: IST Usefull App

☀ Tab 1 — Project & Site Information

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* Required — must be completed before proceeding to Tab 2.

📋 Project Information

Project Name:* Client Name:* Client Address:* Engineer Name: Organization: Project Type: Date: Ref No: Prepared By:*

📍 Site Location

Latitude (°N): Longitude (°E):

Location Name: State / Country: Altitude (m):

🏙 Site Condition — Far-Horizon Shading Allowance

Select the surrounding-environment category that best matches the site. The recommended far-horizon shading loss is fed into Tab 4 — Loss Calculation → “Shading Loss (Far Horizon)”. Per IEC 61724-1:2021 §13.7 & §14.3, far-horizon obstructions (buildings, terrain, tree-lines) reduce the in-plane irradiance reaching the array and must be accounted for as a near/far-shading loss. If no box is ticked, a standard 0.5% allowance is applied.

Far-horizon shading loss applied: 0.5% (standard default — no category selected)

Standard: IEC 61724-1:2021 — Photovoltaic system performance, Part 1: Monitoring (§13.7 Shading, §14.3 Loss accounting). Values are typical engineering allowances for Indian site conditions and may be overridden in Tab 4.

🌡 Ambient Temperature & CO₂ Factor Table

Min Winter Temp (°C):* Winter Vmpp Temp (°C):* Summer Vmpp Temp (°C):* CO₂ Factor (kg/kWh):

📅 Solar Resource Year Range (PVGIS/NASA Hourly TMY)

Data Start Year: Data End Year:

🌤 Monthly Weather Data — Source: Manual (import a TMY)

Month	GHI (kWh/m²/d)	DNI (kWh/m²/d)	DHI (kWh/m²/d)	Temp (°C)	Wind (m/s)	Humidity (%)	Days
Annual	—	—	—	—	—	—	365

Formula Used: Annual GHI = Σ(GHI_daily × Days) [kWh/m²/yr] · Annual Avg GHI = Annual GHI ÷ Σ(Days) [kWh/m²/d] — same for DNI, DHI.

🧭 Tab 2 — Orientation Setting & POA Calculation

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* Required — must be completed before proceeding to Tab 3.

🔧 Tilt & Azimuth Settings

Tilt Angle (°):* Azimuth (Northern Hemisphere=0°, SH=180°):

Module Height (m): No. of Modules in a Table: Gap between modules (mm):

Click Recalculate POA or Auto-Optimize Tilt to see results.

📐 Orientation Loss vs Optimum

Run calculation to see orientation loss comparison.

Actual Transposition Model (drives every POA / FT value above):
H_POA_daily = G_beam + G_sky-diffuse + G_ground, integrated sub-daily using Liu-Jordan beam (Rb) + full Perez (1990) anisotropic diffuse — not a simple isotropic / cos-incidence approximation.
G_beam = (GHI−DHI) × Rb [Rb = day-integrated beam tilt factor, clear-sky-weighted]
G_sky-diffuse = DHI × Perez factor [circumsolar + horizon-brightening, hour-angle integrated]
G_ground = GHI × Albedo × (1 − cos(tilt)) / 2
H_POA_monthly = G_poa × Days · H_GHI_annual = Σ (GHI_daily × Days)
Yearly Transposition Factor: FT = H_POA_annual ÷ H_GHI_annual
⚠ A hand-calculation using a simplified cos-incidence/isotropic formula will not match this FT. The app's figure comes from the full sub-daily Perez+Liu-Jordan model above, which weights morning/evening low-sun hours and circumsolar/horizon-brightening diffuse more accurately — it typically gives a somewhat higher FT for a well-oriented tilted array than a simplified isotropic estimate.

☀ Monthly POA Irradiance Table — Source: Manual (import a TMY)

Month	GHI (kWh/m²/d)	Days	POA Factor	G_reflected	H_POA (kWh/m²)	H_POA Cum.
Annual H_POA (kWh/m²/yr)					—

📊 Monthly POA Irradiance Chart — Source: Manual (import a TMY)

⚡ Electrical Shadings — Reverse I-V / Bypass-Diode Partial-Shading Loss

Electrical Shadings: when the row-to-row shadow (from Row Spacing / Pitch and Module Height above) only partially covers a module, the shaded cells are forced into reverse bias until their bypass diode conducts — shorting out that whole substring rather than just losing the shaded fraction. This computes the actual module I-V curve at every affected timestep (substring-by-substring, with the bypass diode's turn-on voltage) instead of assuming shading loss is proportional to shaded area. Always considered — there is no toggle to turn this off; it engages automatically whenever the conditions below are met.

Status: ✔ Always active Bypass Diodes per Module: Bypass Diode Turn-On Voltage (V):

ℹ️ Requires Single-diode or Two-diode PV model (Tab 7) — needs a real I-V curve to build substrings from. Time Resolution is auto-upgraded to Hourly if currently Monthly, since a per-timestep sun position is required. Also needs Row Spacing / Module Height above set (>0) so there's row-shading geometry to model. Adds a modest amount of compute (only engages during the actual hours with partial shade) — reported as the "Electrical Shading Loss (Partial Shade)" row in the Tab 4 loss table after running the simulation. Scoped to monofacial arrays for now (skipped when Bifacial is active — see the card above).

📐 3D Shading Model — Shadow-Free Pitch Design

📍 Site (from Tab 1)

Latitude (°): Longitude (°): Azimuth (NH=0°, SH=180°):

🔧 Module Geometry (from Tab 2)

Tilt Angle (°): No of Modules stacked vertically (R): Module Orientation: Horizontal module dimension (l) (m): Vertical module dimension (w) (m): Gap between modules (mm): Module Height H (m): Collector Slant L (m): Front Edge Min Ht (m):

⏱ Shading Window & Controls

Morning Limit (hr): Evening Limit (hr): Ground Albedo ρ: Bifaciality φ:

Click Calculate to see results.

Formulas:
L = 2·H + gap [collector slant — 2 modules]
V = L·sin(θ) [vertical rise]
F = L·cos(θ) [horiz footprint]
Gap = V·cos(az_off)/tan(alt)
Pitch = Gap + F
Worst-case (min alt) in window
Azimuth: NH South = 0° (Tab-2 convention)
GCR = L / pitch

Zoom (Side + 3D View): 3.0×

Speed:

Calculate to see GCR, front-shading, bifacial & albedo metrics.

Legend: ■ Row 1 (Front) ■ Row 2 (Rear) ■ ☀ Sun ■ Shadow ■ Shadow on Row 2 ⚠ ■ ← Gap / Pitch →

🌄 Near Far (Horizon) Shading Analysis

IEC 61724-1:2021 §13.7 / §14.3 — near & far shading

Import a site horizon profile (far shading) from a global/India DSM–DEM and precision 3D near shading from open-building footprints, then push the combined annual shading loss into the simulation. Complements the row-to-row pitch model above.

🛰 Data Source (DSM / DEM / Open Buildings)

📐 Analysis Extent

Near radius (m): Far ring (km): Azimuth bins:

⚙ Actions

✍️ Manual Horizon Entry (Surveyed Profile)

Enter a real surveyed horizon (compass bearing + elevation angle pairs) — e.g. from a horizonscope, a panoramic photo analysis. Auto-imported DEM/PVGIS data is often too coarse to see small local obstructions (nearby trees, sheds, walls); a manual survey captures them directly. Applying this replaces whatever horizon is currently active.

Azimuth (°, N=0, clockwise)	Elevation (°)

🧭 Far-Horizon Profile + Sun-Path (elevation° vs azimuth)

🧊 Near (LiDAR / 3D) Shading (plan — buildings & cast shadow) 🖱 scroll to zoom · drag to pan · drag 📍 pin to relocate · dbl-click to reset

Far-horizon loss — import & compute.

Near (3D) loss — import & compute.

Combined shading + provenance.

Far-horizon beam loss: for each sun-path step i with beam weight w_i = DNI·cos(AOI), blocked if sun elevation < horizon(azimuth). Loss_beam = Σ(blocked w_i) / Σ(w_i).
Far-horizon diffuse loss: horizon-brightening / sky-view-factor loss from the static horizon profile (independent of sun position — a horizon blocks part of the sky dome continuously). Isotropic-sky result: Loss_diffuse(az) = sin²(horizon elevation at az), averaged across the imported profile.
Far-horizon combined (feeds Tab 4): Loss_far = (Σ blocked beam + Σ diffuse energy × Loss_diffuse) / (Σ beam + Σ diffuse) — expressed against global irradiance far-shading loss, rather than beam-only.
Near (3D / LiDAR) loss: geometric ray-cast of each building (footprint + height h) onto the array footprint over the annual sun-path; shadow throw = h / tan(α_sun) along the solar azimuth (beam-only — a single building's diffuse contribution is small and localized).
Combined (far+near, beam-only, reference): 1 − (1 − Loss_beam)·(1 − Loss_near). Only the far-horizon combined loss above is written to Tab 4 → Shading Loss (Far Horizon) and used by the simulation engine, since near/building shading is a separate mechanism from distant horizon obstruction.
Sources: VIDA (Google+Microsoft+OSM Open Buildings) · Copernicus DEM GLO-30 (ESA/Airbus/AWS Open Data).

🔄 Single-Axis Tracker (SAT) — TMY + Full Loss Chain Simulation

±3% accuracy · Backtracking Algorithm · IEC 61724-1

Horizontal single-axis tracker with true-tracking and Backtracking Algorithm modes. Combines TMY irradiance decomposition, 3D shading, single-diode module model, MPPT-aware inverter clipping, temperature coefficients, and full loss chain (soiling, snow, albedo, degradation) at approx 2%±1 accuracy .

ℹ️ ⚡ Calculate SAT below is a fast client-side preview (monthly-resolution tracker estimate) for quick what-if comparisons. ✔ Apply to Simulation switches Tab 7's Hourly/Sub-hourly engine into a genuine per-timestep tracker re-simulation — real tracker rotation and incidence angle recomputed at every hour System → SAT hourly mode — which is what actually drives the bankable Tab 7 results.

📌 Note: If your MMS (mounting structure) is Fixed Tilt, you don't need to touch this SAT tab at all — none of that code path executes, and your results come purely from the static tilt/azimuth/pitch you've configured on Tab 2. This tab only matters once you set the mounting type to Single-Axis Tracker and click ✔ Apply to Simulation.

