Renewable Energy Model Checklist
Executive Summary
Key Takeaways
- ✓ Renewable energy models combine standard project finance debt mechanics with technical yield and degradation assumptions that require their own, sector-specific checklist items.
- ✓ Energy yield assumptions (P50/P90) and annual degradation rates directly drive revenue and debt service coverage, and should be sourced from an independent technical report rather than the developer's own estimate alone.
- ✓ PPA and merchant revenue assumptions require separate testing, since a project with partial merchant exposure carries materially different revenue risk than one fully contracted under a PPA.
- ✓ Availability and curtailment mechanics are frequently underweighted in review relative to yield assumptions, despite being an equally direct driver of actual energy revenue.
Objective¶
This checklist verifies the technical and revenue assumptions specific to renewable energy project finance models: energy yield, degradation, availability, curtailment, and PPA/merchant revenue structure. It exists as a distinct checklist because these mechanics do not appear in a generic project finance model and are not covered by the Lender Model Review Checklist or the general Financial Model Audit Checklist, both of which this checklist assumes have already been applied.
Renewable energy models sit at the intersection of technical due diligence and financial model audit: yield and degradation assumptions originate from technical reports but must be correctly and consistently implemented in the financial model's revenue and debt service calculations. This checklist is built around the structural failure modes at that intersection.
Applicability¶
Applicable when a financial model is being built or reviewed to support a solar, wind, storage, or other renewable energy project, ahead of a financing decision, financial close, or investment approval. Relevant to lenders, developers, and advisors, and most directly applicable to project finance style transactions, though the yield, degradation, and revenue structure checks apply to any renewable asset model regardless of financing structure.
Checklist¶
| # | Check Item | Why It Matters | Evidence to Collect |
|---|---|---|---|
| 1 | Energy yield assumption (P50/P90 or equivalent) is sourced from an independent technical or resource assessment report | Developer-generated yield estimates without independent verification are a common source of overstated revenue projections | Independent technical report reference and yield figure cross-check |
| 2 | The correct yield exceedance probability is used for the correct purpose (e.g. P90 for debt sizing, P50 for base case) | Using an optimistic yield case for a conservative purpose, or vice versa, misstates either the debt capacity or the base-case return | Yield case usage cross-check against purpose |
| 3 | Annual degradation rate is applied consistently across the full asset life and sourced from equipment specification or technical report | An understated or omitted degradation rate overstates long-term revenue and therefore long-term coverage ratios | Degradation rate documentation and formula trace |
| 4 | Availability assumption reflects planned and unplanned downtime distinctly, sourced from equipment warranty or O&M contract terms | An overstated availability assumption overstates energy revenue independent of any yield or degradation error | Availability assumption sourcing |
| 5 | Curtailment risk (grid or offtaker-imposed) is modelled as an explicit, separate reduction from theoretical output | Curtailment is a distinct risk from availability and, if omitted, overstates realised energy revenue | Curtailment assumption documentation |
| 6 | PPA-contracted revenue and any merchant-exposed revenue are modelled as separate line items with distinct pricing assumptions | Blending PPA and merchant revenue into a single assumption conceals the project's actual exposure to market price risk | PPA/merchant revenue split and pricing basis |
| 7 | PPA price escalation, if any, matches the actual contract terms rather than a generic inflation assumption | A generic escalation assumption applied to a fixed-price PPA misstates contracted revenue | PPA contract term cross-check |
| 8 | Seasonality of energy production is reflected at a sub-annual level where debt service or covenant testing occurs sub-annually | Modelling only annual output can mask periods of low production that create genuine coverage ratio stress | Sub-annual production profile check |
| 9 | Technology-specific mechanics (e.g. storage cycling degradation, wind wake effects, solar soiling losses) are explicitly modelled, not folded into a single generic loss factor | Folding distinct loss mechanisms into one blended factor obscures which assumption is driving the result and makes sensitivity testing unreliable | Loss factor breakdown documentation |
| 10 | DSCR and LLCR are recalculated incorporating the full yield, degradation, and availability chain, not a simplified proxy | A simplified revenue proxy used for coverage ratio calculation can materially diverge from the fully modelled technical revenue chain | Independent DSCR/LLCR recalculation using full technical assumption chain |
| 11 | Sensitivity analysis specifically tests yield and degradation assumptions, in addition to standard financial sensitivities | Yield and degradation are the assumptions most specific to this asset class and most likely to be under-stress-tested by a generic sensitivity template | Yield/degradation sensitivity test results |
| 12 | Decommissioning or end-of-life costs and any associated reserve are included where relevant to the asset's expected life | Omitting end-of-life costs can overstate terminal value or understate total lifecycle cost obligations | Decommissioning cost and reserve documentation |
Common Failures¶
- Yield assumption sourced from the developer's own estimate rather than an independent technical report, with no disclosed basis for the figure used.
