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Aside from the immediate, visible damage, extreme weather events have a longer lasting impact on PV systems. Here, NREL's Dirk C. Jordan, Kirsten Perry, Robert White, Josh Parker, Byron McDanold and Chris Deline report on research revealing the long-term consequences of hail, wind and other weather phenomena on PV production
Bifacial photovoltaic systems oriented vertically facing east-west are an emerging design, targeting production in morning and afternoon hours and providing competitive annual energy yield to traditional south-tilted modules for high latitude locations. The accuracy of existing bifacial PV models when modules are oriented vertically has yet to be examined in detail. Here, we compare four bifacial PV irradiance models in ~150 locations between 15-80 degrees N on the utility-scale, finding higher inter-model deviations for vertical PV systems than south-tilted across all latitudes less than 75 degrees N. We validate model-predicted irradiance with test-site data collected in Golden, Colorado and Fairbanks, Alaska for E-W vertical and south-tilted arrays. View factor models agree with E-W vertical test-site data in Golden with RMSE=15%. Modelling error increases for the Alaskan test-site to RMSE values between 21-30%, driven in part by high albedo measurement uncertainty during snowy months.
The current popularity of photovoltaic (PV) systems is due in large part to their exceptional reliability and significantly lower cost than other energy sources. Studying cell and module degradation is key to promote further development in the state of the art. Fielded or accelerated aged modules exhibit different failure modes, of which metallization degradation (contacts and interconnections) is prevalent. In this work, we discuss how multiscale characterization methods can be applied to a variety of module technologies that have been field exposed and have undergone accelerated age testing. These methods include performing characterization on the module level, cell level, and finally the materials level. The observed performance losses from the module- and cell-level characterization can be correlated with materials properties to find out the root cause of degradation. We recommend an initial nondestructive characterization suite, including module- and cell-level current-voltage ( I--V ), Suns-V OC , photoluminescence and electroluminescence imaging, quantum efficiency, ultraviolet fluorescence imaging, and thermal infrared imaging. Samples are then extracted from particularly degraded regions of the module and prepared for materials characterization techniques, such as top-down and cross-sectional scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, secondary ion mass spectrometry, Raman spectroscopy, and transmission electron microscopy, allowing a deeper look into the mechanism behind the metallization degradation. This article serves as an instructional review to introduce the different multiscale characterization methods and how they can be effectively applied to perform PV degradation studies. Furthermore, we also share some of our examples and discuss the strengths, limitations, and best practices for each of the characterization techniques.
The CyDER project aimed at developing an open-source, modular and scalable co-simulation platform for power grids with large shares of Distributed Energy Resources (DERs). The project partners are the Lawrence Berkeley National Lab (LBNL), Lawrence Livermore National Lab (LLNL), PG&E, SolarCity, and ChargePoint. The prime recipient is LBNL; SolarCity and ChargePoint were partners for the project’s first two years. Increased DER integration introduces a number of challenges in power grid operation including a more dynamic interaction between the transmission grid and distribution grids, and increased modeling complexity. Although specialized software exists to precisely model different components of the power system, it is far from trivial to integrate all various models and perform a holistic simulation. Instead of replicating all models in a common simulation program, a commonly accepted approach to tackle this model diversity is to couple third-party simulators and models through a co-simulation platform that coordinates information exchange among the various components. Following this line of research, this project’s objective was to develop a co-simulation platform based on a widely accepted industrial standard called Functional Mockup Interface (FMI). Within this process, the project developed models compliant with the FMI standard, called Functional Mockup Units (FMUs), and used them to perform various operational and planning power system analyses. Relying and building upon an industrial standard is the main differentiation of this project compared with previous or parallel efforts in the co-simulation area. Particular emphasis was put on delivering software utilities to facilitate setting up and running co-simulations by end-users. Furthermore, a strong aspect of this project is demonstrating that co-simulation techniques can be used to perform Hardware-in-the-Loop (HIL) simulations that couple software components (e.g., simulated models) with hardware components (e.g., real devices such PV systems and batteries). The long-term goal of CyDER project is to help establish FMI as a powerful standard for co-simulation and promote adoption by electric utilities and other interested stakeholders. The main accomplishments of the project include the development of several FMUs including distribution and transmission grid models, PV inverters with Volt/Var/Watt controllers, batteries, and predictive optimal controllers. Additionally, a unique software package was developed, called SimulatorToFMU, which is capable of exporting any Python-driven simulator or Python script as an FMU. This is an important contribution towards establishing FMI as one of the main co-simulation standards, because more and more third-party programs for sub-system modeling and simulation are delivered with Python APIs. The CyDER platform was used to perform PV hosting capacity analyses in real utility feeders with and without smart inverter controls, battery storage, and EV charging. Smart inverter controls include conventional Volt/Var/Watt controls for reactive power support and active power curtailment, but also predictive controls that optimize the charging and discharging profile of the battery connected on the DC side in order to minimize the customer’s economic benefit. Finally, an important result of this project is delivering an experimental setup that consists of residential-scale PV inverters with battery storage, a real-time grid simulator with an ideal voltage source as grid emulator, and micro Phasor Measurement Units (PMUs). All these components and additional software modules are coupled to one another using the FMI standard and can be co-simulated with the CyDER platform.
