In project finance, understanding how debt sizing works is a foundational skill — especially in renewable energy deals where production output is uncertain. One of the most commonly tested — and misunderstood — topics in interviews is the difference between P50 vs P99 assumptions and how they’re used in structuring debt. This article breaks down everything you need to know, featuring insights from our full 1-hour video case study on the topic.

Why P50 vs. P99 Matters

Lenders want to ensure that debt is repaid reliably — even in downside scenarios. That’s why they rely on yield-based sizing methods, using probability-weighted energy production assumptions:

• P50: The “expected” case. There’s a 50% chance actual production will exceed this value.

• P99: A downside scenario. There’s only a 1% chance actual production will fall below this value.

While P50 assumptions are typically used to size debt under a base case DSCR (e.g., 1.30x for contracted revenues), some lenders impose an additional P99 constraint — usually with a lower DSCR threshold (e.g., 1.00x) to account for rare but plausible underperformance.

In essence, you don’t size debt twice. You calculate two different debt capacity levels — one based on P50, the other on P99 — and then use the lower of the two.

🎥 Watch the Full Modeling Walkthrough (1h 22min)

Understanding Energy Yield Uncertainty

Renewable energy projects — especially solar and wind — have inherent variability in production. Energy yield studies often model this uncertainty using a normal distribution around the P50 value.

For example, if the P50 yield is 2,100 MWh/MWp/year and the standard deviation is 10%, the distribution looks like this:

• P75: ~93% of P50

• P90: ~87% of P50

• P99: ~77% of P50 

This means a project might underproduce relative to expectations — a key risk that must be considered in debt structuring. When debt is sized based solely on the P50 case, the inherent uncertainty of energy production is disregarded. Prior to financial close, lenders typically rely on energy yield assessments prepared by an Independent Engineer or Technical Advisor. These assessments estimate the probability distribution of expected production, including P50, P75, P90, and P99 values. While such assessments remain estimates, they represent the most reliable source available during project structuring. Sizing debt exclusively on the P50 case leaves lenders fully exposed to downside production variability, with no additional sizing constraint to mitigate the risks of underperformance.

Understanding CFADS and Its Role in Debt Sizing

Cash Flow Available for Debt Service (CFADS) is the core metric lenders rely on when assessing how much debt a project can support. It captures the true cash generation available for interest and principal repayments, after all operating obligations and taxes have been considered. In the context of P50 vs. P99 debt sizing, CFADS must be modeled under both scenarios to assess how the project performs across a range of production outcomes.

What CFADS Represents

CFADS is typically calculated as follows:

• Revenue (based on either P50 or P99 energy yield)

• Minus operating expenses (including fixed and variable O&M, insurance, etc.)

• Minus land lease payments

• Minus corporate taxes (based on taxable income after applying depreciation, interest deductions, and other adjustments)

• = Cash Flow Available for Debt Service

It’s important to note that both land lease payments and corporate taxes need special attention in yield-based debt sizing. These line items are always included when calculating CFADS, but their values can differ significantly between P50 and P99 production scenarios:

• Land lease payments often scale with revenue. If structured as a percentage of revenue, they must be recalculated for each production assumption (P50 vs. P99), as revenue will differ between the two.

• Corporate taxes are affected by changes in taxable income, which in turn depends on revenues, operating expenses, and interest deductions. Lower revenues under P99 typically reduce taxable income and taxes—but the exact impact varies depending on the overall structure of the project’s cash flows and financing.

Why CFADS Matters in P50 vs. P99 Scenarios

Because CFADS is production-sensitive, separate calculations under both P50 vs. P99 are required. The P50 scenario typically results in higher CFADS and can support more debt at a given DSCR, while the P99 case ensures lenders test for downside protection in years of underperformance.

Ultimately, each scenario yields a maximum permissible debt service based on its own CFADS and the lender’s DSCR requirement. The lower of the two values becomes the binding constraint, helping lenders ensure the project can service debt even under conservative (P99) production outcomes.

How Dual Constraints Work in Practice

In real-world project finance models, lenders often apply dual constraints to ensure sufficient downside protection. This involves running two parallel CFADS P50 vs. P99 calculations—one based on expected production (P50) and the other on a conservative downside case (P99). Each scenario then informs a separate debt sizing constraint using its respective Debt Service Coverage Ratio (DSCR) target.

