Sascha Lindig

With photovoltaics becoming mainstream and hundreds of gigawatts of capacity being added worldwide each year, it is vital that PV systems reliably generate electricity over extended lifetimes while ensuring sustainability from energy, environmental, and investment perspectives. The introduction of novel PV designs and technologies must meet the demand for improved field performance and reliability through continuous innovation, refinement, and adaptation. This study provides an overview of the state of the art and anticipated future developments in PV technology and examines how changes in the technology and design of individual PV components at module level, balance‐of‐system level, and PV system level can affect the reliability, durability, and energy output of PV installations. We further describe the impact of failures on energy yield, economic viability, and bankability of solar projects, and illustrate how preventive and corrective maintenance, as well as data‐driven mitigation measures, can contribute to lifetime extension and strengthen investor confidence. Finally, we present an overview of the key performance indicators, quality assurance measures, and risk profiles that determine the technical and financial viability of PV projects.

Technical key performance indicators (KPIs) are important metrics used to assess and quantitatively summarize various aspects of photovoltaic (PV) systems, including long‐term performance, economic viability, and carbon footprint. Herein, a group of experts of the International Energy Agency's Photovoltaic Power Systems Programme Task 13 collect and describ the most important technical KPIs used in the industry. Thereby, a set of best practices for reliably handling PV system data is presented and the impact of data quality and climatic variability on KPI calculation is investigated. The effective use of technical KPIs allows triggering data‐driven and informed decisions to optimize PV systems and providing a comprehensive overview of how PV systems operate across different conditions and climates. With the worldwide growth of the PV industry, more companies operate/own PV systems in different regions, where the climatic and seasonal profiles differ. This requires context‐aware evaluation of KPIs, or the judicious application of multiple KPIs, to ensure that each asset is evaluated correctly. Beyond that, there is untapped potential in the utilization of KPIs through geospatial mapping and extrapolation of fleet KPIs. This study demonstrates that the uncertainty in KPI estimation is not well understood and depends on data quality, climatic variability, and system configuration.

Daylight photoluminescence (DPL) is a relatively novel imaging technique utilized in photovoltaic (PV) system inspection, using the sun as excitation source. Filtering the luminescence signal from the strong sun irradiation is its main challenge. Images acquired at two different operating points (OPs) of the module, allow subtraction of the background radiation while maintaining the luminescence signal. A DPL‐ready inverter, which is able to toggle between manually selectable OPs of connected PV modules, is presented in this work. Synchronization of image acquisition and OP switching becomes particularly challenging if the camera is applied to unmanned aerial vehicles. To overcome this challenge, an algorithm is developed to identify OP switches in a set of images taken in the field by investigating image intensities. Further, by working out the detailed dependencies of the signal recorded during DPL, the temperature coefficient of photoluminescence intensity is derived theoretically, and its impact on quantitative inspections. The potential field application of DPL images to identify performance loss in PV modules is investigated by two approaches: recording the signal intensity of images over time and comparing the signal intensity of different PV modules in one image. For both approaches, their hypothetical applicability is shown experimentally.

The increasing focus on sustainability connected to extended lifetimes with guaranteed high energy yields in the photovoltaic (PV) sector brings new challenges in operating and maintaining PV systems. Additionally, as the PV Terawatt Age has been reached in 2022 and a sustained exponential PV deployment growth is expected, operation and maintenance (O&M) companies have an ever‐increasing portfolio of PV plants to operate while maintaining cost competitive. Therefore, a shift from manual fault detection and solution toward smart and automated alternatives is required to keep downtime due to faults at a minimum while ensuring that PV systems can operate safely and efficiently. This work extends past developments of an automated O&M decision support system (DSS) by focusing on the automation of selecting and prioritizing solutions in case of PV system event/failure appearance. Thereby, the TRUST‐PV Solution Matrix is added to the published TRUST‐PV Risk Matrix, providing a standardized list of solutions for all events and failures which can appear in a PV system. Furthermore, the Cost Priority Number, a prioritization methodology to assess technical failures and their economic impact in energy systems, is improved by integrating automated fixing cost calculations into the DSS.

Photovoltaic (PV) modules and systems degrade during operation. It is necessary to understand how those mechanisms develop over time and to find precursors that allow an early detection of potential fatal faults. In this work, we investigated the evolution of performance loss rates (PLR) of 6 PV systems exposed in northern Italy for about 11 years, and we characterized 19 PV modules belonging to the systems via near infrared (NIR) spectroscopy to identify encapsulant and backsheet types. Additionally, we analyzed the spectral data with multivariate data analysis (MVDA) to detect hidden signs of polymer degradation that are not visible with the naked eye.

The application of electroluminescence imaging of photovoltaic modules increased in the last years, due to the reliable and detailed identification of degradation and failures. In future plants the time-consuming connection of power supplies could be overcome by use of inverters with bi-directional functionality, allowing backpowering of connected module strings directly. Temperature influences the open-circuit voltage of photovoltaic modules and must therefore be considered during backpowering. This work investigates the heating due to backpowering of photovoltaic modules of different types during electroluminescence inspection. The temperature increase until saturation is estimated by energy balance calculations and experimentally verified to be around 20 °C, with resulting voltage drops of up to 3 V. Further, these changes have an effect on the recorded luminescence intensity: a decrease of the electroluminescence signal intensity between beginning of backpowering and reaching saturation temperature is shown. For application of the results to a real-world scenario, the electroluminescence window of an electroluminescence-ready inverter is introduced, giving the boundaries of current and voltage that can be supplied. Combined with a simulation of the dark current–voltage curves of a connected photovoltaic module string, the electroluminescence inspection possibilities are visualized. Finally, the applicability of this heating phenomenon for snow melting is discussed.

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.

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.

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.