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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Coordination of Energy Storage and Distributed Generation for Voltage Control and Peak-valley Filling

The increasing penetration of distributed energy resources (DERs) in distribution network (DN) poses challenges on voltage control. In addition, the growing integration of DERs also reshapes the traditional load profile. To comprehensively address the voltage control and peak-valley filling in DN, this paper proposes a model predictive control (MPC) based optimization framework. The proposed method can achieve coordinated voltage control and peak-valley filling by adjusting the reactive power output from distributed generations (DGs) and the charging/discharging of energy storage systems (ESS). The performance of the proposed method is demonstrated by simulations on a modified IEEE-123 bus system.

Zhang, Zhengfa [University of Tennessee, Knoxville↗
DOE OSTI2026

Adaptive Reinforcement Learning (ARL) Control of a Multi-port Resonant Converter in UAV Systems

This study presents an adaptive reinforcement learning (ARL) control framework for a multi-port resonant converter used in hybrid unmanned aerial vehicle (UAV) power systems. The converter integrates high-frequency half-bridge input ports connected to a rectified engine–generator set and a battery energy storage system, along with a semi-bridgeless active rectifier supplying the propulsion load. A deep RL agent is trained to dynamically regulate inter-port phase-shift commands in real time based on flight conditions and load power demand. The ARL controller autonomously identifies phase-shift combinations that maximize conversion efficiency while maintaining stable and coordinated power flow, even under rapidly varying operating scenarios. This data-driven approach eliminates the need for explicit system modeling or extensive manual tuning and enables coordinated control among multiple power ports without inter-port communication. Experimental results validate that the ARL based strategy achieves reliable power sharing and consistently high-efficiency operation across diverse UAV operating conditions.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2025

Design considerations for a Digital Twin built to improve nitrification performance at a water resource recovery facility

A Digital Twin built around Activated Sludge Model No. 1 was deployed at a full-scale water resource recovery facility. Its design included a waste rate recommender system based on automatic scenario analyses, where influent loads and waste rates are varied to determine their impact on nitrification. At the same frequency as these scenario analyses, scheduled auto-calibrations allow for nitrifier maximum specific growth rate (μmax-NITO) soft sensing, the only kinetic parameter shown to require adjustment if the objective is aeration tank effluent ammonia forecasting accuracy. By integrating temperature forecasting over the next three sludge ages, this Digital Twin approach creates opportunities for advancing waste rate decisions in anticipation of seasonal temperature changes, optimizing ammonia control authority under varying influent loads, and furnishing valuable insights for future capital projects requiring nitrifier kinetic understanding and modelling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Real time heat load calculation software based on EPICS for Fermilab PIP-II CM tests

Fermilab has a project to improve the proton beam energy which is called PIP-II (the 2nd Proton Improvement Plan). There is a superconducting linear accelerator, LINAC, to improve the proton beam power and the LINAC consists of 5 types of cryomodules (CM), 1 HWR CM, 2 SSR1 CM, 4 SSR2 CM, LB650 CM, and HB650 CM. The prototypes of these cryomodules are being tested at Fermilab’s CryoModule Test Facility (CMTF). Heat load measurements are an important part of the prototype CM testing. The CMTF cryogenic control system was developed based on the ACNET (Accelerator Control NETwork) for CM testing for other projects, but the PIP-II cryogenic control system will be implemented using the Experimental Physics and Industrial Control System (EPICS). As part of the prototype CM testing campaign, an EPICS based control system has been implemented at CMTF. This EPICS cryogenic control system includes real-time heat load calculation software utilizing the Fortran implementation of Hepak. This paper details the real time heat load calculation software developed for the prototype CM testing including the first results from the HB650 CM.