Take this example from our advanced case study featured in the course How to Ace the Toughest Project Finance Modeling Tests:

• P50 DSCR Target: 1.20x

• P99 DSCR Target: 1.00x

• Maximum Leverage Cap: 90% of total project costs

In the model, you calculate:

• CFADS_P50 → size debt to achieve a minimum 1.20x DSCR

• CFADS_P99 → size debt to achieve a minimum 1.00x DSCR

These two calculations will typically result in different maximum debt service amounts across the tenor of the loan. In a well-structured model, both scenarios produce their own debt service constraints (often through a Present Value of Debt Service calculation), which are then compared period-by-period.

👉 The binding constraint is whichever scenario produces the lower allowable debt service in a given period.
From there, debt is sculpted accordingly—usually using a target DSCR-based approach—to match the most conservative result. This ensures that even under P99 production assumptions, the project can still meet its debt obligations without distress.

This approach does more than safeguard the lender’s interest—it also builds a more bankable, robust capital structure that can withstand revenue volatility. Modeling this properly requires parallel structures for revenue, variable expenses, taxes, and ultimately CFADS across both P50 and P99 cases. We walk through this entire approach step-by-step in our 80+ minute video case study included in the course How to Ace the Toughest Project Finance Modeling Tests.

Modeling P50 vs. P99 Side by Side

To implement this properly in Excel, the full project cash flow needs to be modeled under both energy yield assumptions:

👉 These CFADS outputs are used to determine how much debt the project can support under each scenario. The lower of the two resulting debt service profiles becomes the binding constraint. Debt is then sculpted to ensure compliance with this more conservative profile, providing lenders with downside protection against underperformance.

What This Means for Equity Investors

It’s a common misconception that P99-based debt sizing always results in lower leverage and therefore a lower equity IRR. In reality, the outcome depends on which production case, when paired with its target DSCR, becomes the binding constraint.

• If the P50 case uses a more conservative DSCR target (e.g., 1.30x), while the P99 case assumes only a 1.00x DSCR, the P50-based constraint could still result in lower allowable debt. In that case, P50—not P99—would cap leverage.

• On the other hand, if P99-based CFADS is significantly lower, even a lower DSCR target might not compensate. The resulting allowable debt quantum would drop, requiring more upfront equity.

The implications for the equity IRR vary:

• If most of the equity return comes from early-stage distributions, lower leverage can significantly reduce IRR.

• But if the IRR is driven by later-stage cash flows, the impact may be negligible—even with more equity in the capital structure.

This nuance is exactly why equity sponsors and lenders alike evaluate multiple sizing cases. The goal is to find a structure that balances risk protection (for lenders) with acceptable returns (for equity), while ensuring the project remains bankable.

🎓 Want to Practice This in Excel?

The best way to internalize this concept is by building both cases side by side in Excel and seeing how the constraints interact in real time.

You can do exactly that in our advanced modeling course:

👉 How to Ace the Toughest Project Finance Modeling Tests
Includes downloadable Excel files, model walkthroughs, and realistic interview-style challenges—featuring this exact case study.

Why P50 vs. P99 Debt Sizing Matters in Interviews

If you're preparing for a project finance modeling test, few topics will demonstrate real-world competence as strongly as this one.

Understanding how to:

• Interpret and apply energy yield distributions

• Model revenue and CFADS under multiple production scenarios

• Implement dual DSCR-based debt sizing constraints

• Sculpt debt based on the binding constraint

• And evaluate the impact on IRR and debt capacity

...is exactly what separates top candidates from the rest. These are the real decisions analysts and associates deal with when supporting term sheet negotiations or investment committee memos.

And beyond interviews, mastering this logic is a critical skill in project development, advisory, and lending roles alike.

📺 Watch the Full Breakdown

Want to see the full logic built from scratch?

🧰 Related Resources

• How to Pass a Project Finance Modeling Test – The perfect place to start for beginners

• How to Ace the Toughest Project Finance Modeling Tests – Advanced challenges and real-world case studies

• RVA Certification Program – Master renewable energy financial modeling and valuation from A to Z