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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Model-Free Building Temperature Control and Power Allocation Under Measurement Time Delays

Taking a step towards a greener planet has created an increased need for a higher integration of renewable energy resources into the electric grid. Nonetheless, the intermittency and uncertainty associated with renewable generation have slowed down this integration. Demand response (DR) has been recently adopted to address this challenge by utilizing demand side flexibility and enabling the participation of many grid-interactive efficient buildings (GEBs). However, existing DR methods require significant modeling and/or training efforts and are computationally expensive. To address the aforementioned issues, we propose a model-free control (MFC)-based strategy that is robust to the time delays in the temperature measurements of the thermostatically controlled loads (TCLs). It assigns to each GEB a local controller to maintain the TCLs’ temperatures within desired comfort levels, while the load aggregator (LA) allocates the assigned reference power provided by the distribution system operator (DSO) to support a specific grid service, such as demand peak reduction, load shifting, balancing supply and demand, and consuming the solar photovoltaic power locally. We investigate the effects of such loss of information on the local control action as well as on meeting the power allocation constraint. We conclude that, for an appropriate choice of design parameters, the proposed MFC controller is satisfactorily robust to measurement time delays.

Telsang, Bhagyashri↗
DOE OSTI2024

Integration of Concentrating Solar Power with High Temperature Electrolysis for Hydrogen Production: Preprint

Hydrogen (H2) has been identified as a leading sustainable contender to replace fossil fuels in transportation and electricity generation. H2 production can be achieved by concentrating solar thermal power (CSP) systems collecting thermal energy from the sun to various chemical processes for fuel production. Fuel production via solar thermal chemical processes integrated with CSP uses the full spectrum of sunlight compared with photovoltaic power conversion and stores solar energy directly and efficiently [1]. The solar fuel production can be realized by thermochemical processes (e.g., water splitting for H2 production, carbon dioxide reduction, or methane reforming) or thermal electrochemical methods (e.g., integration with solid oxide electrolysis cell). Technology development for CSP-integrated solar fuel production requires broad technological bases from solar energy collection to chemical energy conversion. H2 generated from renewable sources can be an energy carrier for a carbon-free economy. Integrating CSP with high temperature electrolysis (HTE) using solid oxide electrolysis cells (SOEC) provides a renewable path for H2 generation. The CSP-HTE integration approach provides the benefit of thermal energy storage (TES) for continuous operation, improved capacity, and SOEC life. H2 gas has low energy density for transportation, pipeline networks are expensive, and H2 liquefaction is energy intensive. However, an alternative method for H2 distribution is to use carbon dioxide (CO2) capture and liquid hydrocarbon synthesis to convert solar energy into liquid fuels that are compatible with the existing fossil fuel infrastructure.

concentrating solar thermal power↗
DOE OSTI2021

High-specific-power flexible transition metal dichalcogenide solar cells

Abstract Semiconducting transition metal dichalcogenides (TMDs) are promising for flexible high-specific-power photovoltaics due to their ultrahigh optical absorption coefficients, desirable band gaps and self-passivated surfaces. However, challenges such as Fermi-level pinning at the metal contact–TMD interface and the inapplicability of traditional doping schemes have prevented most TMD solar cells from exceeding 2% power conversion efficiency (PCE). In addition, fabrication on flexible substrates tends to contaminate or damage TMD interfaces, further reducing performance. Here, we address these fundamental issues by employing: (1) transparent graphene contacts to mitigate Fermi-level pinning, (2) MoO x capping for doping, passivation and anti-reflection, and (3) a clean, non-damaging direct transfer method to realize devices on lightweight flexible polyimide substrates. These lead to record PCE of 5.1% and record specific power of 4.4 W g −1 for flexible TMD (WSe 2 ) solar cells, the latter on par with prevailing thin-film solar technologies cadmium telluride, copper indium gallium selenide, amorphous silicon and III-Vs. We further project that TMD solar cells could achieve specific power up to 46 W g −1 , creating unprecedented opportunities in a broad range of industries from aerospace to wearable and implantable electronics.