Yoon, S. [Fermilab]↗
DOE OSTI2024

Deep Reinforcement Learning for Microgrid Cost Optimization Considering Load Flexibility

This paper proposes a novel Soft-Actor-Critic (SAC) based Deep Reinforcement Learning (DRL) method for optimizing the cost of microgrid operation by leveraging load flexibility. The proposed SAC-DRL method is designed to coordinate the control of distributed energy resources (DERs) and flexible load, addressing practical energy billing formation by power distribution utilities. Key contributions include an innovative reward function to mitigate sparse reward challenges and a mixed control strategy for discrete and continuous variables, ensuring radial network topology and minimizing power loss. We evaluate the proposed method on the model of a real microgrid located in Southern California, U.S.. The SAC-DRL model is tested to demonstrate its efficacy in reducing grid dependence, optimizing resource use, and minimizing costs. The results highlight the potential of DRL in modern energy systems, offering a sustainable and economically efficient solution for energy management in microgrids.

deep reinforcement learning↗
DOE OSTI2024

Development of an Optimal Variable-Pitch Controller for Floating Axial-Flow Marine Hydrokinetic Turbines: Preprint

This article discusses the development of an optimal variable-pitch controller for floating, axial-flow marine turbines. Recently, OpenFAST, an open-source wind turbine modeling tool, has been extended to model marine turbines. A controller is necessary to simulate marine turbines for different load cases using OpenFAST, which greatly impacts the performance of the energy system. Previous studies have designed controllers using a linearized model of the marine turbine, which can be timeconsuming and require the expertise of a control engineer. In this study, we use an automated approach that uses generic models of the marine turbine to identify the controller gains, which can expedite the process of designing a controller. Using an optimizer to identify the control system parameters can additionally improve the controller's performance. The optimal controller tuned using such an approach results in a 20% reduction in the towerbase damage equivalent loading and better tracking of the rated generator speed and power.

closed-loop control↗
DOE OSTI2026

Software Control Program For Transportable Microgrid State-of-charge Balancing And Frequency Stability Controls

A deterministic state-of-charge (SOC) balancing approach software control code is introduced as an integral secondary management to primary control layer of an islanded small microgrid or nanogrid system made up of multiple grid-forming inverter/battery/solar combination systems, where each set of batteries with each inverter are on independent DC buses (i.e. non-paralleled on the DC sides). A DERMS-level control approach, algorithm and automation controller program was developed to improve coordination and enable microgrid asset compliance and SOC balancing, enabling provision of a system-level power stability support architecture, load support, and asset scalability. The architecture is configured to treat each unit or micro/nano-grid as a node in a microgrid network, allowing for autonomous DERMS control regarding load and SOC balancing and power stability. As the network grows with the addition of units, greater coordination efforts may be required. The ideal small network microgrid ranges from 2-10 inverter/battery units before additional control parameters must be considered in the existing architecture. The control approach focuses on a deterministic state-of-charge analysis as the primary level control process followed by a secondary control loop using a forced frequency-watt droop strategy to conform off-the-shelf components into behaving under a leader-follower configuration. Adopting this control scheme has been shown to allow for a balanced, unit-coordinated microgrid network, enabling stable power flow. The deterministic state-of-charge approach is introduced as an integral primary control layer of an islanded small network microgrid. A standard strategy for SOC balancing is implementing a battery management system (BMS) to control SOC on the DC side. An alternative approach is to determine how to coordinate sending and receiving power on the AC side with multiple units. The latter approach assesses all the integrated units in the microgrid network. Once the individual units are identified, further system data is required to calculate each unit's total kWh, provided information about its capability to supply or consume kWh and availability. The secondary control layer in the multi-layered small network microgrid methodology uses the primary layer’s decision to initiate frequency setpoint changes, initializing the SOC balancing. The secondary control layer considers numerous system-dependent variables to enable a charging and discharging profile based on adjustable frequency setpoints. The combined architecture will result in stable, coordinated power flow enhancing an AC microgrid's functionalities.

