MultiSolSegment: Trained Model Weights
The resulting model weights for MultiSolSegment to be loaded into the python code for other to use.
14 SOLAR ENERGY↗
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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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Estimating Flexibility Envelopes for Residential Customers From Utility Smart Meter Data: Preprint
Demand response from residential customers has significant potential to support power system operations, but accurate flexibility estimation is challenging due to the limited resolution of advanced metering infrastructure (AMI) data. Most utility AMI measurements are recorded at hourly intervals, with only a small portion at higher resolutions, and even fewer households have appliance-level energy usage data. To address this issue, this paper proposes a two-stage long short-term memory (LSTM) framework for estimating household flexibility envelopes from low-resolution AMI data. In the first stage, the heating, ventilating, and air-conditioning (HVAC) load and non-HVAC loads are estimated by using a model trained on a small set of households with appliance-level profiles. These estimated data are then used to compute the upper- and lower-flexibility bounds, which are subsequently down-sampled to lower-resolution data. In the second stage, these flexibility bounds serve as training inputs for another LSTM model, enabling direct prediction of flexibility envelopes for households with only hourly AMI data. This method is validated using Pecan Street data from two different areas, and the results demonstrate its applicability and effectiveness.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Catalyzing deep decarbonization with federated battery diagnosis and prognosis for better data management in energy storage systems
Industrial data analytics methods play a central role in improving energy storage performance and efficiency, impacting the future of electrified transportation and renewable electricity generation. However, significant challenges hinder the large-scale deployment of batteries. Conventional methods rely on centralized collection and processing of fleet-level data, leading to database size issues and privacy concerns due to potential data breaches. To enable scalable deployment of battery management systems, this article proposes a federated battery diagnosis and prognosis model, which distributes the processing of battery standard current-voltage-time-usage data in a privacy-preserving manner. Instead of transferring the raw data, this approach communicates only the locally processed parameters, thus reducing communication load and preserving data confidentiality. The federated model offers a paradigm shift in battery health management through privacy-preserving distributed methods for battery data processing and lifetime prediction, ensuring the reliable and sustainable deployment of lithium-ion batteries in a rapidly evolving world.
asset health management↗
Visibility-enhanced model-free deep reinforcement learning algorithm for voltage control in realistic distribution systems using smart inverters
Increasing integration of distributed solar photovoltaic (PV) into distribution networks could result in adverse effects on grid operation. Traditional model-based control algorithms require accurate model information that is difficult to acquire and thus are challenging to implement in practice. Here, this paper proposes a surrogate model-enabled grid visibility scheme to empower deep reinforcement learning (DRL) approach for distribution network voltage regulation using PV inverters with minimal system knowledge. In contrast to existing DRL methods, this paper presents and corroborates the adverse impact of missing load information on DRL performance and, based on this finding, proposes a surrogate model methodology to impute load information utilizing observable data. Additionally, a multi-fidelity neural network is utilized to construct the DRL training environment, chosen for its efficient data utilization and enhanced robustness to data uncertainty. The feasibility and effectiveness of the proposed algorithm are assessed by considering DRL testing across varying degrees of observable load information and diverse training environments on a realistic power system.
14 SOLAR ENERGY↗
Fractionation of UF6 and daughter progeny in storage cylinders from external heating
Abstract Uranium hexafluoride (UF 6 ) is a significant concern for material accountancy and verification in the international safeguards community. Verification of the contents of UF 6 cylinders is generally attempted with gamma spectroscopy but the current methods assume a uniform, homogeneous UF 6 mass distribution within the cylinder. In this work, it was found experimentally and confirmed via modeling, that under an external heat load (the sun), the UF 6 and its daughter products undergo fractionation in the cylinder. This fractionation of the UF 6 and daughter products can cause an errant measurement of the enrichment of the cylinder when using the current verification methods.
