Posts by Pablo Caballero

Do your simulations account for the actual temperature conditions at your PV plant site? If you use Typical Meteorological Year (TMY) datasets as inputs, the short answer is “not really.” By analyzing both Solargis time series and Solargis TMY data for a sample site, we show how TMY data can fail to capture high temperatures over the long term, potentially creating unnecessary risks for PV projects.

Weather anomalies and extreme events are constantly challenging the results of regular performance reporting on PV assets around the world. This summer, wildfires and heatwaves are affecting large areas of Europe. On top of that, a total solar eclipse, set to happen in just a few days, deserves attention too.

Most PV projects rely on a single P50 value to represent expected annual energy yield. But one number can't tell the whole story and it certainly can't capture the range of outcomes your project might face.

Watch this webinar to see how Solargis calculates PV yield uncertainty backed by industry-leading solar and meteorological data, proven modeling approaches, and transparent methodology.

Every solar PV project usually starts with one simple number: expected annual PV yield. At first glance, this number looks clear and precise. In reality, however, it always comes with uncertainty. How a project team deals with this uncertainty matters a lot. It influences engineering design, investor expectations, and bank financing, often in ways that only become visible late in the project lifecycle.

One of the most critical outputs from PV simulations is the P50 annual energy yield estimate. Often referred to as the "best estimate," the P50 value represents the annual energy yield that has a 50% probability of being exceeded (with an equal 50% chance that the actual yield will fall below it).
However, relying solely on the P50 value may be too optimistic for project stakeholders. To address this, additional probability-based yield estimates are commonly used e.g. P90 value, which indicates the energy yield expected to be exceeded 90% of the time.

In the context of PV yield simulation, uncertainty helps users understand the potential deviations in the results produced by the software they are using. Understanding these deviations plays a key role in selecting the optimal design of a power plant and in evaluating financial risks and return on investment.

All Solar industry players need to simulate their power plant designs and financial plans at some point. To do so against summarized conditions given by data products like Typical Meteorological Years has been common until recently. However, running energy simulations using more realistic conditions described by Multi-Year Time Series of data is recommended to reduce project risk and evaluate all scenarios.

Solar irradiance modeling involves computing the amount of sunlight reaching the Earth's surface. During eclipses, the moon obstructs solar radiation, leading to a temporary reduction in irradiance.

In this article, we are sharing how Solargis has improved the accuracy of air temperature datasets we provide along with other parameters affecting solar energy power simulations.

The study summarizes the results of comparing the data provided by various institutional or commercial providers of solar irradiance models against ground measurements collected at 129 sites distributed globally. It helps solar developers navigate through all the available databases.

An increasing number of solar PV plant developers, operators and owners require high frequency data (1-min) to make qualitative improvements throughout the entire lifecycle of a solar project.

Due to the impact that surface albedo has in PV yield calculations (mostly when we talk about bifacial modules), we have noticed an increasing interest in knowing more about this parameter. These are the most typical questions we are receiving (with their corresponding answers):