How we calculate.
What we don't.
This page explains exactly how preFeasibility works — the formulas, the assumptions, the data sources, and the boundaries of what the platform can and cannot tell you. We have written it for two kinds of readers: the practitioner who wants to verify the methodology before trusting a number, and the non-technical user who wants to understand what they're looking at before making a decision with it.
Both are legitimate. Both deserve a straight answer.
The renewable energy and infrastructure industry uses a classification system — commonly referred to as Class 1 through Class 5 — to describe how detailed, site-specific, and financially defensible an analysis is. You will see these referenced in financing term sheets, due diligence reports, lender requirements, and internal approval gates.
The classification is not standardised by a single body. Different institutions use slightly different terminology — some use "Order of Magnitude," "Feasibility Study," "Bankable," and so on. The table below reflects the most widely used framework across development finance, independent power producers, and investment committees.
Class 3 vs Class 4 in practice: The difference is mainly input quality, not methodology. The same LCOE formula is used at both levels. A Class 4 result uses regional defaults — the system fills in typical CAPEX, OPEX, and a modelled capacity factor for your region. A Class 3 result uses your own project-specific inputs for CAPEX, OPEX, and WACC, combined with a location-derived capacity factor. The closer your inputs are to your actual project, the closer the output is to Class 3. preFeasibility is designed to help you move from Class 4 to Class 3 as your project matures, without changing tools.
preFeasibility produces Class 3–4 pre-feasibility outputs. This means: credible enough to inform an early-stage development decision, rigorous enough to walk into an internal meeting with, not sufficient on its own to commit capital, seek project finance, or sign a land lease.
The platform is deliberately positioned here. We are not trying to replace a bankable feasibility study. We are trying to make the decision to commission one — or to screen out a site before you spend that money — faster, cheaper, and more defensible than the alternative.
The platform offers two products at this class level. The Site Screener is a Class 4 screening tool — an 8-axis go/no-go verdict designed to answer "Should I even look at this site?" in seconds. The Full Prefeasibility Study (Wind / Solar / Hybrid) operates at Class 3–4 — producing LCOE, IRR, NPV, and AEP with site-specific inputs. Both share the same data spine (GWA 3.0, PVGIS-SARAH3, Natura 2000, OSM, GEM, ENTSO-E, IRENA) and the same methodology for resource estimation and financial modelling. The screener filters; the full study quantifies.
The Site Screener evaluates a wind or solar site across eight independent axes, each producing a GREEN / AMBER / RED traffic-light result. The axes are scored against published thresholds using publicly available datasets. The overall verdict — PASS, CAUTION, or KILL — is synthesised from the individual axis results.
The screener is a Class 4 screening tool. It is designed to answer one question: Should I spend any more time on this site? It is not a substitute for a Class 3 pre-feasibility study or a Class 1–2 bankable analysis.
Where a country has sufficient installed capacity data (≥ 20 projects in our reference dataset), the Resource axis uses country-relative thresholds (P40/P25 percentiles of the operating fleet). Where country data is insufficient, global or latitude-banded defaults are applied. This distinction is noted in the screener output for each run.
What it measures: The estimated capacity factor (CF) for the site, derived from Global Wind Atlas 3.0 (wind) or PVGIS-SARAH3 (solar).
Wind thresholds — country-relative (used when ≥ 20 operating projects in reference fleet):
- GREEN: CF ≥ P40 of the country's operating fleet
- AMBER: P25 ≤ CF < P40
- RED: CF < P25
Wind thresholds — global fallback (when country cohort is unavailable):
- GREEN: CF ≥ 30%
- AMBER: 22% ≤ CF < 30%
- RED: CF < 22%
Solar thresholds — latitude-banded (no country cohort available):
- Tropical (|lat| ≤ 25°): GREEN ≥ 18%, AMBER 14–18%, RED < 14%
- Subtropical (25–40°): GREEN ≥ 15%, AMBER 11–15%, RED < 11%
- Temperate (> 40°): GREEN ≥ 12%, AMBER 9–12%, RED < 9%
Terrain complexity override (wind only): If Weibull k < 1.6 and mean wind speed > 8 m/s, a GREEN result is downgraded to AMBER — indicating that complex terrain introduces resource uncertainty not captured in the atlas data.
