Models used to assess the performance of photovoltaic systems.

Abstract

This report documents the various photovoltaic (PV) performance models and software developed and utilized by researchers at Sandia National Laboratories (SNL) in support of the Photovoltaics and Grid Integration Department. In addition to PV performance models, hybrid system and battery storage models are discussed. A hybrid system using other distributed sources and energy storage can help reduce the variability inherent in PV generation, and due to the complexity of combining multiple generation sources and system loads, these models are invaluable for system design and optimization. Energy storage plays an important role in reducing PV intermittency and battery storage models are used to understand the best configurations and technologies to store PV generated electricity. Other researcher's models used by SNL are discussed including some widely known models that incorporate algorithms developed at SNL. There are other models included in the discussion that are not used by or were not adopted from SNL research but may provide some benefit to researchers working on PV array performance, hybrid system models and energy storage. The paper is organized into three sections to describe the different software models as applied to photovoltaic performance, hybrid systems, and battery storage. For each model, there is a description which includes where to find the model, whether it is currently maintained and any references that may be available. Modeling improvements underway at SNL include quantifying the uncertainty of individual system components, the overall uncertainty in modeled vs. measured results and modeling large PV systems. SNL is also conducting research into the overall reliability of PV systems.

Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems

“…The DC voltage of the PV module is the sum of the cell's voltages in series. It is generally almost stable but decreases when the cell temperature increases [39][40][41], which explains the presence of this negative correlation (i.e., around -0.59). Because the DC voltage is almost stable, the DC power is directly proportional to variations of the DC current.…”

“…The DC current generated by the PV cell, PV module, or PV array is proportional to the tilted irradiance. In the literature, there are many mathematical relationships that explain this high correlation (i.e., more than 0.98) [39,40]. Since the cell temperature is proportional to the irradiance [41][42][43], there is a high positive correlation (i.e., above 0.86) between cell temperature and the following parameters: irradiance, DC current, DC power, AC current, and AC power.…”

Analysis of solar photo‐voltaic for grid integration viability in Uganda

“…Models can also be used to evaluate system performance over time by providing a baseline to compare with if performance suddenly decreases and troubleshooting is necessary. 32 A study on techno-economic feasibility assessment of grid-connected PV systems for residential buildings by Imam et al 33 analyzed key performance indicators using system advisor model software. The study realized that residential PV installations were an effective option for energy management.…”

Fault Diagnostic Methodologies for Utility-Scale Photovoltaic Power Plants: A State of the Art Review

“…Analytical redundancy is based on a comparison of electrical parameters (output power, voltage, and current) with their corresponding parameters taken from the reference model, while the differential indicates a fault [45][46][47][48]. A one diode model (ODM) acts as a reference and the parameters of interest are taken from manufacture datasheets or extracted from performance data applying extraction techniques [49]. The methodology is very sensitive to solar irradiance and thermal parameters which in a sense limits the reliability of the approach [47].…”