38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
Energy storage planning for enhanced resilience of power systems against wildfires and heatwaves
Extreme weather events pose significant risks to power grid stability due to their severe consequences and potential for widespread failures. Energy storage systems hold great potential for enhancing grid resilience against such events by providing reliable power during peak demand periods. However, accurately quantifying the size, location, and investment costs of new energy storage assets is a complex task, as energy storage planning decisions depend on the investment choices of other generation technologies and the integration of new transmission projects. Here, this paper presents a novel capacity expansion planning framework that simultaneously optimizes investments in energy storage, generation, and transmission, determining their optimal size, location, and type, while incorporating extreme weather events into long-term planning. More specifically, our stress-event-informed planning framework integrates the impact of heatwaves and wildfires into the planning process, identifying least-cost investment solutions that comply with policy goals and enhance grid resilience. The proposed framework employs machine-learning-based modeling to project heatwave-induced loads and performance-based risk assessment to evaluate wildfire-driven transmission line derates. Using industry-standard datasets to accurately represent the transmission topology of the Western Interconnection (WI) system, the proposed framework is applied to the WI 40-zone system, with investment decisions reported for the years 2030, 2035, and 2040. Simulation results reveal that with just a 10% increase in investment costs, resilience against extreme events can be significantly improved, with investment decisions heavily favoring energy storage, particularly 4-hour energy storage systems.
25 ENERGY STORAGE↗
Low Activity Waste Glass Optimization with Property Models from Machine Learning, Part 2: Experimental Validation and Active Learning
The United States Department of Energy is responsible for managing legacy nuclear waste stored in underground tanks at the Hanford Site. To treat the waste, it is planned as the current baseline to separately vitrify low-activity waste (LAW) and high-level waste fractions. Previously, machine learning (ML) based glass property models (e.g., chemical durability, viscosity, electrical conductivity and SO3 solubility) were developed with prediction uncertainties. A waste glass optimization approach was then established to enable the capability of using these ML models in LAW glass formulation. In this study, the previous ML models were first experimentally validated, and the results were incorporated back into the database to update the ML models. The updated models and formulations showed increased waste loading while reducing the failure rate, demonstrating improved predictive accuracy, reduced uncertainties, and the effectiveness of active learning in guiding high-dimensional, nonlinear LAW glass design. This represents the first experimental validation of ML based LAW glass formulation, with practical benefits such as higher waste loading, shorter mission duration, and lower operational risk.
Lu, Xiaonan (ORCID:0000000179708148)↗
Pseudo-viscous modeling of transport in dense granular flows for thermal energy storage applications
Dense, granular flows were examined to effectively capture and model bulk viscous properties in thin packed beds. A modified Couette cell with particle image velocimetry was used to experimentally determine pseudo-viscosity properties of four particulate media with varying morphologies: (1) iron oxide-coated SiO 2 particles, (2) CARBOBEAD CP30-60 particles, (3) CARBOBEAD CP40-100 particles, and (4) Al 2 O 3 beads. The pseudo-viscosity functions were fitted using a power law to correlate the measured shear stress as a function of measured shear rate. The pseudo-viscous functions were used as inputs to computation fluid dynamics models for a single-phase viscous fluid to predict granular flow profiles. Steady-state free surface velocity profiles at angular velocities <7 rad/s predicted by the model were in good agreement with the experimental particle image velocimetry measurements, resulting in Pearson correlation coefficients of 0.97 for iron-oxide coated SiO 2 particles and 0.95 for CP30-60 particles. As a result, this alternative approach to measuring pseudo-viscous properties under shearing and modeling bulk transport behavior of granular flow using computation fluid dynamics model offered significant reduction in computational load compared to discrete element methods.
