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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Degradation of Commercial Lithium-Ion Cells as a Function of Chemistry and Cycling Conditions

Energy storage systems with Li-ion batteries are increasingly deployed to maintain a robust and resilient grid and facilitate the integration of renewable energy resources. However, appropriate selection of cells for different applications is difficult due to limited public data comparing the most commonly used off-the-shelf Li-ion chemistries under the same operating conditions. This article details a multi-year cycling study of commercial LiFePO 4 (LFP), LiNi x Co y Al 1–x–y O 2 (NCA), and LiNi x Mn y Co 1–x–y O 2 (NMC) cells, varying the discharge rate, depth of discharge (DOD), and environment temperature. The capacity and discharge energy retention, as well as the round-trip efficiency, were compared. Even when operated within manufacturer specifications, the range of cycling conditions had a profound effect on cell degradation, with time to reach 80% capacity varying by thousands of hours and cycle counts among cells of each chemistry. The degradation of cells in this study was compared to that of similar cells in previous studies to identify universal trends and to provide a standard deviation for performance. All cycling files have been made publicly available at batteryarchive.org, a recently developed repository for visualization and comparison of battery data, to facilitate future experimental and modeling efforts.

25 ENERGY STORAGE↗
DOE OSTI2022

Rapid Evaluation and Response to Impacts on Critical End-Use Loads Following Natural Hazard-Driven Power Outages: A Modular and Responsive Geospatial Technology

The disparate nature of data for electric power utilities complicates the emergency recovery and response process. The reduced efficiency of response to natural hazards and disasters can extend the time that electrical service is not available for critical end-use loads, and in extreme events, leave the public without power for extended periods. This article presents a methodology for the development of a semantic data model for power systems and the integration of electrical grid topology, population, and electric distribution line reliability indices into a unified, cloud-based, serverless framework that supports power system operations in response to extreme events. An iterative and pragmatic approach to working with large and disparate datasets of different formats and types resulted in improved application runtime and efficiency, which is important to consider in real time decision-making processes during hurricanes and similar catastrophic events. This technology was developed initially for Puerto Rico, following extreme hurricane and earthquake events in 2017 and 2020, but is applicable to utilities around the world. Given the highly abstract and modular design approach, this technology is equally applicable to any geographic region and similar natural hazard events. In addition to a review of the requirements, development, and deployment of this framework, technical aspects related to application performance and response time are highlighted.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

REopt Lite Overview & Training Exercise

This training exercise provides users with an introduction to and hands-on, interactive exploration of REopt Lite's capabilities. REopt Lite is a free, publicly available techno-economic optimization web tool for distributed energy systems, developed at the National Renewable Energy Laboratory (NREL). REopt Lite helps organizations evaluate the economic viability of grid-connected solar photovoltaics (PV), wind turbines, and battery storage; identify system sizes and battery dispatch strategies to minimize energy costs; and estimate how long a system can sustain critical load during a grid outage. The model is formulated as a mixed-integer linear program based in an underlying application programming interface (API) that is also free and publicly available. This training activity is structured as a group exercise. Participants split into eight groups and each group is assigned a different hypothetical site to model and assess the opportunity for solar PV + battery storage. Groups work together to develop results for their site and then re-convene to compare and discuss results, inputs/drivers of the analysis, and other factors impacting the decision-making process for behind-the-meter solar PV and battery storage.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

US 2023/0182605 A1 Network constraint energy management system for electric vehicle depot charging and scheduling

Network constraint energy management system for electric vehicle (EV) depot charging and scheduling. In an embodiment, a power schedule is received from an economic dispatch application for a charging depot comprising EV charging station(s) and distributed energy resource(s). The power schedule may be simulated on a distribution network model of the charging depot, according to load flow analysis, to determine whether any grid-code violations occur. In response to the detection of violation(s), a constraint may be generated for each violating node, and the economic dispatch application may be re-executed with the constraint(s) to produce a new power schedule, until no violations are detected. When not all load demand can be satisfied by the power schedule, a charging schedule may be adjusted to ensure that critical energy requirements are satisfied. The final power and charging schedules may be used to schedule and control power generation and charging in the charging depot.

