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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 253 records · Page 14

Technology Pathways Could Help Drive the U.S. West Coast Grid's Exposure to Hydrometeorological Uncertainty

Abstract Previous studies investigating deep decarbonization of bulk electric power systems and wholesale electricity markets have not sufficiently explored how future grid pathways could affect the grid's vulnerability to hydrometeorological uncertainty on multiple timescales. Here, we employ a grid operations model and a large synthetic weather ensemble to “stress test” a range of future grid pathways for the U.S. West Coast developed by ReEDS, a well‐known capacity planning model. Our results show that gradual changes in the underlying capacity mix from 2020 to 2050 can cause significant “re‐ranking” of weather years in terms of annual wholesale electricity prices (with “good” years becoming bad, and vice versa). Nonetheless, we find the highest and lowest ranking price years in terms of average electricity price remain mostly tied to extremes in hydropower availability (streamflow) and load (summer temperatures), with the strongest sensitivities related to drought. Seasonal dynamics seen today involving spring snowmelt and hot, dry summers remain well‐defined out to 2050. In California, future supply shortfalls in our model are concentrated in the evening and occur mostly during periods of high temperature anomalies in late summer months and in late winter; in the Pacific Northwest, supply shortfalls are much more strongly tied to negative streamflow anomalies. Under our more robust sampling of stationary hydrometeorological uncertainty, we also find that the ratio of dis‐patchable thermal (i.e., natural gas) capacity to wind and solar required to ensure grid reliability can differ significantly from values reported by ReEDS.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Queued Up: 2026 Edition, Characteristics of Power Plants Seeking Transmission Interconnection As of the End of 2025 [Slides]

Electric transmission system operators (ISOs, RTOs, or utilities) require proposed power plants seeking to connect to the transmission grid to undergo a series of impact studies before they can be built. This process establishes what new transmission equipment or upgrades may be needed before a project can connect to the system and assigns the costs of that equipment. The lists of projects in this process are known as “interconnection queues”. In collaboration with https://www.interconnection.fyi. Berkeley Lab compiled, aggregated, and cleaned interconnection queue data from >50 transmission grid operators (7 ISO/RTOs and 50 non-ISO balancing areas), which collectively represent ~98% of currently installed U.S. electric generating capacity.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Simulating Impacts of Extreme Events on Grids with High Penetrations of Wind Power Resources

As extreme weather events become more frequent and intense, the demand for connecting grid operation and infrastructure planning with extreme event models will increase as well. We present a methodology for creating damage contingencies and scenarios for electric transmission grids during a hurricane strike. Using WIND Toolkit meteorological data in conjunction with fragility curves for various electric grid elements, we generate stochastic damage scenarios that can be used for short- and long-term planning problems, e.g., emergency asset management. Included is an example case study: Hurricane Dolly damaging a synthetic 2000-bus test system during its landing in Southern Texas. We perform statistical analysis of damages and discuss topological effects on the example synthetic grid. Also, we include a cursory evaluation of impacts using simplified operational models. Finally, we discuss how our method can be extended to use even higher-fidelity meteorological data sets and suggest directions for future work.

contingencies↗

Scalable Unit Commitment with Security Constrained AC Power Flow via ADMM and Hybrid Modeling Strategies

