By Alison Leckie | March 7, 2026
Managing policy uncertainty in renewable energy models
The only thing you know for sure about your model is that it’s wrong. That’s why it’s critical to include scenarios that assess the biggest risks and uncertainties.
Every renewable energy financial model starts with inputs. A generation profile. Energy prices. Tax credits. A few handfuls of key assumptions largely determine project benefits, and you build the rest of the model around it.
But here’s a dirty little secret: the assumptions in your model are wrong. It’s nothing personal. Every model is wrong. That’s why it’s called a model – and not an actual.
Policy shifts, tax changes, and the structural limits of long-term corporate PPAs – among other things – mean the assumptions you set during development may look very different at financial close, and even more different five years into operations. In this post, we look at where assumptions break down and how to build policy uncertainty into your renewable energy model in a way that actually holds up.
The critical assumption that may be way off
One set of assumptions that drives significant value but comes with massive uncertainty is merchant energy prices.
A utility-scale solar or wind project is typically expected to operate for 30 or more years. But most modern corporate PPAs are much shorter. Research from the Federal Reserve Bank of Dallas found that 69% of corporate PPAs signed after 2015 have featured terms of 15 years or less. That means a significant portion of a project’s operating life sits outside any contracted revenue period.
That uncontracted period is merchant exposure. Your model needs to account for it, and it needs to account for it honestly. A simple base case forecast isn’t good enough.
A standard approach is to run sensitivities on the merchant tail using a range of power price assumptions. That is a good start. But if those price forecasts assume a stable policy environment, you may be understating the risk by a wide margin.
How policy changes flow through your model
This is where a lot of renewable energy financial models fall short. The project looks solid, but the economics around it shift when policy moves. And policy is often as certain as the next President or Congress.
In 2022, the US Inflation Reduction Act seemed to create real investment certainty for developers through tax credits including the 45Y and 48E technology-neutral provisions. That certainty was priced into project valuations and deal structures across the market. Long-term forecasts for new renewable generation shot through the roof. At the same time, all that cheap newbuild solar and wind put downward pressure on future merchant price forecasts.
The One Big Beautiful Bill Act, signed into law in July 2025, changed those conditions. The legislation narrowed the construction commencement window for 45Y and 48E credits, tightened eligibility timelines, and eliminated tax credits for projects making payments to prohibited foreign entities. A detailed analysis from Build With Basis breaks down the full financial modeling implications.
For projects where tax credits represent 30 to 50% or more of total return, a scenario where those credits are partially or fully unavailable is not a tail risk. It is a step change, and a scenario you need in your model. If it is not there, the model is incomplete.
If your project expects to utilize tax equity financing, ACORE’s report on renewable energy tax equity risk profiles is worth reading alongside any tax equity-sensitive modeling work. It includes a section on how policy shifts are dealt with by tax equity investors, usually at the cost of sponsor returns.
But – remember those bullish post-IRA renewables forecasts? A less generous tax credit environment has caused market analysts to cut their expectations of future build. Suddenly, merchant price forecasts that may have looked extra rosy just a few years ago may actually prove to have been conservative – but good luck getting your lender to believe it!
Physical vs virtual PPAs are not the same risk
Not all PPAs carry the same policy sensitivity, and your model should reflect that.
Physical PPAs are tied to actual electricity delivery. Virtual PPAs – the structure used for most corporate offtake agreements – are financial contracts settled on the difference between a fixed strike price and the spot market price. When power prices move, or when policy shifts affect market structure, the two products behave differently.
Peer-reviewed research on physical versus virtual corporate PPAs found that virtual structures expose buyers to significantly more price risk under volatile conditions, particularly when negative electricity price hours increase. Virtual PPAs are also much more likely to be subject to curtailment constraints. If your model treats all PPA structures as equivalent revenue certainty, it is missing a meaningful distinction that lenders and reviewers will eventually ask about.
A practical scenario framework
Your model can’t predict what policy will do. The goal should be to understand how your project performs across a range of conditions, especially ones where the base case assumptions no longer hold.
A well-build project finance model needs to be able to easily and flexibly run any number of scenarios and sensitivities that reflect material changes to base case assumptions, whether due to policy change or anything. Here is a straightforward framework to start with:
Base case. PPA holds for full term, tax credits as originally modeled, merchant tail uses a consensus power price forecast.
Tax credit reduction scenario. Model the project with reduced or eliminated IRA credits. How much additional equity is needed? Can any debt be raised to fill the tax credit or tax equity gap? What happens to returns?
Merchant tail stress. Apply a downward-revised power price for the post-PPA period. Identify the floor price at which the project fails to meet its debt service obligations.
Counterparty stress. Corporate buyers carry different credit risk than utilities. Model a scenario where the offtake agreement is renegotiated or defaults mid-term. The Dallas Fed research referenced earlier is a useful anchor for understanding how counterparty profiles have shifted in the corporate PPA market.
Run all four. Look at your capital stack, debt covenants and equity IRR across each scenario. The range of outcomes tells you more than any single base case.
Those are just the start of many scenarios your model needs to be able to run.
What lenders and reviewers expect to see
If you are preparing a model for lenders, advisors, or a model audit, the handling of policy uncertainty is increasingly a focus area.
A well-structured renewable energy financial model should include clearly labelled scenarios, documented assumptions behind power price forecasts, explicit treatment of the merchant tail, and sensitivity tables that isolate the impact of changes. If those elements are missing, your work is far from done.
Documenting your assumptions matters as much as the assumptions themselves. Add clear notes in your model explaining how policy scenarios are structured and what each one is testing. Reviewers will flag anything they cannot trace.
Build models that tell the whole story
If you want to build these mechanics into your renewable energy financial models from scratch, our Renewable Energy Financial Modeling course covers scenario analysis, sensitivity modeling, and how to structure your model to handle policy uncertainty properly.
Pivotal180’s project finance modeling courses are designed to familiarize anyone from analysts to the C-suite with the knowledge and skills to build, analyze and communicate clearly about project finance models:
Course participants will learn how risk is allocated between lenders and sponsors, understand the structure and drivers of project finance transactions, and gain the necessary skills to run and evaluate operational or financing scenarios required to identify the most substantial risks and opportunities for any deal.
Find all of our upcoming courses on our website, including available Australia and APAC courses, Advanced Debt and Battery Storage programs. Contact [email protected] for more info and pricing.
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