Hafiz, Faeza↗
DOE OSTI2022

Flexible cryogenic air separation unit—An application for low-carbon fossil-fuel plants

In this study, the rapid integration of intermittent renewable sources into the electricity grid is driving the need for a flexible cryogenic air separation unit (ASU) coupled with a low-carbon fossil-fuel plant. However, the state-of-the-art ASU process is highly integrated and nonlinear, which can significantly restrict its ramping rate. In this work, we study the fundamental dynamics of a state-of-the-art double-column ASU, focusing on the dynamic characteristics of the highly integrated and nonlinear heat and mass transfers, and we propose basic control methods for achieving high process ramping rates. We found that the vapor–liquid countercurrent flows in the low-pressure column are critical to the cryogenic rectification of air, which governs the ramp rate of the ASU. This countercurrent flow structure is created through a complex heat integration process in the ASU. Here, this process is simplified as a countercurrent heat transfer to reduce the complexity of studying the ASU dynamics. To preserve this flow structure, the heat integration is maintained so that its heat duty follows the ASU load, using several basic controllers on the critical stream flowrates. A flow-driven dynamic ASU model is built in Aspen Plus Dynamics to capture the basic ramping dynamics. This model revealed a fundamental mismatch in the dynamics in the ASU column that causes a significant loss of O 2 product purity when ramping down the ASU and slightly increases the purity when ramping up. Based on these findings, we propose a basic control method for rapidly ramping down/up the ASU while maintaining O 2 product purity. Simulation results show the ASU basic dynamic process successfully ramps at a rate up to 10 %/min (40–100 % load) while maintaining the O 2 product purity at 95.2–95.6 mol%.

42 ENGINEERING↗
DOE OSTI2022

Unleashing the Frequency: Multi-Megawatt Demonstration of 100% Renewable Power Systems with Decentralized Communication-Less Control Scheme

Power systems, which range in size from small microgrids to island systems to large regional grids, are typically managed by a central controller that requires complex communication methods and can be unreliable and pose cyber security risks in certain applications, especially when controlling a larger number of nodes. We propose an inherently robust, scalable method of integration using multiple energy storage systems and distributed energy resources, which does not require any means of dedicated communication. This method moves beyond the paradigm of controlling grid frequency at a fixed value (e.g., 60 Hz), instead allowing the frequency to fluctuate within certain limits (e.g., 59.6-60.4 Hz). With a greater operating range, the frequency can carry necessary information from energy storage systems to highly variable distributed energy resources like photovoltaics, wind, hydro, etc.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Enabling Data Exchange and Data Integration with the Common Information Model: An Introduction for Power Systems Engineers and Application Developers

The Common Information Model (CIM) is an open-source information model that is used to model an electrical network and the various equipment used on the network. CIM is widely used for data exchange of bulk transmission power systems and is finding increasing use for distribution systems. Use of a non-proprietary information model (such as CIM) that has been agreed upon and adopted by numerous utilities, vendors, and researchers allows significant reduction in the effort and cost of data integration. Likewise, adoption of open data platforms built around the CIM increases available functionalities for managing and optimizing the smart grid of the future. This report is intended as an introduction to CIM for utility engineers, power systems researchers, and application developers, providing a broad view of the CIM and how particular profiles can be adapted for various use cases. Unlike most other CIM introduction documents and the International Electrotechnical Commission (IEC) standards (which are mostly targeted to an audience of data scientists, enterprise database managers, and platform developers), this report is intended for users of traditional power systems analysis software and other readers without any prior experience with canonical information models, data profiles, or UML modeling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Fault Detection Utilizing Convolution Neural Network on Timeseries Synchrophasor Data From Phasor Measurement Units

An end-to-end supervised learning method is proposed for fault detection in the electric grid using Big Data from multiple Phasor Measurement Units (PMUs). The approach consists of preprocessing steps aimed at reducing data noise and dimensionality, followed by utilization of six classification models considered for detecting faults. Three of the models were variants of Convolutional Neural Network (CNN) architectures that consider a single type of measurement (voltage, current or frequency) at all PMUs or all types together also at all PMUs. CNN based models were compared to traditional methods of Logistic Regression (LR), Multi-layer Perceptron (MLP) and Support Vector Machine (SVM). Evaluation was conducted on two-year data measured by PMUs at 37 locations in a large electric grid. Here, the response variable for classification were extracted from the grid-wide outage event log. Experiments show that CNN-based models outperformed traditional methods on one year out-of-sample outage detection over the entire grid.

