pvplr-python: Python package implementation of PVplr for Performance Loss Rate (PLR) analysis

Conference · Wed Jun 12 04:00:00 EDT 2024

DOI:https://doi.org/10.2172/2376168· OSTI ID:2376168

Kumar, Suraj ^[1]; Wieser, Raymond; Pierce, Benjamin

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1. Case Western Reserve University

Due to software fragmentation, PV system modeling teams can be limited to language specific packages, preventing cross-sectional analysis of different modeling techniques and workflows. To this end, PVplr, a popular PV performance modeling R software package, has been ported to the Python programming language. To verify and test the robustness of the port, NSRDB data has been used to simulated PV installations at native resolution (~2 million Sites), with a variety of degradation rates, degradation patterns, and modules. Performance Ratios were calculated using the ported functions from pvplr-python and compared against Rdtools YoY values. Due to the complicated nature of degradation, a new metric has been proposed to quantify the performance loss of a system. The cumulative production loss, is the total amount of energy lost due to the degrading performance of the system. Cumulative production loss alleviates the problems with fitting linear functions to non-linear degradation. Cumulative Production loss was shown to better estimate the total lost revenue for non-linear degradation patterns. $XbX + UTC$ was found to most accurately predict the total lost revenue in simulated systems.

• Research Organization: Case Western Reserve University
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
• DOE Contract Number: EE0009535
• OSTI ID: 2376168
• Report Number(s): DOE-CWRU-09353-2406-25
• Country of Publication: United States
• Language: English