Known limitation: Global and latitude-banded thresholds may misclassify sites in regions where the local fleet operates at different CF ranges than global averages. For example, a solar CF of 13% is typical in Northern Europe but would be flagged AMBER under the temperate threshold (≥ 12%). Country-relative thresholds reduce this problem where data is available.
What it measures: Proximity to high-voltage transmission infrastructure and grid saturation from nearby renewable projects.
Distance thresholds (HV / EHV, ≥ 110 kV):
- GREEN: ≤ 15 km to nearest HV line
- AMBER: 15–25 km
- RED: > 25 km
Voltage adjustments: Medium-voltage lines (33–110 kV) tighten thresholds to 10 km (AMBER) and 20 km (RED). Low-voltage lines (< 33 kV) always return RED — they are not suitable for utility-scale injection.
Grid saturation (GEM pipeline data, 20 km radius):
- GREEN: Pipeline capacity ≤ 75% of estimated headroom (default 500 MW)
- AMBER: 75–100%
- RED: ≥ 100% of headroom
The combined Grid verdict is the worse of distance and saturation.
Data source: OpenStreetMap (transmission lines). This is not a substitute for a hosting capacity study with the TSO.
What it measures: The percentage of the project area that is buildable after accounting for slope, settlements, land cover constraints, and aviation buffers.
Buildable footprint thresholds:
- GREEN: ≥ 80% buildable
- AMBER: 60–80%
- RED: < 60%
Deductions from 100% (cumulative): steep slope (> 20°: −60%), moderate slope (15–20°: −40%), settlements within setback zone (wind: 0.5 km, solar: 0.2 km: −30%), high dwelling density (wind: > 5 within 500 m: −40%), forest cover (≥ 50%: −30%), built-up area (≥ 20%: −25%), wetland (≥ 15%: −10%), waterbody proximity (−20%). Mega-projects incur an additional −5% per unit over 500 MW (wind) or 300 MW (solar), capped at −20%.
Aviation override (structural):
- Airport or military installation ≤ 3 km → RED (structural)
- Airport or military installation ≤ 8 km → AMBER
- Heliport ≤ 1 km → AMBER
GEM permitting override: If national permitting data indicates high cancellation rates (> 35%), very long timelines (≥ 84 months), or very high complexity — a GREEN result is downgraded to AMBER.
Footprint defaults: Wind: 40 ha/MW. Solar: 1.5 ha/MW.
What it measures: Proximity of the site boundary to designated protected areas.
Thresholds:
- GREEN: > 2 km from nearest protected area
- AMBER: ≤ 2 km buffer zone (mitigable — may require environmental impact assessment)
- RED (structural): Overlap with protected area boundary
Bird flyway override (wind only): Sites within one of eight major global flyway corridors are flagged AMBER if they would otherwise be GREEN. This is a precautionary flag — not a KILL — but indicates that an ornithological impact assessment should be commissioned early.
Data sources: EU sites — Natura 2000 (EEA, authoritative). Non-EU — OpenStreetMap Overpass query. Query radius: 5 km.
What it measures: The overall market viability of renewable energy investment in the project's country, adjusted for price cannibalisation risk and grid curtailment.
Composite viability score (0–10 scale, pre-computed from country-level indicators including policy strength, investment attractiveness, and market scale):
- GREEN: Composite score ≥ 6.0
- AMBER: 4.0–6.0
- RED: < 4.0
Capture-rate overlay (price cannibalisation):
As renewable penetration increases, the correlation between high-generation hours and low wholesale prices tends to increase — reducing the effective revenue per MWh. The capture rate (actual revenue-weighted price / baseload price) quantifies this effect. Sourced from ENTSO-E (EU), AEMO (Australia), EIA (US), and CEN (Chile).