42 ENGINEERING↗
DOE OSTI2023

Universal Passive Synchronization Method for Grid-Forming Inverters Without Mode Transition

Power systems are transforming with increasing levels of inverter-based resources (IBRs). This transformation requires critical roles of grid-forming (GFM) inverters replacing synchronous generators for bulk power system stabilization and ancillary services, also allowing flexible power system operation, such as microgrid that is operated by multiple GFM IBRs to achieve system resilience against contingencies. To realize the resilient power systems allowing flexible in-and-out operation of GFM IBRs potentially programmed with different primary controls, a synchronization method universally applicable, i.e., independent of control types, would be beneficial to ease the integration process, but it has not been actively studied. To fill the gap, this paper proposes a universal synchronization method that achieves a passive synchronization to enable a smooth transition in a grid with off-nominal system parameters, i.e., voltage and frequency. The logic proposed requires no modification on the primary control, thus applicable to any type of GFMs with a voltage reference input. To validate the concept, a simulation of an IEEE 13-bus benchmark system modified with 3 GFM inverters is presented. It simulates an inverter-driven black start scenario in which GFM inverters autonomously turn on and connect to the grid under heavy loading, using the synchronization logic. The case study demonstrates that GFM inverters can tune their voltage reference to smoothly synchronize without severe transients, and contribute to a seamless black start of the grid under unbalanced load conditions. Two GFM methods - Droop and dispatchable virtual oscillator control - are used for the demo to validate feasibility and interoperability of the passive synchronization.

black start↗
DOE OSTI2020

Advanced Synchrophasor-based Application for Potential Distributed Energy Resources Management: Key Technology, Challenge and Vision

With an increasing number of distributed energy resources (DERs) integrating into the power system, the power grid is becoming more complex and uncertain. Owing to most of DERs are through grid-connected converters to integrate into the power system, the intermittent nature and resource-depended characteristic of the DERs are challenging their monitoring and control. Conventional synchronized measurement devices (SMDs) may be difficult to provide qualified data for DERs management for its accuracy and reporting-rate limitation. The monitoring systems developed basing on SMDs may be insufficient for depicting detailed dynamic manifestations of power systems with a high proportion integration of DERs. This paper shares some advanced synchrophasor-based applications developed in FNET/GridEye, which have a broad prospect for assisting system operator on the control and integration of the DERs.

Wang, Weikang↗
DOE OSTI2023

Improving Frequency Stability and Minimizing Load Shedding Events by Adopting Grid-Scale Energy Storage with Grid Forming Inverters

The upward adoption trend of renewable generation not only means cleaner energy integrated into modern power grids, but also that most new generation sources are based on front-end inverter bridges, used as interfaces to most wind generation and all the solar PV. It is well known that due to their power electronics-based construction rather than rotational shafts, these sources do not provide inertia inherently, nor substantial amounts of short-circuit currents. However, stable energy such as what can be stored in energy storage systems, although interfaced via inverters, can be controlled to respond to system disturbances in a manner that emulates inertial behavior. This paper focuses on the application of such energy storage systems to augment inertia in the island of Puerto Rico. To do so, a user defined inverter model that contains grid forming capabilities and fast frequency response is modeled and integrated into the real transmission system in power flow and dynamics software. Energy storage is then connected to two selected areas so that it not only provides frequency regulation to avoid widespread load shedding events, but also other tangible benefits. The simulated cases suggest that even relatively small energy storage systems can avert load shedding events if adequately placed in the transmission network.

