Photovoltaic (PV) installations heavily depend on connectors for efficient module and string interconnections without requiring skilled labor. Yet this seemingly innocuous component of PV systems is a leading cause of module failures, multiple high-profile fires, and lawsuits in the PV industry. This work aims to answer critical questions regarding why connectors fail and the contributing factors to their failure. The study involves collecting and analyzing more than 17,000 field-harvested connectors from various solar installations across the United States. The vast dataset, which includes connector metadata, visual inspections, and resistance measurements, provides unprecedented insight into the state of health of PV connectors across the US, including the geographic locations, connector types, and installation practices most prone to failures. The work presented here describes a novel rapid characterization method for processing large numbers of connectors and is supported by parallel forensic analysis to discern the root causes of failures as well as a levelized cost of lifetime model to determine the economic ramifications of connector failure. Ultimately, the findings may inform PV developers about the best practices to extend connector longevity and lead to more resilient and reliable PV systems.

Connectors have traditionally been considered crucial yet somewhat overlooked elements within a PV system, playing a functional role that has been sidelined by the solar industry's emphasis on module efficiency and cost reduction in manufacturing and installation. However, faulty or deteriorated connectors significantly impact system performance, leading to power losses, heightened operational and maintenance demands, and potentially catastrophic failures, including fire hazards. Furthermore, degraded or failed connectors raise concerns about increased insurance premiums, higher levelized cost-of-energy (LCOE), and reduced confidence in the reliability of solar power generation. This work describes an integrated technical and economic analysis to quantify the impact of connector failure on PV life cycle economic metrics, including energy yield, O&M expenses and LCOE. This work includes the modeling of O&M expenses and energy production losses related to degraded and failed connectors based on operating temperature and the impact of connector degradation and failure on a plant’ LCOE. This data matters because DC power losses lower the amount of kWh (energy yield) that could otherwise be converted to PV project revenues, and replacement of failed connectors impacts project O&M expenses. The results of this analysis provide insights into the value of quality connectors, proper installation methods, and/or more rigorous testing standards for connector product qualification.

The U.S. Department of Energy's (DOE's) Solar Energy Technologies Office (SETO) aims to accelerate the advancement and deployment of solar technology in support of an equitable transition to a decarbonized economy no later than 2050, starting with a decarbonized power sector by 2035. Its approach to achieving this goal includes driving innovations in technology, hardware, and soft cost reductions to make solar affordable and accessible for all. As part of this effort, SETO must track solar cost trends so it can focus its research and development (R&D) on the highest-impact activities. The benchmarks in this report are bottom-up cost estimates of all major inputs to PV and energy storage system installations. Bottom-up costs are based on national averages and do not necessarily represent typical costs in all local markets. Like last year's report, this year's report includes two distinct sets of benchmarks: minimum sustainable price (MSP) benchmarks and modeled market price (MMP) benchmarks. MSP benchmarks can be interpreted as the minimum price a company needs to charge to remain financially solvent in the long term based on the minimum sustainable prices of all inputs including minimum sustainable profit margins. MMP benchmarks can be interpreted as the actual cash sales price a company charges in the given benchmark period. These simplified estimates are useful for tracking technological progress, but they do not reflect all experiences. In fact, no individual estimate under any approach can reflect the diversity of the PV and storage manufacturing and installation industries. Our residential MMP benchmark ($2.90 per watt direct current [Wdc]) is 24% higher than the MSP benchmark ($2.34/Wdc) and 9% lower than our MMP benchmark ($3.18/Wdc) from Q1 2022 in 2022 U.S. dollars (USD). For community solar, our MMP benchmark ($1.75/Wdc) is 18% higher than our MSP benchmark ($1.49/Wdc). Our Q1 2022 benchmark report has no community solar system for comparison. For utility-scale systems with one-axis tracking, our MMP benchmark ($1.17/Wdc) is 22% higher than our MSP benchmark ($0.96/Wdc) and 10% higher than its counterpart ($1.07/Wdc) in Q1 2022 in 2022 USD.

This effort improves the effectiveness and reduce uncertainty in O&M cost through four primary objectives/tasks: 1) institutionalize standards for reliability and availability reporting for large PV power plants; 2) bridge systemic O&M knowledge gaps around important topics affecting O&M; 3) characterize systemic failure modes and patterns and accelerate O&M experiential learning cycles using field data; and 4) establish a baseline understanding of UPVS O&M cost drivers. Key results of this effort include publication of IEC standards, published topical papers on O&M topics, training, and characterize field data for climate- and service-related patterns (additional details below). Integrating these results serves to reduce performance risk and facilitate improvement in the way solar projects are operated and maintained. Results are well received and two publications are among the most successful SETO publications at NREL ("Model of Operation and Maintenance Costs for Photovoltaic Systems with over 40,000 downloads and "Best Practices in Operation and Maintenance of PV Systems, 3rd Ed." with over 90,000 downloads).

