Solar resource uncertainty has the potential to significantly contribute to the financial risk of a PV project. This uncertainty has been studied in depth for satellite-derived global horizontal irradiance (GHI), which are often used as long-term average dataset for project valuations. Resource uncertainty, however, is not as well quantified in the plane of the array of the solar modules. This paper presents results that quantify the residual errors in plane of array irradiance (POAI) at locations where ground-measured GHI and POAI instruments are installed. Reductions in error to the modeled PAOI are observed when using satellite derived GHI that has been tuned with ground measured GHI for reduction in respective model errors.

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.

A variety of test methodologies are commonly used to assess if a photovoltaic (“PV”) system is able to perform in-line with expectations generated by a computer simulation. Four commonly used methodologies include: the PVUSA rating as implemented in ASTM E2848 and E2939 (“ASTM”), a Performance Ratio Test (“PR”), the Power Performance Index (“PPI”) and the Adjusted Energy Test (“AET”). This paper compares the results of a one year AET to short term ASTM, PR, PPI, and AET test results in an attempt to determine which test can best reproduce the results of a one-year AET. Test durations of 3, 7, 15, and 30 days were evaluated to examine the effect of test duration on the residual between the short term test result and the long term AET test result. Seasonality was also examined. This study was not able to identify a single test methodology which consistently outperformed the others, nor was this study able to determine the optimum test duration.

This paper explores methods with which developers and technology providers can fully monetize energy production estimates of photovoltaic plants, thereby leaving little value on the table. The methods discussed in this paper describe comprehensive model validation and performance testing. Risk allocation through commercial means is also briefly discussed.