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DOE OSTI2024

Clear Sky Toolkit: Decision Support User Guide

The Clear Sky Decision Support Template is a Microsoft Excel-based resource that enables users to screen and prioritize critical facilities for the addition of solar + storage. The Decision Support Template consolidates and streamlines the most relevant resources from the emergency management discipline, the electric utility sector, and national energy research institutions to assist in further site analysis and decision-making. The Decision Support Template is intended for use by local government and private sector decision-makers who aim to mitigate the effects of power outages on critical infrastructure, understand the potential for deploying photovoltaic (PV) and storage solutions on critical facilities, and align emergency management planning processes and utility needs with community resilience objectives. The Decision Support Template is divided into five modules that provide a structured, step-by-step approach users can follow and apply to unique circumstances. This process guides a more thorough and accurate assessment of a site's potential to accommodate a solar + storage system, provide grid benefits to utilities, and support community resilience and disaster mitigation for the broader region. After identifying potentially viable and preferred sites for solar + storage using the Decision Support Template, users should conduct a more detailed evaluation and consultation with a solar professional.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Assessment of Accelerated Stress Testing Data for Silicon Photovoltaics Using Tensor Decomposition Methods

In this work, we examine the use of high-order tensor decompositions to analyze degradation pathways emerging from accelerated stress testing of silicon photovoltaic (PV) modules. Matrix-based decompositions are powerful tools for studying two-dimensional data arrays and form the foundation of a host of classical data analysis techniques. Tensors are high-order extrapolations of matrices that are able to account for more parameter dimensions, and a variety of tensor decomposition methods have been developed that similarly seek to extend insights from matrix decompositions to higher dimensions. Applying and interpreting tensor decomposition methods to sequences of PV module image data, we seek to uncover and isolate different degradation modes occurring from accelerated stress testing procedures. Further, we consider the contributions of different modes to PV module performance degradations.

data analysis↗

DOE OSTI2022

Arrhenius Analysis of the Degradation Modes in BackFLIP Study of Emerging Photovoltaic Backsheets: Preprint

With increasingly large-scale deployments of photovoltaic (PV) modules to meet global energy demands, interest in better establishing module lifespans is growing. We report on the performance of co-extruded polyolefin- (PO) based backsheets as environmentally friendly alternatives to fluoropolymer-reinforced polyethylene-terephthalate- (PET)-based backsheets using three hygrometric accelerated test conditions. After completing 4000 cumulative hours of aging, we analyzed data from electrical performance (I-V), surface roughness (gloss), and appearance (L, a*, b* color) characterizations are analyzed to quantify degradation rates, quantify the corresponding activation energy, and cross-correlate between the characteristics examined, thereby providing insights into the relationship between physical characteristics and operating performance.

BackFLIP↗

DOE OSTI2021

Best Practices at the End of Photovoltaic System Performance Period

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.

14 SOLAR ENERGY↗

DOE OSTI2021

REopt ® Lite Overview - Resilience Analysis: Clear Sky Tampa Bay [Slides]

Through the Solar Energy Innovation Network (SEIN), the Clear Sky Tampa Bay team, led by the Tampa Bay Regional Planning Council (TBRPC), developed a toolkit to help local governments prioritize sites for solar photovoltaics (PV) and battery energy storage in the context of resilience and emergency management. At a high level, the Clear Sky Tampa Bay process involves: (1) Identifying sites based on community resilience needs and priorities ("Where do we need a resilient energy solution?"); (2) Screening and prioritizing sites for solar PV and storage suitability based on site conditions and other factors; and (3) Using REopt Lite to screen down-selected facilities to prioritize sites based on resilience project economics. This slide deck focuses on Step 3 above, and provides a general overview of REopt Lite and suggestions and examples tailored to the Clear Sky Tampa Bay team about how to use REopt Lite for additional analysis.

