Degradation Modelling
Executive Summary
Key Takeaways
- ✓ Degradation curves are not always linear — some technologies exhibit a higher initial "light-induced" or early-life degradation rate followed by a lower steady-state rate, and the model should reflect the actual curve shape rather than a uniform annual percentage throughout.
- ✓ Degradation profiles are technology- and manufacturer-specific, and the model's assumption should be sourced from the specific equipment's warranty and specification documentation rather than a cross-technology default.
- ✓ Once operational, measured actual degradation should be reconciled against the original warranty-guaranteed rate, with the model updated to reflect actual performance where it diverges materially from the original assumption.
- ✓ Degradation directly affects refinancing and repowering decisions later in an asset's life, since the actual remaining output capability, not the original nameplate capacity, determines the cash flow basis for a refinancing or life-extension decision.
- ✓ Degradation modelling should be reconciled consistently with the resource yield and generation forecast assumptions used elsewhere in the model, since an inconsistency between the two produces an internally contradictory output schedule.
Objective¶
This guide covers the methodology behind degradation modelling within Energy Financial Modelling, extending the basic degradation rate mechanic with curve-shape, sourcing, and reconciliation practice.
Curve Shape: Linear vs. Non-Linear¶
Degradation is not always linear. Some technologies — notably certain solar panel types — exhibit a higher initial degradation rate in the first year or so of operation (sometimes referred to as light-induced degradation), followed by a lower, more steady-state annual rate for the remainder of the operating life. The model should reflect the actual curve shape specified for the equipment used, rather than applying a single uniform annual percentage throughout, since a linear approximation of a genuinely non-linear curve can materially misstate output in the early years of operation.
Technology- and Manufacturer-Specific Sourcing¶
Degradation profiles vary meaningfully not just between technologies (solar versus wind versus other generation types) but between manufacturers and equipment vintages of nominally the same technology. The model's degradation assumption should be sourced from the specific equipment's warranty and technical specification documentation, giving the model a defensible, equipment-specific basis rather than a generic cross-technology default that may not reflect the actual equipment installed.
Reconciling Warranty-Guaranteed Against Measured Performance¶
Once the asset is operational, actual measured degradation should be reconciled periodically against the original warranty-guaranteed rate used in the pre-construction model. Where measured performance diverges materially from the original assumption — whether better or worse — forward assumptions should be updated to reflect the asset's actual demonstrated performance, consistent with the general principle described in Generation Forecast Models of updating forecasts against operating data rather than holding a pre-construction assumption fixed indefinitely.
Implications for Refinancing and Repowering¶
A refinancing or life-extension decision later in an asset's life should be based on its actual remaining output capability — reflecting cumulative degradation to date and projected forward degradation — rather than its original nameplate capacity, since nameplate capacity substantially overstates what a degraded asset can actually still produce by that point in its life. See Repowering Models for how this feeds the broader repower-versus-decommission decision framework.
Internal Consistency with the Generation Forecast¶
Degradation is applied on top of the resource yield assessment's base output figures within the generation forecast. An inconsistency between the degradation schedule used in one part of the model and the schedule feeding the generation forecast produces an internally contradictory output schedule — different calculations implicitly assuming different degraded output levels for the same year — and this consistency should be checked explicitly whenever either assumption is updated.
Common Construction Pitfalls¶
Linear curve applied to a non-linear technology. Using a uniform annual percentage for a technology with a genuinely non-linear degradation curve misstates early-year output.
Generic degradation default used. Applying a cross-technology or industry-average degradation rate rather than the specific equipment's warranty and specification basis understates the model's defensibility.
Warranty rate never reconciled against actual performance. Continuing to rely on the original pre-construction degradation assumption indefinitely, without checking it against measured operating data once available, misses the most valuable evidence available once the asset is operational.
Recommended Practices¶
- Model the degradation curve's actual shape (linear or non-linear) as specified for the equipment used.
- Source degradation assumptions from the specific equipment's warranty and technical specification documentation.
- Reconcile the warranty-guaranteed rate against measured actual performance periodically once operational.
- Base refinancing or repowering decisions on the asset's actual remaining output capability, not nameplate capacity.
- Check consistency between the degradation schedule and the generation forecast whenever either is updated.
Continue Reading¶
Related Pillars¶
Related Technical Guides¶
Related Glossary¶
Related Industries¶
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Frequently Asked Questions
Is degradation always linear over an asset's operating life?
No — some technologies (notably certain solar panel types) exhibit a higher initial degradation rate in the first year or so of operation, sometimes called light-induced degradation, followed by a lower, more steady-state annual rate thereafter, and the model should reflect this actual curve shape rather than applying a single uniform annual percentage across the full operating life.
Why should degradation profiles be sourced per technology and manufacturer?
Because degradation characteristics vary meaningfully between technologies (solar versus wind) and even between manufacturers and equipment vintages of the same nominal technology, making a cross-technology or generic default a weaker basis than the specific equipment's own warranty and specification documentation.
What should happen once the asset has actual operating history?
Measured actual degradation should be reconciled against the original warranty-guaranteed rate used in the pre-construction model, with forward assumptions updated where actual measured performance diverges materially — continuing to rely on the original warranty assumption indefinitely, once contradicting evidence exists, understates or overstates the asset's actual remaining output capability.
How does degradation affect refinancing and repowering decisions?
A refinancing or life-extension decision should be based on the asset's actual remaining output capability — reflecting cumulative degradation to date and projected forward degradation — rather than its original nameplate capacity, since nameplate capacity overstates what the asset can actually still produce at the point such a decision is being evaluated.
Why does degradation need to reconcile with the generation forecast?
Because the generation forecast applies degradation on top of the resource yield assessment's base output figures — an inconsistency between the degradation schedule and the forecast it feeds into produces an internally contradictory output schedule, where different parts of the model implicitly assume different degraded output levels for the same year.
References
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Degradation Rate
Degradation rate is the annual decline in equipment output over a generation asset's operating life, reflecting expected panel, turbine, or other equipment performance decline. It should be applied as an explicit, consistent annual schedule reconciled to the technical basis used elsewhere in the model, since even a small inconsistency compounds materially over a multi-decade asset life.
Generation Forecast Models
A generation forecast translates a resource yield assessment's confidence-level output figures into the full time-series generation schedule a financial model actually runs on — monthly or hourly granularity, weather-pattern-driven variability, and an explicit uncertainty band around the central forecast. This guide covers how a generation forecast should be built and updated, and why it is a distinct modelling exercise from the resource yield assessment it draws on.
Repowering Models
As a power project approaches the end of its original design life or PPA/incentive tenor, its owner faces a repower-versus-decommission-versus-life-extension decision, each with a distinct capital, timeline, and risk profile. This guide covers how to model this end-of-life decision: comparing repowering capital cost against greenfield development economics, valuing the retained permitting and interconnection position a repowering project keeps that a greenfield project must acquire from scratch, and the timing considerations that shape when this decision should actually be made.
Financial Model Audit for Renewables
Renewable energy financial models combine standard project finance debt sculpting with technical assumptions specific to the energy source, resource yield (solar irradiance or wind speed), equipment degradation over the asset life, and curtailment risk, that directly determine the cash flow feeding the debt structure. Power purchase agreement pricing and tenor, and the merchant tail risk once a PPA expires, add a further layer of revenue structure specific to this sector. This page sets out the modelling risks specific to renewables, the audit findings that recur across solar, wind, and storage financings, and what lenders typically expect before financial close.