Myers, KurtS [Idaho National Laboratory (INL), Ida↗
DOE OSTI2024

PV Generation and Load Forecasting for Adjuntas PR Community Microgrids

Existing frameworks to forecast time-series photovoltaic (PV) output power and consumer load for microgrid operations and controls assume a near-continuous availability of real-time input features from the field assets such as PV inverters, energy meters, and weather station. These incoming data points are used to periodically retrain models and update forecast snapshots over a moving horizon window, be it one hour-ahead, one-day ahead, or one-week ahead. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. Hence, there is a need for programs that assume no availability of real-time microgrid asset data and still make reliable forecasts that can be used for decision-making. Such programs would be apt to function in extreme weather events such as hurricanes and would use lightweight recursive time-series models to independently forecast solar irradiance and ambient temperature, then compute PV power from those forecasts, as well as independently forecast consumer load. The codebase performs forecasting for the scenario of when the microgrid does not have a reliable access to forecasts or real-time observations of solar irradiance (I) and ambient temperature (AT) and load (Load) to be able to adequately forecast, in real-time, the PV power production or a business' load. In this case, using historical values of PV power and load, a univariate forecasting of generation and consumption are respectively made. The use-case in particular has two sub-scenarios: one, a normal 7-day ahead forecast where the unavailability of real-time data is assumed due to infrastructure issues such as loss of communication or sensor maintenance or service downtimes. Whereas a hurricane-caused unavailability of real-time data requires a second model trained specifically on historical hurricane days to be able to capture the extreme day behavior of generation in particular, and load if applicable. A gradient boosted regression tree comprises an ensemble of additive models that map between the input of historical values (be it irradiance, temperature, or load) and their corresponding output forecasts of a given horizon such that the individual learner predictions are summed up over the total number of such learners in the ensemble to produce an aggregate forecast. A weighting mechanism is applied to the training data in each iteration, where actual and forecast values are compared to penalize incorrect forecasts by increasing the weight and reducing it to reward correct forecasts. The code's benefits are that it: (a) accounts for a contingency where communication loss renders newly measured real-time data unavailable for model tuning and snapshot updates; (b) presents blind forecasting that recursively determines the next time-step value in a horizon using the forecast of the same attribute from a prior step; and (c) employs lightweight models that, once trained, can reliably generalize for different horizons, which make them suitable for enhancing the resilience of field microgrids prone to extreme events that encounter disruptions to data availability.

Sundararajan, Aditya [Oak Ridge National Laborator↗
DOE OSTI2024

Challenges in Rotor Aerodynamic Modeling for Non-Uniform Inflow Conditions

Within an international collaboration framework, the accuracy of rotor aerodynamic models used for design load calculations of wind turbines is being assessed. Where the use of high-fidelity computation fluid dynamics (CFD) and mid-fidelity free-vortex wake (FVW) models has become commonplace within the wind energy community, these still fail to meet the requirements in terms of execution time and computational cost needed for design load calculations. The fast but engineering fidelity blade-element/momentum (BEM) method can therefore still be considered the industry workhorse for design load simulations. At the same time, upscaling of wind turbine rotors makes inflow non-uniformities (e.g. shear, veer, turbulence) more important. The objective of this work is to assess model accuracy in non-uniform inflow conditions, which violate several BEM assumptions. Thereto a comparison in turbulent inflow has been executed including a wide variety of codes, focusing on the DanAero field measurements, where a 2.3-MW turbine was equipped with, among other sensors, a pressure measurement apparatus. The results indicate that, although average load patterns are in good agreement, this does not hold for the unsteady loads that drive fatigue damage and aero-elastic stability. A simplified comparison round in vertical shear was initiated to investigate the observed differences in a more controlled manner. A consistent offset in load amplitude was observed between CFD and free-vortex codes on the one hand and BEM-type codes on the other hand. To shed more light on the observations, dedicated efforts are ongoing to pinpoint the cause for these differences, in the end leading to guidelines for an improved BEM implementation.