14 SOLAR ENERGY↗

DOE OSTI2025

Comprehensive techno-economic and life cycle greenhouse gases analysis of green ammonia production utilizing PV and wind energy: Jordan as a case study

Ammonia (NH 3 ) has emerged as a critical player in the global energy transition due to its potential as a low-carbon fuel. Conventional ammonia production methods, primarily through steam methane reforming, are significant contributors to global CO 2 emissions. This study investigates the potential of green ammonia production in Jordan by leveraging the country's abundant solar photovoltaic and wind energy resources. A comprehensive techno-economic and life cycle greenhouse gas analysis was conducted to compare green ammonia production with conventional grey and blue ammonia pathways. Here, the study utilized spatial mapping to assess renewable energy capacity factors across Jordan and optimized the integration of hybrid PV/wind systems with proton exchange membrane (PEM) electrolyzers. The results indicate that the levelized cost of green ammonia (LCOA) in Jordan varies between 900 USD/kg NH 3 and 2500 USD/kg NH 3 which is significantly higher than grey ammonia (similar to 360 USD/kg NH 3 ). Nevertheless, the carbon intensity of green ammonia production (between 0.1 kg CO 2eq /kg NH 3 and 0.5 kg CO 2eq /kg NH 3 ) is much lower than the grey ammonia (1.8 kg CO 2eq /kg NH 3 ). Finally, the results indicate that considering future reductions in capital costs and advancement in renewable energy systems and PEM electrolyzers as well as the ability to sell the co-product O 2 can reduce the LCOA by up to 92 % and make it competitive with grey ammonia.

Green Ammonia↗

DOE OSTI2023

IEA PVPS Task 13 Techno-Economic Study of Bifacial Photovoltaic Systems on Single Axis Trackers

The International Energy Agency PVPS Task 13 has assembled an international team of photovoltaic (PV) researchers from government labs, academia, and industry to study the specific application of bifacial PV modules deployed on tracking systems. This configuration is of particular interest as it has been identified as the PV system design with the lowest Levelized Cost of Electricity (LCOE) for over 90% of the world [1]. As such, much research, development, and commercial activity are focused on further optimizing this system design for different regions and markets, including field deployment and advanced modeling activities. Despite these efforts, many challenges remain to overcome with this system design. This conference paper will report on several areas the team is working on as part of this project. The first part of this study focuses on extensive interviews with PV tracker companies (we have identified 35) and customers worldwide to identify emerging technology trends, design diversity, market environments, and the reliability and performance of these systems. We will provide a market summary of technologies such as advanced tracking algorithms, methods for dealing with topography within the PV plant, different system configurations and layouts, backside irradiance optimization from albedo enhancement, and ground sculpting. We will also review dual-use applications of tracked bifacial systems, such as Agrivoltaics and Building-Integrated PV. These interviews are underway now, and we will be able to report our initial findings at the conference. The second part of the study focuses on summarizing performance monitoring and evaluation methods, with particular attention to capacity and acceptance testing for such systems. The third part of the study reviews performance modeling and yield assessment methods for these tracked bifacial PV systems. The final part of the study covers reliability and operations, and maintenance data from these systems.

albedo↗

DOE OSTI2022

Characterization of Contact Degradation in Crystalline Silicon Photovoltaic Modules (Final Technical Report)

In this project, the aim was to develop highly-automated metrology methods that can provide more insights into degradation and failure, while still relying on the types of measurements the PV industry and RD communities are comfortable with (e.g., I – V and EL). In particular, our focus was to develop techniques to better understand contact and interconnect degradation in crystalline silicon (c-Si) PV modules and apply those techniques to conventional and emerging c-Si cell architectures and cell interconnection technologies.

14 SOLAR ENERGY↗

DOE OSTI2020

Detection and Diagnosis of Data Integrity Attacks in Solar Farms Based on Multilayer Long Short-Term Memory Network

Photovoltaic (PV) systems are becoming more vulnerable to cyber threats. In response to this emerging concern, developing cyber-secure power electronics converters has received increased attention from the IEEE Power Electronics Society that recently launched a cyber-physical-security initiative. Here this letter proposes a deep sequence learning based diagnosis solution for data integrity attacks on PV systems in smart grids, including dc–dc and dc–ac converters. The multilayer long short-term memory networks are used to leverage time-series electric waveform data from current and voltage sensors in PV systems. The proposed method has been evaluated in a PV smart grid benchmark model with extensive quantitative analysis. As a comparison, we have evaluated classic data-driven methods, including K-nearest neighbor, decision tree, support vector machine, artificial neural network, and convolutional neural network. Comparison results verify performances of the proposed method for detection and diagnosis of various data integrity attacks on PV systems.

