Perspective: Performance Loss Rate in Photovoltaic Systems

Detailed knowledge of the performance of photovoltaic (PV) systems over their full lifetime is invaluable for evaluating their profitability and sustainability. PV systems are known to have gradually declining performance; for long‐term yield assessments, typically a performance loss rate (PLR) of ‐0.5%/year is assumed. Here, we evaluate monitoring data of thousands of commercial PV systems, to determine how performance loss trends and PLR values are affected by operational climate, PV module technology, and other parameters. Using performance ratio (PR) measurements from almost 6,000 PV systems, we evaluate the long‐term trends in PR and overall PLR value. Subsequently, we evaluate the relation between climate, performance loss and PLR for three different climate classification schemes. Finally, we evaluate how long‐term performance loss trends and estimated PLR values differ for PV module technology and other parameters. Our results show that in general, PV systems that have operated now for at least 3 but up to 25 years show a PLR of roughly ‐1%/year, indicating substantially higher performance loss compared to typical estimates used for new PV systems. Climate by itself does not seem to be the leading factor that determines overall performance loss trends or PLR values in the dataset analyzed.This article is protected by copyright. All rights reserved.

Section: Plr Estimation Methodsmentioning

confidence: 99%

Climate‐ and Technology‐Dependent Performance Loss Rates in a Large Commercial Photovoltaic Monitoring Dataset

Louwen,

Lindig,

Chowdhury

et al. 2024

“…Alternatively, PLR can be calculated in a 2-year moving window and report multiple values representing the nonlinear PLR changes (see ref. [2]).…”

Section: Nonlinearitiesmentioning

confidence: 99%

“…

The performance loss rate (PLR) represents both reversible (e.g., soiling) and irreversible (e.g., material degradation) losses [1,2] that can occur in a photovoltaic (PV) power plant and is an important parameter for performance modeling, monitoring, and operation and maintenance (O&M). In PV performance modeling, PLR is applied to account for the power reduction over time whereas in monitoring and O&M, it can provide a high-level indication of a power plant's health state.

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mentioning

confidence: 99%

How Climate and Data Quality Impact Photovoltaic Performance Loss Rate Estimations

Theristis,

Anderson,

Ascencio-Vasquez

et al. 2023

Self Cite

Different data pipelines and statistical methods are applied to photovoltaic (PV) performance datasets to quantify the performance loss rate (PLR). Since the real values of PLR are unknown, a variety of unvalidated values are reported. As such, the PV industry commonly assumes PLR based on statistically extracted ranges from the literature. However, the accuracy and uncertainty of PLR depend on several parameters including seasonality, local climatic conditions, and the response of a particular PV technology. In addition, the specific data pipeline and statistical method used affect the accuracy and uncertainty. To provide insights, a framework of (≈200 million) synthetic simulations of PV performance datasets using data from different climates is developed. Time series with known PLR and data quality are synthesized, and large parametric studies are conducted to examine the accuracy and uncertainty of different statistical approaches over the contiguous US, with an emphasis on the publicly available and “standardized” library, RdTools. In the results, it is confirmed that PLRs from RdTools are unbiased on average, but the accuracy and uncertainty of individual PLR estimates vary with climate zone, data quality, PV technology, and choice of analysis workflow. Best practices and improvement recommendations based on the findings of this study are provided.

“…The accumulation of dust, dirt, and contaminants is a serious issue for these systems, as it decreases the amount of light reaching a PV cell or a CST receiver, resulting in reduced electricity or heat generation. [ 8 ] This phenomenon, known as “soiling”, affects systems worldwide and is significantly site specific. [ 9 ] The magnitude of soiling varies depending on factors such as geographical position, system configuration, physicochemical dust properties, and weather patterns, which also change with seasons.…”

Section: Introductionmentioning

confidence: 99%

Soiling in Solar Energy Systems: The Role of the Thresholding Method in Image Analysis

Micheli,

Smestad,

Khan

et al. 2023

The use of image analysis has often been suggested as a practical way to monitor the soiling accumulated on the surfaces of solar energy conversion devices. Indeed, the deposited soiling particles can be counted and characterized to calculate the area they cover, and this area can be converted into an energy loss. However, several particle counting methodologies exist and can lead to dissimilar results. This work focuses on the role of thresholding, an essential step where particles are distinguished from a background based on the pixel brightness. Sixteen automatic thresholding methods are assessed using 13 200 micrographs of glass coupons soiled at nine locations globally. In low‐to‐intermediate soiling conditions, the “Triangle” method is found to return the minimum coefficient of variation and a mean deviation closer to zero. On the other hand, methods assuming a bimodal distribution of pixel brightness underestimate the area coverage. In addition, since soiling can be unevenly distributed over a surface, different loss estimations can be returned when the same image analysis process is used on different spots on a sample's surface. For these reasons, image analysis should be repeated at multiple locations on each investigated surface.