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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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NREL evaluated the statistical equivalence between SAVs calculated by Enphase Energy using using their Solargraf software to perform remote shading analysis, and SAVs measured by NREL on-site using a Solmetric SunEye 210 shade measurement tool.
Assigning capacity value to renewable energy sources (RES) is a challenge faced in planning their integration with the grid. The difficulties stem from the natural characteristics of variability and intermittency of wind and solar sources. The capacity credit (CC) analysis evaluates the system’s actual power output compared with a constant capacity generator, i.e., conventional generator and determines an effective capacity to use for planning and operation. Herein this paper presents different factors that could affect the CC of a system. Two methods are proposed to determine the CC, namely equivalent firm capacity (EFC) and effective load carrying capability (ELCC). Since these methods are based on satisfying reliability criteria, daily loss of load expectation (LOLE), hourly loss of load (LOLH), and expected energy not served (EENS) have been employed as indices. To obtain the CC value, both methods apply two techniques: traditional and optimization. Genetic algorithm (GA) is the optimization approach used in this paper. Then, this work compares the two techniques and shows the superior performance of the optimization approach. Two hybrid systems, stand-alone (SA) and grid-connected (GC) modes, are proposed and used as case studies. The hybrid systems consist of photovoltaic (PV), wind turbine (WT), and battery energy storage system (BESS). In this work, three different scenarios are used to compare capacity credit: system as a whole, only wind, and no batteries. Finally, sensitivity analysis is carried out to examine the impact of varying the wind speed, solar irradiation, and load. It is demonstrated that the choice of reliability index plays an important role in determining the capacity credit and it is shown that EENS is a more comprehensive and consistent index of reliability.
Inorganic-based thin-film photovoltaics (TFPV) represents an important component of the growing low-carbon energy market and plays a vital role in the drive toward lower cost and increased penetration of solar energy. Yet, commercialized thin-film absorber technologies suffer from some non-ideal characteristics, such as toxic or non-abundant element use (e.g., CdTe and Cu(In,Ga)(S,Se) 2 , which bring into question their suitability for terawatt deployment. Numerous promising chalcogenide, halide, pnictide and oxide semiconductors are being pursued to bridge these concerns for TFPV and several promising paths have emerged, both as prospective replacements for the entrenched technologies, and to serve as partner (i.e., higher bandgap) absorbers for tandem junction devices—e.g., to be used with a lower bandgap Si bottom cell. The current perspective will primarily focus on emerging chalcogenide-based technologies and provide both an overview of absorber candidates that have been of recent interest and a deeper dive into an exemplary Cu 2 BaSnS 4 -related family. Altogether, considering the combined needs of high-performance, low-cost, and operational stability, as well as the experiences gained from existing commercialized thin-film absorber technologies, chalcogenide-based semiconductors represent a promising direction for future PV development and also serve to highlight common themes and needs among the broader TFPV materials family.
Thin-film tandem photovoltaic (PV) technology has emerged as a promising avenue to enhance power conversion efficiency beyond the radiative efficiency limit of single-junction devices. Combining a tunable wide-bandgap perovskite cell with a commercially established narrow-bandgap cadmium selenium telluride (CdSeTe) cell in a comparatively easy-to-fabricate four-terminal (4-T) arrangement is a great step in that direction. Herein, the impact of the transparent back contact and the perovskite absorber bandgap on the performance of 4-T perovskite–CdSeTe tandem solar cells is investigated. 4-T perovskite–CdSeTe tandem device architecture with ≈25% efficiency is demonstrated and a feasible pathway is shown to improve the tandem efficiency to more than 30%. Furthermore, the results show that the integration of CdSeTe with perovskite in 4-T tandem PV configurations represents a significant advancement toward achieving higher efficiency and low-cost tandem PVs.
Natural soiling and the subsequent requisite cleaning of photovoltaic (PV) modules result in abrasion damage to the cover glass. The durability of the front glass has important economic consequences, including determining the use of anti-reflective and/or anti-soiling coatings as well as the method and frequency of operational maintenance (cleaning). Artificial linear brush abrasion using Nylon 6/12 bristles was therefore examined to explore the durability of representative PV first-surfaces, i.e., the surface of a module incident to direct solar radiation. Specimens examined include silane surface functionalized-, roughened (etched)-, porous silica-coated-, fluoropolymer-coated-, and ceramic (TiO2 or ZrO2/SiO2/ZrO2/SiO2)-coated-glass, which are compared to monolithic-poly(methyl methacrylate) and -glass coupons. Characterization methods used in this study include: optical microscopy, ultraviolet-visible-near-infrared (UV-VIS-NIR) spectroscopy, sessile drop goniometry, white-light interferometry, atomic force microscopy (AFM), and depth-profiling X-ray photoelectron spectroscopy (XPS). The corresponding characteristics examined include: surface morphology, transmittance (i.e., optical performance), surface energy (water contact angle), surface roughness, scratch width and depth, and chemical composition, respectively. The study here was performed to determine coating failure modes; identify characterization methods that can detect nascent failures; compare the durability of popular contemporary coating materials; identify their corresponding damage characteristics; and compare slurry and dry-dust abrasion. This study will also aid in developing an abrasion standard for the PV industry.