17 WIND ENERGY↗
DOE OSTI2026

Machine learning-enhanced MPC for demand flexibility in small commercial buildings: An experimental study

Small- and medium-sized commercial buildings (SMCBs) represent the majority of U.S. commercial building stock and a significant share of peak electricity demand, yet they often lack centralized building automation systems, representing a significant untapped resource for urban energy management. This infrastructure gap makes advanced control implementation challenging, limiting the potential for widespread demand flexibility. Model Predictive Control (MPC) has shown strong potential for load shifting, peak demand reduction, and cost savings, but its effectiveness is hindered by unmeasured disturbances such as internal heat gains. This paper presents a Hybrid MPC framework that integrates a physics-based gray-box building thermal model, identified using a lumped disturbance (LD) approach, with a machine learning (ML) model for forecasting unmeasured disturbances. The hybrid approach is designed for buildings with multiple individually controlled heat pump and thermostat pairs, common in SMCBs, and aims to optimize coordinated scheduling of multiple heat pumps under dynamic electricity pricing while respecting comfort constraints. The methodology is validated through both simulations of case study buildings and experimental studies at a highly-instrumented test facility. Simulation results show that the Hybrid MPC achieves substantial load shifting and peak demand reduction, approaching the performance of an ideal MPC with perfect disturbance knowledge, and outperforming a conventional MPC without disturbance forecasting. In experiments, the Hybrid MPC reduced daily HVAC energy costs by 8.7%, peak-price time load (load shifting) by 41.7%, and peak demand by 29.2% compared to baseline control, demonstrating comparable benefits to the 11.6% cost savings, 42.9% load shifting, and 23.2% peak reduction of the ideal MPC. These results demonstrate that the proposed hybrid modeling approach can significantly improve MPC performance in real-world SMCB applications without requiring additional disturbance measurements.

Demand Flexibility↗
DOE OSTI2025

Techno-Economic Analysis for the Addition of Thermal Energy Storage to a Campus With Existing Battery Storage

Rising global temperatures and increasing energy demands pose significant challenges for energy management, particularly in institutional and commercial settings. As cooling needs grow, campuses must balance operational efficiency, cost control, and grid stability. Energy storage solutions, such as thermal energy storage (TES) systems, offer a promising approach to shifting energy consumption from peak to off-peak periods, alleviating peak demand, reducing utility costs, and enhancing grid resilience. When integrated with existing battery energy storage systems (BESS), TES can further optimize load management and improve energy savings, especially in buildings with diverse energy needs. This article presents a techno-economic analysis of integrating a chilled water TES system into the central plant at California State University, Dominguez Hills, which already operates a BESS. We assess three TES sizing strategies—full storage, load leveling, and peak demand limiting—by modeling and simulations based on historical energy loads. Our findings show that we can control TES systems to complement BESS operation, with campus-level load leveling providing the greatest cost savings by reducing peak demands. Furthermore, the study also evaluates the long-term economic viability of TES, considering installation costs, energy savings, and payback periods under varying tariffs. This research offers practical guidance for institutions seeking to enhance energy resilience and reduce operational costs through energy storage solutions.

25 ENERGY STORAGE↗
DOE OSTI2024

Customer enrollment and participation in building demand management programs: A review of key factors