Battery storage systems are increasingly being installed at photovoltaic (PV) sites to address supply-demand balancing needs. Although there is some understanding of costs associated with PV operations and maintenance (O&M), costs associated with emerging technologies such as PV plus storage lack details about the specific systems and/or activities that contribute to the cost values. This study aims to address this gap by exploring the specific factors and drivers contributing to utility-scale PV plus storage systems (UPVS) O&M activities costs, including how technology selection, data collection, and related and ongoing challenges. Specifically, we used semi-structured interviews and questionnaires to collect information and insights from utility-scale owners and operators. Data was collected from 14 semi-structured interviews and questionnaires representing 51.1 MW with 64.1 MWh of installed battery storage capacity within the United States (U.S.). Differences in degradation rate, expected life cycle, and capital costs are observed across different storage technologies. Most O&M activities at UPVS related to correcting under-performance. Fires and venting issues are leading safety concerns, and owner operators have installed additional systems to mitigate these issues. There are ongoing O&M challenges due the lack of storage-specific performance metrics as well as poor vendor reliability and parts availability. Insights from this work will improve our understanding of O&M consideration at PV plus storage sites.

Photovoltaic (PV) technology is a rapidly developing technology in response to supply-demand balancing needs. Although there is some understanding of costs associated with PV O&M, costs associated with emerging technologies such as PV plus storage lack details about the specific systems and/or activities that contribute to the cost values. This study aims to address this gap by exploring the specific factors and drivers contributing to utility-scale PV plus storage (UPVS) systems O&M costs, how particular storage technologies were selected, O&M data collection, and ongoing challenges in this space. Here, we present an initial analysis of data collected from 10 semi-structured interviews and questionnaires representing 50 MW of installed battery storage capacity. More detailed analysis of collected results and insights into additional cost drivers will be presented in the full conference presentation.

and insights from utility-scale owners and operators. Data was collected from 14 semi-structured interviews and questionnaires representing 51.1 MW with 64.1 MWh of installed battery storage capacity within the United States (U.S.). Differences in degradation rate, expected life cycle, and capital costs are observed across different storage technologies. Most O&M activities at UPVS related to correcting under-performance. Fires and venting issues are leading safety concerns, and owner operators have installed additional systems to mitigate these issues. There are ongoing O&M challenges due the lack of storage-specific performance metrics as well as poor vendor reliability and parts availability. Insights from this work will improve our understanding of O&M consideration at PV plus storage sites.

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Responsible and cost-effective dissolution of photovoltaic (PV) system hardware at the end of the performance period has emerged as an important business and environmental consideration. Alternatives include extending the performance period and existing contracts for power purchase, lease, and utility interconnect; refurbishing the plant by correcting any deficiencies; repowering the plant with new PV modules and inverters; or decommissioning the plant and removing all the hardware from the site. Often key decisions are made very early in the project development and might require decommissioning by some certain date after the end of a power purchase agreement. To "abandon in place" is not an alternative acceptable to landowners and regulators, so any financial prospectus should include costs associated with decommissioning, even if those costs are deferred by extending operations, refurbishment, or repowering. Decommissioning costs are driven by regulations regarding the handling and disposal of waste, with reuse and recycling of PV modules and other components preferred as a way to reduce both costs and environmental impact. Each alternative is discussed with order-of-magnitude costs, and recommendations are provided considering site-specific details of that situation, such as estimated costs to refurbish or repower, projected revenue from continued operations, and tax considerations.

We report on 250 PV systems throughout the United States, comprising 157 MWdc of system capacity and more than 10,000 monthly performance index (PI) values. Loss factors were isolated including first-year start-up issues, snowfall, soiling and inverter downtime. Inverter availability was found to contribute significant system energy loss during the first six months of operation, with an average of 8% loss occurring during this period, and 2.3% on average thereafter. Other start-up issues beyond inverter downtime, such as partial string outage, contributed additional underperformance in the first year of operation across the fleet. Winter performance was also found to be below summer performance on average, likely due to snowfall. A relationship was found between monthly snowfall accumulation in centimeters and monthly under-performance, indicating a 6%-40% loss in months with measured snowfall, depending on climate. After correcting for availability, snow and startup loss, over 90% of systems were performing within 10% of monthly expectation based on satellite resource data and PVWatts production estimates.