A high-performance p -type transparent conductor (TC) does not yet exist but could lead to advances in a wide range of optoelectronic applications and enable new architectures for, e.g., next-generation photovoltaic (PV) devices. High-throughput computational material screenings have been a promising approach to filter databases and identify new p -type TC candidates and some of these predictions have been experimentally validated. However, most of these predicted candidates do not have experimentally achieved properties on par with n -type TCs used in solar cells and therefore have not yet been used in commercial devices. Thus, there is still a significant divide between transforming predictions into results that are actually achievable in the laboratory and an even greater lag in scaling predicted materials into functional devices. In this perspective, we outline some of the major disconnects in this materials discovery process—from scaling computational predictions into synthesizable crystals and thin films in the laboratory to scaling laboratory-grown films into real-world solar devices—and share insights to inform future strategies for TC discovery and design. Published by the American Physical Society 2024
Sustainability and Circular Economy have many metrics for evaluation. Calculating mass intensity, energy return on investment, financial payback, and recycling rate for proposed technology changes and lifecycle management can support decision making. Robust data with modeling tools can perform these calculations, informing good decision making. Analyses show that reliability is more critical than recyclability. Improved data gathering and tool accessibility will support our industry to make more circular choices for PV lifecycle management.
Consistent cost and performance data for various electricity generation technologies can be difficult to find and may change frequently for certain technologies. With the Annual Technology Baseline (ATB), the National Renewable Energy Laboratory annually provides an organized and centralized set of such cost and performance data. The ATB uses the best information from the Department of Energy national laboratories' renewable energy analysts. The ATB has been reviewed by experts and it includes the following electricity generation and storage technologies: land-based wind, offshore wind, distributed wind, utility-scale solar photovoltaics (PV), commercial-scale solar PV, residential-scale solar PV, concentrating solar power, geothermal power, hydropower, utility-scale battery storage, commercial battery storage, residential battery storage, pumped storage hydropower, coal, and natural gas. EIA data for nuclear and conventional biopower are included for reference. This webinar presentation introduces the 2022 update to the ATB Electricity data and documentation.
This report describes the modeling methodology for an upgrade package of two end-use savings shape measures - Thermostat Control for Load Shedding and Lighting Control for Load Shedding - and briefly introduces key results. The package combines thermostat control for load shedding, lighting control for load shedding, and PV with 40% rooftop coverage measures to reduce the net building load during the building's electricity peak window every weekday. The measure takes daily peak load schedule inputs generated by the method "Dispatch Schedule Generation" described in the "Supplemental Documentation: Dispatch Schedule Generation for Demand Flexibility Measures" to determine the start and end times of the predicted peak window, and then relaxes the thermostat setpoints and dims the lighting level from the original schedules during the peak window to reduce the peak demand, while applying the fixed rooftop PV application for onsite electricity generation. The measure is applicable to (large, medium and small) offices, warehouses, and primary and secondary schools, which correspond to approximately 68% of the stock floor area of commercial buildings in ComStock analysis. The measure demonstrates 5%-15% daily peak demand reduction performance for applicable buildings, and around 1% total site energy savings (0 trillion British thermal units [TBtu]) for the U.S. commercial building stock modeled in ComStock.
Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.