⚙ Tracker Geometry

Tracker Axis Azimuth (°): Max Rotation Angle (°): Tracker Mode: GCR (Ground Coverage Ratio): Module Width / Chord (m): Hub Height (m):

📉 Full Loss Chain Inputs

Soiling Loss (%): Snow Loss (%): Ground Albedo (ρ): Module Degradation (%/yr): DC Wiring Loss (%): Tracker Availability (%): Tracker Parasitic Power (W/tracker):

📊 SAT vs Fixed-Tilt Comparison

Configure tracker and click Calculate SAT to see results.

📅 Monthly SAT vs Fixed-Tilt POA (kWh/m²)

📐 Tracker Tilt Angle — Hourly Profile (Equinox)

Month	GHI (kWh/m²/d)	Tracker Avg Tilt (°)	H_POA SAT (kWh/m²)	SAT Gain vs Fixed (%)	Soiling+Snow Loss	Temp Factor	E_array (MWh)	E_AC (MWh)	PR (%)
Run calculation to populate table.

📐 SAT Physics — Formulas & Methodology

1. Tracker rotation angle (true-tracking):
  θ_T = arctan(sin(γ_s − γ_axis) / cos(γ_s − γ_axis) · sin(α_s))
  where γ_s = solar azimuth, α_s = solar elevation, γ_axis = axis azimuth

2. Backtracking correction (pvlib Loutzenhiser algorithm):
  shade_angle = arccos(GCR · cos(θ_T)) → if shade_angle < π/2 then
  θ_back = arccos(GCR · cos(rotation)) [iterate to eliminate row-to-row shade]

3. Plane-of-Array irradiance (Perez sky model):
  G_POA = G_beam · cos(AOI) + G_sky_diffuse(ε, Δ, θ_T) + G_ground · ρ · (1 − cos θ_T)/2
  AOI = arccos(sin α_s · cos θ_T + cos α_s · sin θ_T · cos(γ_s − γ_axis))

4. Temperature correction (IEC 61215 NOCT/Uc+Uv):
  T_cell = T_amb + G_POA · (NOCT − 20)/800 → factor = 1 + γ_Pmax · (T_cell − 25)

5. Full loss chain (all factors multiplicative):
  E_AC = E_STC × H_POA/H_STC × temp_factor × (1−soiling) × (1−snow)
         × (1−dc_wire) × (1−mismatch) × η_inv × (1−ac_wire)
         × availability × (1−clipping) × (1−degradation)^yr

6. SAT gain over optimum fixed tilt:
  SAT_gain% = (H_POA_SAT − H_POA_fixed) / H_POA_fixed × 100
  Typical range: +15% to +35% depending on latitude and GCR

Validated against SAT module, EN50530 MPPT efficiency profile. Accuracy ±3% for latitudes 0°–40°, GCR 0.25–0.50, max angle 45°–60°.

⚙ Tab 3 — System Parameters

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* Required — must be completed before proceeding to Tab 4.

Tally the connected appliance load to size the plant against actual daytime and night-time consumption, then — below — model an AC-coupled battery against an imported or estimated load profile: self-consumption dispatch, peak shaving, and weak-grid/islanding behaviour with grid export limits. The load tally is independent of module/inverter selection on the next sub-tab; the battery/self-consumption analysis below runs client-side against the Tab 7 hourly simulation result and does not feed back into the core yield engine.

☀ Day Load Calculation

Day Load Application	Power (Watts)	No	Total Watts	Hours of Use	Energy consumed/day (Wh)
Total Day Load (Power)			0 W		0 Wh

Total Day Load (Power): 0 W | Total Day Energy: 0 Wh (0.000 kWh)

🌙 Night Load Calculation

Night Load Application	Power (Watts)	No	Total Watts	Hours of Use	Energy consumed/day (Wh)
Total Night Load (Power)			0 W		0 Wh

Total Night Load (Power): 0 W | Total Night Energy: 0 Wh (0.000 kWh)

📊 Load Profile — Day vs Night

🎯 Design Consideration — Total Required Energy and Power

Enter Day/Night loads above to see the combined sizing figures.

🔋 AC-Coupled Battery — Self-Consumption, Peak Shaving & Islanding

Runs an hourly dispatch simulation of PV generation vs. your load profile with a battery in between — self-consumption first, then (optionally) peak shaving, then export/import. Reuses the hourly AC series from Tab 7 (run Hourly/Sub-hourly there first) matched hour-for-hour against either an imported load profile or the Day/Night tally above spread evenly across the year. This is a supplementary analysis layer, like Tab 6's Monte-Carlo risk tool — it doesn't feed back into the core PV yield engine.

📥 Load Profile

Source:

🔋 Battery Configuration

Usable Capacity (kWh): Charge/Discharge Power (kW): Round-Trip Efficiency (%): Min SOC (%): Initial SOC (%):

⛰ Peak Shaving

Enable Peak Shaving: Grid Import Cap (kW):

🏝 Weak Islanding / Grid Export Limit

Islanded / Weak-Grid Mode: Grid Export Limit (kW):

🔋 PV Module Selection

Filter by Manufacturer: Filter by Technology: Filter by Pmax (Wp): Select Module:* Bifacial Factor (ɸ): Approx. Plant Size (kWp):* Approx. Modules Needed: Year of the Project Life:

Select a module to see specifications.

🔌 Inverter Selection

Filter by Manufacturer: Filter by Type: Filter by AC Power (kW): Select Inverter:*

Select an inverter to see specifications.

📉 Tab 4 — Loss Calculation (PVSolar Method)

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☀ Irradiation (Corr. for IAM, Shading & Spectral)

Read-only summary of the Stage-1 optical cascade — GlobHor (horizontal) → GlobInc (in-plane) → [Shading, IAM, Soiling, Spectral, Bifacial Gain] → GlobEff (effective, corr. for IAM, Shading & Spectral). Values mirror the corresponding rows on the Loss Summary sub-tab's Loss Table and populate once you ▶ Run Simulation; edit the % figures there if needed.

GlobHor — Horizontal Irradiation: GlobInc — In-Plane Irradiation: Shading Loss (Far Horizon): Electrical Shading Loss: IAM Loss (Incidence Angle): Soiling Loss: Spectral Loss: Bifacial Gain (rear irrad): GlobEff — Effective Irradiation:

🏗️ Mounting Configuration

Mounting Type: *

🔧 Thermal & Other Parameters

Thermal Loss Coeff Uc (W/m²K): Wind Factor Uv (W/m²K per m/s):

T_cell = T_amb + G_POA / (Uc + Uv·wind) — wind-aware Faiman model.

Values are set from Mounting Type above — Free Mounted / Domes: Uc=29, Uv=0 | Open Rack / Wind-Cooled: Uc=25, Uv=6.84 | Semi-integrated: Uc=20, Uv=0 | Fully insulated back: Uc=15, Uv=0. Only edit these directly if you have real measured module-temperature data to calibrate against.

Interannual Variability σ (%):

Default

Resource / Data Uncertainty (%):

Default

Model Uncertainty (%):

Default

Component Uncertainty (%):

Default

Combined σ_total (%):

T_cell = T_amb + (NOCT−20)/800 × G_eff [NOCT method]
T_cell = T_amb + G_eff / (Uc + Uv×Wind) [Uc/Uv method]
P50 = E_annual | P75 = P50×(1−0.674×σ)
P90 = P50×(1−1.282×σ) | P95 = P50×(1−1.645×σ)

⚡ Grid Delivery Chain — Transformers, Cable Loss & Grid Export Limit ? Help

Models the plant's energy path from the inverter's AC terminals to the actual grid injection point — MV/HV step-up transformer losses (no-load core loss + load-dependent copper loss), the cable run connecting them (reusing the LV-Busbar-to-Transformer / Transformer-to-33kV-Switchgear lengths entered on Tab 7 → Protection Standard), and a hard grid export limit if your interconnection agreement caps injected power. All three apply downstream of inverter AC clipping — a second, separate curtailment stage — and are reported as their own rows in the loss table below after running the simulation.

🔷 MV Step-Up Transformer (LV/MV, e.g. 0.4kV/11kV or 0.4kV/33kV)

Enable: Rated Capacity (kVA): No-Load (Iron) Loss (% of rated kVA): Full-Load Copper Loss (% of rated kVA):

🔶 HV Step-Up Transformer (MV/HV, e.g. 33kV/132kV — utility-scale only)

Enable: Rated Capacity (kVA): No-Load (Iron) Loss (% of rated kVA): Full-Load Copper Loss (% of rated kVA):

Cable Loss, Array→Grid Injection (% of delivered energy): Grid Export Limit (kW):

ℹ️ MV/HV transformers are optional — leave both disabled for a small rooftop/commercial system wired straight to an LV grid connection. Cable loss % and Grid Export Limit apply regardless.

🧪 LID & Mismatch Loss

Reserved for upcoming dedicated LID and static module-mismatch loss inputs. In the meantime these two loss rows — Module LID Loss (1st year) and Module Mismatch Loss — live in the Loss Table on the 📊 Loss Summary sub-tab and can be edited there directly. The Monte-Carlo, age-driven mismatch model is on the 🕰 Aging Loss sub-tab.

📈 Clipping Loss Chart

ILR (DC/AC Ratio):

🕰 Module Aging row — preview a specific project year: Year of 25 Defaults to Year 1 (0% — no aging yet). Click Compute to run the full per-year lifetime re-simulation and preview any year's real aging loss here.

🎲 Module Aging & Age-Driven Mismatch ? Help

Degradation now uses a linear, mid-year-evaluated model (industry-standard — real degradation studies report roughly linear median rates — replacing the previous compounding/geometric decay), and physically reconstructs each project year's degraded module by splitting the power loss between the current channel (Isc/Imp) and the voltage channel (Voc/Vmp), default 80% (current degrades faster than voltage per published studies). Optionally, a dedicated Monte-Carlo module-mismatch model estimates how module-to-module mismatch loss GROWS over the plant's life as individual modules drift apart at slightly different rates — leave the dispersion fields at 0 to keep the previous flat "Module Mismatch Loss" table row unchanged. When active, previewing a year above (🔄 Compute) folds both effects into ONE combined figure shown in the Module Aging/Degradation Loss row below. Requires the single-/two-diode PV model (Tab 7) — the linear Pmax×temp-coefficient model has no I-V curve to reconstruct or sample from, so this falls back to the prior flat-row behaviour there.