- P50 yield used for debt sizing rather than a more conservative exceedance case, overstating the coverage ratio a lender is actually protected by.
- Degradation rate omitted or applied only partially across the asset life, overstating revenue in later years of the debt tenor.
- PPA and merchant revenue blended into a single assumption, concealing the project's actual exposure to market price risk during any merchant-exposed period.
- Curtailment risk left unmodelled entirely, with realised revenue assumptions equal to theoretical output net of availability only.
- Multiple technical loss factors (soiling, wake effect, transformer losses) folded into one blended derate factor, making it impossible to sensitivity-test any single driver.
Recommended Evidence¶
A completed renewable energy model review should be accompanied by the underlying independent technical report, a yield and degradation assumption sourcing log, and an independent DSCR/LLCR recalculation incorporating the full technical assumption chain. The table above is structured for direct use in model governance documentation, a lender due diligence file, or an audit working-paper file supporting a financing decision.
How to Use This Checklist¶
Apply the Lender Model Review Checklist first for general debt and covenant mechanics, then work through this checklist with the independent technical report in hand, cross-checking each yield, degradation, and availability assumption against its documented source. See Financial Model Audit for Renewables for industry context and Circular Reference in a Renewable Energy Debt Model Caught Before Drawdown for an applied example.
Continue Reading¶
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Frequently Asked Questions
What makes a renewable energy model different from a standard project finance model, for audit purposes?
It combines standard project finance debt mechanics with technical assumptions specific to the energy source — resource yield, equipment degradation, availability, and curtailment — that directly drive the revenue feeding the debt structure, none of which appear in a generic infrastructure model.
What is P50/P90 energy yield, and why does this checklist test it?
P50 is the yield level expected to be exceeded in 50 percent of years; P90 is the more conservative level expected to be exceeded in 90 percent of years. Lenders typically size debt to a P90 or similar conservative case, and this checklist verifies the model uses the correct yield case for the correct purpose.
What is degradation, and why is the assumed rate checked closely?
The gradual decline in equipment output over its operating life, typically modelled as an annual percentage reduction. An understated degradation rate overstates long-term revenue and therefore long-term debt service coverage.
How does a PPA differ from merchant revenue, and why does this matter for the model?
A power purchase agreement (PPA) contracts revenue at a fixed or formulaic price for a defined term; merchant revenue is sold at prevailing market price with no such contract. A project with material merchant exposure carries price risk a fully PPA-contracted project does not, and the model should reflect this distinction explicitly.
What is curtailment, and why is it checked separately from availability?
Curtailment is a reduction in output due to grid or offtaker-imposed limits, distinct from availability, which reflects the equipment's own operating uptime. Both reduce actual energy revenue relative to theoretical output and should be modelled as separate, explicit mechanics.
Who typically uses this checklist?
Lenders financing a renewable energy project, developers preparing a model for financing, and advisors conducting an independent structural and technical-assumption review ahead of financial close.
How does this checklist relate to the lender model review checklist?
The lender model review checklist covers general project finance debt and covenant mechanics. This checklist adds the renewable-energy-specific technical assumptions — yield, degradation, availability, PPA structure — that feed into those same debt mechanics.
Does this checklist apply to storage as well as generation assets?
The core structure applies; storage-specific mechanics such as cycling assumptions and degradation tied to charge/discharge cycles rather than time should be reviewed with equivalent scrutiny to generation yield and degradation.
Related Articles
What Is a Financial Model Audit?
A financial model audit is an independent, structured examination of an Excel based financial model to confirm that its mechanics, logic, and outputs are reliable enough to support a decision. It is not a check of whether the assumptions are optimistic or conservative. It is a check of whether the model actually calculates what its author believes it calculates. Every year, lenders extend debt, investment committees approve capital, and boards sign off on transactions using numbers that came out of a spreadsheet nobody outside the immediate deal team has independently verified. A financial model audit exists to close that gap before it becomes expensive.
What Is a Project Finance Model Audit?
A project finance model audit is a financial model audit applied to the specific class of model used to finance infrastructure, energy, and long dated capital projects: debt sculpted, multi decade, cash flow driven structures with mechanics that do not appear in a typical corporate model. It is frequently a formal condition of financial close, not an optional check, and lender requirements for it exist almost entirely inside non public bank credit policy rather than any single consolidated public source. This page defines what makes project finance models structurally distinct, why lenders require independent verification of them specifically, and what the audit process looks like in this context.
Audit vs Validation — What's the Difference?
Financial model audit and model validation are frequently used as interchangeable terms, and specifying the wrong one in a lender requirement or an internal policy leads to real confusion about what has actually been checked. They test different things. An audit tests whether a model's mechanics are correct. Validation tests whether the model's methodology and assumptions are appropriate for its intended purpose. Both are legitimate, useful exercises. They are not substitutes for each other.