42 ENGINEERING↗
DOE OSTI2022

Farm-level Interactions Study of a Novel Tri-port Soft-switching Medium-Voltage String Inverter (MVSI) based Large-scale PV-Plus-Storage Farms

A medium-voltage photovoltaic power conversion unit supported by battery based on a novel topology of soft switching solid state transformer is presented in this paper. It can reduce the Levelized Cost of Energy (LCOE) of PVS farm in an extended degree. However, its capability to provide grid-support services, such as frequency response, reactive power support, etc, have not been demonstrated. A series/parallel combination of these modules form a large 20 MVA solar farm. To validate them with reduced computation burden, a small-signal model of MVSI is firstly derived and validated in this paper. The model has been validated to match the full-switch model in both steady-state and dynamic conditions. Next, a farm-level simulation based on the proved small-signal model was implemented to demonstrate grid-support services. Real irradiation date from NREL is used to show the operational capabilities of the system. As a result, the overall farm is simulated based on MATLAB/Simulink domain to check the performance for various cases namely: power transfer from dc-ac side, reactive power support during grid voltage sag and utilization of battery during grid voltage disturbance or partial shading over PV panels.

14 SOLAR ENERGY↗
DOE OSTI2022

North Carolina Water Utility Builds Resilience with Distributed Energy Resources

As the frequency and duration of grid outages increase, backup power systems are becoming more important for ensuring that critical infrastructure continues to provide essential services. Most facilities rely on diesel generators, which may be ineffective during long outages owing to limited fuel supplies and high generator failure rates. Distributed energy resources such as solar, storage, and combined-heat-and-power systems, coupled with on-site biofuel production, offer an alternative source of on-site generation that can provide both cost savings and resilience (i.e., the ability to respond to catastrophic events with longer-term consequences). A mixed-integer linear program minimizes costs and maximizes resilience at a wastewater treatment plant in Wilmington, North Carolina. We find that the plant can reduce life-cycle energy costs by 3.1% through the installation of a hybrid combined-heat-and-power, photovoltaic, and storage system. When paired with existing diesel generators, this system can sustain full load for seven days while saving $664,000 over 25 years and reducing diesel fuel use by 48% compared with the diesel-only solution. This analysis informed a decision by the Cape Fear Public Utility Authority to allocate funds for the implementation of a combined-heat-and-power system at the wastewater treatment plant in fiscal year 2023. Finally, the benefits of deploying hybrid combined-heat-and-power technologies and the utilization of on-site biofuel production extend, on a national scale, to thousands of wastewater treatment facilities and other types of critical infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

pvlib python: 2023 project update

pvlib python is a community-developed, open-source software toolbox for simulating the performance of solar photovoltaic (PV) energy components and systems. It provides reference implementations of over 100 empirical and physics-based models from the peer-reviewed scientific literature, including solar position algorithms, irradiance models, thermal models, and PV electrical models. In addition to individual low-level model implementations, pvlib python provides high-level workflows that chain these models together like building blocks to form complete “weather-to-power” photovoltaic system models. It also provides functions to fetch and import a wide variety of weather datasets useful for PV modeling. pvlib python has been developed since 2013 and follows modern best practices for open-source python software, with comprehensive automated testing, standards-based packaging, and semantic versioning. Its source code is developed openly on GitHub and releases are distributed via the Python Package Index (PyPI) and the conda-forge repository. pvlib python’s source code is made freely available under the permissive BSD-3 license. Here we (the project’s core developers) present an update on pvlib python, describing capability and community development since our 2018 publication (Holmgren, Hansen, & Mikofski, 2018).

14 SOLAR ENERGY↗
DOE OSTI2020

Single Model Characterization

A characterization framework has been developed, demonstrated and deployed for the quantification and categorization of all losses from Nominal Energy to Energy Delivered for operational photovoltaic power plants. Of equal importance is that the same characterization methods can be applied to a priori model outputs. Placing measured and modeled performance data in a single model framework allows a full quantitative, apples to apples, comparison of actual and expected performance.