The modern power grid has seen a rise in the integration of non-linear loads, presenting a significant concern for operators. These loads introduce unwanted harmonics, leading to potential issues such as overheating and improper functioning of circuit breakers. In pursuing a more sustainable grid, the adoption of electric vehicles (EVs) and photovoltaic (PV) systems in residential networks has increased. Understanding and examining the effects of high-order harmonic frequencies beyond $1.5$ kHz is crucial to understanding their impact on the operation and planning of electrical distribution systems under varying nonlinear loading conditions. This study investigates a diverse set of critical power electronic loads within a household modeled using PSCAD/EMTdc, analyzing their unique harmonic spectra. This information is utilized to run the time-series harmonic analysis program in OpenDSS on a modified IEEE 34 bus test system model. The impact of high-order harmonics is quantified using metrics that evaluate total harmonic distortion (THD), transformer harmonic-driven eddy current loss component, and propagation of harmonics from the source to the substation transformer.
This project advances the understanding of how roof solar PV systems and battery energy storage systems (BESS) can be effectively deployed in multifamily residential buildings, a sector that has historically faced barriers due to misaligned incentives between landlords and tenants. By leveraging high-resolution building stock data and simulation tools, the research demonstrates how energy consumption patterns vary across building types, climates, and occupant characteristics, and how these variations influence the optimal sizing and operation of distributed energy resources. A key contribution is the development of a publicly accessible, web-based tool named RESIDE (Residential Energy Systems & Infrastructure Data Evaluation) that allows users to explore building energy use and evaluate solar and battery configurations without requiring specialized expertise. This significantly lowers the barrier to entry for stakeholders such as property owners, utilities, and policymakers. From a technical perspective, the project shows that integrating rooftop solar PV with battery energy storage can substantially reduce electricity costs and peak demand through strategies such as energy arbitrage and peak shaving. The modeling framework incorporates real-world constraints, including time-of-use electricity pricing and battery degradation, providing realistic and actionable insights. Economically, the results indicate that properly sized systems can deliver meaningful cost savings, improving the feasibility of energy investments in multifamily housing. More broadly, the project benefits the public by supporting the transition to affordable and reliable energy, particularly in rental multifamily housing where adoption has traditionally lagged.
PV Field Performance Data Initiative 2022 aggregated results including PV system performance loss rates and site metadata.
In 2014, the Mickey Leland Federal Building in Houston, Texas installed a 192 kW PV system on top of its parking structure. To monitor and optimize its performance, the General Services Administration, which manages the site, responded to the Federal Energy Management Program’s request for federal agency participation in the Federal Energy Management Program Solar PV Performance Initiative.
There is growing concern that severe weather events are negatively affecting solar photovoltaic (PV) systems and pose ongoing and unrecognized risks. Clearly research is needed to quantify and characterize the risks so that properly calibrated improvements can be implemented. To begin this process, this report is a collection of information of severe weather impacts on solar PV available today taken from research, insurance data, and field inspections. While it is not comprehensive enough to draw conclusions, this information provides a baseline for action and indicates an urgent need to investigate further.
The Muckleshoot Indian Tribe (MIT) collaborated with our Project Partner, GRID Alternatives (GRID), to install 132 kilowatts of direct current (kW-DC) of rooftop solar on three Tribal facilities. The three facilities are the Tribe’s Youth Drop-In Center, Canoe Shed, and Water Treatment Facility. The solar PV systems were originally anticipated to offset approximately 45% of the aggregate annual electricity usage of the three buildings. A major aspect of the MIT-ED project was providing hands-on paid training to five Muckleshoot Building Maintenance workers in solar PV installations, operations, and maintenance. The scope of work aligns with the Tribe’s goals of building local capacity and providing real world work experience and potential career opportunities in solar PV to its citizens. The Building Maintenance Department committed five of its current FTE employees to the project. GRID provided guidance for the MIT project team on identifying paid trainees as well as end goals of skill development through training, including a long-term Operations and Maintenance (O&M) plan tailored to the Tribe’s goals of local capacity building and stewardship of natural resources.