A technoeconomic analysis of grid to low temperature electrolysis (Grid-LTE), photovoltaic to low temperature electrolysis (PV-LTE), concentrating solar power to high temperature electrolysis (CSP-HTSE) and a concentrating solar power with PV to high temperature electrolysis (CSP-PV-HTSE) centralized hydrogen production systems are analyzed to assess the economics of system and to provide a baseline for comparing these technologies against hydrogen production cost targets. A framework integrating the system advisor model (SAM) and US Department of Energy hydrogen production models (H2A) is developed to assess these systems. The hydrogen levelized cost given current and future assumptions for technology cost and performance is evaluated at optimal system configurations. The framework described in this report integrates SAM with H2A electrolyzer technologies and provides analysts a detailed technoeconomic method to analyze concentrating and photovoltaic solar technologies to produce energy that are directly coupled to LTE and HTSEs that use that energy to split water into hydrogen and oxygen. The baseline hydrogen levelized cost (HLCs) for the GRID-LTE, PV-LTE, CSP-HTSE, and CSP-PV-HTSE systems in Daggett, CA are 2.82, 3.86, 3.68, and 2.90 $\$$USD 2016/kg H2 and 2.50, 2.13, 2.84, 2.15 $\$$USD 2016/kg H2 in the 2020 and 2050 scenarios respectively. To achieve the $\$$2/kg H2 target in locations with excellent solar resources, cost parameters values aligned with aggressive R&D targets will need to be achieved for all the systems configurations. In Daggett, PV costs of $\$$0.68 /Wac or moderate ATB PV CAPEX projections result in HLCs of $\$$2 /kg H2. Similarly for PV-MSALT-HTSE systems, $\$$0.60 /Wac result in $\$$2/kg H2. For the MSALT-HTSE systems, better than aggressive 2050 ATB salt tower CAPEX projections would be needed to reach $\$$2/kg H2. Molten salt tower capital costs of $\$$2400/kW would enable $\$$2/kg H2 in 2050.
Natural soiling and the subsequent necessary cleaning of photovoltaic (PV) modules result in abrasion damage to the cover glass. The durability of the front glass has important economic consequences, including determining the use of anti-reflective and/or anti-soiling coatings as well as the method and frequency of operational maintenance (cleaning). Artificial linear brush abrasion using Nylon 6/12 bristles was therefore examined to explore the durability of representative PV first-surfaces, i.e., the surface of a module incident to direct solar radiation. Specimens examined include silane surface functionalized-, roughened (etched)-, porous silica-coated-, fluoropolymer-coated-, and ceramic (TiO 2 or ZrO 2 /SiO 2 /ZrO 2 /SiO 2 )-coated-glass, which are compared to monolithic-poly(methyl methacrylate) and -glass coupons. Characterization methods used in this study include: optical microscopy, ultraviolet–visible–near-infrared (UV-VIS-NIR) spectroscopy, sessile drop goniometry, white-light interferometry, atomic force microscopy (AFM), and depth-profiling X-ray photoelectron spectroscopy (XPS). The corresponding characteristics examined include: surface morphology, transmittance (i.e., optical performance), surface energy (water contact angle), surface roughness, scratch width and depth, and chemical composition, respectively. The study here was performed to determine coating failure modes; identify characterization methods that can detect nascent failures; compare the durability of popular contemporary coating materials; identify their corresponding damage characteristics; and compare slurry and dry-dust abrasion. This study will also aid in developing an abrasion standard for the PV industry.
The progression of electrification in the building and transportation sectors brings new opportunities for energy decarbonization. With higher dependence on the grid power supply, the variation of the grid carbon emission intensity can be utilized to reduce the carbon emissions from the two sectors. Existing coordinated control methods for buildings with distributed energy resources (DERs) either consider electricity price or renewable energy generation as the input signal, or adopt optimization in the decision-making, which is difficult to implement in the real-world environment. This paper aims to propose and validate an easy-to-deploy rule-based carbon responsive control framework that facilitates coordination between all-electric buildings and electric vehicles (EVs). The signals of the grid carbon emission intensity and the local photovoltaics (PV) generation are used for shifting the controllable loads. Extensive simulations were conducted using a model of an all-electric mixed-use community in a cold climate to validate the control performance with metrics such as emissions, energy consumption, peak demand, and EV end-of-day state-of-charge (SOC). Our study identifies that 4.5% to 27.1% of annual emission reduction can be achieved with limited impact on energy costs, peak demand, and thermal comfort. Additionally, up to 32.7% of EV emission reduction can be obtained if the EV owners reduce the target SOC by less than 21.2%.
This paper presents a framework that expands the URBANopt(TM) modeling platform to include 3D-printed concrete wall assemblies and assess energy efficiency and backup power trade-offs in new housing developments. Applied to a planned mixed-use neighborhood in Oil City, Pennsylvania, the workflow integrates building energy and distributed energy resource (DER) modeling to evaluate envelope and equipment upgrades alongside DER operations. Results show that advanced 3D-printed envelopes combined with efficient systems and onsite photovoltaics (PV) and storage reduce energy use intensity and sustain critical loads during outages. The framework supports planning for emerging construction technologies by quantifying key trade-offs between energy efficiency and backup power performance.