14 SOLAR ENERGY↗
DOE OSTI2024

Review of Technical Photovoltaic Key Performance Indicators and the Importance of Data Quality Routines

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

14 SOLAR ENERGY↗
DOE OSTI2026

Different Damp-Heat-Induced Series Resistance Degradation Behaviors in Fielded Crystalline Silicon Photovoltaic Modules Due to Difference in Bill of Materials

This case study investigates mono-crystalline silicon modules from underperforming portions of a utility-scale photovoltaic power plant. Field-collected I-V curves and electroluminescence imaging suggested that increased series resistance was a primary factor driving module degradation. Selected modules were removed from the field for further analysis, including incremental damp heat accelerated testing, which confirmed a progression in series resistance degradation. Two distinct cell degradation behaviors became apparent during the investigation. Cross-sectional scanning electron microscopy (with elemental analysis) and scanning spreading resistance microscopy identified key differences between the two degradation mechanisms, primarily grid finger width and contact resistance. Additionally, the study highlights the reliability implications of retest requirements in International Electrotechnical Commission 61215 for material changes and how they may have mitigated the degradation observed at this site.

14 SOLAR ENERGY↗
DOE OSTI2025

End-to-end microgrid protection using distributed data-driven methods

This paper introduces an end-to-end microgrid protection framework that offers real-time system monitoring, fault-related decision making, and circuit breaker control. This is achieved through the design of distributed data-driven techniques based on the support vector machine method, where each relay is responsible for distributed data collection, fault detection, fault localization, and fault isolation. Local communication is established among neighboring relays, fostering cooperative fault localization and isolation. This decentralized design not only reduces the computational and communication requirements but also enables the adaptability of each relay under varying operational dynamics. The proposed end-to-end protection framework was validated using MATLAB/Simulink simulations on a 100% renewable microgrid, achieving an accuracy of 93.1% with response time of 0.0523 s, in protecting against a range of fault scenarios that are characterized by various types, locations, impedances, load conditions, photovoltaic power levels, and microgrid operating modes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Improving Low-Bandgap Tin–Lead Perovskite Solar Cells via Contact Engineering and Gas Quench Processing

Low-bandgap Sn/Pb ABX 3 perovskites have reached photovoltaic power conversion efficiencies >20%, but they usually have poor stability due to the common use of acidic poly(3,4-ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS) hole transport layers and A-site cation compositions containing methylammonium (MA). In this work, we develop a process to enable high-quality MA-free Sn/Pb perovskite films grown using a gas quenching process instead of the conventional antisolvents, which provides improved control of the film growth and eliminates wrinkling. Using this method in a device structure with poly[bis(4-phenyl)(2,4,6-trimethylphenyl)amine] (PTAA) instead of PEDOT:PSS as the hole transport layer, devices can reach efficiencies up to 20% mppt at 0.06 cm 2 and up to 17.5% mppt at 1 cm 2 active area. With these improvements, the devices are characterized for thermal stability and show 80% of the initial power output remaining after 4000 h at 85 °C.

14 SOLAR ENERGY↗
DOE OSTI2021

Surface Brightening in Eastern and Central China Since the Implementation of the Clean Air Action in 2013: Causes and Implications

Surface brightening has been observed in China since 2005. However, it remains unclear whether the brightening has accelerated recently in response to the strictest ever air pollution control policies since 2013. By combining intensive surface and satellite observations, we find an unprecedented rapid increasing trend in surface solar radiation (SSR) of 0.70–1.16 W m-2 yr-1 over the eastern and central China for 2014–2019. Using a novel method to identify the relative contributions of aerosol and cloud radiative effects to the SSR trends, we find that the strongly declining aerosol radiative effect due to the strict air pollution controls is the main cause of the upward SSR trends; cloud variations should not be the main reason. Distinction exists in seasonal trends of SSR, with decreasing trends in winter and increasing trends in other seasons. Air pollution controls play an important role in regulating SSR, which has valuable implications for photovoltaic power generation.

Shi, Hongrong↗
DOE OSTI2025

An AI-accelerated pathway for reproducible and stable halide perovskites

Halide perovskites (HPs) have remarkable optoelectronic properties, and in the last decade their photovoltaic power conversion efficiency and light-emitting diode efficiency have skyrocketed. Despite the surge in research on these burgeoning materials, two key challenges in the field remain: material irreproducibility and instability. Their behavior is especially dynamic in response to environmental stressors, due to complex interactions with the perovskite crystal lattice. Here, in this review, we survey the latest achievements in HP materials research accomplished with the assistance of artificial intelligence (AI), through the implementation of automated experimentation and machine learning (ML) data analysis. Automated synthesis and characterization tackle problems with material irreproducibility by systematically controlling parameters with very high precision, creating massive datasets, and allowing methodical comparisons from which unbiased conclusions can be drawn. AI can reveal otherwise unnoticed trends, inform future experiments with the highest potential information gain, and forecast future performance. The review concludes with a forward viewpoint of how human-assisted closed-loop laboratories and shared databases allow halide perovskite materials’ processing, properties, and performance to be potentially optimized with AI, accelerating the development of highly reproducible and stable optoelectronic devices.

