Solar Shading Analysis Software Saudi Arabia: 2026 Guide

Saudi solar shading analysis is governed by a number that has nothing to do with geometry. Under the Kingdom’s Small-Scale Solar PV framework, surplus exported to the grid is credited at roughly 5 halalas per kWh, while commercial import tariffs run in tiers up to around 32 halalas and industrial up to around 20. That is a gap of up to six to one between what a self-consumed kilowatt-hour is worth and what an exported one is worth. A shading report that gives you one annual percentage has told you the quantity of the loss and withheld its price. This guide covers what shading analysis has to do differently in Saudi Arabia, and which tools do it.

Short version. Solar shading analysis software for Saudi projects has to run 8,760 hourly steps at module level, carry soiling in the same electrical model rather than as a flat annual derate, and price each lost kilowatt-hour against the correct side of a very wide net billing spread. In a market where a shaded kilowatt-hour can be worth 5 halalas or 32, the tariff model is not an optional extra bolted onto the shading engine. It is the part that makes the shading number mean anything.

The general methodology is covered in our solar shading analysis software guide, and the simulation approach in our PV yield simulation software guide. This page covers what changes in the Kingdom. For the wider platform comparison see best solar design software Saudi Arabia.

The Six To One Gap And What It Does To Design

The Small-Scale Solar PV framework covers grid-connected systems from 1 kW to 2 MW and settles exports through net billing rather than net metering. The consequence for design is that surplus generation is close to worthless relative to displaced consumption.

This reshapes the whole design brief. In markets with generous export compensation, the objective is maximum annual generation and the roof is filled. In Saudi Arabia the objective is maximum displaced consumption, so systems are sized to the daytime load curve and generation beyond it earns very little.

Shading analysis inherits that logic. The question is not how many kilowatt-hours the shadow destroys but how many billable kilowatt-hours it destroys, and those are only the ones that would have been consumed on site. On a system deliberately sized below peak daytime load, essentially every shaded kilowatt-hour is expensive. On a system that overshoots at midday, the midday shadow is nearly free.

Soiling At Saudi Rates

Measured work in the Kingdom puts daily dust-driven efficiency loss in the region of 2 to 5 percent, a single sandstorm has been recorded reducing module power output around 20 percent, and a module in Dhahran left uncleaned for six months lost more than half its output. Studies in Madinah found losses near 28 percent over sixty days of accumulation.

Those are not numbers a flat annual derate represents well, for two reasons.

The first is shape. Soiling accumulates between cleans and resets when the array is washed, so the loss is a sawtooth, not a constant. A flat annual figure gets the total roughly right and the timing entirely wrong, which matters because the peaks may coincide with the facility’s highest-tariff consumption.

The second is interaction with shading. Modules in a string are wired in series, so current is limited by the weakest cells. Soiling weakens cells unevenly and geometric shading weakens a different set. When both apply, the constraint is whichever combination is worst, not the arithmetic sum of two independent percentages. Stacking the derates typically overstates the total loss.

Why Cleaning Schedules Need The Shading Map

The practical payoff from modelling the two together is maintenance spend, which in the Kingdom is a substantial line item.

Cleaning recovers soiling loss. But on a module that spends several hours a day in parapet or inter-row shadow, part of that recovery is unavailable, because during those hours the module is constrained by shading regardless of how clean it is. The return on washing that module is therefore lower than on a clear one.

A uniform roof-wide cleaning schedule ignores this and over-invests in the shaded portions of the array. A schedule informed by the shading map cleans the unshaded rows more often and the parapet-adjacent rows less, and delivers more recovered energy for the same spend. Getting there requires shading resolved at module level and soiling in the same model, which is a fairly short list of tools.

There is a second operational trigger worth building into the schedule. After a sandstorm, the sensible response is immediate cleaning rather than waiting for the next scheduled visit, because the step change in soiling is large enough to dwarf the routine accumulation curve.

Self-Shading Is The Main Event

Saudi commercial and industrial buildings are typically flat-roofed with perimeter parapets, and arrays are mounted on tilted frames in rows. So the dominant shading sources are the parapet and the array’s own preceding row, with rooftop plant, chillers, water tanks and lift overruns behind them.

That is good news, because these obstructions are known before anything is installed. Two design levers follow.

Parapet setback should vary by edge. The usual practice applies one setback figure around the entire perimeter, sized for the worst case. But the eastern, western and northern parapets shade the array at different times of day and for different durations, with different amounts of energy and different tariff value behind those hours. Modelling each edge separately usually returns usable roof area.

Row spacing is an economic optimisation. The solstice-avoidance rule produces wide rows and fewer modules. The better method simulates several candidate spacings across all 8,760 hours and prices the result against the tariff and the load curve.

Here the Saudi capacity position changes the answer relative to a purely area-constrained market. Where the 5 MW aggregate cap per distribution area is the binding constraint on a multi-site portfolio, the scarce resource is approved capacity rather than roof, and squeezing extra modules onto a roof at the cost of shading them is less attractive. Where roof area binds instead, tighter spacing usually wins. A competent tool should let you optimise for either, because Saudi developers face both cases.

Worked Example: The Same Array Against Two Load Profiles

An illustrative calculation showing why the load profile decides the recommendation, not a specific customer project. Both sites have a 300 kWp rooftop array with an identical 8% annual shading loss of roughly 42 MWh, arising mostly from parapet and inter-row geometry.

Same array, same shading percentage, and more than double the cost on one site. The shading report was identical for both. The information that decided the recommendation was the load curve, which is why shading analysis that lives apart from consumption modelling cannot answer the question a Saudi client is actually asking.

Who Should Choose What

• Choose SurgePV if you want 8,760-hour module-level shading with soiling in the same loss chain and net billing pricing on both sides of the loss.
• Choose PVsyst for utility-scale projects and wherever a lender or a large developer names it.
• Choose HelioScope for commercial rooftop engineering where the customer-facing document is produced elsewhere.
• Avoid annual-percentage shading reports and flat soiling derates. In the Kingdom, both of those shortcuts hide the numbers that decide the project.

The Bottom Line

In Saudi Arabia the tariff decides what a shadow costs, and dust decides how you maintain around it.

• Split the loss by destination. Exports settle near 5 halalas and self-consumption displaces up to 32, so only one of them justifies mitigation.
• Model soiling and shading together. Stacked derates overstate the loss and send the cleaning crew to the wrong rows.
• Design the self-shading out. Parapet setbacks and row spacing are decisions, not constraints, and they are worth more attention than any tree.