The Cyber-Informed Engineering Photovoltaic Analysis Tool (CIEPAT) was developed in collaboration with the U.S. Department of Energy's Office of Cybersecurity, Energy Security, and Emergency Response (CESER). This tool is an energy source subcomponent integrated into the CIEMAT ecosystem and is developed to enhance the security and resilience of Photovoltaic installations by incorporating Cyber-Informed Engineering (CIE) principles into the deployment of PV systems.
With support from the U.S. Department of Energy Solar Energy Technologies Office, the National Renewable Energy Laboratory (NREL) partnered with Xcel Energy, Schneider Electric, Varentec, and Electric Power Research Institute (EPRI) to meet the goals of the Enabling Extreme Real-Time Grid Integration of Solar Energy (ENERGISE) program. This project developed and validated an innovative data-enhanced hierarchical control architecture that enables the efficient, reliable, resilient, and secure operation of future distribution systems with a high penetration of distributed energy resources like solar energy. The architecture enables a hybrid control approach where a centralized control layer is complemented by distributed control algorithms for solar inverters and autonomous control of grid edge devices. It is fully interoperable and includes all the cybersecurity aspects necessary for reliable and secure system operation. The hybrid approach can seamlessly integrate multiple voltage-regulation technologies, both at central and grid-edge levels, which enables reliable and efficient system operation in the face of unpredictable conditions. The overarching goal of the Eco-Idea project is to develop, validate, and deploy a unique and innovative Data-Enhanced Hierarchical Control (DEHC) architecture that comprehensively addresses the formidable challenges associated with proliferation of high penetration of distributed PV such as reverse power flows, transients from variability of PV systems, feeder load balancing, and voltage stability. These issues are exposing the weaknesses of existing grid operations and controls - including, but not limited to, lack of grid situational awareness, heuristic and slow-acting control actions, latency of control for emergency situations, and points of failure in communications. The proposed architecture will comprehensively resolve the deficiencies of current operational settings - where monitoring and control solutions proposed across industry and academia may not be interoperable and may not coexist in the same system - and will enable an efficient, reliable, resilient, and secure operation of future distribution systems with penetration of solar energy well beyond current limits. The DEHC architecture was developed and validated rigorously through hardware-in-loop simulations in the laboratory environment and deployed on the field.
This paper presents a convolutional neural network (CNN) developed to identify voltage events in photovoltaic (PV) inverters. The CNN is trained on synthetic data generated using the IEEE 13-bus distribution feeder model and evaluated on field measured data collected from Energy Northwest’s Horn Rapids Solar, Storage, and Training (HRSST) facility. The study focuses on two common voltage events: faults and voltage sags. The CNN is configured to analyze voltage and current waveforms from three-phase PV systems, demonstrating excellent accuracy during training. Field data from the HRSST facility is employed to assess its real-world performance, where the CNN achieves perfect identification of faults and voltage sags in a sample of nine events. This work highlights the potential of the proposed method to enhance PV protection schemes, providing a robust foundation for improved voltage event detection and grid reliability.
EMOS software performs a real-time hierarchal optimal control for energy systems like multi-port electric vehicles charging site with distributed energy resources (DERs) and energy storage systems (ESSs). It gathers information in real-time from electric vehicles, power grid, and DERs, solves a multi-objective energy management optimization problem, and output setpoint for chargers, ESS converters, DERs converters, and grid converters. The software incorporates a novel integration of two control tasks: a) An Energy Management Optimization (EMO), which is the brain of EMOS controller that gathers information in real-time from EVs [e.g., battery size, state-of-charge (SOC), desired SOC, and charge acceptance curve], grid (e.g., electricity price, allowed feeder capacity, ramp rate limit, and reactive power), and DERs (e.g., prediction for solar generation for PV systems). It solves a control optimization problem in real-time to find optimal setpoint for ESSs power dispatch, EVs charging rate, and grid inverters. The objectives are to (1) minimize the charging cost considering grid energy, demand charges, and battery energy, (2) minimize charging time to meet fast charging criterion, (3) keep high energy level on ESSs at the end of an operating period, while satisfying constraints related to grid, EVs, and power converters. b) Real-Time Energy Management System (RT-EMS) is a rule-based algorithm that has faster response than EMO. It receives optimal setpoint from EMO and actual measurements from the system and modify the setpoint to compensate for any fast disturbance in the system, until a new optimum solution is received. Fast disturbances may include vehicle connect/disconnect, unpredicted variation in DERs profiles, variation in grid voltage, errors in PV generation prediction, and others. In addition, RT-EMS regulates voltage at point of common coupling (PCC) by managing reactive power of grid converters.