Hering, Abigail R. [Univ. of California, Davis, CA↗
DOE OSTI2022

Nanometer-Scale Measurement of Grid Finger Electrical Conduction Pathways to Detect Series Resistance Degradation of Utility-Scale Silicon Solar Cells

Here, we report on an electrical conduction mechanism for series resistance (Rs) degradation observed in a utility-scale photovoltaic power plant by imaging the local resistance at the Ag/Si interface of the c-Si front metallization. Scanning spreading resistance microscopy imaging with nanometer (nm) scale spatial resolution revealed that the number of point or small-area electrical contacts decreased substantially in a degraded cell compared to an unaffected cell, demonstrating the root cause of Rs degradation. The decrease in electrical contacts is likely caused by a structural change - the Ag particles in contact with the Si cell (or via a nm-thickness tunneling glass layer) aggregate into bulk Ag, and a highly resistive glass frit forms a belt shape at the Ag/Si interface. This resistive belt, with a thickness of ~1 um, blocks the current conduction from the cell emitter to the Ag grid. Our microscopy results help to unravel unambiguously the degradation mechanism and demonstrate an example of the multiscale characterization approach for understanding degradation in field-weathered PV module.

14 SOLAR ENERGY↗
DOE OSTI2024

Short-Term Probabilistic Solar Forecasting via Reinforcement Learning over ECMWF

In this paper, we present an innovative reinforcement learning approach for short-term solar forecasting, leveraging data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The methodology begins with the application of the System Advisor Model (SAM) to transform various ECMWF numerical weather prediction members into predictive photovoltaic power generation. To enhance the precision of deterministic forecasting, we introduce a dynamic model selection algorithm based on Q-learning. This algorithm dynamically identifies and utilizes the most accurate ensemble member for forecasting purposes. Furthermore, we employ a support vector regression surrogate model with a Gaussian distribution to generate probabilistic forecasts, providing a holistic view of solar energy generation uncertainty. To expedite the training process and make it more practical for real-world applications, we integrate a rolling update workflow. This innovative workflow reduces the training period from months to a mere 19 days, making our method highly efficient. Numerical results of the case study show that in comparison to benchmark models, the proposed method improves the deterministic and probabilistic solar forecasting accuracy by up to 40.84% and 48.42%, respectively.

ensemble forecasting↗
DOE OSTI2021

Consensus International Solar Resource Standards and Best Practices Development

Standardization and best practices of data sets and models enable the industry to develop widely accepted protocols adapted to various stages of solar project development and operations. In collaboration with the International Energy Agency Photovoltaic Power Systems Programme Task 16, the National Renewable Energy Laboratory (NREL) developed and regularly updates the Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications. In collaboration with ASTM's radiometry subcommittee, NREL leads the development of various radiometric standards. These contribute to increased accuracy in the measurement of the solar resource available to conversion systems and boost the development and improvement of radiative models. These models can then provide the long-term geographic or climatic distribution of solar radiation for areas where measured solar data are not available. Further, these standards play a preeminent role in all aspects of solar energy projects, including standard conditions, methods and instrumentation, accelerated testing, and service lifetime of materials.

ASTM↗
DOE OSTI2024

Automatic Loss Factor Modeling and Attribution on Unlabeled PV Energy Data

We present a novel approach for modeling the loss factors of photovoltaic power generation systems (PV systems). This method is a white-box machine learning model built on convex optimization that is fast, interpretable, and auditable. It takes as an input the measured daily energy produced by the system, over a multi-year period, and returns a multiplicative decomposition model of the daily energy signal and full attribution of the total energy loss to each feature. The methods section of this paper has two major components: (1) the description of the signal decomposition (SD) model, expressed in the SD framework, and (2) the attribution of total energy losses via Shapley values. We validate the method on synthetic and open-source data sets and compare to similar methods from the literature.

artificial intelligence↗