14 SOLAR ENERGY↗
Role of dislocations on martensitic transformation temperatures and microstructure: A molecular dynamics study
Microstructure and defects strongly affect martensitic transformations in metallic alloys. Significant progress has been made in understanding the atomic-level processes that control the role of grain boundaries and precipitates in these solid-to-solid phase transformations. Yet, the role of dislocations and their structures on martensitic transformation temperature and the resulting microstructure remains unclear. Therefore, we used large-scale molecular dynamics simulations to study the forward and reverse transformation of a martensitic material modeled after Ni63Al37 under cyclic thermal loading. The simulations reveal that dislocations in the austenite phase act as one-dimensional seeds for the martensite phase, which is present at temperatures significantly above the martensite start value. We find a reduction in the dislocation density during cyclic thermal loading, which results in the increase in martensite and austenite transition temperatures, in agreement with experiments. Importantly, we extracted a critical martensitic nuclei size for developing stable domains and found that relatively low dislocation densities are needed to grow independent martensitic variants resulting in a multi-domain structure.
Physics↗
A Hierarchical OPF Algorithm with Improved Gradient Evaluation in Three-Phase Networks
Linear approximation commonly used in solving alternating-current optimal power flow (AC-OPF) simplifies the system models but incurs accumulated voltage errors in large power networks. Such errors will make the primal-dual type gradient algorithms converge to solutions with voltage violation. In this paper, we improve a recent hierarchical OPF algorithm that rested on primal-dual gradients evaluated with a linearized distribution power flow model. Specifically, we propose a more accurate gradient evaluation method based on an unbalanced three-phase nonlinear distribution power flow model to mitigate the errors arising from linearization. The resultant gradients feature a blocked structure that enables our development of an improved hierarchical primal-dual algorithm to solve the OPF problem. Numerical results on the IEEE 123-bus test feeder and a 4,518-node test feeder show that the proposed method can enhance voltage safety at comparable computational efficiency with the linearized algorithm.
approximation algorithms↗
Adaptive Power Flow Approximations With Second-Order Sensitivity Insights
The power flow equations are fundamental to power system planning, analysis, and control. However, the inherent non-linearity and non-convexity of these equations present formidable obstacles in problem-solving processes. To mitigate these challenges, recent research has proposed adaptive power flow linearizations that aim to achieve accuracy over wide operating ranges. The accuracy of these approximations inherently depends on the curvature of the power flow equations within these ranges, which necessitates considering second-order sensitivities. In this paper, we leverage second-order sensitivities to both analyze and improve power flow approximations. We evaluate the curvature across broad operational ranges and subsequently utilize this information to inform the computation of various sample-based power flow approximation techniques. Additionally, we leverage second-order sensitivities to guide the development of rational approximations that yield linear constraints in optimization problems. In conclusion, this approach is extended to enhance accuracy beyond the limitations of linear functions across varied operational scenarios.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Modeling the Moisture Content and Dry Matter Loss in Dynamic Woody Biomass Storage Piles with Variable Extraction
The urgent need to mitigate climate change has spurred significant interest in renewable energy sources. This paper explores the storage and processing of woody biomass for biofuel production, considering the dynamic nature of biomass piles in real-world scenarios. A model has been developed to analyze moisture content changes and dry matter loss in woody biomass stored in piles prior to processing, taking into account varying extraction methods and environmental conditions. Case studies that examine the effects of different unpiling methods (FIFO, LIFO, and homogeneous) on moisture content and dry matter loss under various feedstock arrival rates and weather conditions are presented. Results indicate that unpiling methods significantly impact moisture content, with LIFO typically resulting in higher moisture content due to the utilization of fresher feedstock. Dry matter loss increases with pile size and time, emphasizing the importance of accurate modeling for assessing carbon emissions and feedstock quality. Furthermore, the model highlights the importance of process loading order and extraction methods in biomass storage, suggesting potential cost benefits associated with decreased moisture content. The difference between different extraction methods could vary the moisture content in the feedstock reaching the biofuel process by as much as 37.6%, however dry matter loss varies minimally for realistic pile changes. Overall, this research contributes to a better understanding of biomass storage dynamics and informs sustainable biofuel production practices.
