Prospect PV simulation chain

In this document

This document provides a high-level introduction to the Prospect PV simulator used in Solargis Prospect. It describes the purpose and structure of the simulation, summarizes each stage of the simulation chain, and explains where to find detailed methodology documentation for each stage.

Usage in Solargis platform
This simulator is used in Solargis Prospect application.

Overview

The Prospect PV simulator is a fast, statistics-based energy yield simulation engine integrated into Solargis Prospect. It estimates the electricity output of a PV power plant for a given location based on Solargis solar radiation data, meteorological conditions, and a simplified representation of the energy system. The simulator is designed for speed and scalability, making it suitable for site screening, portfolio-level assessments, and early-stage feasibility studies where rapid results across many locations or scenarios are required.

Unlike the Argus PV simulator used in Solargis Evaluate, the Prospect simulator does not perform time-series simulation over full historical records. Instead, it operates on a statistically aggregated input dataset: 12 representative days (one per month), each sampled at 15-minute resolution and provided at seven percentile levels. The simulator runs independently for each percentile and time step, and combines the results using weighted averaging to produce the final energy yield output. This approach preserves the statistical distribution of solar resource variability while keeping computation times orders of magnitude shorter than a full time-series simulation.

The Prospect simulator shares several core physical models with the Argus simulator - the First Solar spectral correction model (Lee & Panchula), De Soto's single diode model, and the Sandia inverter model. Its key architectural difference is in the optical simulation: Prospect uses a view-factor-based shading model rather than Monte Carlo raytracing, trading some accuracy in complex shading geometries for the speed required in preliminary assessments.

Prospect PV simulator — the simulation stages

The simulation chain is organized into three sequential stages, each building on the results of the previous one.

Stage 1: Simulation inputs

Simulation inputs define the site conditions and the PV system configuration used throughout all calculations. The Prospect simulator operates on a statistically aggregated input dataset rather than full historical time series — solar radiation and meteorological data are provided as percentile-based representative day profiles, and PWAT is provided as monthly averages. System configuration combines user-specified parameters with configuration-dependent defaults that are applied automatically based on the selected mounting type.

For more details on simulation inputs, see Prospect PV simulator inputs.

Stage 2: Optical simulation

The optical simulation calculates the effective GTI on the PV module surface for each time step and percentile, accounting for all relevant optical losses, using an optimized Perez model that splits per-time-step and per-sample-point computations for efficiency.

Sky irradiance model: The Perez sky model decomposes GHI and DNI into direct, isotropic diffuse, circumsolar, and horizon band components, distributing diffuse radiance across the sky dome. The theoretical no-loss GTI and the effective GTI (GTI_eff, accounting for angular reflectivity losses) are both calculated at this stage.
Terrain and horizon shading: The horizon profile, provided at evenly spaced azimuth intervals, is applied as a pre-computed shading mask that removes irradiance blocked by distant terrain features.
Near shading — view-factor model : PV tables are arranged on a regular rectangular grid. Each unique table (a table whose shading conditions are distinct from others in the field) is identified for each time step.
Soiling losses : A soiling correction factor is applied as a multiplicative attenuation to GTI.
Angular reflectivity losses: The pv-angular model accounts for optical losses caused by the angle of incidence on the module surface, reducing the effective GTI for each radiation component.
Spectral correction: The First Solar spectral correction model (Lee & Panchula) adjusts GTI_eff for the spectral mismatch between incident sunlight and the module's spectral response, using optical air mass and precipitable water content as inputs.

For more details on optical simulation, see Prospect PV simulator optical simulation.

Stage 3: Electrical simulation

The electrical simulation converts the spectrally corrected cell-level GTI from the optical stage into AC power output at the inverter terminals, accounting for all electrical losses along the way.

Cell temperature — Cell temperature is calculated from air temperature and the effective GTI using a NOCT-based thermal model, optionally incorporating module efficiency to refine the estimate.
Single diode model and I-V curves — De Soto's single diode model generates current-voltage (I-V) curves for every PV cell using five reference parameters adjusted to actual GTI and temperature conditions.
PV field simulation — The simulator iterates over all strings in the field. Cells and sub-modules are classified as fully lit, fully shaded, or partially shaded. I-V curves are aggregated from cell to sub-module to string level, with bypass diodes applied at sub-module boundaries. String I-V curves are combined at constant voltage for all strings connected to one inverter, and blocking diodes are applied.
DC losses — DC mismatch and cabling losses are applied as a combined DC loss factor to the maximum power point before the inverter.
Inverter model — The Sandia inverter model converts DC input power to AC output power as a function of DC voltage and power. The model is taken from the SAM/SNL (System Advisor Model / Sandia National Laboratories) implementation.
AC losses — Transformer and AC cabling losses are applied to the AC output.

For more details on electrical simulation, see Prospect PV simulator electrical simulation.

Output and percentile weighting

The simulator internally calculates energy output for each of the 12 × 96 time slots across each of the seven input percentiles as an intermediate step. These per-percentile results are not exposed as outputs — they are immediately combined as a weighted average to produce the final result. Per-time-step results are then combined as a weighted average across percentiles, using the fixed weights:

p01: 2/40
p10: 5/40
p25: 8/40
p50: 10/40
p75: 8/40
p90: 5/40
p99: 2/40

Monthly totals are obtained by multiplying the representative-day result by the number of days in each month. Annual totals are the sum of monthly values. The final output includes specific PVOUT [kWh/kWp], total PVOUT [kWh], performance ratio (PR), and a full stage-by-stage loss breakdown covering far horizon shading, soiling, angular reflectivity, spectral correction, inter-row shading, DC losses, inverter losses, AC losses, snow losses, and technical availability.

Note: The Prospect simulator operates on a statistically representative input dataset rather than full historical time series. Results represent the p50 energy yield by default. For bankable energy yield assessments requiring full uncertainty analysis, use Solargis Evaluate with the Argus PV simulator.

For more details see Prospect PV simulator post-processing and Prospect PV simulator outputs.

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