Increasing the efficiency and flexibility of electricity demand is necessary for ensuring a cost-effective and reliable transition to zero-carbon electricity systems. Such demand-side management (DSM) resources have been procured by utilities for decades via energy efficiency and demand response programs; however, the key drivers of program enrollment and customer participation levels remain poorly understood — even as governments and grid planners seek to scale up the deployment of DSM assets to meet climate targets. Here we systematically review the evidence on multiple factors that may influence customer enrollment and participation in building DSM programs, focusing primarily on residential and commercial buildings. We examine the contexts in which relationships between DSM factors and outcomes are most often explored and with which methods; we also score the strength, direction, and internal consistency of each factor's reported impact on the enrollment and participation outcomes. We find that studies most commonly assess the effects of economic incentives for load flexibility on program participation levels, often using simulation-based methods in lieu of measured data. Few studies focus on program enrollment outcomes or regulatory drivers of either enrollment or participation, and gaps are also evident in the coverage of emerging DSM opportunities like load electrification. Removal of structural barriers (e.g., the lack of controls infrastructure) and the use of third party services (e.g., load aggregators) are the factors with the largest positive impacts on DSM outcomes, but no single factor emerges as clearly most impactful. For a given factor, the range of reported impacts typically varies widely across the relevant studies reviewed. Our findings provide a snapshot of the state of knowledge about building DSM and customer decision-making, and they expose key gaps in understanding that must be filled if building DSM is to expand as a critical resource for operating clean power grids.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Polyalkenamers as Drop-In Additives for Ring-Opening Metathesis Polymerization: A Promising Upcycling Paradigm

Here we report a distinct strategy to upcycle waste polyalkenamers such as polybutadiene into new, performance-advantaged materials by using them as drop-in additives for ring-opening metathesis polymerization (ROMP). The polyalkenamers serve as competent chain-transfer agents in ROMPs of common classes of cyclic olefin monomers, facilitating good molecular weight control, allowing low Ru catalyst loadings, and enabling efficient incorporation of the polyalkenamer into the synthesized polymeric material. We successfully demonstrate ROMP using model polyalkenamers and translate these learnings to leverage commercial polybutadiene and acrylonitrile butadiene styrene (ABS) as chain transfer agents for ROMP copolymerizations. Critically, our strategy is shown to be highly efficient and operationally simple, quantitatively incorporating the polyalkenamer and inheriting aspects of its thermomechanical performance. Our results highlight a promising pathway for the upcycling of polyalkenamers and provide an alternative to existing deconstruction and functional upcycling strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Overview of the ICRF heating system in SPARC

Ion Cyclotron Range of Frequencies (ICRF) heating is a critical system for the SPARC mission of demonstrating net positive fusion gain. In the first campaign, 20 MW of installed power at 120 MHz will be supplied to 10 four-strap antennas, arranged in poloidal pairs. The matching network is designed to accommodate for a range of loading conditions and a fast controller adjusts the matching during a pulse with frequency modulation of ±1 MHz. For the main L-mode scenario of the first experimental campaign, the power coupling and absorption is analysed in this work. Several feeding schemes are compared and limits on the amount of the total coupled power are evaluated. Coupling of the full 20 MW power is possible well within the limit of the peak electric field in the transmission line of 15 kV/cm. The 3 He minority and 2 nd harmonic T heating scheme is validated in full-wave modelling to be efficient for heating plasma in the SPARC L-mode Q>1 scenario, with dominant ion heating and efficient single-pass absorption. A range of ICRF wave parameters and minority concentration would be suitable for operation and allow tailoring the heating properties for plasma conditions and experimental objectives.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Tokamak level performance in the optimized stellarator Wendelstein 7-X with stable peaked density profiles

Progress towards achieving net gain from nuclear fusion is typically characterized by the triple product 𝑛⁢𝑇⁢𝜏. Although stellarators have several benefits for a reactor, such as the inherent steady-state capability and the lack of disruptions, the maximum achieved triple product has historically lagged behind that of tokamaks. In the large optimized stellarator Wendelstein 7-X, a triple product of (1.10 ± 0.15) × 10 20 m −3 ⁢ keVs has recently been achieved and held stable for 1.9 s. This marks an important step in stellarator core plasma performance and puts it in line with 𝐻-mode performance in similar sized tokamaks. The progress in Wendelstein 7-X performance is due to the suppression of ion-temperature-gradient turbulence by strong density gradients. In the record case, these are maintained by a fine balance of the core particle transport and central fueling using neutral beam injection. The duration of the scenario was limited in time only by technical restrictions of the neutral beam injectors and control of the divertor heat loads. This paper presents the result in the context of the leading magnetic confinement devices along with details of the scenario.