Grid-forming inverters, IBR, Inertia↗
DOE OSTI2022

Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems

The transition to a smarter grid is empowered by enhanced sensor deployments and smart metering infrastructure in the distribution system. Measurements from these sensors and meters can be used for many applications, including distribution system state estimation (DSSE). However, these measurements are typically sampled at different rates and could be intermittent due to losses during the aggregation process. These multi timescale measurements should be reconciled in real-time to perform accurate grid monitoring. This paper tackles this problem by formulating a recursive multi-task Gaussian process (RGP-G) approach that sequentially aggregates sensor measurements. Specifically, we formulate a recursive multi-task GP with and without network connectivity information to reconcile the multi time-scale measurements in distribution systems. Here, the proposed framework is capable of aggregating the multi-time scale measurements batch-wise or in real-time. Following the aggregation of the multi time-scale measurements, the spatial states of the consistent time-series are estimated using matrix completion based DSSE approach. Simulation results on IEEE 37 and IEEE 123 bus test systems illustrate the efficiency of the proposed methods from the standpoint of both multi time-scale data aggregation and DSSE.

42 ENGINEERING↗
DOE OSTI2022

Model predictive control for demand flexibility: Real-world operation of a commercial building with photovoltaic and battery systems

Hundreds of studies have investigated Model Predictive Control (MPC) for the optimal operation of building energy systems in the past two decades. However, MPC field tests are still uncommon, especially for small- and medium-sized commercial buildings and for buildings integrated with onsite renewables. This paper describes the implementation and the long-term performance evaluation of an MPC controller in a small commercial building equipped with behind-the-meter photovoltaics and electrochemical batteries. MPC controls space conditioning, commercial refrigeration, and the battery system. We tested two types of demand flexibility applications in the field: electricity bill minimization under time-of-use tariffs and responses to grid flexibility events. Results show that the proposed controller achieves 12% of annual electricity cost savings and 34% peak demand reduction against the baseline, while respecting thermal comfort and food safety. The field tests also demonstrate the ability of the MPC controller to provide a multitude of grid services including real-time pricing, demand limiting, load shedding, load shifting, and load tracking, using the same optimization framework.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Artificial correlation heating in PIC simulations

The Particle-in-Cell (PIC) method, a cornerstone in plasma modeling, is widely employed for its ability to simulate kinetic phenomena in device-scale domains. Part of what makes this possible is that computational macroparticles represent many physical particles. It converges under certain constraints, including a grid spacing that resolves the Debye length and a time step small enough to respect the Courant–Friedrichs–Lewy condition and plasma frequency stability limit. Here, we introduce a new constraint necessary to avoid Artificial Correlation Heating (ACH). This requires that the macroparticle coupling strength be smaller than one, Γ ω < 1, where Γ ω ≡ Γω 2/3 ⁠, Γ = Z 2 e 2 /(4πε 0 ak B T) is the physical coupling strength, and w is the macroparticle weight. This is particularly relevant to 3D simulations of dense plasmas, which are becoming common with modern computing power. If this condition is violated, the finite macroparticle weight artificially enhances the coupling strength and causes the plasma to heat until the macroparticle coupling strength is near unity, depending on the grid resolution. A comprehensive model of ACH is developed that incorporates electron density, temperature, macroparticle weight, and grid resolution. It is then tested using PIC simulations, delineating the boundaries of the method's applicability and offering a predictive framework for ACH. Moreover, the research explores a runaway heating process induced by ACH in the presence of ionization, which can lead to numerical instability. A conclusion of this study is that the onset of ACH can impose a more stringent constraint on the macroparticle weight and average number of macroparticles per cell than what is typically expected, particularly in 3D simulations of dense plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Cooperative Research and Development Agreement between National Energy Technology Laboratory and VariGrid Explorations Inc. [Final Report]

The scope of this project is to advance the National Energy Technology Laboratory’s (NETL) Variable Grid Method (VGM) technology to commercial status thought enhancements upon prior work to produce a VGM application that can rapidly quantify multiple types of data and uncertainty commonly encountered by the subsurface energy industry. Along with development, the project will also include technology demonstrations, through applications using common industry data types and uncertainty metrics, to stress the value the VGM can offer the subsurface energy industry by providing an improved understanding of the strengths and limitations of their data, models, and analytical results. The resulting commercially ready VGM will offer interoperability with common industry software applications to ensure broad ‘plug and play’ functionality that will help keep costs relatively low and foster more rapid industry adoption.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Uncertainty in Synthetic Tropical Cyclone Hazard and Risk Estimates: Insights from RAFT, CHAZ, MIT, STORM, and CLIMADA

