statistical distributions, tailored for specific RE projects. We can achieve this objective for clients, as we have full access control over our R source code.

Samples are generated from the input distributions that best describe the volatility per uncertain input variable.

The optimal amount of simulations are reached when the outputs start converging to a level of confidence.

The stochastic method of Monte Carlo simulation is used to measure the risks underlying the RE project financing.
MONTE CARLO SIMULATION

Probabilistic likelihood of variation in load factor, electricity price increase, & O&M rate variables eventuating.
BLACK SCHOLES MODEL

Using options pricing theory to calculate the economic benefit of undertaking an option early on in the project.
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product features.

"A representation of relationships among variables, or events using statistical, financial, economic, mathematical, or scientific concepts and equations" - American Academy of Actuaries.
R CODE & SPREADSHEET MODELS.
Flexibility.

ADAPTABLE - algorithm design is efficient & support rapid tailoring & evolution
Appropriateness.

MINIMALIST - optimized & clutter-free with precision fit for purpose
Model governance checklist.

STANDARDS - modeling process, assumption setting, input data/tables mapping, access control, versioning, consolidation reporting, & validation analysis
BENCHMARKED AGAINST GLOBAL GUIDELINES.
Structured.

EVALUATE - Sensitivities, scenarios, Monte Carlo simulations & options pricing
Transparent.

CLARITY - logic structure, neat layout, simple to understand, no viruses, full commentary, & scalable for additional features as information becomes available


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modeling & simulation process.

Feature requirements

Engage with project sponsors & research with OEMs to formulate project assumptions.

Algorithm tailoring

Customisation of proprietary algorithms to generate desired investment metrics.

Simulation engine

Vary multiple variables at once to determine chances of particular risks occurring.

Model reporting

Scorecard summary of pass/fail outcomes across a pre-determined list of thresholds.
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