In this analysis, we report on 250 PV systems throughout the US, comprising 157 MWdc of system capacity and over 10,000 monthly PI values based on high-frequency (subhourly) energy data and satellite-based resource data. The distribution of PI values is analyzed, and multiple causes of underperformance are assessed, including first-year startup issues, snowfall and inverter downtime. An initial distribution of raw monthly PI values was collected with mean measured / modeled performance of PI = 0.935. After correcting for the three identifiable loss factors mentioned above, an average monthly performance of PI = 0.994 was obtained, with a distribution closely following a Gumbel Extreme Value distribution. In particular, inverter availability was found to contribute a system energy loss of 2.3% on average, except in the first six months of operation when availability losses are closer to 8%. Other startup issues beyond inverter downtime such as partial string outage contributed an additional 5% underperformance in the first year of operation across the fleet. Winter performance was also found to be 5%-10% below summer performance on average, likely due to snowfall. A simple linear relationship was found between snow loss and monthly snowfall accumulation in cm, indicating between 6% - 40% loss in months with nonzero snowfall, depending on climate.

This paper describes how performance problems can be “masked,” or not readily evident by several causes: by photovoltaic (PV) system configuration (such as the size of the PV array capacity relative to the size of the inverter and the resultant clipped operating mode); by instrumentation design, installation, and maintenance (such as a misaligned or dirty pyranometer); by contract clauses (when operational availability is transformed to contractual availability, which excludes many factors); and by identified management and operational practices (such as reporting on a portfolio of plants rather than individually). A simple method based on a duration curve is introduced to overcome shortcomings of Performance Ratio based on nameplate capacity and Performance Index based on hourly simulation when quantifying masking effects, and inverter clipping and pyranometer soiling are presented as two examples of the new method. With a better understanding of the non-transparency of masking issues, stakeholders can better interpret performance data and deliver improved AC and DC plant conditions through PV system operation and maintenance (O&M) for improved performance, reduced O&M costs, and a more consistently delivered, and reduced, levelized cost of energy (LCOE).

This paper provides practical information for PV plant operators regarding cybersecurity. Plans to integrate photovoltaic generation into utility systems requires that cybersecurity be considered in every aspect- from customer data to business information to plant monitoring and control operations. This paper describes the types of threats encountered in operation of PV plants; challenges faced by photovoltaic plant operators in implementing cybersecurity; cybersecurity standards that apply to photovoltaic plant operations; cybersecurity response plans and recommended best practices to ensure photovoltaic plant cybersecurity.

Inverters are a leading source of hardware failures and contribute to significant energy losses at photovoltaic (PV) sites. An understanding of failure modes within inverters requires evaluation of a dataset that captures insights from multiple characterization techniques (including field diagnostics, production data analysis, and current-voltage curves). One readily available dataset that can be leveraged to support such an evaluation are maintenance records, which are used to log all site-related technician activities, but vary in structuring of information. Using machine learning, this analysis evaluated a database of 55,000 maintenance records across 800+ sites to identify inverter-related records and consistently categorize them to gain insight into common failure modes within this critical asset. Communications, ground faults, heat management systems, and insulated gate bipolar transistors emerge as the most frequently discussed inverter subsystems. Further evaluation of these failure modes identified distinct variations in failure frequencies over time and across inverter types, with communication failures occurring more frequently in early years. Increased understanding of these failure patterns can inform ongoing PV system reliability activities, including simulation analyses, spare parts inventory management, cost estimates for operations and maintenance, and development of standards for inverter testing. Advanced implementations of machine learning techniques coupled with standardization of asset labels and descriptions can extend these insights into actionable information that can support development of algorithms for condition-based maintenance, which could further reduce failures and associated energy losses at PV sites.

This paper provides an overview of property and casualty insurance industry functions, insurance terminology descriptions, and special insurance coverage considerations for photovoltaic (PV) system owners, asset managers, operators, PV operation-and-maintenance service providers, utilities, and other parties.The paper includes the results of an analysis of 6 years of property and casualty insurance claims for PV equipment and discusses considerations for purchasing property and casualty insurance for owners and operators of PV systems.PV is a relatively new asset type, and insurance companies are revisiting rates and offerings as actuarial data become available.This paper seeks to inform decisions that optimize the balance of the cost of insurance with enabling benefits to finance, permitting and utility connections, and the operation of PV plants.