Solar simulation

Background

For our simulations, we need to be able to predict solar generation in the future for a certain asset. These assets do not exist yet, so training a machine learning model is impossible without past data. For this reason, we employ a physical solar simulation model that takes in input the asset characteristics and the weather data to simulate the asset generation.

The asset characteristics include location, DC capacity, inverter capacity, panel tilt and azimuth, and characteristics of the panel, such as temperature coefficient. We then use weather data from our weather model to calculate how much the panels would produce.

Methodology

We use technology originally developed at the US Sandia National Laboratories. Our technology has been extensively tested and validated with real data.

Weather variables

We use the following weather variables for the physical simulation:

Variable	Unit	Time resolution
Air temperature at 2 m	°C	1 hour
Wind speed	m/s	1 hour
Global horizontal irradiance (GHI)	W/m2	1 hour
Diffuse horizontal irradiance (DHI)	W/m2	1 hour
Direct normal irradiance (DNI)	W/m2	1 hour

Far-shading effects

By default, solar simulations account for terrain-based shading effects. We use horizon profile data from PVGIS for all assets to estimate the resulting loss of solar radiation caused by distant objects such as hills and mountains

Inverter efficiency

An inverter is not equally efficient at every power level. It reaches its rated efficiency near full load, and becomes progressively less efficient as the DC input falls — at dawn, at dusk, and under heavy cloud. Below a startup threshold it produces no AC output at all.

We model this with the NREL PVWatts inverter model, which expresses efficiency $\eta$ as a function of the load factor $\zeta$, the ratio of DC input power to the inverter's DC input limit:

$$\eta = \frac{\eta_{nom}}{\eta_{ref}}\left(-0.0162\,\zeta - \frac{0.0059}{\zeta} + 0.9858\right), \qquad \zeta = \frac{P_{dc}}{P_{dc0}}$$

$\eta_{nom}$ is the solar inverter efficiency you configure on the asset. $\eta_{ref} = 0.9637$ is a fixed reference constant, and the AC output is capped at the inverter's AC capacity.

Why the AC/DC conversion loss never reaches zero

The efficiency you configure scales this curve, but it does not change its shape. The relative droop at partial load is therefore the same no matter what value you enter:

Load factor ζ	Efficiency relative to your configured value
100%	100.0%
60%	100.3%
30%	99.8%
10%	96.0%
5%	90.0%
2%	71.7%
1%	41.1%
0.6%	0.2%
below 0.6%	0%

Setting the efficiency to 100 percent therefore does not produce a lossless inverter. It produces an inverter that is 100 percent efficient at rated load and still follows the same droop everywhere else. Sunny hours contribute almost no loss, while the dim edges of each day contribute a little, and the AC/DC conversion entry in the solar losses chart stays slightly above zero.

The size of that residual depends on how much time the system spends at partial load, which is driven by the DC/AC ratio. For a system with 100 percent configured efficiency it typically falls between 0.1 and 0.5 percent of DC energy — smallest for a heavily oversized array, which spends more of its generating hours near full load.

note

This also means the realized annual conversion efficiency of an asset is always slightly below the value configured on it.