In photovoltaic (PV) projects, it is important to establish a common practice for professional risk assessment, which serves to reduce the risks associated with related investments. The objective of this paper is to present a methodology on how to improve the current understanding of several key aspects of technical risk management during the PV project lifecycle, with the identification of technical risks and their economic impact. To achieve this, available statistical data of failures during a PV project have been collected with the aim to (i) suggest a guideline for the categorisation of failure and (ii) develop a methodology for the assessment of the economic impact of failures occurring during operation but which might have originated in previous phases. The risk analysis has the aim to assess the economic impact of technical risks and how this can influence various business models and the levelised cost of electricity. This paper presents the first attempt to implement cost-based failure modes and effects analysis to the PV sector and to define a methodology for the estimation of economic losses because of planning failures, system downtime and substitution/repair of components. The methodology is based on statistical analysis and can be applied to a single PV plant or to a large portfolio of PV plants in the same market segment. The quality of the analysis depends on the amount of failure data available and on the assumptions taken for the calculation of a cost priority number. The overall results can be linked to the cost of periodic and corrective maintenance and form the basis to estimate the impact of various risk and mitigation scenarios in PV business models.
The IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the performance loss rate value. In this study, several high‐quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually.
Due to the overall declining costs of photovoltaic systems, market players in the operation and maintenance sector are under increasing price pressure when offering their services. The automation and standardization of maintenance and failure tickets as well as their statistical and economical evaluation are key to ensure optimal yield and long lifetime. A thorough understanding of typical faults, classified through a standardized taxonomy, can be a pathway of developing location and technology specific decision support, offering cost‐time efficient solutions to reduce component downtime and costs in case of failure appearance. A useful method for this approach is the Cost Priority Number, a methodology to assess technical failures and their economic impact in energy systems. In this work, this method is further improved to be applied to individual use cases to make it useful in the actual operation of PV plants. A standardized ticket taxonomy for the operational phase of PV systems has been developed where more than 35,000 PV systems' tickets have been statistically evaluated, and a fully automated methodology to calculate the cost of individual maintenance tickets has been developed.
Technical risks are important criteria to consider when investing in new and existing PV installations. Quantitative knowledge of these risks is one of the key factors for the different stakeholders, such as asset managers, banks or project developers, to make reliable business decisions before and during the operation of their PV assets. Within the IEA PVPS Task 13 Expert Group, we aim to increase the knowledge on methodologies to assess technical risks and mitigation measures in terms of economic impact and effectiveness. The developed outline provides a reproducible and transparent technique to manage the complexity of risk analysis and processing in order to establish a common practice for professional risk assessment. Semi‐quantitative and quantitative methodologies are introduced to assess technical risks in PV power systems and provide examples of common technical risks described and rated in the new created PV failure fact sheets (PVFS). Besides the PVFS based on expert knowledge and expert opinion, an update on the statistics of the PV failure degradation survey is given. With the knowledge acquired and data collected, the risk and cost–benefit analysis is demonstrated in a case study that shows methods for prioritising decisions from an economic perspective and provided important results for risk managing strategies.
Project investment has been and still is a primary financial factor in enabling sustainable growth in photovoltaic (PV) installations. When assessing the investment worthiness of a PV project, different financial stakeholders such as investors, lenders, and insurers will evaluate the impact and probability of investment risks differently depending on their investment goals. Similarly, risk mitigation measures implemented are subject to the investment perspective. For the calculation of the economic impact of technical risks during PV plant operation, a method was developed to assign a cost priority number (CPN) to each failure given in €/kWp or €/kWp per year. This CPN method was introduced in the Solar Bankability Project and can be described as a cost-based failure mode effect analysis. The methodology has been further developed for the evaluation and effectiveness of the identified mitigation measures. Risk mitigation factors are introduced, which quantify the reduction of the economic impact of technical risks, achieved via the reduction of failure occurrence or reduction of costs for fixing the failures (ie, repair of existing component, substitution by spare component, and substitution by new component). Mitigation measures were identified along the entire value chain and assigned to various technical risks. The new CPN (CPN new ) value arises from the cost-benefit analysis by adding the CPN after mitigation to the cost of the mitigation measures. The FIXING costs for selected failures were calculated when applying combinations of 8 selected mitigation measures. Theaim of this work is to create a framework of well-defined mitigation measures, which have an impact on the global CPN (given as sum of CPNs of all technical risks). The cost-benefit analysis can then include the combination of various mitigation measures and derive the best strategy depending on market segment and PV plant typology. In addition to this, it is important to assess in the CPN analysis who will bear the cost and the risk to derive considerations not only on the overall economic impact of the technical risks, but also on cost and risk ownership. KEYWORDS assessment of PV technicak risks, cost priority number, levelised cost of energy, mitigation measures, PV technical risks 1 | INTRODUCTIONIn the project report "Technical Risks in PV Projects," 2 technical risks were identified and categorised for components (modules, inverters, mounting structure, connection and distribution boxes, cabling, potential equalization and grounding, lightning and protection system, weather station, communication and monitoring, transformer station, infrastructure and environmental influence, storage system, and miscellaneous) and phases (product testing, photovoltaic (PV) plant planning/development, installation/transportation, operation/maintenance, and decommissioning) of the value chain of a PV project into a so-called risk matrix. The technical risks were broadly divided into risks to which one can assign an uncertainty to the initial yield assessment...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.