Niska, Janel↗
How Improved Forecasting Can Increase the Bulk Power System Value of Price-Responsive Electric Vehicle Managed Charging
Personal light-duty vehicle (LDV) electric vehicle managed charging (EVMC) can reduce power system costs by better aligning electric vehicle (EV) charging with locations and times of low energy cost or infrastructure use. The need to coordinate charging demand across thousands to millions of vehicles while preserving mobility service is a barrier to realizing the value of EVMC. Price-responsive dispatch mechanisms like time-of-use rates (TOU) and hourly real-time prices (RTP) are attractive compared to direct load control (DLC) because they only require one-way communications and local controls. However, increasing participation in price responsive mechanisms can induce costly-to-serve spikes in load and otherwise increase, rather than decrease, production costs. We quantify the ability of improved EVMC forecasting to sustain savings from price responsive mechanisms beyond the limit of 14% of LDVs actively participating observed in previous work. Perfect forecasting of price-responsive EV load makes TOU and RTP value-competitive with a low-error DLC formulation with up to 27% (within-week flexibility) to 45% or more (within-session flexibility) of LDVs participating in EVMC in an envisioned New England power system with 84% clean energy. Additional costs of implementing DLC should be no more than tens of dollars per vehicle-year if DLC is to be value-competitive with accurately forecast price-responsive EVMC for double-digit percentage shares of LDVs participating.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Side-by-Side Comparison of Subhourly Clipping Models
Over the past several years there have been numerous attempts at quantifying the inherent power clipping of inverters due to sub-hourly irradiance variability that is not captured in hourly PV performance models. Different models have been proposed to correct for these clipping losses in PV performance estimates, including matrix lookup models, distribution modeling of the PV power performance within a given hour, and machine learning methods. To date, there have been few comprehensive quantitative comparisons of these inverter clipping correction modeling approaches to evaluate the effectiveness of these approaches in predicting the actual behavior of PV system inverter clipping. In this study, we perform such a comparison, evaluating the Allen and Walker correction loss modeling approaches recently implemented in the System Advisor Model (SAM) against clipping losses modeled with 1-minute climate data. These comparisons were performed across a variety of climate locations and inverter loading ratios to thoroughly analyze the effectiveness of these modeling approaches relative to each other. Results from this analysis reveal that both clipping correction approaches improve annual energy accuracy to within 2% of 1-minute modeled energy yield. The two models predict annual clipping loss more accurately than simple hourly power limit clipping, with the Allen method typically being slightly more accurate at typical ILR values and the Walker method often being slightly more accurate at high ILR values The models can improve accuracy over the status quo clipping approach up to 3 percentage points in systems with ILR of 2.0, showing the importance of this modeling factor in energy yield estimates.
ENERGY PLANNING, POLICY, AND ECONOMY,MATHEMATICS A↗
Toward engineering lattice structures with the material point method (MPM)
This study examines the potential of two variants of the material point method—the generalized interpolation material point (GIMP) and dual domain material point (DDMP) methods—in developing a robust computational framework for engineering lattice structures under different loading conditions. The study begins with assessing the ability of the two methods in predicting elastic buckling phenomena using column geometries with and without initial geometric imperfections. The results indicate that both methods effectively capture buckling phenomena when initial geometric imperfections are introduced. After this verification step, we create several models of tetrahedral lattice structures with varying strut diameter and orientation and subject them to quasi-static loading. We then validate the numerical results using laboratory test results. The results show that, while both methods accurately predict load–displacement curves in the pre-buckling regime, their predictive capabilities diminish in the post-buckling regime. Through visual comparison between the numerical and experimental deformed shapes, it appears that the discrepancies between model and experimental results are attributed to initial geometric imperfections in the lattices that occurred during 3D printing. We then establish a second set of lattice models where different types of initial geometric imperfections are considered. The results from these models show that imperfections have a negligible influence in the pre-buckling regime but affect the behavior considerably in the post-buckling regime. As a final step in this work, we subject the lattice models to impact loading and employ hypothetical soft and stiff materials. These results show that the lattice stiffness, which depends on material stiffness, strut diameter, and orientation, significantly influences the ability of a lattice structure to resist impact. In particular, we find that a stiffer lattice (i.e., one made with a stiff material and thicker struts) is capable of absorbing more energy than a softer one during impact. Although material nonlinearities, inelasticity, and detailed contact formulations are not considered in this study, the findings obtained herein lay the groundwork for engineering lattice structures under extreme loading conditions through a simulation-driven framework based on particle-based methods.