- RED (structural): Capture rate ≤ 60%
- AMBER downgrade: Capture rate 60–75%
- Normal range: 75–90%
- Premium: ≥ 90%
Curtailment overlay: Sourced from ENTSO-E (EU) and equivalent national TSO publications. Not available for all markets.
- RED (structural): Curtailment ≥ 15%
- AMBER downgrade: 7–15%
- Normal: 3–7%
- Low: < 3%
The combined Market verdict is the worst of the base composite score, capture-rate overlay, and curtailment overlay.
What it measures: The cost and risk of financing a project in the given country, using WACC as the primary metric.
Thresholds (project WACC midpoint):
- GREEN: WACC mid < 10% AND FX risk ≠ High AND Offtaker risk ≠ High
- AMBER: WACC mid 10–15%, OR either FX or Offtaker risk = High
- RED: WACC mid > 15%
Data sources: Damodaran country risk premium, OECD country risk classification, internal country financing database.
What it measures: Country-level social and political risk using the INFORM Risk Index, with a settlement-awareness check for the specific site.
Thresholds:
- GREEN: INFORM Risk ≤ 3.4 AND nearby settlements identified
- AMBER: INFORM ≤ 3.4 but no settlement data available, OR INFORM 3.5–6.5
- RED: INFORM Risk > 6.5
The settlement check acts as a confidence modifier: if the INFORM score is low but we cannot confirm nearby population centres (which may indicate data gaps rather than absence of social risk), the result is downgraded to AMBER.
Data sources: INFORM Risk Index 2024 (EU JRC), OpenStreetMap settlement data.
What it measures: Physical accessibility for equipment delivery and installation, scored across three sub-components.
Sub-component thresholds:
| Sub-component | GREEN | AMBER | RED |
|---|---|---|---|
| Port distance | ≤ 100 km | 100–250 km | > 250 km |
| Road distance | ≤ 20 km | 20–50 km | > 50 km |
| CAPEX benchmark | ≤ $2,000/kW | $2,000–3,000/kW | > $3,000/kW |
The combined Logistics verdict is the worst of the three sub-components. If both port and road data are unavailable, the axis defaults to AMBER (mitigable).
Data sources: OpenStreetMap Overpass (ports within 300 km, roads within 100 km), IRENA country CAPEX benchmarks.
Each axis produces a traffic-light result (GREEN / AMBER / RED) and a tag (structural or mitigable). The overall verdict is synthesised from the individual axis results:
Structural KILL: At least one RED axis is tagged structural — meaning the constraint is physical, regulatory, or market-level and cannot be mitigated through project redesign. Examples: overlap with a protected area, capture rate ≤ 60%, airport within 3 km, WACC > 15%.
Data-coverage exception: Axes where data coverage is rated INSUFFICIENT are excluded from the verdict calculation (treated as informational only) — unless they return RED, in which case the RED stands. This prevents missing data from inflating verdicts while still flagging confirmed risks.
Every axis result includes a confidence rating (HIGH, MEDIUM, LOW) that reflects the quality and completeness of the underlying data, not the verdict itself. A RED result with LOW confidence still means "proceed with extreme caution" — but you should verify the data before acting on it.
base_score × coverage_multiplier. The base score reflects the inherent reliability of the data source (e.g., Global Wind Atlas = 0.70, OpenStreetMap = 0.60, INFORM = 0.80). The coverage multiplier reflects data availability: FULL = 1.0, PARTIAL = 0.75, INSUFFICIENT = 0.5.The screener report includes a pipeline intelligence panel sourced from the Global Energy Monitor (GEM) database. This is displayed for context only — it does not affect the verdict. It is computed client-side from GEM project data within the specified radius.
operating.n / active.n × 100 where active = operating + construction + planned + announced.(cancelled + shelved).n / total.n × 100.operating_mw / (year_max − year_min) for operating projects within 100 km, commissioned 2010–present (MW/yr deployed).total_operating_mw / (year_max − year_min) for the country, 2015–present.pipeline_mw / national_velocity — the number of years of development backlog at the current deployment rate.LCOE (Levelised Cost of Energy) is the average cost per unit of electricity generated over the lifetime of a project, expressed in $/MWh. It is the standard metric for comparing the economics of different energy technologies and projects, independent of scale or market context.
