Production Modeling for Grid-Tied PV Systems

Production modeling meets multiple needs. Integrators seek to optimize PV system designs or to provide production guarantees; investors look to verify the right return on investment; operators need performance expectations to compare to measured performance.

All sectors of the maturing solar industry demand accurate production estimates, which require a clear understanding of how the estimates are produced and an ability to interpret the results. In this article we provide an overview of productionmodeling theory and review available production-modeling tools. We compare the tools’ performance to each other and to real systems, and provide a summary of the key uses of production modeling in PV projects. At the most basic level, production modeling comes down to two questions:

  1. How much sunlight falls on an array?
  2. How much power can a system produce with that sunlight?

Answering these questions requires location-specific parameters, such as shading and weather data; educated assumptions about system derating due to soiling, module mismatch, system availability; and complex algorithms to model available radiation as well as module and inverter performance.


A PV system’s geographical location, surroundings and configuration determine the amount of sunlight that falls on the modules. Where a system is located geographically determines how much sunlight is available; the surroundings dictate the amount of available sunlight that is blocked before reaching the array; and the array configuration determines how efficient the system is at exposing the modules to sunlight.

Meteorological data. The first factor in determining how much sunlight falls on an array is meteorological data that accurately represent the weather at a system’s location. Meteorological data typically include solar radiation (global horizontal, direct beam and horizontal diffuse), temperature, cloud cover, wind speed and direction, along with other meteorological elements. The data are based on ground or satellite measurements and in some instances are modeled rather than measured.

Typically a large amount of analysis is involved in taking raw data and producing a data set suitable for use. Meteorological data are typically measured by government agencies and utilized by a variety of organizations that make the data available in formats suitable for use in production-modeling tools. These organizations include the National Renewable Energy Laboratory (NREL) and NASA, which provide the information free of charge, and also organizations such as Meteonorm and 3Tier, which provide the data for a fee.

The most common sources of data for US solar projects are the Typical Meteorological Year (TMY) files published by NREL and based on analysis of the National Solar Radiation Data Base (NSRDB). TMY data comprise sets of hourly values of solar radiation and meteorological elements representing a single year. Individual months in the data record are examined, and the most “typical” are selected and concatenated to form a year of data. Due to variations in weather patterns, these data are better indicators of long-term performance rather than performance for a given month or year. According to the online document “Cautions for Interpreting the Results” that NREL publishes along with its PVWatts tool (see Resources), these data may vary as much as ±10% on an annual basis and ±30% on a monthly basis.

The first TMY data set was published in 1978 for 248 locations throughout the US. The data set was updated in 1994 from the 1961–1990 NSRDB to create a set of TMY files, called TMY2, for 237 US locations. A subsequent 2007 update utilized an expanded NSRDB from 1999–2005 to create TMY3, which covers 1,020 locations across the US. TMY3 data are categorized into three classes that reflect the certainty and completeness of the data, with Class I being the most certain, Class II less certain and Class III being incomplete data. TMY, TMY2 and TMY3 present changes in reference time, format, data content and units from set to set. The data sets are incompatible with each other, but conversion tools are available. The TMY2 and TMY3 data sets are either utilized by or can be imported into all of the major PV performance-modeling tools used in the US.


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