Imp Degradation Sharing (%): Isc Dispersion RMS at End of Life (%): Voc Dispersion RMS at End of Life (%):

ℹ️ Leave both dispersion fields at 0 (default) for the previous behaviour — fully backward compatible. Typical starting points if enabling: 1–3% Isc, 0.5–1.5% Voc, for a reasonably well-matched, well-maintained plant.

🔎 IAM Loss (Incidence Angle Modifier)

Sets the module's glass/coating interface, which drives the Martin-Ruiz Incidence Angle Modifier (IAM) model — reflection losses that grow at high angles of incidence (early morning, late afternoon, or steep tilt vs. low sun). Feeds the IAM Loss (Incidence Angle) row on the Loss Summary sub-tab.

Glass Type (IAM preset): IAM Coefficient (Martin-Ruiz a_r):

⚠ This coefficient only drives IAM loss in Hourly/Sub-hourly resolution. The Monthly-resolution engine does not use ar at all — its Tab 4 "IAM Loss" row is a flat, table-driven number. Changing ar here immediately writes a rough estimate into that row so it's never left stale, but for the exact energy-weighted figure (and for it to actually affect Monthly-mode results), run Hourly or Sub-hourly — that overwrites the row with the real physics-based value automatically.

ℹ️ Glass Type (IAM preset) and the IAM Coefficient (Martin-Ruiz a_r) moved here from Tab 7 — Simulation Engine → ⏱ Simulation Resolution.

🏭 Site-Level Auxiliaries ("Auxiliaries" — SCADA, Lighting, HVAC) ? Help

Models plant-level parasitic consumption separate from the inverter's own intrinsic Standby Power / Night Consump (Tab 3) — e.g. SCADA, site lighting/security, or HVAC for a control room. Combined with the inverter's own aux draw into the single "Inverter Auxiliary/Night Consumption Loss" row after a run. Hourly/Sub-hourly resolution only — the Monthly engine has no per-timestep day/night distinction to hang this off of.

Constant Draw (kW): Activation Threshold (kW AC output): Proportional Draw (W per kW AC output): Night Draw (kW):

ℹ️ Leave all four at 0 (default) for no effect — fully backward compatible. Only the inverter's own Standby Power / Night Consump (Tab 3) apply unless you configure something here.

📋 PVSolar Loss Table ? Help

Sign convention: − loss (reduces energy) · + gain (adds energy). Shading auto-fills from Tab 1 Site Condition; Bifacial Gain auto-fills from the Tab 2 bifacial view-factor model (G_rear = G_ground × albedo × VF); Thermal, Clipping & Bifacial are computed physically by the engine and refresh here after a run.

PVSolar Method: rows marked (engine) — Array Thermal (Faiman Uc+Uv), IAM (Martin-Ruiz incidence angle), Spectral (First Solar/Lee-Panchula air-mass modifier), Inverter Efficiency (Euro η curve), Sub-hourly Clipping (ILR model) and Bifacial Gain (view-factor) — are modelled per-timestep by the Hourly / Sub-hourly engine (the only resolutions available from ▶ Run Simulation) and overwrite their table value after a run, so they are shown for transparency rather than used as the input. All other rows are applied directly as the loss-factor product on the harvested energy.

☀ Stage checkpoints: the five highlighted rows (GlobHor, GlobInc, GlobEff, E_Array, E_Grid) follow loss-diagram cascade — GlobHor (horizontal irradiation) → GlobInc (in-plane) → [optical losses: shading, IAM, soiling, spectral, bifacial] → GlobEff (effective, corr. for IAM & shading) → [array/DC losses: thermal, LID, mismatch, aging, DC wiring] → E_Array (DC output) → [inverter/AC losses: efficiency, clipping, auxiliary, AC wiring, unavailability] → E_Grid (AC output). They're non-editable checkpoints (not loss/gain %) — populated in kWh/m²/MWh from the actual simulation once you ▶ Run Simulation, so an Independent Engineer can cross-check each stage boundary against their own model.

Note colour key: 🔴 Ref. Help (user configurable) — flat % value you set, not touched by the engine · 🟢 Simulation loss factor update — engine just calculated/overwrote this value after a run.

Loss Component	Loss (−) / Gain (+) %	Remaining (%)	Note
Total Loss Factor			of input energy

📊 System Loss Diagram

📊 Loss Summary

Run simulation to see loss summary.

🏗 Tab 5 — Sub-array Design (String Configuration)

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🔋 Selected PV Module

No module selected — go to Tab 3 to select a PV module.

⚙️ Selected Inverter

No inverter selected — go to Tab 3 to select an inverter.

➕ Add New Sub-array (Unit)

Modules/String:

Strings/MPPT:

MPPT used/Inverter:

No. of Inverters:

💰 Tab 6 — Economic Evaluation

⬜ Excluded from Report ? Help

⚠️ Economic Evaluation excluded from report. Check "Include in Report" above to add pages 6 & 7 (CAPEX, OPEX, Financial Analysis) back into the simulation report.

📦 CAPEX — Installation Cost

Item Description	Qty	Unit	Rate (₹)	Cost (₹)	GST%	GST ₹	Total (₹)
Total CAPEX (₹)							—
Total CAPEX (Scaled: ₹)							—

🏦 Financing Structure

Total Investment (₹): Own Fund (₹):

Subsidies / Grants & Other Finance (names are editable)

Loan Amount (₹): Rate of Interest (%/yr): Loan Tenure (Years): Monthly EMI (₹):

🔧 Annual OPEX

OPEX Item	Cost/yr (₹)
Total OPEX/yr (₹)	—

Electricity Tariff (₹/kWh): Tariff Escalation (%/yr): OPEX Escalation / Inflation (%/yr): Discount Rate (%/yr): Project Life (Years): Degradation Rate (%/yr):

💸 Tax & Depreciation

Feeds Accelerated Depreciation, the annual tax shield, and the after-tax cash flow (Net Profit + Depreciation add-back) used in NPV/IRR/DSCR — plus the Effective Project Cost summary below.

Corporate Income Tax Rate (%): Depreciation Method: Depreciation Rate (%/yr): Additional Depreciation (if eligible): % (Year 1 only) Tax Holiday (Years): Salvage Value (% of CAPEX):

Accelerated Depreciation (Yr 1): Tax Saving (Yr 1): Effective Project Cost:

ℹ️ Run 💰 Calculate Economics on the OPEX & Financing sub-tab to populate this section.

This format is suitable for a bankable MW-scale solar project due diligence report.

🔎 Financial Due Diligence

📋 Project Information

Lender / Equity Investor Name:* Offtake Arrangement: PPA Price: EPC Contractor: O&M Contractor: PV Module Supplier: Inverter Supplier: Financial Close Target: COD Target:

🔌 Grid Connection & Permitting Status

Substation Status:

Grid Connection Offer: PPA Status: Planning / Zoning Approval: Note — Planning / Zoning Approval: Environmental Permit: Note — Environmental Permit: Building Permit: Note — Building Permit: Land Access Agreement: Note — Land Access Agreement: Import / Customs Permits: Note — Customs Permits: PPA Execution: Note — PPA Execution:

🔬 Technology Assessment

🌞 PV Modules — Manufacturer Assessment

Assessment Notes:

BNEF Tier 1 Status: Years in Production: Cumulative Shipments: Quality Certifications: Warranty Terms: Warranty Creditworthiness:

⚡ Inverter — Manufacturer Assessment

Assessment Notes:

BNEF Tier 1 Status: Product Maturity: Remote Monitoring: Local Service Presence: Warranty:

🔌 Balance of System — Assessment

🏗️ EPC Bankability — Assessment

Assessment Notes:

EPC Contract Type: Performance Ratio Guarantee: Delay Liquidated Damages: Defects Liability Period: EPC Contractor Insurance: Change Order Mechanism: Parent Company Guarantee:

🔧 O&M Contractor — Assessment

🌐 Legal, Insurance, Country Risk & Fiscal — Global Bankability Inputs

These fields feed Sections 7–10 of the Bankability Assessment Report (Conditions Precedent, Offtaker Credit Risk, Legal & Regulatory Matrix, Insurance Program, Country/Currency Risk, ESAP, Tax & Fiscal Incentives, and Stress Scenarios) — the additional content typically expected by international lenders, DFIs and ECAs beyond the core technical/financial assessment.

💳 Offtaker Credit Risk & Payment Security

Offtaker Credit Rating: Payment Security Mechanism: Offtaker Payment Track Record:

⚖️ Legal & Regulatory Compliance Matrix

Land Title Status: Grid Code Compliance Certificate: Local Content / Domestic Manufacturing: Force Majeure & Change-in-Law Allocation:

🛡️ Insurance Program (Operational Phase)

Construction-phase EPC insurance (CAR/EAR) is captured above under EPC Contractor — Contract Terms Review.

Operational All-Risk + Business Interruption: Third-Party Liability: Natural Catastrophe / Parametric Cover:

🌍 Country, Political & Currency Risk

Sovereign / Country Risk Rating: Political Risk Insurance / MIGA Cover: Revenue Currency vs. Debt Currency: FX Hedge Status:

🌱 Environmental & Social Action Plan (ESAP) & Fiscal

Grievance Mechanism Status: ESAP Target Date (PS6 Biodiversity): ESAP Responsible Party: Depreciation Method: Tax Holiday / Fiscal Incentive: Import Duty / GST-VAT Treatment:

A structured lender / investor checklist covering the key financial, contractual and technical items typically reviewed during independent engineer (IE) and bank due diligence for solar project financing. Mark each item as you complete it; the panel auto-saves progress in your browser session.

Due Diligence Completion

Complete

Done

Open

📝 Due Diligence Notes / Open Issues

🚀 Tab 7 — Simulation Engine

? Help

⏱ Simulation Resolution

Time Resolution: PV Cell Model:

Hourly mode: fetches a representative year of NASA POWER hourly irradiance/temperature data (or synthesizes an hourly profile from the monthly table if unavailable), then runs a full 8,760-step time-series simulation: exact sun position, Perez (1990) POA transposition, Martin-Ruiz IAM, air-mass spectral correction, Faiman cell-temperature model, row-to-row self-shading, and true sub-hourly inverter clipping. Typical compute time: 1–3 s. Use Sub-hourly for the highest fidelity (15-min steps), recommended for final bankable reports. Use Monthly only for quick what-if checks during early design.

Enter project details in Tabs 1–6, then click RUN SIMULATION.

