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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Sparse Linear Solvers for Large-scale Electromagnetic Transient Simulations

Linear solvers form the basis for electromagnetic transient (EMT) simulations. There is a need to speed up EMT simulations as larger regions are analyzed using EMT simulations. For the same, the performance of linear solvers plays an important role. Exploiting the sparsity of the matrices generated in EMT simulations could assist with speed-up. Scalability is also crucial as power grids expand, demanding solutions capable of accommodating the increasing system size. Recent studies from the North American Electric Reliability Corporation (NERC) increasingly emphasize that EMT simulation models of the power grid will grow larger with the inclusion of power electronics components. Parallelisms in sparsity patterns exploit modern central processing units (CPUs), multi-core CPUs, and graphics processing units (GPUs) architectures in sparse solver designs. Therefore, this paper explores publicly available existing linear solvers and investigates their efficiency in large-scale power grid simulations. A large-scale power grid is developed by increasing the size of the IEEE 39 bus test system to up to 39000 bus systems.

Hsu, Kuan-Chieh↗
DOE OSTI2025

A Comprehensive Machine Learning Model for Metal–Ligand Binding Prediction: Applications in Chemistry and Biology

A machine-learning (ML) model that predicts metal–ligand binding constants was developed using the open-source Chemprop software. The model was trained on over 30,000 experimental log K 1 values, which include both protonation and metal–ligand stability constants, comprising over 3500 ligands and 10 2 metal ions from 73 total elements, thus generalizing beyond existing limited approaches, which focus only on specific metals or ligand families. The best-performing model included a combination of SMILES-based molecular representations along with descriptors for the metal ion and experimental conditions. It had an external test R 2 value of 0.942, and MAE value of 0.834. A “SMILES-only” simpler version also produced accurate predictions and preserved the binding trends, serving as a quick and easily accessible alternative for users without computational expertise. The SMILES-only model performed comparably to density functional theory (DFT) calculations but utilized a fraction of the computational resources. The model was successfully applied across diverse domains, including bioinorganic chemistry, heavy metal remediation, and sensor development and demonstrated its effectiveness as a rapid and reliable screening tool for both academic and industrial uses.

Ligands↗
DOE OSTI2026

The NREL Sensor Laboratory Detection of Hydrogen Emissions

The development of a functional hydrogen detection system is a multifaceted process that integrates hardware, deployments strategies, and analytics which can be supported by the NREL Sensor Laboratory: 1. Support of the design, validation and optimization of sensing prototypes; 2. Guide optimized sensing element development, including control electronics; 3. Laboratory testing to validate/optimize metrological performance (measurement range, detection limit, etc.); 4. Provide test sites for field deployments representative of real-world scenarios with controlled hydrogen releases; 5. Develop sensor placement and operation guidance; 6. Provide guidance on electronics to accommodate facility integration; 7. Electrical safety designs to allow for operation within restricted zones; 8. Integration into facility monitoring and control systems; 9. Guide incorporation of cyber security elements to protect facilities from malicious attacks; 10. Modeling and application of advanced analytics to detect and quantify emissions; 11. Higher Order dispersion models to guide sensor placement for reliable detection; 12. Advanced analytics for improved metrological performances, and to inform inverse modeling; 13. Market support and commercialization (national and international markets); 14. Commercial deployments in H2@SCALE markets (e.g., HUBs and other large-scale hydrogen markets); and 15. Leverage off international collaborations/partnerships (e.g., NREL is on the advisory board for the European initiative "pre-Normative Research on Hydrogen Releases Assessment"-NHyRA).

