PV Fleet Performance Data Initiative Program and Methodology

Deline, Christopher (ORCID:0000000298678930); White, Robert; Muller, Matthew; Anderson, Kevin; Perry, Kirsten; Deceglie, Michael; Simpson, Lin; Jordan, Dirk

doi:10.1109/pvsc45281.2020.9300583

DOE OSTI · 1765616

PV Fleet Performance Data Initiative Program and Methodology

Deline, Christopher (ORCID:0000000298678930)·White, Robert·Muller, Matthew·Anderson, Kevin·Perry, Kirsten·Deceglie, Michael·Simpson, Lin·Jordan, Dirk

View original source ↗Export metadata ↓

Abstract

The US Department of Energy’s PV Fleet Performance Data Initiative has been launched in order to collect and evaluate production data across multiple PV fleet partners. Performance statistics are anonymized, aggregated and shared to represent a snapshot of the US commercial and utility-scale fleet. Production data have been collected from over 1500 systems representing more than 1.3 GWdc capacity. Preliminary analysis indicates median performance loss rates are in line with previous publications of system degradation, on the order of –0.6%/yr to –0.9%/yr (preliminary numbers subject to change). These values are higher than module-only degradation rates which are often used in pro-forma estimates of project performance and economics, potentially exposing owner/operators to increased risk if systems under-perform over time.

Explore related subjects

41 EE - Solar Energy Technologies Office (EE-4S)availability fleet analysis performance loss rate photovoltaic system degradation RdTools

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Deline, Christopher (ORCID:0000000298678930), White, Robert, Muller, Matthew, Anderson, Kevin, Perry, Kirsten, Deceglie, Michael, Simpson, Lin, Jordan, Dirk. 2021-01-05. PV Fleet Performance Data Initiative Program and Methodology. https://doi.org/10.1109/pvsc45281.2020.9300583

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2021

A Machine Learning Framework for Bridging the Gap Between the Steady-State Scheduling and Dynamic Security Operation for Future Power Grids

This presentation was part of an invited talk for a panel session presented at the 2021 IEEE Power & Energy Society General Meeting, held 26-29 July 2021.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2021

Assessment of Cloud Mask Forecasts from the WRF-Solar Ensemble Prediction System: Preprint

Numerical weather prediction (NWP) models are important tools used by government agencies and the renewable energy enterprise to forecast solar radiation. Cloud prediction, a key process of NWP models, has the highest impact on the accuracy of solar forecasting. This study uses satellite observations from the National Solar Radiation Data Base (NSRDB) to evaluate the cloud mask forecast by the WRF-Solar Ensemble Prediction System (WRF-Solar EPS). Preliminary analysis of the data in 2018 demonstrates the need of further improvement in predicting thin and low-level clouds. The information obtained from this work will be used to enhance the WRF-Solar EPS in reproducing the cloud field over the contiguous U.S. and reducing solar forecasting errors.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2021

Evaluating Cell Temperature Models and the Effect of Wind Speed in PV System Capacity Testing: Preprint

Capacity testing is a routine procedure for assessing a photovoltaic system's performance relative to expectations. The most common test method involves fitting a regression model that predicts system output power using operating weather conditions including wind speed. Structural modifications to the regression model to incorporate wind in different ways improved the model's ability to fit measured system performance, but the observed improvements were small and unlikely to change the result of a capacity test. However, the results showed that the choice of reporting wind speed and inclusion or exclusion of wind speed in the performance model used as the test benchmark can significantly change the test result.

41 EE - Solar Energy Technologies Office (EE-4S)↗