📐 Formulas & Functions Used in Simulation

1. POA Transposition: cos_incidence = cos(tilt)×cos(θz) + sin(tilt)×sin(θz)×cos(az_diff)
2. POA Factor: POA_factor = max(0.7, min(1.4, cos_incidence + 0.15))
3. G_reflected: GHI × Albedo × (1 − cos(tilt_rad)) / 2
4. H_POA_monthly: GHI_daily × Days × POA_factor + G_reflected × Days
5. Cell Temperature (NOCT): T_cell = T_amb + (NOCT−20)/800 × G_eff
6. Cell Temperature (Uc/Uv): T_cell = T_amb + G_eff/(Uc + Uv×Wind)
7. DC Energy: E_array = Pnom × (H_POA/1000) × [1 + γ×(T_cell−25)] × Loss_factor × Bif_gain
8. Bifacial Gain: Infinite-sheds view-factor model (pvlib-equivalent): G_eff = poa_front + ɸ×poa_back, from tilt/height/pitch/GCR/albedo/sun-position geometry
9. AC Output: E_AC = E_array × η_inv × (1 − AC_wiring_loss)
10. Performance Ratio: PR(%) = [E_AC / (H_POA × Pnom)] × 100
11. Specific Yield: Yf = E_AC_annual / Pnom [kWh/kWp/yr]
12. Array Yield: Ya = E_array_annual / Pnom
13. Capacity Factor: CF(%) = E_AC / (Pnom×8760) × 100
14. P50/P75/P90/P95: Px = P50 × (1 − z×σ) where z=0, 0.674, 1.282, 1.645
15. LCOE: [CAPEX + Σ(OPEX_t/(1+r)^t)] / Σ(E_t/(1+r)^t)
16. NPV: −CAPEX + Σ[(Revenue_t − OPEX_t)/(1+r)^t]
17. IRR: Newton-Raphson iteration on NPV=0
18. EMI: P×r×(1+r)^n / [(1+r)^n−1] where r=monthly rate, n=months
19. CO₂: CO₂_annual = E_AC×factor | Trees = CO₂/21.77 | Cars = CO₂/4600

ℹ️ Click ▶ RUN SIMULATION on the Run Simulation sub-tab to populate these results.

📊 Monthly Simulation Results Table

Month	GHI (kWh/m²/d)	H_POA (kWh/m²)	T_cell (°C)	E_array (MWh)	E_AC (MWh)	PR (%)	Yf (kWh/kWp)
Annual	—	—	—	—	—	—	—

📊 Eac vs Earray vs GPOA Chart

📈 Probability - P50/P75/P90/P95

✂️ Monthly Clipping Loss ? Help

Clipping is a summer/high-sun-angle effect, so it should concentrate in the sunniest, highest-output months rather than spread evenly across the year. Bars show each month's clipped energy (kWh); the dashed line shows that month's clip loss as a % of its own array energy.

📋 Probability Analysis

Exceedance	Annual Energy (kWh)	Annual Energy (MWh)	Specific Yield (kWh/kWp)	Typical Users

📊 Year vs Energy into Grid (25 Yrs)

📊 Year vs Energy & Revenue

📊 Performance Ratio vs Year

ℹ️ The protection & cable schedule is generated after you run the simulation.

ℹ️ AI/ML performance recommendations appear after you run the simulation.

ℹ️ CO₂ savings are calculated after you run the simulation.

📋 Proposal Card

Fill in company, customer, and financial details below, then click Print Proposal for an industry-quality PDF (requires Tab 7 simulation + Tab 6 Calculate Economics).

🏢 EPC Company Details

Company Name: Registered Address: City / State / PIN: GSTIN / VAT / Tax Reg. No: Contact No: Email: Website: Project Authorized Person: Company Logo (PNG / JPG / SVG):

🏦 Bank Account Details

Bank Name: Account Number: Type of Account: IFSC Code: Branch:

📖 About Us (Company Profile)

Fill in company & bank details, then Save company.json to reuse across projects.

✉ Cover Letter

📜 Terms & Conditions

Cover Letter & Terms/Conditions save separately from company.json — reuse the same standard wording across proposals.

🧾 Quotation & Customer Details

Quotation No: Quotation Date: Project Type: Customer Name: Customer Address: Customer Phone: DISCOM Consumer ID: Project App ID:

Fill in quotation & customer details, then Save customer.json.

📈 Financial Analysis (from Tab 6 Economics)

💼 Bankability Perspective

📊 Financial Analysis Results — Year-by-Year Projection (P90 energy basis)

Requires Tab 7 simulation (P90) and Tab 6 "Calculate Economics" to have been run.

🧱 CAPEX — Installation Cost

Show item-wise cost (uncheck to blank Rate/GST/Amount per item — Total still shown — for client-facing scope-only proposals)

🏦 Financing Structure (from Tab 6 OPEX & Financing)

🧮 BOQ Card — Bill of Quantities

Auto-derived from Tab 3 (module/inverter) and Tab 5 (units) — a technical quantities list, separate from the CAPEX ₹ table above. Editable after generating.

Requires Tab 3 module/inverter selection and Tab 5 units.

🗓 Project Implementation Schedule

Editable phase-by-phase schedule for the proposal. Pre-filled with typical EPC phases — add, remove, rename, or re-order as needed for this project.

Project Start Date:

Set a Project Start Date, adjust phase durations, then click Auto-Chain Dates to fill in Start/End automatically — every field stays editable afterwards.

🔬 Research Analysis — Per-Design Diagnostics

? Help

Research-grade diagnostics for the simulated plant and for each sub-array design (Tab 5 units). Metrics follow IEC 61724-1 performance accounting (yields, capture/system losses, weather-corrected PR), with operating-point distributions and a first-order parameter sensitivity sweep. Run a simulation in Tab 7 first; Hourly / Sub-hourly resolution unlocks the time-series panels.

Run a simulation in Tab 7, then click Refresh Analysis.

🏗 Per-Design (Sub-array) Diagnostics

📐 IEC 61724-1 Yield & Loss Accounting

🌡 Weather-Corrected Performance

📊 POA Irradiance Regime (energy-weighted)

🌡 Cell-Temperature Distribution

⚡ Part-Load Signature — PR vs Irradiance

🔥 Thermal Signature — PR vs Cell Temp

🗓 Generation Heatmap — Hour-of-Day × Month

🎚 Parameter Sensitivity (Tornado)

First-order sensitivity of annual AC energy to design / site parameters, each perturbed independently about the current design point. Runs on the fast monthly engine for responsiveness — use it to rank which inputs most influence yield, then confirm magnitudes with a full Hourly run.

🎲 Uncertainty Analysis — Simulation Uncertainty & Exceedance (P50/P75/P90/P95)

Combines the independent simulation-uncertainty sources below into a total 1σ by root-sum-square (σ_total = √Σσ_i²), then derives exceedance probabilities on the simulated P50 annual energy as P_x = P50 × (1 − z·σ), with z = 0 / 0.674 / 1.282 / 1.645 for P50 / P75 / P90 / P95 (Gaussian one-sided, per IEC 61724-3 bankability practice). Values are 1σ percentages of annual yield.

Irradiance uncertainty (%): Model uncertainty (%): Temperature uncertainty (%): Soiling uncertainty (%): Degradation uncertainty (%): Measurement uncertainty (%):

⚠ Irradiance uncertainty caveat: if this value was set from a TMY's computed year-to-year GHI variability (Tab 1 "Use as Irradiance Uncertainty"), note that satellite-derived climatologies (PVGIS/NASA) tend to understate true variability — their spatial pixel-averaging and retrieval smoothing typically report lower interannual variability than real ground-station measurements at the same site (published cross-comparisons show satellite products often understate what ground stations measure directly). Treat a satellite-derived figure as a lower bound, not the final word — if you have a ground-station-anchored source for this site (e.g. Meteonorm, a nearby pyranometer network), prefer that number here instead.

Enter the 1σ uncertainty values and click Compute Uncertainty (a simulation must be run first).

Variance contribution

Annual-energy probability distribution

🤖 AI & Machine Learning Research

Trains lightweight, fully transparent models on the simulated hourly time-series, entirely in your browser (ordinary least squares — no data leaves the page). Each model reports its R² / RMSE and offers an interactive predictor. Because the models learn the simulator's own physics, they are for methodology research and teaching; field deployment still needs real monitored data. Requires an Hourly or Sub-hourly run in Tab 7.

Run an Hourly/Sub-hourly simulation, then click Train Models.

🌡 Module Temperature Deep Research — Model Comparison & Characterisation

Compares four thermal models (Faiman, NOCT, Sandia, Ross) calibrated on the simulated hourly dataset. Each model is characterised using AI/ML regression — coefficients are extracted from the physics of the simulator's own computed cell temperatures — then evaluated on cell temperature accuracy (RMSE, MAE, R²) and energy prediction accuracy (annual energy error, hourly RMSE). Requires an Hourly / Sub-hourly run in Tab 7.

📍 Measured Validation — Thermal Models vs Field / SCADA Module Temperature

Validates the four thermal models against a measured module / back-of-module temperature series instead of the simulator's own output — turning the panel from inter-model consistency into a true field validation. Each model's predicted module temperature is computed a priori from (G, T_amb, wind) and compared with the measurement, reporting MBE (bias) alongside RMSE / MAE / R², a predicted-vs-measured regression, and error stratified by month, irradiance band, wind band and hour-of-day. Every model is also re-fitted to the measured data — the Faiman u₀/u₁ pair, the Ross k, the Sandia a/b coefficients and an effective NOCT — so each standard model is shown next to its measured-data-calibrated variant. Import a CSV (timestamp + module temperature; POA / ambient / wind optional), or reuse a series already mapped in SCADA Benchmarking.

🔌 Inverter Performance Deep Research — Parameter Characterisation & AI/ML Study

Characterises inverter operating behaviour across five key parameters: MPPT efficiency, Clipping, Partial loading, Voltage window, and Reactive power operation. Each parameter is studied through regression models and data-driven metrics derived from the simulated time-series. Requires an Hourly / Sub-hourly run in Tab 7.