08 HYDROGEN↗
DOE OSTI2026

Innovative Interfacial Engineering for Simultaneous Enhancement in Efficiency, Stability, and Reliability in Perovskite Devices

The overarching objective of this project was to increase significantly the mechanical adhesion toughness of the key interfaces in PSCs, thereby improving the durability and reliability of PSCs and minimodules, while simultaneously enhancing their PCE. This is achieved by incorporating rationally-chosen self-assembled monolayers (SAMs) at key PSC interfaces. Inherently scalable methods for the deposition of MHP light-absorber thin films and SAMs are employed to ensure a viable path towards manufacturability. Furthermore, detailed understanding of mechanisms of PSCs and minimodules degradation, and SAMs-induced mitigation, is provided. As part of the project accomplishments, >60% increase in the adhesion toughness of the key PSC interfaces over their SAMs-free control counterparts was achieved. Also, PCEs >20% and >19% in rigid and flexible, respectively, single-junction PSCs (~1 cm 2 area) were achieved. It was shown that <20% PCE loss in flexible PSCs with SAMs can be achieved after bend testing. It was demonstrated that T80 (80% retention of initial PCE) of >5,000 hours in rigid PSCs (~1 cm2 area) can be achieved. Comprehensive understanding of the degradation mechanisms in PSCs was obtained. These strategies were adopted towards developing minimodules. PCE of >21% was obtained in an 6-cm 2 area minimodule, with T91 of 1,000 hours.

14 SOLAR ENERGY↗
DOE OSTI2025

Designing reinforcement learning algorithms for building HVAC control: From experimental observation to simulation comparisons

Advanced supervisory-level control with reinforcement learning (RL) is regarded as a promising solution for HVAC systems to minimize energy consumption while maintaining thermal comfort and indoor air quality. However, most RL applications were conducted in the simulation environment rather than real-world HVAC systems. This paper developed a value-based RL controller termed Deep Q-Network (DQN) for a typical central HVAC system and evaluated its performance in a building test facility. By comparing DQN with a rule-based controller, the study not only demonstrated the cases where DQN could properly maintain indoor comfort but also discussed possible reasons why DQN failed in some other situations. Recognizing the limitations of value-based RL algorithms from the experimental tests, a simulation study was conducted to compare DQN with an alternative RL approach, an actor–critic algorithm termed Deep Deterministic Policy Gradient (DDPG). In scenarios with a relatively large action space, DDPG outperformed DQN by requiring fewer computational resources and achieving better thermal comfort, lower energy consumption, and more stable control actions. The findings suggest that the ability of DDPG to handle continuous control variables more effectively allows for faster convergence in training and more precise control in practice, which enhances the overall efficiency and reliability of the HVAC system.

Guo, Fangzhou↗
DOE OSTI2025

High-Temperature Active Magnetic Bearing Development for Supercritical CO 2 Machinery Applications

Hermetic machinery utilizing gas bearings for MW-scale supercritical CO 2 (sCO 2 ) machinery applications can have significantly lower power loss and enable improved cycle efficiency compared to conventional machinery with oil-lubricated bearings. Active magnetic bearings (AMBs) are another option anticipated to have similar power loss and load capacity to gas bearings as well as offering larger mechanical tolerances, the ability to tune properties, and high reliability due to lack of mechanical wear. AMBs also have proven commercial experience at MW-scale, though environments for high-temperature sCO 2 power cycle machinery conditions are novel. Besides the potential impact of AMBs for sCO 2 turbomachinery, the technology also offers promising benefits for steam and gas turbines for power generation, compressors and expanders for industrial heat and power, and in other oil and gas and space applications. The goals of this project were to conceptual design an AMB and perform material testing. Conceptual designs for radial and thrust AMBs were produced based on a hermetically-sealed sCO 2 machinery waste-heat recovery (WHR) application for sizing and loads, and choosing a target design temperature of 540°C useful for high-temperature sCO 2 turbines for concentrating solar power (CSP) applications. Conceptual designs were initially developed for multiple radial and thrust AMBs with spreadsheet-based calculations before selecting one of each to develop further using higher-fidelity design methods for magnetic and structural performance. It was found that the radial AMB at 540°C was feasible, but the thrust AMB needed to be limited to 315°C for high-speed operation. The decision for a reduced-temperature thrust AMB was the result of several significant conclusions: 1) Hiperco 50A, originally chosen for good magnetic performance at high temperature, had insufficient strength for high-speed operation, so it was replaced with 17-4 PH. 2) The reduced magnetic performance from 17-4 PH yielded a larger bearing size, reducing the strength margin. 3) This ultimately led to a creative design implementing a more-compact E-core topology, compared to the original (conventional) C-core, and an integral shaft-disk with Hirth joint connection. These conceptual designs are unique for the size and temperature in CO 2 , relevant for MW-scale CSP applications. Long term, high temperature test data was generated for several materials, filling a void in the current body of literature. Corrosion and magnetic performance measurements were produced for PM materials (Alnico 5-7C, Alnico 9C, and SmCo) with and without nickel-coating for environments of high-temperature CO 2 up to 550°C at atmospheric pressure and 450°C at 103 bar for up to 6,000 hours. Corrosion measurements were also produced for Hiperco 50, a SM material relevant for AMB laminations, with and without C5 coating. Comparisons were also made for 450°C and 550°C, atmospheric pressure air exposures up to 5,000 hours. Results generally show that coatings can be effective at improving oxidation resistance of the bare materials, and Alnicos generally outperformed SmCo after high-temperature exposure. In addition to technical feasibility demonstrated by the design, economic feasibility was demonstrated by updating the TEA from the reference machine, re-evaluating it with CAPEX and OPEX to reflect estimated changes from process-lubricated bearings to AMBs. AMBs were shown to be comparable in performance to process-lubricated bearings, still showing a notable improvement over conventional machinery architecture with oil-lubricated bearings.

