Economic Evaluation of Maintenance Strategies for Ground-Mounted Solar Photovoltaic Plants

Peters, Lennart; Madlener, Reinhard

doi:10.2139/ssrn.2883140

Cited by 7 publications

(9 citation statements)

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“…Common ways to present the failure probability functions are, e.g. Weibull distributions for age-related failures (Nguyen and Chou, 2018; Sarker and Faiz, 2016) and exponential functions for constant failure rates (Peters and Madlener, 2017). In this paper, we pursue a more general model to support the decision makers in evaluating the profitability of an investment which would, later on, include the FMEA to study the failure distributions of the system(s) in question.…”

Section: Case Applicationmentioning

confidence: 99%

Modeling the wasted value of data in maintenance investments

Marttonen-Arola

Baglee

Ylä-Kujala

et al. 2020

JQME

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PurposeBig data and related technologies are expected to drastically change the way industrial maintenance is managed. However, at the moment, many companies are collecting large amounts of data without knowing how to systematically exploit it. It is therefore important to find new ways of evaluating and quantifying the value of data. This paper addresses the value of data-based profitability of maintenance investments.Design/methodology/approachAn analytical wasted value of data model (WVD-model) is presented to quantify how the value of data can be increased through eliminating waste. The use of the model is demonstrated with a case example of a maintenance investment appraisal of an automotive parts manufacturer.FindingsThe presented model contributes to the gap between the academic research and the solutions implemented in practice in the area of value optimization. The model provides a systematic way of evaluating if the benefits of investing in maintenance data exceed the additional costs incurred. Applying the model to a case study revealed that even though the case company would need to spend more time in analyzing and processing the increased data, the investment would be profitable if even a modest share of the current asset failures could be prevented through improved data analysis.Originality/valueThe model is designed and developed on the principle of eliminating waste to increase value, which has not been previously extensively discussed in the context of data management.

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Section: Case Applicationmentioning

confidence: 99%

Modeling the wasted value of data in maintenance investments

Marttonen-Arola

Baglee

Ylä-Kujala

et al. 2020

JQME

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“…sending out a service team) against performance lost (e.g. due to faults awaiting resolution) (Peters and Madlener, 2017). When making this decision, it is important to have comprehensive monitoring and modelling to understand what impact faults are having; for example, strings of PV modules are electrically connected in parallel so any performance mismatch can adversely affect the entire system in complex ways (Bosman et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Digital Twinning Proof of Concept for Utility-Scale Solar: Benefits, Issues, and Enablers

Starkey

Hancock

Chen

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. A Digital Twin is a virtual representation of a physical asset or system with the purpose of optimizing intelligent behaviour of said physical entity. Digital Twin is a promising tool for asset management as the virtual entity can exist and aid at every stage of a systems life. However, the infancy of the concept means implementation remains at an early stage and particularly poorly defined within an asset management context. Practical case studies of digital twinning (the modelling process of generating and updating Digital Twins) are an important tool to ensure definitions from research are applied rigorously and to aid in their deployment with practitioners in real industrial applications. This-being-said, there are insufficient case studies for asset management digital twinning. In particular, the Digital Twinning process for utility-scale solar has not been considered. Utility-scale solar asset management often suffers challenges due to remoteness and scale of assets, contributing to high labour costs and thus could benefit enormously from an effective Digital Twin to increase precision and accuracy of fault detection and efficiency of labour for O&M tasks. In addition, the data sharing and analysis Digital Twins provide is vital for the immature solar sector. However, Digital Twinning of utility-scale solar has not been well considered and presents issues around cost-effective data collection and modelling. Therefore, this paper details the current state-of-the-art and challenges surveying utility-scale solar and the progress and application of Digital Twin to utility-scale solar. Then a novel proof of concept process for digital twinning of utility-scale solar is presented with a focus on geometric data capture for updating as-is models. Furthermore, the paper will consider Digital Twin requirements and their prescription to current O&M methods in utility-scale solar. Finally, the paper highlights currently available required technology as well as highlighting future technological improvements that would benefit the proposed proof of concept.

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“…Photovoltaic (PV) modules are often regarded as the most reliable elements of PV systems. [1][2][3][4][5][6] Their high reliability is indirectly reflected in the power output guarantees, which are currently in the range of 25 years (and may reach 30 years in the near future). In fact, PV modules have a very low total number of returns.…”

Section: Introductionmentioning

confidence: 99%

Warranty assessment of photovoltaic modules based on a degradation probabilistic model

Prieto‐Castrillo

Núñez

Vázquez

2020

Progress in Photovoltaics

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Evaluating the probability of failure of a product during the warranty is key to estimating the cost of its entire life cycle. Photovoltaic (PV) modules are the most reliable elements of a PV system, and their high reliability translates into long warranty periods (typically 25 to 30 years). The number of PV modules that fail during warranty and when they failed is an important issue to estimate the life cycle cost of PV modules. In this paper, we propose a coupled degradation‐warranty model for PV modules. The proposed model is aimed to assess the probability of failure over time covered by warranty based on degradation data. This allows us to estimate the time of PV modules failure covered by the warranty. Although the warranty model has been applied to some forms of degradation and sales functions, our method can be applied to any real application. This allows the key parameters of the warranty to be obtained in a simple way. The required degradation parameters to fulfill warranty depend on the allowable ratio of warranted elements. However, the main conclusion of the influence of degradation rates on warranty is that it is not possible to fulfill 25 years of warranty if degradation rates are larger than 0.8%/year. The required degradation rates values, for 25 years of warranty, will be lower than 0.5%/year if the allowed warranted element is in the range of 1%.

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Economic Evaluation of Maintenance Strategies for Ground-Mounted Solar Photovoltaic Plants

Cited by 7 publications

References 0 publications

Modeling the wasted value of data in maintenance investments

Modeling the wasted value of data in maintenance investments

Digital Twinning Proof of Concept for Utility-Scale Solar: Benefits, Issues, and Enablers

Warranty assessment of photovoltaic modules based on a degradation probabilistic model

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