42 ENGINEERING↗

DOE OSTI2024

Benchmark High-Fidelity EMT Models for Power Grid with PV Plants

In recent times electromagnetic transient (EMT) modeling tools have been identified as one of the most important requirements in replicating, analyzing, and investigating the dynamics of the power grid with photovoltaic (PV) plants. However, there are no benchmark models for power grid with PVs to investigate emerging challenges with higher penetration of PVs (like trips and momentary cessations during faults from a region far away). To this end, in this paper, synthetic benchmark high-fidelity EMT dynamic models of power grid with large-scale PV plants are presented. The models are developed in PSCAD and PSCAD/Fortran. Simulation results for different use cases (events) and scenarios are presented.

Marthi, Phani Ratna Vanamali↗

DOE OSTI2024

Network science can improve the sustainable development of solar energy

Abstract The recent emergence of agrivoltaic and ecovoltaic approaches to ground-mounted photovoltaic (PV) energy provides a much-needed alternative to the environmentally disruptive practices employed in utility-scale solar development. Research on such land-sharing approaches has grown rapidly, with an emphasis on characterizing how PV arrays impact ecosystem processes and agricultural productivity. Although these studies have done well to quantify a variety of dual-use solar practices by employing site-specific sampling designs, this approach has limited our ability to synthesize results across sites, regions, and globally. We call for a network science approach for improved cross-site synthesis of dual-use solar research. We contend that a common approach for data collection and synthesis will facilitate a more rigorous investigation of the agricultural and ecological impacts of PV development across space and over time. The products of this scientifically informed approach can be directly applied to improve sustainable land management.

Bacon, Taylor (ORCID:0009000518578569)↗

DOE OSTI2023

AI-Driven Smart Community Control for Accelerating PV Adoption and Enhancing Grid Resilience

The U.S. Department of Energy (DOE) has launched a Connected Communities program that supports projects that expand DOE's network of grid-interactive, efficient building communities nationwide. As an early pilot of Connected Communities, the National Renewable Energy Laboratory (NREL) and its partners have developed and demonstrated a community-scale, hierarchical control solution to address the potential grid issues arising from high penetration of solar photovoltaics (PV) as well as to improve grid reliability and resilience in this residential community. A field pilot study has been performed at an affordable housing development called the Basalt Vista Community, which was built for school teachers and other professionals in the local workforce and represents an autonomous energy grid with all-electric, energy-efficient homes. With no natural gas line in the community, this is the first all-electric net-zero community in rural Colorado. In the field pilot study, NREL has validated the performance of the hierarchical control solution, which consists of NREL's foresee home energy management systems (HEMS) and community aggregators, in increasing demand flexibility and self-consuming PV, reducing potential over-voltages, and supporting critical loads during emergency events. In this presentation, we will present the methodology, simulation and field pilot results, and lessons learned from the project.

all-electric community↗

DOE OSTI2025

Digital Assurance Checklist for Homeowners and Installers

This document provides a comprehensive Digital Assurance Checklist for securing behind-the-meter energy assets, focusing on both installers and homeowners. As distributed energy resources (DERs) such as solar PV and battery storage become integral to residential energy systems, cybersecurity emerges as a critical component of reliability and safety. The guide outlines actionable steps for installers during pre-installation, commissioning, and post-installation phases, emphasizing practices like network segmentation, credential management, firmware validation, and homeowner education. For homeowners, the document introduces a tiered approach to cyber hygiene—from essential measures like strong Wi-Fi credentials and automatic updates to advanced strategies such as network segmentation, DNS filtering, and intrusion detection. By adopting these practices, stakeholders can mitigate cyber risks, safeguard energy infrastructure, and ensure resilient, secure operation of DER systems. Additional resources and references to industry standards are included to support implementation.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Digital Assurance Checklist for Homeowners and Installers

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Enhanced deep neural networks with transfer learning for distribution LMP considering load and PV uncertainties