The growing energy demand of the industrial sector and the need for sustainable solutions highlight the importance of efficient decision making in solar photovoltaic (PV) implementation. Selecting optimal PV configuration is complex due to the interdependent technical, economic, environmental, and social factors involved. This study introduces an integrated decision-making method combining a morphological matrix and fuzzy TOPSIS to systematically select and rank optimal PV system configurations for manufacturing firms. While the morphological matrix exhaustively examines possible design solutions based on sensing, smart, sustainable, and social (S4) attributes, the fuzzy TOPSIS method ranks the alternatives by handling uncertainty in decision making. A case study conducted in a Mexican manufacturing company validates the methodology’s effectiveness. The optimal PV configuration identified comprehensively addresses operational and sustainability criteria, covering all lifecycle stages. This approach demonstrates quantitative superiority and greater robustness compared to existing fuzzy TOPSIS-based methods for solar PV applications. The findings highlight the practical value of data-driven, multi-criteria decision making for industrial solar energy adoption, enhancing project feasibility, cost efficiency, and environmental compliance. Future research will incorporate discrete event simulation (DES) to further refine energy consumption strategies in manufacturing.
Here, this study presents a novel approach for the precise monitoring and prognosis of photovoltaic (PV) inverter status, which is crucial for the proactive maintenance of PV systems. It addresses the gaps in traditional model-based methods, which tend to neglect the overall reliability of inverters, and the limitations of data-driven approaches that largely depend on simulated data. This research presents a robust solution applicable to real-world scenarios. The proposed data-driven model for PV inverter failure prognosis employs actual inverter measurements, integrating various operational and weather-related factors based on domain knowledge. This approach effectively represents inverter stressors and operational status. Utilizing an Enhanced Siamese Convolutional Neural Network (ESCNN), the model merges operational data with domain knowledge features, redefining the prognosis challenge as a classification task. Furthermore, the paper discusses an ESCNN-based real-time inverter failure monitoring method developed on the well-trained model. The proposed models are rigorously trained and tested with real inverter data and a novel filtering method is included to address accidental failures in practical scenarios. The results validate the model's efficacy, and the directions for future research are also outlined.
Photovoltaic (PV) modules work best in the sunniest environments. Unfortunately, often the sunniest places also have substantial amounts of airborne "dust" that deposits on the front surface of the modules and blocks the sunlight; reducing energy output. In fact, natural soiling has reduced the energy output of PV systems since the technology was first used, and viable mitigation strategies have remained elusive ever since. With the ever-increasing deployments around the world, especially in dusty environments, soiling is becoming a billion-dollar problem, worldwide. While substantial work has been done to examine and resolve some of the issues with PV soiling, often mitigation comes down to physically cleaning the modules. However, a more systematic evaluation of the different module properties correlations to soiling mitigation needs to be done. In many instances, the causal connections between module properties and soiling are simply not known. This lack of knowledge results in a substantial increase in time and effort to evaluate and qualify appropriate soiling mitigation protocols based on site specific issues and the intrinsic module properties that are typically not optimized for mitigating soiling in a given environment. Thus, module property protocols and/or standards are needed to more quickly help identify appropriate module and site-specific mitigation.
Photovoltaic (PV) module qualification standards, IEC 61215 and IEC 61730, were designed to apply to “general open-air climates” and IEC 61730 specifically indicated applicability of ambient air temperature of 40°C. Additionally, these standards provided allowances for so-called “open rack mounted PV modules” without a clear definition of “open-rack.” These implied restrictions and allowances meant that hotter climates or thermally restrictive installation methods may not be covered by these often customer-mandated certification standards. This is particularly salient for the significant growth regions of the Middle East and India that would be expected to operate at significantly higher temperatures. The applicability of these documents raised issues over the definition of “open rack” and the fact that the geographic location is just as important as the mounting configuration in assessing the impact of the micro-environment of a PV module. This work summarizes the scientific background for IEC Technical Specification 63126:2020 ED1, titled “Guidelines for qualifying PV modules components and materials for operation at high temperatures.” This standard was recently published by the IEC and is the first step in a systematic effort to rework these standards to address the question of temperature more directly. Instead of specifying a mounting condition, we specify different suites of tests suitable for a system (PV module, mounting style, and location) defined by the 98th percentile cell temperature. With a defined temperature regime to work from, this allowed us to use existing literature research combined with additional modeling work to determine, which tests would need to be modified. This resulted in suggested changes to material thermal indices, thermal cycling temperatures, hot spot testing, ultraviolet testing, and bypass diode testing among other tests and characteristics described in this study.