Best practices

While TMY datasets are useful for early-stage, pre-feasibility assessments, they are no longer sufficient for performance modeling and financial analysis. Just as we once moved from low-resolution to ultra-high-definition TV screens, the solar industry must now make the same leap and embrace high-resolution time series data as the new standard.

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.

While the growth in PV has brought fast development of a variety of products to the market, it has also created a new problem: It’s becoming increasingly difficult for project developers to manage and accurately evaluate the technical specifications of the modules, inverters and other components of a PV power plant.

Bifacial photovoltaic (PV) modules dominate in modern solar projects, as they are more efficient, capturing sunlight on both sides of the cells, and generating more energy than monofacial PV modules. However, the shift to bifacial photovoltaics requires a different approach to feasibility evaluation compared to traditional monofacial technology.

The higher frequency of the extreme weather puts growing pressure on the PV industry to design power plants that can withstand harsher conditions. Beyond this, developers must consider the potential losses from the extreme weather events and the likelihood of their occurrence.

Managing multiple applications for different stages of PV project evaluation is both costly and inefficient. While most of the developers have long accepted this as the norm, the solar industry needs a comprehensive solution that brings all functionalities together in one solution.

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.

Depending on the source and desired application, solar data can have distinctive temporal resolutions, such as sub-hourly (1-, 2-, 5-, 10-, 15-, 30-minute) or hourly intervals. But how are you supposed to know the difference, and why should you care?

One of the key challenges for solar project financiers is to assess the expected energy production and revenue of a project over its lifetime, considering the uncertainties and fluctuations of the solar resource.

To design optimal PV projects, designers must consult 1-minute data which paint the most accurate picture of a plants’ PV power potential and output, while providing increased financial certainty to solar investors.

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.