42 ENGINEERING↗
DOE OSTI2026

Data from: Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N₂O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N₂O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N2O fluxes from US cropland. Trained and validated on approximately 12,000 N2O chamber measurements at 17 U.S. Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N2O at both training (R² = 0.84, RMSE = 16.4 g N ha⁻¹ d⁻¹) and held-out testing sites (R² = 0.84, RMSE = 6.2 g N ha⁻¹ d⁻¹). Analyses identified six dominant N₂O drivers: soil organic carbon (SOC), NH₄⁺, NO₃⁻, water-filled pore space (WFPS), soil temperature, and biomass production. Wet, warm soils produced large N₂O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N₂O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

agricultural sciences↗
DOE OSTI2024

Investigating the Crosslinking, Degradation, and Adhesion Behavior of Photovoltaic Encapsulants Under Thermal Accelerated Aging

Degradation of photovoltaic (PV) module encapsulant characteristics that lead to mechanical embrittlement and delamination remains a cause of failure in solar installations. A multiscale reliability model connecting the encapsulant mechanical and fracture properties to the degraded molecular structure and interfacial bonding to adjacent solar cell and glass substrates was previously published. The model, developed primarily for poly(ethylene-co-vinyl acetate) acetate (EVA) encapsulants, remains to be experimentally validated. Determining the degradation and crosslinking kinetics of alternative encapsulants, such as polyolefin elastomer (POE) and EVA/POE/EVA composites (EPE), can generalize the model. In this work, we subject fully cured EVA, POE, and EPE encapsulants to accelerated thermal aging to determine how high temperatures impact reaction kinetics. An increase in gel content (crosslinking) and decrease in crystallinity of the encapsulants under hot-aerobic (90 degrees C, 22% RH) and hot-anaerobic (90 degrees C, sealed in N 2 air) aging were observed, even in the absence of UV and crosslinking initiators. Fourier transform infrared spectroscopy (FTIR)-attenuated total reflectance analysis showed insignificant encapsulant degradation, demonstrating the critical role of UV and moisture in accelerating degradation. Adhesion testing performed on coupon-level specimens (cell/encapsulant/glass laminates) showed decreases in adhesion energy, Gc , from 5000 h of hot-dry (90 degrees C, ~1% RH) and hot-humid (90 degrees C, 60% RH) aging. POE coupons demonstrated the best stability, followed by EPE then EVA. For EVA and POE, hot-humid aged coupons experienced a larger decrease in Gc due to enhanced hydrolytic degradation. Hot-dry aging condition demonstrated that thermal degradation of the interface could be significant even if the encapsulant experiences negligible degradation in the absence of UV and elevated humidity.

accelerated aging↗
DOE OSTI2025

Ensemble cure kinetics network (ECK-Net): A method to derive cure kinetics of thermosetting resin