We synthesize five complementary tropical cyclone (TC) hazard frameworks—RAFT (physics-based machine learning), CHAZ and MIT (statistical–dynamical), STORM (fully statistical), and CLIMADA (observation-driven resampling)—to characterize uncertainty in wind-related TC metrics relevant to energy applications. All datasets and the IBTrACS observational record are harmonized to a common 6-hourly, 2.5° grid. We compare basin-wide and coastal properties using consistent definitions for TC frequency, mean and maximum intensity, 24-hour intensification, and 6-hour translation speed, and quantify agreement with Pearson r, RMSE, and Kling–Gupta efficiency (KGE) alongside resampling-based confidence intervals. CLIMADA is included for basin context but excluded from coastal skill scoring because it resamples historical IBTrACS; if supplied with projected future tracks from an external hazard model, CLIMADA can be used to simulate future TC scenarios. Results show robust, cross-model signals: (i) a corridor of activity from the tropical Atlantic through the Caribbean into the Bahamas and western subtropical Atlantic; (ii) a meridional dipole in 24-hour intensification (low-latitude strengthening, subtropical weakening); and (iii) a transition from slower tropical motion to faster midlatitude translation. Coastal winds (mean and maximum) consistently cluster from the eastern Gulf into the Bahamas–western Atlantic transition. The largest structural spread occurs in the amplitude and footprint of lifetime maximum intensity and, secondarily, in translation speed; intensification exhibits similar central behavior across frameworks with variability in extremes. Translation speed shows the most uniform coastal agreement. These findings provide a decision envelope for wind-focused risk screening and clarify where uncertainty should be carried forward; wind-only results represent a lower bound on total hazard, motivating integration of surge and rainfall modules and a companion, asset-level damage analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Editorial: Advanced water splitting technologies development: Best practices and protocols

As the level of deployment and utilization of renewable energy sources, including wind and solar, continues to rise, large-scale, long-term energy storage technologies that could accommodate weekly and seasonal energy fluctuations will play a significant role in the overall deployment of renewable energies in the future. Harnessing and storing renewable energy resources via electrochemical, photoelectrochemical, or thermochemical processes by converting renewable energy into sustainable (energy storage) fuels have the potential to meet the long-term, terawatt scale energy storage challenge. Renewable hydrogen production is the cornerstone for sustainable fuel production and deep decarbonization of multiple sectors in our society. Cost-competitive clean hydrogen provides value to applications, such as 1) in the transportation sector for fuel cell vehicles, 2) in the electric grid sector for system stability and load balancing, and 3) in the industrial sector with metal refineries, cement production, and biomass upgrading (carbon-free fertilizer production). In addition, coupling clean renewable hydrogen with the carbon and nitrogen cycles enables known and well-established thermal-chemical processes to generate renewable hydrocarbon fuels and ammonia. The Advanced Water Splitting Technologies (AWST): low temperature electrolysis (LTE), high temperature electrolysis (HTE), photoelectrochemical (PEC) and solar thermo-chemical hydrogen (STCH) provide four unique and parallel approaches to produce low cost, low greenhouse gas (GHG) emission hydrogen at scale (Figure 1). Cost competitive clean hydrogen production using these four technologies is a current high priority focus for governments and industry. In June of 2022, the U.S. Department of Energy (DOE) launched the first in a series of Earthshot Initiatives. The Hydrogen Shot, “1 1 1” aims to reduce the cost of clean hydrogen by more than 80% to one dollar per one kilogram in 1 decade ($\$$1/kg H 2 ). The European Green Deal and the International Energy Agency (IEA) have implemented a strong focus on green hydrogen production for a clean and secure energy future.

benchmarking, low temperature electrolysis↗