🌑 Shading Deep Research — Model Comparison & Characterisation

3-D spatial analysis of shading losses derived from terrain and obstacle datasets. Supports LiDAR point clouds (.las/.laz), DEM (bare-earth elevation), DSM (surface model including structures and vegetation), and imported 3D models (OBJ / KML / IFC). Studies five shading source categories — Row-to-row, Building, Tree, Terrain, and Horizon shading — and compares geometric ray-casting, SkyView-factor, isotropic-diffuse, Perez anisotropic, and linear-shading models. Outputs annual shading loss (%), monthly shading maps, and a full 8 760-step time-series shading factor. Requires an Hourly / Sub-hourly run in Tab 7.

📡 LiDAR (.las/.laz) 🗺 DEM — bare earth 🏗 DSM — surface model 📦 3D Models (OBJ/KML/IFC) 🧭 Horizon profile (.HOR)

🔍 Shading sources studied

● Row-to-row shading — inter-row blocking at low sun angles ● Building shading — nearby permanent structures ● Tree shading — vegetation obstruction (LiDAR / DSM) ● Terrain shading — topographic self-shading and cast shadows ● Horizon shading — far-field elevation profile (8-sector / continuous)

📊 Outputs

● Annual shading loss (%) ● Monthly shading map (12 × azimuth × elevation) ● Time-based shading factor (8 760-step hourly) ● Shadow-mask raster overlay ● Irradiance loss heatmap (hour-of-day × month)

⚖ Model comparison

⚡ Bifacial Gain Deep Research — Model Comparison & Characterisation

Characterises bifacial rear-side irradiance gain across key system parameters: Ground albedo, Mounting height, Row spacing / GCR, and Tracker systems (fixed-tilt, SAT, DAT). Compares PVsyst view-factor, NREL bifacial_radiance, SAM simple-bifacial, IEC TS 60904-1-2, and Marion et al. (2017) models. Studies five real-world ground surface albedo classes — Grass, Concrete, White gravel, Snow, and Sand — and outputs expected bifacial gain (%), rear-side irradiance fraction, and height / GCR sensitivity curves. Requires an Hourly / Sub-hourly run in Tab 7.

🌍 Ground albedo 📏 Mounting height ↔ Row spacing / GCR 🔄 Tracker systems 🔋 Bifaciality factor

🌿 Ground surface albedo examples

Grass

ρ = 0.15 – 0.25

Concrete

ρ = 0.25 – 0.35

White gravel

ρ = 0.40 – 0.55

Snow

ρ = 0.60 – 0.90

Sand

ρ = 0.20 – 0.40

⚙ Albedo sensitivity — quick estimator

Ground albedo: 0.20

Mount height (m): 0.8

GCR: 0.40

Est. bifacial gain

3.8%

Rear irradiance fraction

19%

Height factor

0.82

🔬 Bifacial Gain Simulation Model — Perez Diffuse + View Factor (PDVF) Method

A research-grade rear-irradiance engine that couples Perez (1990) anisotropic sky transposition (front) with a segmented ground-irradiance map and 2-D cell-level view factors (sky / ground / obstruction, via Hottel crossed-strings reduction of the configuration integral) to estimate rear POA, effective irradiance, cell temperature and DC power. Inputs are pulled from the previous simulation, the selected module and the design tabs: Latitude, Longitude, Time, Day-of-year, Solar zenith θz, azimuth γs, AOI, GHI, DNI, DHI, ambient temperature, wind speed, tilt, azimuth, pitch, GCR, height, module width/length, bifaciality φ, IAM, efficiency, ground albedo, seasonal variation and surface type. Geometry is built once, so the model is fast enough for full annual (8,760-hour) bankability runs and supports fixed-tilt and single-axis-tracker layouts. Requires an Hourly / Sub-hourly run in Tab 7.

☀ Perez front POA 🟫 Ground irradiance map 📐 View factors (sky/ground/obstr.) 🌡 Faiman thermal ⚡ Single-diode DC

⚙ Discretisation & ground surface

Ground segments

Rear cells

Surface type

Seasonal albedo variation

Latitude, longitude, tilt, azimuth, pitch, height, module geometry, bifaciality φ, efficiency and IAM are read automatically from Tabs 1–4 and the previous run.

📡 Measured vs Simulated (SCADA Benchmarking)

Import a plant SCADA CSV, map its columns, and benchmark the measured generation against the simulated hourly series. The data is resampled to hourly and aligned to the 8,760-step year (timestamps read as wall-clock; Feb-29 folded out), with IEC 61724-style filtering of night, gaps, curtailment and outages. Reports MBE, MAE, RMSE, nRMSE, R² and regression slope overall, by month, by irradiance band and by hour-of-day. Everything stays in your browser. Requires an Hourly / Sub-hourly run in Tab 7. New to this? Grab the Download Sample CSV button below for a ready-made 1 MW plant example (8,760 hourly rows) to see the column format and try the workflow end-to-end.

⬇ Download Sample CSV

Run an Hourly/Sub-hourly simulation, then import your SCADA CSV.

🔄 Single-Axis Tracker (SAT) Research

A dedicated research workbench for horizontal single-axis trackers (HSAT). It runs an independent hourly representative-day simulation (pvlib-equivalent backtracking, Erbs DNI/DHI decomposition, Duffie–Beckman tracker geometry) using the site latitude and the Tab 1 monthly GHI. Explore the four studies below — none require a full Tab 7 run, though one is used when present to anchor absolute energy.

Open this tab to auto-run the Fixed vs SAT comparison.

⚖ Compare Fixed-Tilt vs Single-Axis Tracker

Axis azimuth (°, N–S=0)

Max rotation (±°)

GCR

Albedo

Fixed tilt (°, blank=opt)

🔋 Battery / BESS Deep Research — Duty-Cycle & Sizing Characterisation

Runs the same hourly self-consumption dispatch model as Tab 3, but characterises it in depth: a full State-of-Charge trace and duty-cycle histogram, an implied cycle-life check, and two sensitivity sweeps (Capacity×Power, and Round-Trip Efficiency). Requires an Hourly / Sub-hourly run in Tab 7, a configured battery on Tab 3, and a load profile (Tab 3 Day/Night rows, or an uploaded 8,760-row load CSV).

Configure the battery on Tab 3, run a simulation in Tab 7, then click Run Battery Analysis.

🌦 Weather Source Benchmarking — Default vs PVGIS vs NASA

Compares every weather resource currently available to this project — the app's built-in default monthly profile plus any imported PVGIS TMY and/or NASA Hourly TMY (Tab 1) — side by side: monthly GHI/temperature, annual irradiation totals, year-to-year variability, and the resulting annual H_POA at your current Tab 2 orientation. Import PVGIS and/or NASA in Tab 1 first to compare more than one source.

Click Compare Weather Sources to benchmark whichever resources are currently loaded.

🧹 Soiling & Degradation Deep Research — Model Comparison & Characterisation

The Tab 4 loss table treats Soiling Loss as one flat annual %, and Module Aging/Degradation as a straight-line (linear) decline — the industry default, and what the simulation engine actually applies. This panel characterises two well-known alternative models against that baseline, purely as a diagnostic/planning tool: a seasonal, cleaning-schedule-driven soiling profile, and non-linear LID (Light-Induced Degradation) / LeTID (Light & elevated-Temperature Induced Degradation) power-retention curves. Nothing here changes the simulation automatically — use the Apply buttons if you want a derived figure written into Tab 4.

🧹 Soiling — Flat vs Seasonal/Cleaning-Schedule Model

Daily soiling accumulation rate (%/day): Cleaning interval (days):

Months with natural rain-cleaning (soiling resets to 0% — typically the monsoon/wet season for your site):

Set the parameters above, then click Compare Soiling Models.

📉 Degradation — Linear vs LID vs LeTID Power-Retention Models

LID step, Year-1 only (%): Post-LID/LeTID linear rate (%/yr): LeTID peak additional loss (%): LeTID time-to-peak (years): LeTID recovery time-constant (years):

Set the parameters above, then click Compare Degradation Models.

🌈 Spectral Mismatch Deep Research

Every hourly/sub-hourly simulation already computes a per-timestep spectral modifier M (First Solar / Lee-Panchula 2016 model — the same model family PVsyst's optional FirstSolar spectral correction and pvlib's spectral_factor_firstsolar use) and folds it into GlobEff, but nothing in the app visualizes it. This panel characterises the real simulated M series, and separately re-runs the same physics for the other four module technologies and a couple of "what if" site conditions, purely as a diagnostic — none of it changes the simulation.

📖 Reference — How Spectral Mismatch Works

Standard Reference Spectrum. Modules are rated at STC against AM1.5G (IEC 60904-3 / ASTM G173) — the standardised terrestrial global spectrum for 1.5 air masses, 1000 W/m², 25°C cell temperature. Any real spectrum that differs in shape from AM1.5G — bluer or redder — produces a spectral mismatch, even at identical broadband irradiance.

Air Mass Effect. Air Mass (AM) is the relative path length sunlight travels through the atmosphere, AM ≈ 1/cos(zenith) — AM1 near solar noon at low latitudes, rising sharply at low sun angles (dawn/dusk, winter, high latitude). Higher AM means more atmospheric scattering/absorption of shorter (blue) wavelengths, so the spectrum reddens as AM rises.

Atmospheric Influences. Beyond air mass itself, precipitable water (Pw) drives strong near-infrared absorption bands (~940, 1100, 1400 nm) — humid, coastal, or tropical sites see more red-shifting than dry/high-altitude sites at the same AM. Aerosols/turbidity and ozone further modify the UV/blue portion, and overcast/diffuse-dominant conditions skew bluer than clear-sky direct beam.

Influence on PV Parameters. Spectral shift mainly affects Isc (and hence Pmax) — a cell's spectral response only converts photons within its band-gap window into current, so a mismatch between the incident spectrum and AM1.5G under/over-produces current relative to STC rating. Sensitivity is technology-dependent: wider-band-gap thin films (a-Si ~1.7eV, CdTe ~1.5eV) respond differently to blue-rich/red-rich shifts than crystalline silicon (~1.12eV) — which is why the model below uses separate coefficients per technology.

Geographic Influence. Low-latitude, high-altitude, arid sites (e.g. high Andean plateau, desert interiors) run lower average AM and lower Pw — generally a small net spectral gain for crystalline silicon. Humid tropical coastal sites and high-latitude winter conditions push both AM and Pw up, typically a small net spectral loss for c-Si, while thin-film technologies can respond differently. The "Geographic Influence" sensitivity tool below lets you test this for your own site.