This paper introduces an Ensemble Cure Kinetics Network (ECK-Net), a neural network (NN)–based framework for modeling the cure kinetics of thermosetting resins within a phenomenological context. ECK-Net replaces traditional analytic models, which require extensive chemical insight and multiple isothermal/non-isothermal experiments, with a data-driven surrogate that maps nonlinear relationships between temperature, degree of cure, and reaction rate from differential scanning calorimetry data. The proposed approach predicts input-dependent kinetic coefficients of a generalized nth-order reaction equation rather than reaction rates directly, enabling a single unified model to represent various epoxy systems without relying on iso-conversional analysis or predefined functional forms. To ensure robustness, multiple independently trained networks under different random initializations are blended through an ensemble strategy, effectively mitigating the stochastic variability inherent to neural networks. The framework is validated using experimental datasets from multiple resin systems, including aerospace-grade materials (Toray 3900-2, Cycom 5320-1, and Hexcel 8552) and a windmill-grade resin (RIMR 035c). The model accurately reproduces the temporal evolution of the degree of cure under manufacturers’ recommended cure cycles across all tested resins systems, yielding Pearson’s correlation coefficients of 0.992, 0.994, 0.993, 0.997, respectively. To demonstrate process-level applicability, the trained network was implemented within the Abaqus environment to simulate out-of-autoclave (OOA) curing process of the CFRP panel composed of Toray T830H-6K/3900-2D prepreg. The simulation results showed excellent agreement with experimental temperature response (maximum peak temperature, simulation: 189.6 °C, experiment: 188.5 °C) and the final degree of cure (simulation: 0.948, experiment: 0.960 ± 0.013), confirming ECK-Net’s capability as a reliable alternative to conventional cure kinetics modeling methods.

Composite curing↗
DOE OSTI2025

ACTIVE

The Automated Control Testbed for Integration, Verification, and Emulation (ACTIVE) framework is a software platform designed to support the optimized operation and management of a wide range of building types. It enables the development, testing, and validation of diverse control strategies, including AI-based, rule-based, and model-based approaches. The platform facilitates a seamless transition from simulation-based evaluation of control strategies to real-world field validation and deployment. ACTIVE supports the full building management lifecycle, encompassing data acquisition and management, system monitoring, optimized control, adaptive learning services, device dispatch and coordination, as well as advanced analytics and visualization. Together, these capabilities provide an integrated environment for improving building performance, operational efficiency, reducing energy cost, and reliability.

Smith, Robert [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2024

Ultrafast High Voltage Kicker System Hardware for Ion Clearing Gaps

Jefferson Lab (JLab) will collaborate with Radiabeam, LLC in a DOE SBIR Phase II Project (DOE Grant No. DE-SC0019684, title: “Ultrafast High Voltage Kicker System Hardware for Ion Clearing Gaps”) to develop and test a MHz high voltage nanosecond kicker system that enables the time structure required by the ion traps for high current electron beam cooling. High current (in particular energy recovery) linac based electron cooling facilities for medium to high energy bunched proton or ion beams are of great interest for the recently funded Electron-Ion Collider (EIC) to which Jefferson Lab plays a critical role. The successful execution of this project will enable the capability to mitigate the ion trapping effect and circumvent a major obstacle preventing reliable operation of the crucial cooling facilities.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Establishing model credibility for process-microstructure-property relationships in additive manufacturing using exascale computing

