Mark A. Mikofski

Validation of the PVLife model using 3 million module-years of live site data

Establishing a strong basis for confidence in a solar technology requires being able to prove a low-degradation track record in the real world, and rationalize it with strong physical understanding and investigation. This paper briefly reviews our previously-published physical model for calculating degradation and reliability, PVLife, which computes hour-by-hour degradation of PV modules using weather files and degradation sub-models developed from accelerated test data. We then demonstrate a validation of this model against a large statistical data set obtained from 266 systems powered by SunPower modules (data from over 179 systems installed by Powerlight, using non-SunPower modules are also shown). In total these data represent over 800,000 modules and a total of 3.2 million module-years of experience. The data analysis technique requires little manual data processing and can be derived from live sites without special experimental treatment. We also discuss returnrate data on modules incorporating SunPowers back-contact cell, as well as front contact modules in SunPowers fleet. Implications for failure prediction are discussed.

PVLife: An integrated model for predicting PV performance degradation over 25+ years

We report results of an integrated model called PVLife that predicts the performance and degradation of a PV system over its entire lifetime. The model solves the coupled electro-thermal equations to calculate PV panel performance for a given set of weather conditions. Based on this calculated operating point and a series of physical sub-models for key degradation and failure modes, the progressive degradation of the panel performance is simulated, creating a second level of coupling. The sub-models describing the different degradation modes are developed based on data from the field and accelerated laboratory tests. To close the loop, the overall model is compared with both laboratory and field data. Coupled degradation modes, including possible feedback loops, are investigated.

Evaluation and correction of the impact of spectral variation of irradiance on PV performance

Spectral shift from the ASTM G173-03 reference spectrum was calculated for different cell types from measured quantum efficiency and predicted spectral irradiance using the Bird Simple Spectral Model with measured aerosol optical depth and water vapor. Our predictions correlated well with measured spectral shift for different sites and cell technologies. However we observed significant differences between spectral correction methods. We found that the factors affecting spectral shift differ depending on cell technology, making the formulation of a single linear expression of all factors difficult for all technologies. Predicted power at several geographically different sites using the Sandia Array Performance Model with its spectral correction function versus our predicted spectral shift showed seasonal variations.

pvlib python: a python package for modeling solar energy systems

pvlib python is a community-supported open source tool that provides a set of functions and classes for simulating the performance of photovoltaic energy systems. pvlib python aims to provide reference implementations of models relevant to solar energy, including for example algorithms for solar position, clear sky irradiance, irradiance transposition, DC power, and DC-to-AC power conversion. pvlib python is an important component of a growing ecosystem of open source tools for solar energy (William F. Holmgren, Hansen, Stein, & Mikofski, 2018).

A fast parameterized model for predicting PV system performance under partial shade conditions

Accurately modeling the performance of partially shaded photovoltaic systems is well-known to be a difficult problem. Power loss is not only nonlinear with shade coverage, but also has a strong dependence on system configuration and location of the shade on a system. This paper presents a parameterized shade loss model (called the “Fast Shade Model” or FSM) that allows for the calculation of system-level power loss based on three input parameters. This model was developed through the statistical analysis of hundreds of thousands of shade scenarios modeled with a cell-level, 2-diode model. Model validation was performed using real systems under shaded conditions.