Conceptually, it answers one question: at what minimum electricity price does this project break even over its lifetime? If the LCOE is $42/MWh and the available PPA is $55/MWh, there is headroom. If the LCOE is $68/MWh and the PPA is $55/MWh, there is not.
The summation runs over 25 years. Both the cost stream (OPEX) and the energy stream (AEP) are discounted by the same WACC — a standard approach that avoids the need to separate nominal and real cash flows and is consistent with IRENA and IEA methodology.
The degradation rate of 0.5% per year is a conservative industry standard applied to both wind and solar in the current model. For solar PV, this is consistent with crystalline silicon manufacturer warranties and falls within the typical 0.4–0.7%/yr range observed across utility-scale deployments. The specific yield base is derived from PVGIS-SARAH3 irradiance data per the installed panel technology.
Some parameters are fixed in the current version rather than user-adjustable. This is a deliberate choice: pre-feasibility analysis benefits from consistency, and allowing every parameter to vary freely can produce results that are technically calculated but practically meaningless. Here is every fixed assumption, its value, and the rationale.
preFeasibility calculates Project IRR — the internal rate of return on total invested capital, before debt service. This is the discount rate at which the project's NPV equals zero, treating the entire CAPEX as an equity investment and the PPA revenue stream minus OPEX as the cash inflow.
This is not the same as Equity IRR, which is what most developers and investors ultimately care about. Equity IRR requires:
Rule of thumb: For a typical utility-scale wind project with 70% debt at 6–8% interest, Equity IRR will be approximately 1.5–2.5× the Project IRR shown here — the leverage amplifies returns (and risk). A Project IRR of 9% might correspond to an Equity IRR of 14–18% depending on the capital structure. Use the Project IRR as a screening metric; commission a full financial model before making capital allocation decisions.
Every LCOE result is compared against a country benchmark — the published median LCOE for that technology and market from our primary sources. The comparison shows you where your project sits in the distribution: whether your assumed inputs are conservative, typical, or aggressive relative to what has actually been built and financed in that market.
The benchmark is not a target. A project with an LCOE above the benchmark isn't necessarily unviable — it may reflect a lower-wind site, higher WACC due to market risk, or a smaller project scale. A project below the benchmark isn't automatically a winner. The benchmark is context, not verdict.
Benchmark data is updated annually following the publication of IRENA's Renewable Power Generation Costs report (typically released Q1 each year). The current dataset is from the most recently published edition. Publication year and methodology version are cited on every benchmark shown.
Every benchmark shown in the platform includes a source tag, publication year, and methodology note. If we are using a modelled figure rather than a transaction-based one, we say so. We do not blend sources without disclosure.
Every analytical tool has a boundary. Here is ours, stated plainly. These are not bugs or oversights — most are deliberate decisions to keep the tool appropriate for its class. But you need to know about them before you rely on an output.
Capture-rate handling: The Site Screener uses ENTSO-E capture-rate data (and equivalents for non-EU markets) to flag cannibalisation risk on the Market axis. The full prefeasibility LCOE/IRR engine does not yet adjust revenue projections for cannibalisation — the PPA price you input is applied as a flat rate. This gap will be addressed in the PPA structuring tool. For markets with significant cannibalisation (capture rate < 75%), treat the screener's Market flag as a signal to apply a haircut to the PPA price you use in the full study.
preFeasibility is the right tool at the right stage — until it isn't. Here are the signals that your project needs more rigorous analysis:
We version every change to the calculation methodology, benchmark datasets, and fixed assumptions. There are no silent revisions. If a change affects previous outputs materially, we note it here and notify registered users by email.
Questions about the methodology? Spotted an error? Write to methodology@prefeasibility.com. We read every message and respond to technical questions directly — not with a support ticket.