97 MATHEMATICS AND COMPUTING↗
Kinetics of HCP-BCC Phase Transition Boundary in Magnesium at High Pressure
Under high pressures, many crystalline metals undergo solid–solid phase transformations. In order to accurately model the behavior of materials under extreme loading conditions, it is essential to understand the kinetics of phase transition. Using molecular dynamics simulations, this work demonstrates the feasibility of characterizing the speeds of a moving phase boundary using atomistic simulations employing a suitable empirical potential for single-crystal magnesium. The model can provide temperature- and tensorial stress-dependent velocity of a moving phase boundary as a rate-limiting contribution to the kinetics of phase transformation in continuum codes. Results demonstrate that a nonlinear interaction exists between plasticity and phase transition, facilitating a jump in the velocity of a moving phase boundary, facilitated by activated plastic deformation mechanisms.
36 MATERIALS SCIENCE↗
Side-by-Side Comparison of Subhourly Clipping Models: Preprint
Over the past several years there have been numerous attempts at quantifying the inherent power clipping of inverters due to inter-hourly irradiance variability that is not captured in hourly PV performance models. Different models have been proposed to correct for these clipping losses in PV performance estimates, including matrix lookup models, distribution modeling of the PV power performance within a given hour, and machine learning methods. To date, there have been few comprehensive quantitative comparisons of these inverter clipping correction modeling approaches to evaluate the effectiveness of said approaches in predicting the actual behavior of PV system inverter clipping. In this study, we perform such a comparison, evaluating two different clipping correction loss modeling approaches recently implemented in the System Advisor Model (SAM) against clipping losses modeled with 1-minute climate data. These comparisons will be performed across a variety of climate locations and inverter loading ratios to thoroughly analyze the effectiveness of these modeling approaches relative to each other. Results from this analysis reveal that both clipping correction approaches improve annual energy accuracy to within 2% of 1-minute modeled energy yield. The models can improve accuracy up to 3% in systems with ILR of 2.0, showing the importance of this modeling factor in energy yield estimates.
clipping↗
OC6 project Phase IV: validation of numerical models for novel floating offshore wind support structures
Abstract. This paper provides a summary of the work done within Phase IV of the Offshore Code Comparison Collaboration, Continued with Correlation and unCertainty (OC6) project, under International Energy Agency Wind Technology Collaboration Programme Task 30. This phase focused on validating the loading on and motion of a novel floating offshore wind system. Numerical models of a 3.6 MW horizontal-axis wind turbine atop the TetraSpar floating support structure were compared using measurement data from a 1:43-Froude-scale test performed in the University of Maine's Alfond Wind–Wave (W2) Ocean Engineering Laboratory. Participants in the project ran a series of simulations, including system equilibrium, surge offsets, free-decay tests, wind-only conditions, wave-only conditions, and a combination of wind and wave conditions. Validation of the models was performed by comparing the aerodynamic loading, floating support structure motion, tower base loading, mooring line tensions, and keel line tensions. The results show a relatively good estimation of the aerodynamic loading and a reasonable estimation of the platform motion and tower base fore–aft bending moment. However, there is a significant dispersion in the dynamic loading for the upwind mooring line. Very good agreement was observed between most of the numerical models and the experiment for the keel line tensions.
17 WIND ENERGY↗