This presentation will explore the land, food, water, and energy implications of changing energy systems. With an increase in solar development, there are potential conflicts between land use for solar and agricultural. Under the DOE’s InSPIRE project, researchers are gathering data and performing experiments for the co-location of solar development and the agricultural production of crops, pollinator habitat, and grazing. Initial findings from this project highlight mutual benefits for both farmers and the PV installation themselves, all while providing an avenue to reduce land use conflicts. Also, in this presentation, the intersection of water and energy systems will be explored. Energy production requires water and water treatment and transport requires energy. This presentation will highlight past work examining oil and gas water use and detail upcoming water treatment research within the National Water Alliance for Innovation (NAWI).
The Austin SHINES project and solution is a software management platform, for an electric grid with a high penetration of dispersed photovoltaic (PV) solar generation sites, which maintains the traditional power quality and reliability associated with grid service. This project developed and deployed the platform as a Distributed Energy Resource Management System (DERMS), engaging multiple advanced controls, to evaluate operation and optimization of a fleet of diverse DER assets, installed at several locations among Austin Energy’s customers and distribution system. The project also produced a methodology to create a replicable DERMS template, adaptable to other regions and market structures. Last, Austin SHINES aimed to demonstrate the solution’s methodology would enable the DER grid ecosystem to serve load at a technical cost (System Levelized Cost of Electricity, or System LCOE) of less than the U.S. Department of Energy SHINES program metric of $0.14/kWh, in a defined boundary, while enabling a high penetration of distributed PV. Research was categorized in 6 reports (Final Deliverables = FD) listed below, with titles and descriptions indicating which area of understanding was investigated: FD-1: System Levelized Cost of Electricity (System LCOE) Methodology The creation and use of the System LCOE to Serve Load metric that encompasses the holistic, system-level costs and benefits of all resources, and enables them to be evaluated based on their ability to support an efficient and low-cost integrated grid ecosystem. FD-2: Software Platform Product Description The creation of new DER control methodologies deployable within a utility-grade software platform that enable DER's to maximize their benefit within a grid, that is capable of serving load enabling a high penetration of distributed PV generation. FD-3: Optimal Design Methodology Optimal design methodologies for individual DER installations that enable utilities to determine the optimal combinations and sizing for individual DER sites. FD-4: Austin SHINES Ownership and Operation Models for DER System Performance A comparison of multiple DER aggregation and ownership methodologies including direct utility control, third-party aggregator, and autonomous. FD-5: Economic Modeling & Optimization A comparison of multiple DER technology mixes and configurations within the distribution system, providing insight into an optimal blend of technologies that best enable the distribution system to serve load at the lowest cost at high penetrations of solar. FD-6: Fielded Assets Deployed DER assets within the Austin Energy SHINES circuits. Austin SHINES provided an opening for state-of-the-art technology products to be deployed, providing a rich opportunity for improving how each of the products perform as stand-alone products, and in concert with other complementary products. The Austin SHINES project comprised of two key metrics for System LCOE: SystemLCOE_SHINES<$0.14/kWh Modeled ΔSystemLCOE_SHINES/ΔSystemLCOE_Base≥20% at same solar penetration The System LCOE calculation uses the costs of the utility-owned infrastructure as it exists today, the cost of the DERs that exist in the system today, and the cost of the purchase of energy from ERCOT wholesale markets over the course of the calendar year. All costs are on an annualized basis. The capital and operating costs are derived from the rate case, which produces a yearly cost. The net cost of energy and services imported to the system is integrated over the test year, as is the load served and solar penetration. The first metric was easily achieved by every scenario considered. The goal was set when the Department of Energy’s SHINES Funding Opportunity Announcement was written in 2015 and was a more difficult target at the time. Due mostly to rapidly declining costs for DERs and the significant decrease in the Electric Reliability Council of Texas (ERCOT) energy market prices, which results in lower net cost of energy purchases, the System LCOE is well below this target for all scenarios considered. A fleet of DERs can assume different mixtures, each of which serves the load at a different LCOE. The optimal mixture of DERs serves load at the smallest System LCOE. The second metric (hereinafter %delta metric) asks that the holistic DERMS controls reduce the incremental cost above the baseline of going to a high solar penetration future by at least 20% as compared to the case of a DER deployment with no sophisticated controls (autonomous). Many comparison sets were created throughout this project. Physical technology was installed for informing utility engineering and testing several types of operational control schemes, through