Additive Manufacturing (AM) of alloys holds significant promise as a disruptive technology in various industries, yet its adoption is often hindered by challenges in achieving consistent part quality. These issues are primarily due to the complex process-microstructure-property (PSP) relationships inherent to AM. Computational models can greatly aid in understanding these relationships, but their widespread impact and adoption has been limited by a lack of validated, open-source, and computationally efficient PSP modeling frameworks and hardware limitations. Here, this study leverages the ExaAM software suite and data from the AMBench-2018 series of laser powder bed fusion (LPBF) benchmark experiments to perform a comprehensive model assessment, including verification, validation, sensitivity analysis, and uncertainty quantification. The RADICAL-EnTK workflow manager was used to perform an ensemble of heat transport, solidification, and mechanical response simulations on the exascale computer Frontier, considering uncertainties in critical model inputs such as laser spot size and nucleation parameters, and consisting of 125 explicit grain structure simulations and 7875 crystal plasticity simulations. For a selected location within the Inconel 625 AMBench-2018 test artifact, sensitivity analysis and uncertainty quantification were performed using the predicted distributions of grain structure and mechanical properties. Qualitative agreement was found between the predicted grain size and texture and the observed AMBench-2018 microstructure, the mean predicted yield stress was within 5% of the experimental measurement mean, and the mean predicted engineering stress at 5% strain was within 10% of the experimental measurement mean. The insights gained from development and validation of the ExaAM PSP modeling framework will help guide future directions for enhancing the credibility and reliability of PSP models in AM, thereby accelerating the adoption of AM technologies in various industries.

Additive manufacturing↗
DOE OSTI2026

Measurement of mean excitation energies of neutrino-relevant materials

Modern neutrino experiments require precision reconstruction of events. A crucial component of this reconstruction is the stopping power for charged particles, calculated using the Bethe equation. The main free parameter of the Bethe equation is the mean excitation energy (the "I-value"), which in most cases cannot be calculated, but must be measured for each substance. In many cases, the values are derived from very old experiments with large quoted uncertainties, or worse, small quoted uncertainties and inadequate treatment of systematics. Even if the tabulated values were reliable to the necessary degree, the I-value is affected by the phase of the substance and by chemical bonding, and only rough heuristics have been developed to convert measurements of single elements into I-values for compounds or from one phase to another. Modern neutrino experiments which need to measure an absolute energy scale, while lacking calibration sources, suffer the most from uncertainties in the I-value. DUNE is a primary example. We are performing measurements with the 400MeV Fermilab LINAC beam at the Irradiation Test Facility. We describe our efforts to measure the I-value of liquid argon, as well as those for several other materials used in past, present and potential future neutrino experiments, including water, NOvA scintillator, MINOS steel, zirconium, and molybdenum. These measurements use a set of degraders to scan the beam energy around the Bragg peak for each substance to determine the proton range. Experimental results are compared to results from Geant4 and FLUKA to determine the I-value.

Strait, Matthew L. [Fermilab] (ORCID:0000000157088↗
DOE OSTI2025

Utilizing machine learning to predict tensile ductility and yield strength of CoNiV-based multi-principal elements alloys

This study explores the use of machine learning (ML) as a computational tool to accelerate the design of multi-principal element alloys (MPEAs) with improved tensile elongation. An ML model was trained using available experimental data from the literature along with theoretically derived features to predict yield strength (YS) and ductility. A subset of ML-predicted compositions—CoNiVFe, CoNiVTi, CoNiVTiFe, and CoCrNiVTi—was synthesized and evaluated through tensile testing. The ML model underpredicted YS by approximately 20–30 % and overpredicted ductility by 60–70 % for Ti-containing alloys. Microstructural analysis revealed that Ti segregation at interdendritic regions contributed to early fracture, leading to discrepancies in ductility predictions. Ti segregation at these regions likely drives the increased YS due to segregation strengthening. In contrast, the CoNiVFe alloy showed good agreement with both experimental YS and elongation, with prediction errors of ∼10.2 % and ∼20.7 %, respectively. Microstructural characterization revealed minimal segregation in this alloy, suggesting that the ML model can reliably predict the properties of alloys with little to no segregation. These findings highlight the capability of ML in predicting YS with good accuracy but underscore its limitations in capturing defect-driven failure mechanisms such as segregation-induced embrittlement.