As the flexibility of generation and demand increases in distribution systems, the residential loads are emerging as a promising means to participate in demand response and the transactive energy market. Market pricing is an instrumental mechanism for the distribution system operator to exploit the full potential of the flexible resources. The distribution locational marginal price (DLMP) can be used to guide the residential load consumption. This type of market signal helps the distribution system operator to optimize the scheduling of all resources while satisfying related network constraints through a day-ahead market. However, solving the optimization problem for large-scale systems can be computationally expensive. To address the scalability and practicability limitations of the DLMP framework, a learning-based approach is proposed in this paper to complement the day-ahead distribution market framework. Here, the proposed approach combines long short-term memory and transfer learning to develop deep neural network that can capture the spatial–temporal correlation of the input data. The model can determine the optimal DLMP for each node in a distribution system without the system parameters required to formulate the optimization problem. Testing results on IEEE 33-bus and 123-bus systems show that the proposed approach can generate a comparable DLMP against the optimization solutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Structural Stability of Tin-Lead Halide Perovskite Solar Cells

One of the key features that makes halide perovskite solar cells such an attractive and intensely researched photovoltaic (PV) technology, is the tunability of the bandgap of these halide perovskite materials [1], [2]. Shortly after it was established that the bandgap could be increased to 1.7-1.8 eV, investigations into the application for silicon based tandem solar cells launched. Within a few years, conversion efficiencies of up to 28 % have been reported [3]. Concurrently, the interest in commercial application of this technology emerged and have rapidly increased. Whereas there are advantages to piggy backing on an established PV technology such as silicon, efforts to develop all-perovskite tandem solar cells are of considerable interest. Partly substituting the metal cation in the ABX3 perovskite enables the low bandgap absorbers required for an all-perovskite tandem solar cell. One common approach is to partially substitute the lead with tin, however these materials often suffered from poor stability. Therefore the development of an efficient, stable mixed tin-lead perovskite is key to enabling all-perovskite tandem solar cells.

degradation↗

DOE OSTI2020

A review of Sb 2 Se 3 photovoltaic absorber materials and thin-film solar cells

Energy generated from environmentally friendly, cost-effective solar cells is a key aspect for developing a clean renewable-energy economy. Non-toxic and Earth-abundant materials with high absorption coefficient (>10 5 cm -1 ) and optimal bandgap (1-1.5 eV) have received great attention as photovoltaic (PV) absorber layers during the last few decades. Among them, antimony selenide (Sb 2 Se 3 ) has been a promising PV absorber, with steadily increasing power-conversion efficiency (PCE) compared to other emerging compounds. Very recent studies showed that high-quality ZnO:Al/ZnO/CdS/TiO 2 /Sb 2 Se 3 /MoSe 2 /Mo devices with PCE of 9.2% can be fabricated using cost-effective novel compounds. Considering these recent advances, this article provides an overview of the material properties of Sb2Se3 thin films and the recent progress made with Sb 2 Se 3 -based solar cells. Furthermore, analysis of Sb 2 Se 3 -based thin-film solar cells has also shown that the devices have relatively good light management due to their suitable bandgap and high absorption coefficient, whereas carrier management, i.e. collection efficiency of photo-generated carriers, needs significant improvement. Overall, this study provides background knowledge on material properties and device performance and suggests main research directions to overcome the limiting factors of solar cell performance.

14 SOLAR ENERGY↗

DOE OSTI2025

Exploring Capability of Multimodal Foundation Model for Image-based Fault Detection of Photovoltaic Modules

Multimodal Foundation Model (MFM), like ChatGPT and Gemini, have emerged as powerful tools for their exceptional natural language processing capabilities and their emerging potential in image analysis. This paper investigates the application of MFMs for photovoltaic (PV) fault detection through image analysis, focusing on ChatGPT 4.0 and Gemini 1.5 Pro. Three types of PV images and the corresponding common PV faults are detected: bird droppings using visible images, cell cracks via electroluminescence (EL) images, and hotspots using infrared (IR) images. Among the two models, Gemini 1.5 Pro demonstrated superior performance, achieving near-perfect results with an average F1 score of 0.97, consistently outperforming ChatGPT 4.0 in accuracy and reliability. Unlike traditional machine learning (ML) models, MFMs can operate in a zero shot manner that does not require additional training by the user, and the input images are not limited by size, angle, scope, or PV technology. The strong adaptability and user-friendliness make MFM a promising tool for analyzing PV images and advancing health monitoring for PV modules.

Li, Baojie↗