Monthly / Yearly / Lifelong Effect. Spectral mismatch is a recurring, not a cumulative, effect — unlike degradation, it doesn't compound year over year. The monthly pattern below repeats every year of the project life (seasons don't shift), so the single annual % figure applies unchanged in every future year — multiply it by the project life for a simple lifelong total, with no year-on-year growth.

Spectral Models Used in This and Other Simulation Software. This app uses the two-variable First Solar / Lee-Panchula (2016) M(AM_a, P_w) polynomial for all technologies. Loading reference comparison…

Run an Hourly/Sub-hourly simulation in Tab 7 first, then click Run Spectral Analysis.

🎯 Tilt/Azimuth/GCR Optimization Surface — Utility-Scale & Rooftop Solar PV

Tab 2's Auto-Optimize Tilt button sweeps tilt alone, at a fixed south-facing azimuth, for maximum unshaded irradiance — it can't show whether a slightly lower tilt paired with a different azimuth beats it, or how tightening row spacing trades irradiance loss for more DC capacity per hectare. This panel runs genuine 2D grid searches — Tilt×Azimuth (real transposition physics via the same engine Tab 2 uses) and Tilt×GCR (real row-to-row shading geometry) — to find the true joint optimum, complementing the one-variable-at-a-time Sensitivity Analysis tornado chart elsewhere in Research.

📖 Reference — Tilt, Azimuth & GCR Effects

Effects of Tilt. Tilt trades off several effects simultaneously: annual POA irradiation (peaks near tilt ≈ latitude for a due-equator azimuth); self-cleaning from rainfall and soiling accumulation (shallow tilts <10° retain more dust — rain sheets off a steep module far more effectively); wind loading (steeper tilt = larger projected area normal to prevailing wind = higher structural loads, especially at array edges/corners); snow shedding in cold climates (steep tilt sheds snow quickly; shallow tilt can lose most of a winter month's production under an unshed snow layer); row-to-row shading (steeper tilt casts a longer shadow at a given sun angle, needing wider pitch); module temperature (tilt changes the sky view factor and natural convection slightly — a secondary effect versus wind speed and ambient temperature); and for bifacial modules, rear irradiance (steeper tilt at a given clearance height generally improves rear-side view of the ground, up to a point).

Optimum Tilt by Latitude. The classic rule of thumb is tilt ≈ |latitude| for maximum annual yield at due-equator azimuth — flatter tilts shift the seasonal balance toward summer-weighted yield (useful for summer-peaking loads), steeper tilts weight toward winter and improve snow shedding at some annual-yield cost. The Tilt×Azimuth grid below finds this numerically for your actual weather data rather than the rule of thumb.

Effect of Azimuth. Due-equator (South in the Northern hemisphere) gives the maximum annual yield and the highest midday production peak. East-facing shifts production earlier (higher morning output), West-facing shifts it later (higher afternoon output) — both trade some annual energy for a better match to time-of-use tariffs or specific load shapes. East-West row layouts (alternating East/West-facing rows, common in close-packed bifacial/high-GCR designs) give a flatter, longer production profile that can better match some commercial daytime loads, and — because East-West rows can be packed edge-to-edge with far less row-to-row shading penalty than a South-facing array at the same pitch — enable higher DC capacity per hectare on constrained land.

Ground Coverage Ratio (GCR) & Row-to-Row Shading. GCR = collector area ÷ ground area. Lower GCR (wide pitch) maximises energy per module but uses more land per MW; higher GCR (tight pitch) increases DC capacity per hectare but raises row-to-row shading loss — the classic utility-scale land-vs-yield trade-off. The Tilt×GCR grid below quantifies exactly where that trade-off sits for your module geometry and site latitude, using the same row-shading formula (rowShadingFactor()) the actual Hourly simulation engine applies.

Optimization Variables & Bifacial Optimization. The two grids below sweep the four variables that jointly define array orientation and density: tilt, azimuth, GCR (via row spacing), and — where a bifacial module (Tab 5's Bifacial Factor > 0) is selected — an approximate rear-irradiance gain term that falls as GCR rises (denser rows reduce both ground view factor and rear-side sky view). Bifacial arrays often favour a slightly lower GCR and higher clearance than an equivalent monofacial design, since rear gain is more GCR-sensitive than front-side shading loss alone.

Software Capabilities. Loading reference comparison…

🗺 Tilt × Azimuth — Annual H_POA Heatmap

Real transposition physics (the same 'poa' calculation Tab 2's Auto-Optimize Tilt uses) at every grid point — tilt 0–60° × azimuth −90…+90°. Finds the true joint optimum, not just the best tilt at a fixed azimuth.

This runs ~169 lightweight irradiance calculations — takes a few seconds.

🗺 Tilt × GCR — Relative Annual Energy & DC Density Heatmap

Combines real H_POA(tilt) with the actual row-shading geometry model at each GCR (azimuth fixed at your Tab 2 value), plus an approximate bifacial rear-gain term if a bifacial module is selected. All-client-side, so this grid is quick regardless of resolution.

Set the desired GCR range then click Compute Tilt×GCR Grid.

💰 Financial/Economic Risk Tornado — Bankable Solar PV Sensitivity

Tab 6's Sensitivity/Uncertainty tools (elsewhere in Research) only perturb technical inputs and read out a Δ in annual energy. This panel runs the SAME real cash-flow/tax/depreciation/debt engine Tab 6 & the Financial Risk Monte-Carlo tool use (calcNPV/calcIRR/ calcDepreciationSchedule/Equity-IRR/DSCR), perturbing one variable at a time across technical, CAPEX, OPEX, and financial risk categories, and reports the impact on Equity IRR — the metric lenders actually underwrite to.

📖 Tornado vs Monte Carlo — What's the Difference?

Tornado (this panel): deterministic, one variable at a time — every other input stays at its base-case value while one is shifted to its assumed adverse/favourable case. Ranks variables by individual impact and shows direction — ideal for a risk register or IC memo appendix ("what matters most, and which way"). It does not capture correlated or simultaneous shocks.

Monte Carlo (the Financial Risk tool, Tab 6): draws every variable simultaneously, at random, from a probability distribution, thousands of times — producing a full probability distribution of outcomes (P10/P50/P90 of IRR, NPV, DSCR). This is what lenders use to size debt against a P90 DSCR, and it does capture combined/correlated effects, at the cost of being a distribution rather than a simple ranked list.

Bankable Solar PV Tornado Analysis means: built on the exact same financial engine as the headline base case (not a simplified side-calculation), covering technical and commercial/financial risk together, expressed against the metrics lenders underwrite to (Equity IRR / DSCR), with every assumed range fully visible and editable — not a black box.

⚙ Assumed Risk Ranges (editable — sensible defaults pre-filled)

Core (mirrors the Financial Risk Monte-Carlo tool)

Tariff ±% Discount Rate ±pp CAPEX (overall) ±% Degradation Rate ±pp/yr

Solar Resource / Technical Uncertainty (± energy %, reuses Uncertainty Analysis σ where available)

Irradiance ±% Model ±% Temperature ±% Soiling ±% Availability ±% Inverter Efficiency ±% Clipping Losses ±% Spectral Effects ±% Shading ±%

CAPEX Risks (± price %, auto-weighted from Tab 6 CAPEX table where item names match, else default weights)

Module Prices ±% Inverter Prices ±% MMS/Structure Prices ±% Cable Prices ±% Civil Works ±% EPC Overrun / Contingency Usage ±%

OPEX Risks (± cost %, auto-weighted from Tab 6 OPEX table where item names match, else default weights)

O&M Contract ±% Spare Parts ±% Insurance ±% Cleaning ±% Security ±% Vegetation Mgmt ±%

Financial Risks

Interest Rate ±bps Debt Ratio ∓pp (lender gearing) Refinancing Rate Step-up ±bps Inflation (OPEX Esc.) ±pp Exchange Rate ±% (imported equip.) Corporate Tax Rate ±pp Accelerated Depreciation Loss % GST/VAT Change ±%

Requires Tab 6 "Calculate Economics" to have been run at least once (needs a base-case Project IRR/NPV to perturb from).

🔁 Repowering / Mid-Life Revamp Comparison

Reuses the same full year-by-year lifetime engine as Tab 4's "preview a specific project year" feature (a genuine hourly re-simulation for every project year, not a flat degradation formula) to compare three real cash-flow scenarios: As-Is (no intervention), Module Replacement at a chosen mid-life year, and Inverter Upgrade at a chosen year — each spliced from real lifetime-engine output and run through the same financial engine as the Financial Risk Tornado.

⏳ This runs multiple full hourly-engine lifetime simulations back to back (potentially 40+ individual year-simulations in total) — expect this to take noticeably longer than a normal Tab 7 run.

⚙ Scenario Configuration

Module Replacement Scenario

Replacement Year: Replacement Module: Module Replacement CAPEX:

Inverter Upgrade Scenario

Upgrade Year: Replacement Inverter: Inverter Replacement CAPEX:

Configure the scenarios above, then click Run — needs Tab 3 module/inverter selected, Tab 5 units configured, and Tab 6 "Calculate Economics" run at least once.

🌪 Climate / Extreme-Weather Risk Panel

Tab 6's due-diligence checklist collects free-text notes on natural-catastrophe exposure (insNatCat, cyclone/hail exposure, etc.) but nothing quantifies them. This panel turns six climate-change projection categories into an actual energy-yield and financial analysis — using the module's real temperature coefficient, the same soiling model as the Soiling & Degradation panel, and the same financial engine as the Financial Risk Tornado — so the due-diligence notes get a number attached, not just a description.

📖 Climate Change Projections (IPCC AR6)

Higher Temperatures. Global mean warming continues to raise average and peak ambient temperatures across virtually all regions, directly reducing PV output via the module's negative power temperature coefficient and raising thermal stress on inverters/BOS.

Changing Rainfall. Precipitation patterns are shifting — some regions drier, some wetter, and monsoon timing/reliability becoming less predictable — directly affecting how often rainfall naturally cleans panels (soiling accumulation between "resets").

Stronger Extreme Precipitation. Extreme daily precipitation is projected to intensify by roughly 7% for every 1°C of global warming (high confidence), raising flooding/waterlogging risk at low-lying or poorly-drained sites — a direct downtime/damage risk, not just an energy-yield one.

More Intense Tropical Cyclones (Region-Dependent). The proportion of the most intense (Category 4–5) tropical cyclones is projected to increase with further warming (high confidence), though projected changes in cyclone frequency vary considerably by ocean basin — some basins show projected declines in frequency alongside increased intensity of the storms that do occur. Sites in cyclone-prone coastal regions face structural-damage and extended-downtime risk from the more intense end of the distribution.