Bannmann, Sebastian [Max Planck Institute for Plas↗
DOE OSTI2024

Environmental Validation of Materials and Design Concepts to Enable Operational Flexibility of Existing Coal Power Plants

Increasingly, coal-fired power plants are required to balance power grids by compensating for the variable electricity supply from renewable energy sources. Fossil-fueled power plants, originally designed to be base loaded, will increasingly need to operate on a load following or cyclic basis. This demanding requirement for operational flexibility will need to be evaluated for resilience to frequent start-ups, meeting major and rapid load changes, and providing frequency control duties. Our research objective was to evaluate and improve flexibility of existing power plants by improving and redesigning components and defining new operational strategies, with acceptable impacts on component life, efficiency and emissions.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Cellulose molecular weight of corn stover with cotreatment

Here we present a dataset of cellulose molecular weights from corn stover after a cotreatment process. The corn stover sample was subjected to a combination of mechanical disruption (ball milling) and consolidated bioprocessing (CBP) process using the bacterium C. thermocellum at 60 grams/L solids loadings. Cellulose was isolated from control, no cotreatment control, and cotreatment corn stover. Cellulose was then derivatized using anhydrous pyridine and phenyl isocyanate over 48 h at 70 ºC. The reaction was quenched by anhydrous methanol. Methanol and water mixture (7:3, v/v) was added dropwise to promote precipitation of the cellulose derivative. The solids were collected by filtration and thoroughly washed with the methanol and water mixture followed by water. The cellulose derivative was dried overnight under vacuum at 40°C. The obtained cellulose tricarbanilates was then dissolved in tetrahydrofuran (THF) at 1.0 mg/mL and the solution was filtered through a 0.45 µm PTFE filter and placed in an auto-sampler vial. The molecular weight of cellulose was measured by Agilent GPC SECurity 1200 system equipped with four Waters Styragel columns (i.e., HR0.5, HR2, HR4, and HR6) and a UV detector (270 nm). THF was used as the mobile phase. Data collection and processing was performed by Polymer Standards Service WinGPC Unity software (Build 6807). The molecular weight was calculated relative to the polystyrene calibration curve. The data on molecular weights provided information about the effects of cotreatment on corn stover cellulose molecular changes.

Cellulose, molecular weights, corn stover, cotreat↗
DOE OSTI2026

Impact of Selenium and Vitamin E Deficiency on Zika Virus Pathogenesis and Immune Response in Mice

Micronutrient status is recognized to influence host susceptibility to viral infections, yet its impact on Zika virus (ZIKV) pathogenesis remains incompletely understood. We investigated the effects of dietary selenium and combined selenium plus vitamin E deficiency on ZIKV infection outcomes in a type I interferon α/β receptor knockout (Ifnar1 −/− ) murine model. Mice maintained on deficient diets exhibited significantly lower neutralizing antibody titers and reduced levels of key antiviral cytokines (IFN-γ, TNF-α, IFN-α, IFN-β, IL-12p70, CCL5) compared to controls. Correspondingly, higher viral RNA loads were detected in the brains of double-deficient mice, which also experienced greater weight loss and increased mortality. Deep sequencing revealed no major differences in overall viral genome diversity across diet groups; however, specific mutations, including V330L and D67E in the E gene, and V360I in the NS3 gene, were enriched or detected in nutritionally deficient animals. These findings suggest that antioxidant micronutrient deficiency impairs both humoral and cellular immune responses to ZIKV, potentially facilitating enhanced neuroinvasion. While the functional consequences of the identified mutations warrant further investigation, our results underscore the importance of adequate micronutrient intake for optimal antiviral defense. Further studies are needed to clarify the epidemiological significance of these observations.

Biological and medical sciences↗