Impact of duration and missing data on the long-term photovoltaic degradation rate estimation

Romero-Fiances, Irene; Livera, Andreas; Theristis, Marios; Makrides, George; Stein, Joshua S.; Nofuentes, G.; Casa, J. de la; Georghiou, George E.

doi:10.1016/j.renene.2021.09.078

Cited by 37 publications

(25 citation statements)

References 43 publications

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“…Similarly, appropriate routines to process and analyze data to extract long-term PLR is crucial, as reviewed by several recent literature. [19][20][21][22] A prerequisite for the reliability of the monitoring system is the quality and regular servicing (including a regular recalibration) of on-site radiometric sensors. 4) Furthermore, we have demonstrated that unplanned module replacements (or more in general revamping) may have a considerable impact on a solar project's profitability.…”

Section: Discussion and Indications To Project Developers And Investorsmentioning

confidence: 99%

Profitability of Solar Photovoltaic Projects: A Sensitivity Analysis of Performance Loss Curves and Operation and Maintenance Expenses

Virtuani¹,

Morganti²

2022

Solar RRL

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Literature highlights the fact that degradation curves are frequently not linear. This technical knowledge is, however, neglected by developers, which continues to use simplified linear degradation rates to assess the profitability of solar photovoltaic (PV) projects. Herein, the unleveraged equity interest return rate (IRR) of utility‐scale (50 MWp in size) PV projects deployed in different parts of Europe is computed and a sensitivity analysis of how the profitability of the plant is impacted when nonlinear degradation curves are used is carried out. In general, a 2‐step performance loss curve leads to the highest project‐IRRs at equal total loss, whereas the lowest IRRs are achieved if module degradation is undergoing a negative logarithmic or exponential curve. Further, the sensitivity of profitability to the different degradation trajectories increases at higher latitudes for which the absolute project‐IRR is lower. Then, a similar analysis is performed mimicking situations that would lead to a significant revamping of the PV plant after 10 years of operation. This allows us to identify thresholds for overall module degradation that would justify this kind of intervention. Finally, the impact that loss rates and combined operation–maintenance and insurance costs have on the project IRR is highlighted.

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Section: Discussion and Indications To Project Developers And Investorsmentioning

confidence: 99%

Profitability of Solar Photovoltaic Projects: A Sensitivity Analysis of Performance Loss Curves and Operation and Maintenance Expenses

Virtuani¹,

Morganti²

2022

Solar RRL

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“…In the case of performance losses, continuous CPs indicate a variation in the rate at which soiling accumulates or a nonlinear degradation pattern 60 . In case of nonlinear degradation, changes in the variability of PR time series are detected with the different segments exhibiting different slopes 61 . On the other hand, discontinuous CPs can either indicate soiling cleaning events, snow shedding, or corrective maintenance actions 60 …”

Section: Methodsmentioning

confidence: 99%

“…60 In case of nonlinear degradation, changes in the variability of PR time series are detected with the different segments exhibiting different slopes. 61 On the other hand, discontinuous CPs can either indicate soiling cleaning events, snow shedding, or corrective maintenance actions. 60 The TLRs consist of the Facebook Prophet (FBP) 59 Module temperature measurements can be simulated using an empirical model (i.e., using the Sandia module temperature model, 52 the Ross thermal model, 53 or the open-source Faiman module temperature available in pvlib-python library 40 ).…”

Section: Tlrsmentioning

confidence: 99%

Failure diagnosis and trend‐based performance losses routines for the detection and classification of incidents in large‐scale photovoltaic systems

Livera

Theristis

Micheli

et al. 2022

Progress in Photovoltaics

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Fault detection and classification in photovoltaic (PV) systems through real-time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend-based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change-point (CP) models.Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.

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“…Although these can significantly affect the accuracy of the applied statistical methods, the majority of PLR pipelines treat missing and invalid measurements by filtering them out. However, it was shown that when missing data rates exceed 10% [36,41], the PLR estimate deviates significantly from its true value. Furthermore, the missing data rate limit depends also on the length of the dataset meaning that shorter time series will require the highest data availability possible.…”

Section: Data Imputationmentioning

confidence: 99%

Best practices for photovoltaic performance loss rate calculations

2022

Self Cite

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The performance loss rate (PLR) is a vital parameter for the time-dependent assessment of photovoltaic (PV) system performance and health state. Although this metric can be calculated in a relatively straightforward manner, it is challenging to achieve accurate and reproducible results with low uncertainty. Furthermore, the temporal evolution of PV system performance is usually nonlinear, but in many cases a linear evaluation is preferred as it simpliﬁes the assessment and it is easier to evaluate. As such, the search for a robust and reproducible calculation methodology providing reliable linear PLR values across diﬀerent types of systems and conditions has been the focus of many research activities in recent years. In this paper, the determination of PV system PLR using diﬀerent pipelines and approaches is critically evaluated and recommendations for best practices are given. As nonlinear PLR assessments are fairly new, there is no consent on how to calculate reliable values. Several promising nonlinear approaches have been developed recently and are presented as tools to evaluate the PV system performance in great detail. Furthermore, challenges are discussed with respect to the PLR calculation but also opportunities for diﬀerentiating individual performance losses from a generic PLR value having the potential of enabling actionable insights for maintenance.

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Impact of duration and missing data on the long-term photovoltaic degradation rate estimation

Cited by 37 publications

References 43 publications

Profitability of Solar Photovoltaic Projects: A Sensitivity Analysis of Performance Loss Curves and Operation and Maintenance Expenses

Profitability of Solar Photovoltaic Projects: A Sensitivity Analysis of Performance Loss Curves and Operation and Maintenance Expenses

Failure diagnosis and trend‐based performance losses routines for the detection and classification of incidents in large‐scale photovoltaic systems

Best practices for photovoltaic performance loss rate calculations

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