Janine Freeman

Validation of multiple tools for flat plate photovoltaic modeling against measured data

In this validation study, comprehensive analysis is performed on nine photovoltaic systems for which NREL could obtain detailed performance data and specifications, including three utility-scale systems and six commercial-scale systems. Multiple photovoltaic performance modeling tools were used to model these nine systems, and the error of each tool was analyzed compared to quality-controlled measured performance data. This study shows that, excluding identified outliers, all tools achieve annual errors within ±8% and hourly root mean squared errors less than 7% for all systems. Finally, the acceptability of this range of annual error is discussed with regard to irradiance data uncertainty and the use of default loss assumptions, and two avenues are proposed to reduce photovoltaic modeling error.

System Advisor Model: Flat Plate Photovoltaic Performance Modeling Validation Report

The System Advisor Model (SAM) is a free software tool that performs detailed analysis of both system performance and system financing for a variety of renewable energy technologies. This report provides detailed validation of the SAM flat plate photovoltaic performance model by comparing SAM-modeled PV system generation data to actual measured production data for nine PV systems ranging from 75 kW to greater than 25 MW in size. The results show strong agreement between SAM predictions and field data, with annualized prediction error below 3% for all fixed tilt cases and below 8% for all one axis tracked cases. The analysis concludes that snow cover and system outages are the primary sources of disagreement, and other deviations resulting from seasonal biases in the irradiation models and one axis tracking issues are discussed in detail.

PV system energy test

The performance of a photovoltaic (PV) system depends on the weather, seasonal effects, and other intermittent issues. Demonstrating that a PV system is performing as predicted requires verifying that the system functions correctly under the full range of conditions relevant to the deployment site. This paper discusses a proposed energy test that applies to any model and explores the effects of the differences between historical and measured weather data and how the weather and system performance are intertwined in subtle ways. Implementation of the Energy Test in a case study concludes that test uncertainty could be reduced by separating the energy production model from the model used to transpose historical horizontal irradiance data to the relevant plane.

Solar input data for photovoltaic performance modeling

One of the most critical inputs to a photovoltaic (PV) energy model is the solar data set, which establishes the sites irradiance and weather variability. For long-term energy estimates, the solar data set is expected to represent the long-term climatological conditions on-site. While modeled solar data sets are available, the quality of these data vary by data source as well as regionally. The result of using a poor quality solar input data set is higher uncertainty in the energy production estimated from the model; conversely, a more accurate solar input data set can improve the confidence in the energy production estimate. As the solar industry begins to recognize the value of increasing confidence in PV performance modeling predictions, an increased focus on quality input solar data for PV energy estimation models is expected. Publicly available data sources were evaluated with respect to their suitability as input data for PV energy estimation. These included modeled data sources, publicly, available reference station data, and site-specific measured data. The results of a research study conducted at nine locations throughout the United States show that both the magnitude and the distribution of input solar data sets affect energy. The value of on-site solar data collection and its ability to reduce uncertainty from between 2% to 5% is presented, as demonstrated from a case study from a site in the United States Desert Southwest.

PV system “Availability” as a reliability metric — Improving standards, contract language and performance models

The use of the term “availability” to describe a photovoltaic (PV) system and power plant has been fraught with confusion for many years. A term that is meant to describe equipment operational status is often omitted, misapplied or inaccurately combined with PV performance metrics due to attempts to measure performance and reliability through the lens of traditional power plant language. This paper discusses three areas where current research in standards, contract language and performance modeling is improving the way availability is used with regards to photovoltaic systems and power plants.