Increased Drought Frequency. More regions are projected to see increased agricultural and ecological drought frequency. For solar, this is a mixed effect: fewer clouds can mean modestly higher GHI, but longer dry spells between rains mean soiling accumulates for longer, and water-based cleaning regimes may become costlier or restricted.

Longer Heat-Wave Duration. It is considered virtually certain that hot extremes, including heatwaves, have become more frequent and intense, with continued increases projected. Sustained multi-day heat events can push ambient temperature above a plant's original design values (Tab 1) for extended periods, stressing inverters/transformers beyond their original thermal design margin, not just cutting output for a few hours.

⚙ Scenario Assumptions (editable)

🌡 Higher Temperatures

Projected ambient temperature rise (°C):

🌧 Changing Rainfall / Reduced Natural Cleaning

Reduction in effective rain-cleaning events (%):

🌊 Stronger Extreme Precipitation (Flood/Waterlogging)

Expected extra outage (days/yr):

🌀 Tropical Cyclones (region-dependent — set probability to 0 if not applicable)

Annual probability of a damaging event (%): Expected downtime if it occurs (days): Expected repair cost if it occurs (% of CAPEX):

☀ Increased Drought Frequency

Clear-sky GHI uplift during drought (%): Extra soiling from prolonged dry spells (%):

🔥 Longer Heat-Wave Duration

Projected peak heat-wave ambient (°C): Assumed inverter derating threshold (°C): Heat-wave days/yr above threshold:

Set the assumptions above, then click Run — a completed Tab 7 simulation and Tab 6 "Calculate Economics" give the most accurate energy/financial baseline, but the panel will run with reasonable defaults either way.

🌾 Agrivoltaics / Land-Use Trade-off

Relevant mainly for Ground Mounted projects with a co-location or dual-use land component. Extends the same row-geometry technique as the Tilt×GCR panel — but computes light reaching the ground/crop beneath and between rows, not row-to-row shading between panels — and combines it with the Land Equivalent Ratio (LER), the standard metric for whether co-locating crops and PV on one plot beats farming and generating power on two separate plots of the same total area.

📖 Reference — Agrivoltaic System Types & the Core Trade-off

Elevated Fixed-Tilt Arrays. Standard fixed-tilt modules raised to 2.5–5 m clearance (vs. ~1–1.5 m conventional) so farm machinery and crops can operate beneath. Usually paired with a wider row pitch (lower GCR) than a conventional ground-mount to let more light reach the ground — the main design lever this panel quantifies.

Elevated Single-Axis Trackers. SAT tracking (Tab 2's SAT model) at agrivoltaic clearance heights — tracking can be programmed to open up ("de-tilt" toward horizontal at midday, or skew off-sun) specifically to let more light through during critical crop growth periods, trading some PV yield for crop yield on demand rather than through fixed geometry alone.

Vertical Bifacial Arrays. Modules mounted near-vertical (tilt ≈ 90°) in East-West-facing rows, wide apart — minimal ground shading (light passes largely unobstructed at midday), a flatter two-peak (morning/evening) generation profile, and full use of both faces via bifacial gain. Land-use efficiency is often the best of any configuration, at some monofacial-equivalent energy-density cost.

Orchard / Interspace Agrivoltaics. Panels installed between existing tree/vine rows (orchards, vineyards) rather than over the crop itself — minimal new shading design needed since the crop canopy and the array occupy separate strips; land-use gains come mainly from using otherwise-unused inter-row land rather than from a light-sharing trade-off.

⚖ The Central Trade-off

Every agrivoltaic design balances three competing goals: maximum electricity generation (favours low clearance, high GCR, steep tilt toward latitude-optimum), maximum agricultural productivity (favours high clearance, low GCR, tilts/tracking chosen for light transmission over energy yield), and minimum environmental impact (favours designs preserving soil, water infiltration, and biodiversity — often correlated with, but not identical to, high light transmission). This panel quantifies the first two directly; the third stays qualitative here.

Land Equivalent Ratio (LER) = (Crop Yield_AV ÷ Crop Yield_Reference) + (PV Yield_AV ÷ PV Yield_Reference). LER > 1 means the co-located site produces more combined output than farming and generating power on two separate reference-sized plots of the same total area — the standard justification metric for agrivoltaics.

⚙ Configuration

Panel Clearance Height (m): Reference (conventional) GCR: This Design's GCR: Crop Shade-Tolerance (0=full-sun crop, 1=fully shade-tolerant):

Uses Tab 1 latitude, Tab 2 azimuth/module slant length — set the configuration above, then click Run.

📚 Journal Papers Guide — Core Research Roadmaps

Eight publication-ready study roadmaps drawing on the Uncertainty, Sensitivity, Inverter, Temperature, Degradation, Spectral and AI/ML modules above. Each card outlines the aim, recommended methodology & standards, key variables, datasets, expected figures/results, candidate journals, and exactly which tools in this simulator produce the evidence. Click a title to expand.

🗒 Research Notebook — Versioned Run History

Saves a snapshot of the current design (modules, inverter, layout, weather source) and result summary with a stable ID, so you can reproduce or cite the exact parameter set behind a figure later — useful when responding to peer-review requests. Snapshots are stored locally in this browser; use Share / Import below to hand a config to a co-author or reviewer on another machine.

Run a simulation in Tab 7, label it above, then click Save.

Import accepts a single-snapshot file from "📤 Share", a full "Export Notebook JSON" file, or several of either at once. Imported snapshots are tagged 📥 and never overwrite an existing local snapshot with the same ID — a colliding ID is re-tagged so both are kept.

📑 Citations & References

One-click BibTeX export of the physics models, standards and weather data source relevant to the current configuration (e.g. Perez transposition, IEC 61724, tracker/bifacial references when applicable). Review and trim the file to what you actually used before submission.

📋 Table Export (CSV / LaTeX)

Export any results table currently rendered anywhere in the Research tab as CSV or a LaTeX tabular environment (booktabs style). Visit the relevant sub-tab first so its table is populated, then refresh this list.

Select a table above and choose a format.

📦 Zenodo-Style Dataset Export

Builds a ready-to-upload package (dataset CSV, Zenodo-format metadata.json, citations.bib and a README) for archiving your simulated dataset with a DOI. This runs entirely in your browser and does not contact Zenodo — actual DOI minting happens once you upload the package and click Publish on Zenodo's site, since submitting via their API would require your personal access token, which isn't safe to embed in a public web page.

🗄 Databases — PV Modules & Inverters

? Help

🔋 PV Module Database (panel.JSON)

	ID	Manufacturer	Model	Tech	Pmax(W)	Eff(%)	Voc(V)	Bif	Actions

🔌 Inverter Database (inverter.JSON)

	ID	Manufacturer	Model	Type	AC(kW)	Eff(%)	MPPT	Phase	Actions

⚙ Settings — File Paths & Defaults

Database Path: Project Path: Report Path: Start Year: Currency Symbol: Applicable Tax Name: CO₂ Grid Factor (kg/kWh):

✨ AI features (Auto-Create from Datasheet, AI curve digitizer, in-app assistant) run through this server so your API key is never exposed to the browser. Set ANTHROPIC_API_KEY in ai_config.php (or the ANTHROPIC_API_KEY environment variable) to enable them.

🌍 Country/Region-Specific CAPEX/Wp Bankability Bands

Editable, per-currency CAPEX/Wp benchmark ranges, expressed in each currency's own units (installed costs vary materially by country due to labour, land, import duties and local EPC competition — a fixed USD threshold is not appropriate across markets). Two segments are tracked separately since they have structurally different cost bases: 🏭 Utility-Scale Ground-Mount (economies of scale, simple racking) and 🏢 Rooftop / C&I (mounting complexity, working-at-height labour, smaller BOS economies — typically ~30–40% higher per Wp). These bands are used by the Bankability Assessment Report (Section 5.2 CAPEX benchmark and the Project Bankability verdict), which automatically picks the matching segment from Tab 1's Project Type (Ground Mounted → Utility-Scale; Rooftop Commercial/Residential → Rooftop/C&I). CAPEX/Wp at or below "Low" is assessed as reasonable, between "Low" and "High" as slightly elevated, and above "High" as materially above benchmark. Values are approximate, indicative defaults — edit them to reflect current EPC market pricing for your project's country.

Symbol	Code	Market	🏭 Utility-Scale Ground-Mount		🏢 Rooftop / C&I
Symbol	Code	Market	Low (/Wp)	High (/Wp)	Low (/Wp)	High (/Wp)

📋 Default Parameters Table

Reference list of every default/fallback value used by the simulation engine when a field is left blank or not overridden by the user, an imported TMY, or a selected library component.