the DERMS. The types of operational control which were compared for valuation of the System LCOE Metric were: Holistic control = using the full suite of the DERMS platform to decide and optimize how/why the systems operate depending on weather, market, and reliability signal input. Autonomous control = a local mode at the asset site, wherein a schedule operates the asset, with visibility into performance only No control = the baseline for comparing value against the other two types of control The types of ownership control included: Direct Utility control = the utility dispatches a signal to each asset Third-Party Aggregator = a third party aggregates a fleet of assets and the utility dispatches one signal for all Autonomous = a local mode is set for operation at the asset site, wherein a schedule operates the asset, with visibility into performance only The types of control methodologies deployable within a utility-grade software platform included: Utility Peak Load Reduction = Lower transmission cost obligation Day-Ahead Energy Arbitrage = Realize economic value through price differential Real-Time Price Dispatch = Realize economic value from real-time price spikes Voltage support = Reduce losses and increase solar generation Distribution Congestion Management = Increase local grid reliability Demand Charge Reduction = Lower customer bills and realize system benefit The fielded assets deployed for the project were: Utility Scale Kingsbery Energy Storage System: 1.5 MW / 3 MWh Li-Ion battery storage Mueller Energy Storage System: 1.75 MW / 3.2 MWh Li-Ion battery storage, 7 Energy Storage Units (250 kW each) La Loma Community Solar: 2.6 MW Commercial Scale Aggregated storage installations at 3 sites, with existing solar (300+ kW): One 18 kW / 36 kWh Li-Ion battery storage Two 72 kW / 144 kWh Li-Ion battery storage Residential Scale Aggregated storage installations: -Six stationary battery storage systems (10 kWh each) at homes with existing solar -One Electric Vehicle installed as Vehicle-to-Grid (V2G) Utility-Controlled Solar via Smart Inverters at 12 homes Autonomously-Controlled Smart Inverters at 6 homes Over the course of the project, Austin SHINES undertook installing more than 3 MW of distributed battery energy storage, smart PV inverters, a DER control platform, and other enabling technologies utilizing customer and utility locations and aggregation models. All of these resources were to be integrated and optimized at the utility level. DER assets and control methodologies were designed to achieve a credible pathway to a System LCOE for energy delivered to load of $0.14//kWh or less by 2020, while maximizing distributed solar generation and maintaining acceptable standards of power quality. The project also established a template for other regions to follow, to maximize the adoption of distributed solar PV in support of an economic and efficient grid. In total, the Austin SHINES project added value to the DER subject area in each layer of integration. From utility, to commercial to residential scales, the sheer hierarchy of communication and coordination was a significant accomplishment in addition to learnings from what these communications revealed was unique to each. Economically, the most effective method demonstrated was the criticality of planning phases. Contingencies and multiple projection scenarios helped guide the project to deploy optimal design as close as feasible, in real world conditions. The project and reports will serve public benefit by outlining specific areas of DER strategy and installation where many stakeholders and needs can be addressed with improved efficiency. Overall, communities and utilities should use the results to guide the increasing options available for powering the grid with DER, renewables, and carbon considerate energy.
EPRI formed a working group of utilities and software vendors called the Distribution Advanced PV Model Working Group (DAWG). The working group has been focused on building consensus regarding the models that represent the reactive power capabilities outlined in Section 5 of IEEE Std 1547™-2018. This report provides an overview of the reactive and active power capabilities specified in IEEE Std 1547™-2018 and outlines a specification of key functions for usage during steady state snapshot and time series power flow analysis. Additionally, it presents a framework for performing tests to verify the correct operation of functions during power flow analysis.
The objective of this study is to complete a life cycle assessment (LCA) of a novel technology that separates the crystalline silicon (c-Si) photovoltaic (PV) module front glass from the backsheet using hot knife technology. This is known to be the most challenging step in module recycling, where the choice of delamination approach can determine the process selection for the next steps of the recycling process, the economic value of the recovered materials, and environmental performance. The life cycle inventory (LCI) reported here is based on primary data from the technology manufacturer. Different LCIs are established following the cut-off approach and the end-of-life (EOL) approach. The environmental impacts of the hot knife delamination process are analysed based on six indicators, and the main contributors to delamination efforts are identified. For the EOL approach, the potential environmental benefits from the recovered materials are compared to the environmental impacts caused by the delamination process. The functional unit of this analysis is the delamination of 1 kg of used framed c-Si PV modules at the place of installation.