36 MATERIALS SCIENCE↗
DOE OSTI2026

pyEF: A Python Framework for QM and QM/MM Atom-Wise Electric Field Analysis

We introduce pyEF, a software package for computing molecular electric fields, electrostatic interaction energies, and electrostatic potentials from quantum mechanical (QM) atom-centered multipole expansions with atom-wise decomposable contributions. We demonstrate the computational efficiency and accuracy of this QM-derived electric field evaluation tool through several tests. To assess the influence of the underlying QM method and charge partitioning scheme on these electrostatic quantities, we analyze over 250 configurations of an acetone solute molecule in five solvents of variable polarity. We find that electric field calculations are highly sensitive to the choice of charge partitioning method. Even among real-space charge schemes, acetone Stark tuning rates differ by up to a factor of 2. Benchmarking computed solvent dipole moments against experimental bulk values, we conclude that the CM5, ADCH, and Hirshfeld-I charge schemes most reliably capture solvent electrostatics and therefore provide a more faithful foundation for computing electric fields. When constructed from these real-space charges, electric fields are nearly insensitive to basis set size and monotonically increase in magnitude with higher Fock exchange. We also demonstrate efficient convergence of QM electrostatics when more distant molecules are represented solely by MM point charges, reducing computational overhead. Leveraging these findings, we demonstrate the use of pyEF to deduce environmental effects on a transition metal complex from a Ga 4 L 6 12– nanocage and quantify the dominant role of organic linkers in orchestrating electrostatic preorganization.

electric fields↗
DOE OSTI2025

Data-driven model validation for neutrino-nucleus cross section measurements

Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We review the difficulties associated with modeling neutrino-nucleus interactions that lead to a dependence on event generators in oscillation analyses and cross section measurements alike. We then describe data-driven model validation techniques intended to address this model dependence. The method relies on utilizing various goodness-of-fit tests and the correlations between different observables and channels to probe the model for defects in the phase space relevant for the desired analysis. These techniques shed light on relevant mismodeling, allowing it to be detected before it begins to bias the cross section results. We compare more commonly used model validation methods which directly validate the model against alternative ones to these data-driven techniques and show their efficacy with fake data studies. These studies demonstrate that employing data-driven model validation in cross section measurements represents a reliable strategy to produce robust results that will stimulate the desired improvements to interaction modeling.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Next Generation Solid Oxide Fuel Cell Module Development

The overall objective of this project was to develop a transformative Solid Oxide Fuel Cell (SOFC) building block configuration comprised of multi-stack arrays that can be utilized in large-scale power plants. This transformative design signified the benefits of lower performance degradation coupled with improved reliability, low cost, smaller packaging for easier transport and installation, and improved maintenance and field serviceability characteristics. The project goal was to design and fabricate a scalable hot module for housing an array of SOFC stacks and to demonstrate the characteristics of the module gas distribution, insulation and instrumentation, and DC power take-off. The approach was to validate the design of a stack prototype scalable to megawatt (MW) class systems using FuelCell Energy’s Compact SOFC Architecture (CSA) stacks. The scope of work was intended to design, build, and test a compact and low-cost multi-stack sub-module with flexibility to house CSA stacks and scalable to 350 kW which could ultimately be deployed in construction of MW-class systems.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Control Strategies and Validation in the Hybrid Optimization and Performance Platform (HOPP)

The Hybrid Optimization and Performance Platform (HOPP) is a tool that simulates hybrid power plants in various configurations, and also calculates the financial feasibility of these plants. This report outlines an overview of HOPP and the energy storage dispatch strategies available. It then presents three case studies which demonstrate different applications of HOPP. The first case looks at the profitability of hybrid power plants in different locations in the USA. The second case examines the availability of hybrid power plants to provide energy reliability services. The third case presents a plant that produces both hydrogen and electricity, and demonstrates a dispatch strategy that chooses the most profitable energy vector based on price signals. The next section shows the validation of HOPP on operational data, using data from both unit-scale and utility-scale power plants. This validation process demonstrated that HOPP can simulate the power output of both wind and solar PV plants at both scales with comparable fidelity to an existing commercial software tool. Finally, HOPP is applied in a field test which applies an optimal dispatch strategy to a physical battery in a unit-scale hybrid plant at NREL. HOPP's optimal dispatch strategy, applied in a real-world setting, improved this hybrid plant's ability to meet a load signal while minimizing operational costs.

14 SOLAR ENERGY↗