Scenario analysis compares complete project versions under different assumptions. Select multiple alternate values together across terms and blocks to model distinct futures—optimistic conditions, pessimistic outcomes, or design alternatives.Each scenario runs the full project model and produces complete financial outputs. Compare NPV, IRR, and cash flows across scenarios to understand outcome ranges and identify which assumptions drive viability.
How does the project perform in optimistic versus pessimistic conditions?
Should we design with battery storage or without?
Which financing structure produces better returns—tax equity or project finance?
What combination of assumptions makes the project viable?
Use scenario analysis when you need to compare distinct alternatives or understand the range of potential outcomes. Use sensitivity analysis when you need to identify which individual inputs matter most.
Base Model: Your complete project model with all terms, blocks, and calculations definedAlternate Values: Different values or configurations defined for specific terms or blocks—for example, “low”, “base”, “high” electricity prices, or “lithium_ion”, “flow_battery” storage technologiesScenarios: Named combinations of alternate value selections that represent distinct futures—“Optimistic” might select high_price + low_cost + aggressive_incentives; “Pessimistic” might select low_price + high_cost + conservative_incentivesOutputs: Complete financial results for each scenario including NPV, IRR, LCOE, cash flows, and proformas
Build a scenario by selecting which alternate values to use for each term or block where multiple options exist. The platform runs your complete project model with those selections and generates full financial outputs.Example scenarios:
Design Alt A: base prices/costs + flow_battery + merchant_revenue
Design Alt B: base prices/costs + no_storage + PPA_revenue
Each scenario produces a complete set of outputs. Compare across scenarios to evaluate alternatives, understand outcome ranges, or test viability under different conditions.
Select alternate values that make sense together. Optimistic scenarios typically pair high revenue assumptions with favorable cost conditions—these tend to co-occur in reality. Pessimistic scenarios pair low revenue with unfavorable costs.Avoid contradictory combinations like “high electricity demand + low prices” unless testing a specific hypothesis.
Build scenarios that represent materially different futures or alternatives. If two scenarios differ by only one minor input, the comparison won’t inform decisions.Focus on the uncertainties or design choices that actually matter to project viability and investor returns.
Three to five scenarios works well: typically base, optimistic, pessimistic, plus one or two design or financing alternatives. More scenarios become difficult to compare effectively.If you need to test many input combinations systematically, use sensitivity analysis instead.
Compare key metrics to evaluate scenarios:NPV and IRR: Which scenarios meet return thresholds? If only the optimistic scenario clears your hurdle rate, the project carries high risk.Outcome Range: How wide is the spread between optimistic and pessimistic results? Narrow spreads indicate resilient returns. Wide spreads signal high sensitivity to assumptions—consider whether you can control or hedge the driving variables.Downside Protection: Does the pessimistic scenario still deliver acceptable returns? If yes, the project has margin of safety. If no, understand what conditions would cause failure.Alternative Performance: Which design or financing structure produces better risk-adjusted returns? Make build-or-buy decisions, technology selections, or capital structure choices based on scenario performance.Use scenario comparisons to inform decisions: pursue battery storage if that scenario significantly outperforms; select financing structure A if it delivers superior equity returns; reconsider the project if viability depends entirely on optimistic assumptions.
Sampled, Not Exhaustive: Scenarios test specific points in the possibility space, not every potential outcome. You select which combinations to model—the analysis shows those scenarios, not all possible futures.Subjective Bounds: What constitutes “optimistic” or “pessimistic” involves judgment. Different analysts might define different bounds for the same project. Document your rationale for alternate value definitions.Unweighted Probability: Scenarios don’t include likelihood estimates. The optimistic scenario isn’t necessarily “likely”—it’s one possible future. Don’t assume equal probability across scenarios.Fixed Combinations: Each scenario locks in all alternate value selections simultaneously. If you want to understand every possible combination of inputs, you’d face combinatorial explosion. Instead, use sensitivity analysis to test inputs independently.
Build optimistic, base, and pessimistic scenarios. If base meets return requirements and pessimistic isn’t catastrophic, the project is attractive. If only optimistic works, understand whether you can control or hedge the key drivers.
Create scenarios for design alternatives: different system sizes, with or without storage, alternative technologies. Compare NPV and IRR to select the optimal configuration. Consider both expected performance and downside scenarios.
Model scenarios for capital structure alternatives: tax equity versus project finance, different debt-to-equity ratios, merchant versus contracted revenue. Compare equity returns and cash flow stability to choose the structure that best fits project risk and investor requirements.
Present multiple scenarios to demonstrate you’ve analyzed uncertainty. Base, optimistic, and pessimistic scenarios show the outcome range and give investors confidence you understand project risks. Scenarios turn abstract uncertainty into concrete financial projections.