Sl No	Parameter	Default Value	Notes
📍 Site / Location
1	Latitude	19.076°	Fallback only — used when the Lat field is blank/unparsable (Mumbai)
2	Longitude	72.877°	Fallback only — used when the Lon field is blank/unparsable (Mumbai)
3	Ground Albedo	0.20	Generic ground reflectance
4	NASA TMY Start/End Year	2005 – 2024	For NASA Hourly TMY import
🧭 Orientation
5	Tilt Angle	15°	Fallback only — used when the Tilt field is blank
6	Azimuth	0° (South, N. Hemisphere)	0° = due south for Northern Hemisphere
7	Plant Size	10 kWp	Required field, no on-screen default; 10 kWp used only as a calc fallback
☀ Weather
8	Weather Source	Generic Mumbai-region monthly profile (DEFAULT_WEATHER)	Used unless a location-specific PVGIS or NASA TMY is imported in Tab 1
9	GHI (annual avg.)	~4.8–6.5 kWh/m²/day (monthly)	Jan 4.82 → Dec 4.70; varies by month, monsoon dip Jun–Aug
10	DNI / DHI (monthly)	1.5–6.1 / 1.4–2.5 kWh/m²/day	Monsoon months (Jun–Aug) are diffuse-heavy, dry months are beam-heavy
11	Ambient Temperature (monthly)	23.5–33.0°C	Peaks in Apr–May
📉 System Losses
12	Array Thermal Loss (Uc+Uv)	−5.0%	IEC 61724 / Uc/Uv model
13	DC Ohmic Loss (Wiring)	−1.5%	Typical 1–2% for DC wiring
14	AC Ohmic Loss (Cabling)	−0.5%	Typical 0.3–1% AC cable losses
15	Module LID Loss (1st year)	−2.0%	Light-induced degradation
16	Module Mismatch Loss	−1.0%	Module-to-module variation
17	Soiling Loss	−2.0%	Dust/dirt accumulation
18	IAM Loss (Incidence Angle)	−2.5%	Incidence angle modifier
19	Module Aging / Degradation	0.0%	Computed separately per year in lifetime model
20	Grid Unavailability Loss	−1.0%	Outage/curtailment loss
21	Spectral Loss	−0.5%
22	Inverter Efficiency Loss	−2.0%
23	Auxiliary Consumption Loss	−0.5%
24	Shading Loss (Far Horizon)	−0.5%	Auto-synced from Tab 1 Site Condition once computed
25	Sub-hourly Clipping Loss	−0.5%
26	Bifacial Gain (rear irrad.)	+2.0%	Auto-synced from Unlimited-Sheds bifacial model
27	ILR (DC/AC Pnom Ratio)	1.1
28	Thermal Loss Coefficients Uc / Uv	25 / 6.84	Thermal model coefficients
🏗 Row Spacing / Shading
29	Row Spacing	3.0 m
30	Module Height (slant)	1.5 m (1.05 m slant)
31	Module Stack Count	2
32	Module Gap	20 mm
33	Front Edge Height	0.3 m	rsc_frontEdge
34	Module Height (RSC)	2.12 m	rsc_modh
35	Shading Window (Start–End)	9:00 – 15:00	rsc_tStart / rsc_tEnd
36	Albedo (RSC)	0.20
37	Bifaciality (RSC)	0.70
38	Azimuth (RSC)	0°
39	Roof (Local Shading Studio)	20 × 12 m, height 9 m, pitch 18°, azimuth 0°
40	Obstruction (LSS)	1.2 × 1.2 × 1.5 m @ 1.2 m distance
41	Parapet Height (LSS)	1.0 m
42	Setback (LSS)	0.5 m
43	Module Size / Power (LSS)	1.13 × 2.28 m @ 585 Wp
44	Shading Drop Threshold (LSS)	12%
🌗 Single-Axis Tracker (SAT)
45	Axis Azimuth	0°
46	Max Rotation Angle	±60°
47	GCR (Ground Coverage Ratio)	0.40
48	Module Width	2.13 m
49	Hub Height	1.5 m
50	Soiling Loss (SAT)	2.0%
51	Snow Loss (SAT)	0.0%
52	Albedo (SAT)	0.20
53	Degradation (SAT)	0.55%/yr
54	DC Wiring Loss (SAT)	1.5%
55	Availability (SAT)	99.0%
56	Parasitic Tracker Power	8 W
57	GCR (SAT Research/Bankability)	0.35	satr_gcr
58	Max Angle (SAT Research)	55°	satr_maxAngle
59	Backtracking Albedo Slider	0.25 (range 0.10–0.85)	satr_bAlb
60	Backtracking GCR Slider	0.35 (range 0.20–0.65)	satr_bGcr
61	Backtracking Hub Height Slider	1.5 m (range 0.5–3.0)	satr_bHt
62	Backtrack Tolerance	0.10	satr_bTt
63	CAPEX / OPEX (SAT Research)	35 / 1.2	satr_bCapex / satr_bOpex
64	Discount Rate (SAT Research)	9%	satr_bDisc
65	Project Life (SAT Research)	25 yrs	satr_bLife
66	φ (SAT Research)	0.70	satr_bPhi
🔲 Bifacial Settings
67	Bifacial Factor	0 (mono default)
68	Bifacial Albedo Slider	0.20 (range 0.05–0.90)
69	Bifacial GCR Slider	0.40 (range 0.20–0.80)
70	Bifacial Height Slider	0.8 m (range 0.3–3.0)
💰 Financial / Economic
71	Tariff	₹7.5/kWh
72	Tariff Escalation	5%/yr
73	Discount Rate	10%
74	Loan ROI	9%
75	Loan Tenure	5 yrs
76	Own Funds	₹150,000	Placeholder
77	Total Investment	₹500,000	Placeholder
78	Project Life	25 yrs	Mirrored from Tab 3 "Year of the Project Life" — auto-updates to whichever is higher: 25, or the selected module's warranty period
79	CAPEX Reference Items	Modules ₹15,000/unit (×20), Inverter ₹45,000, Structure ₹30,000, DC/AC cables ₹12,000/₹8,000, Earthing ₹5,000, Net-meter/DISCOM ₹15,000, Installation ₹20,000, AMC ₹5,000	Reference ~10 kWp system; GST 0–18% per item
80	OPEX Reference Items	O&M ₹5,000/yr, Insurance ₹2,000/yr, Land lease ₹0
81	Simulation Start Year	2025
💸 Tax & Depreciation
82	OPEX Escalation / Inflation	5%/yr	Compounds Annual OPEX the same way Tariff Escalation compounds revenue
83	Corporate Income Tax Rate	0%	Defaults to 0% so results match the app's original pre-tax behavior — tax modeling is opt-in
84	Depreciation Method	WDV (Written Down Value)	Alternatives: SLM (Straight Line) or a Custom Schedule
85	Depreciation Rate	40%/yr	India: standard WDV rate for solar power generating equipment under the Income Tax Act
86	Additional Depreciation	Off (20% if enabled)	Applied in Year 1 only — India: Sec 32(1)(iia)-style, new plant & machinery
87	Tax Holiday	0 yrs	Years with zero income tax regardless of profit
88	Salvage Value	0% of CAPEX	Floor below which depreciation cannot reduce book value, in any method
📊 Uncertainty (P50–P95 / Monte Carlo)
89	Irradiance Uncertainty	3.5%
90	Model Uncertainty	2.5%
91	Measurement Uncertainty	1.0%
92	Temperature Uncertainty	1.0%
93	Soiling Uncertainty	1.5%
94	Degradation Uncertainty	1.0%
95	Component Uncertainty	2%
96	Data Uncertainty	5%
97	Monte Carlo Trials	5,000

☀ Tab 1 — Project & Site Information

📋 Project Information

📍 Site Location

🏙 Site Condition — Far-Horizon Shading Allowance

🌡 Ambient Temperature & CO₂ Factor Table

📅 Solar Resource Year Range (PVGIS/NASA Hourly TMY)

🌤 Monthly Weather Data — Source: Manual (import a TMY)

🧭 Tab 2 — Orientation Setting & POA Calculation

🔧 Tilt & Azimuth Settings

📐 Orientation Loss vs Optimum

☀ Monthly POA Irradiance Table — Source: Manual (import a TMY)

📊 Monthly POA Irradiance Chart — Source: Manual (import a TMY)

🪟 Unlimited Sheds 2D Model — Bifacial Rear-Irradiance Geometry

⚡ Electrical Shadings — Reverse I-V / Bypass-Diode Partial-Shading Loss

📈 Simulated Bifacial Gain — Monthly & Hourly ▸ Show

Hourly Profile (energy-weighted gain % by hour of day, per month)

🌡 Array Temperature vs Effective Irradiance

📐 3D Shading Model — Shadow-Free Pitch Design

📍 Site (from Tab 1)

🔧 Module Geometry (from Tab 2)

⏱ Shading Window & Controls

🌄 Near Far (Horizon) Shading Analysis

🛰 Data Source (DSM / DEM / Open Buildings)

📐 Analysis Extent

⚙ Actions

✍️ Manual Horizon Entry (Surveyed Profile)

🔄 Single-Axis Tracker (SAT) — TMY + Full Loss Chain Simulation

⚙ Tracker Geometry

📉 Full Loss Chain Inputs

📊 SAT vs Fixed-Tilt Comparison

📅 Monthly SAT vs Fixed-Tilt POA (kWh/m²)

📐 Tracker Tilt Angle — Hourly Profile (Equinox)

📋 Monthly SAT Simulation Table — Full Loss Chain

🔽 SAT Energy Loss Waterfall — Full Chain

📐 SAT Physics — Formulas & Methodology

⚙ Tab 3 — System Parameters

☀ Day Load Calculation

🌙 Night Load Calculation

📊 Load Profile — Day vs Night

🎯 Design Consideration — Total Required Energy and Power

🔋 AC-Coupled Battery — Self-Consumption, Peak Shaving & Islanding

📥 Load Profile

🔋 Battery Configuration

⛰ Peak Shaving

🏝 Weak Islanding / Grid Export Limit

Battery Size vs. Self-Consumption Ratio

🔋 PV Module Selection

🔌 Inverter Selection

📉 Tab 4 — Loss Calculation (PVSolar Method)

☀ Irradiation (Corr. for IAM, Shading & Spectral)

🏗️ Mounting Configuration

🔧 Thermal & Other Parameters

⚡ Grid Delivery Chain — Transformers, Cable Loss & Grid Export Limit ? Help

🔷 MV Step-Up Transformer (LV/MV, e.g. 0.4kV/11kV or 0.4kV/33kV)

🔶 HV Step-Up Transformer (MV/HV, e.g. 33kV/132kV — utility-scale only)

🧪 LID & Mismatch Loss

📈 Clipping Loss Chart

🎲 Module Aging & Age-Driven Mismatch ? Help

🔎 IAM Loss (Incidence Angle Modifier)

🏭 Site-Level Auxiliaries ("Auxiliaries" — SCADA, Lighting, HVAC) ? Help

📋 PVSolar Loss Table ? Help

📊 System Loss Diagram

📊 Loss Summary

🏗 Tab 5 — Sub-array Design (String Configuration)

🔋 Selected PV Module

⚙️ Selected Inverter

➕ Add New Sub-array (Unit)

📊 Total System Summary

💰 Tab 6 — Economic Evaluation

📦 CAPEX — Installation Cost

🏦 Financing Structure

🔧 Annual OPEX

💸 Tax & Depreciation

📊 Financial Analysis Results computed on P90 (90% exceedance) energy

📋 Year-by-Year Projection (P90 energy basis)

🏦 Bankability Perspective — Project IRR, Equity IRR, DSCR & Payback Rating

📈 Hourly Perez Engine — 8,760-step Profile & Financial Risk (Monte Carlo)

8,760-step hourly AC energy profile

Monte-Carlo financial risk — P10 / P50 / P75 / P90 / P95 (exceedance)

🔎 Financial Due Diligence