This research introduces a more efficient way to optimize power grid operations, breaking the problem into manageable steps and using advanced mathematical techniques to speed up calculations. By incorporating smart heuristics, improved preprocessing, and contingency analysis, the approach allows operators to make better decisions faster. These innovations enhance our understanding of how to optimize energy generation, making it possible to anticipate failures before they happen, reduce system costs, and improve overall grid performance. Ultimately, this research helps bridge the gap between theoretical models and real-world applications, paving the way for a smarter, more resilient power grid. This research directly benefits the public by making electricity more affordable, reliable, and sustainable. By improving how power grids schedule and distribute electricity, the project helps energy providers reduce operational costs, which can lead to lower electricity prices for consumers. Additionally, the ability to predict and prevent power system failures enhances grid reliability, reducing the likelihood of blackouts that can disrupt homes, businesses, and critical infrastructure such as hospitals. From an environmental perspective, optimizing power generation reduces energy waste and lowers carbon emissions, contributing to cleaner air and a more sustainable energy system. Furthermore, with extreme weather events becoming more frequent, these advancements make the power grid more resilient, ensuring communities are better prepared for emergencies and natural disasters. By strengthening the nation's energy infrastructure, this research plays a crucial role in improving economic stability, public safety, and environmental sustainability.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Evaluating the Impact of Managed EV Charging for Reliable Operation of Bulk Power Systems with High Non-Dispatchable Generation

The growth of electric vehicles (EVs) and variable-generation (VG) sources introduces new challenges for power-system operations. This study introduces a modeling framework and evaluates five EV charging strategies under projected 2040 grid conditions in the Evergy service territory with high non-dispatchable generation. Using realistic EV behavior and generation models, their impacts on system peak demand, ramp rate, and reserve capacity are evaluated. Results show that only the peak-avoidance strategy effectively reduces system peak demand, while decentralized strategies-particularly cost based dynamic charging-can exacerbate peaks due to synchronized user behavior. However, ramp-rate minimization strategy significantly reduce the stress on dispatchable generation achieving the lowest maximum absolute ramp rate (MARR) (56.81 MW) and lowest reserve requirement (2.39 GW). In contrast, unmanaged and TOU random strategies increase the stress on dispatchable generation sources with increased MARR and reserve requirements. These findings highlight the importance of coordinated, system-aware managed charging strategies to ensure reliable and affordable grid operation in the presence of EVs and VG sources.

14 - SOLAR ENERGY↗

Novel Data Driven Noise Emulation Framework using Deep Neural Network for Generating Synthetic PMU Measurements

Sensors play a critical role in supporting day-to-day grid operations and they are essential to operator’s decision-making process. Furthermore, sensors and sensor behaviors need to be emulated with grid simulations to perform modeling studies and to design cutting edge power systems applications. Ensuring the accurate behavior of these applications requires accurate emulation of sensors and pertinent signals. However, most grid simulators and modeling tools assume either zero error scenarios or simplistic noise models that may not always correlate to real-world sensors. To address the above issue, this work presents an initial study on the noise characteristics of phasor measurement units (PMUs), along with models for recreating their unique noise signatures. The proposed methods (both analytical and machine-learning-based) provide a substantial increase in a sensor’s model fidelity, a feature that can be leveraged by an end-user application to yield more accurate system representations. The proposed methods were then applied to micro PMU data from the EPFL microgrid campus to extract sensor noise profiles. This data was used to train a deep learning model, which was tested to emulate the noise characteristics present in actual signals. Based on the observed results and the employed data-driven methodology, the proposed methods may be adapted to replicate the behavior of other grid sensors and power new applications capable of detecting sensor degradation and eventual device failures in near real-time.

PMU, noise emulation, synthetic measurements, deep↗

Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI-XFC)

Increasing vehicle electrification will require extensive use of extreme fast charging (XFC), especially for larger vehicles. Uncoordinated XFC can create grid challenges, particularly at the distribution level. Two strategies can support widespread XFC: gold-plate the grid (i.e., upgrade all systems to enable worst-case, fully coincident loads) or use integrated planning to codesign a smart system based on advanced controls that leverage load flexibility and distributed energy resources. With the right design and control, XFC can simultaneously support both mobility and grid operations. Fully realizing the potential of XFC, however, will require unprecedented coordination among the charging infrastructure, grid, and vehicles. GEMINI-XFC combines high-fidelity grid and transport modeling at an unprecedented level of resolution and codesigns a smart system based on advanced controls that leverage load flexibility and distributed energy resources to optimize the integration of extreme fast charging (XFC) across a full regional scale with individual customer resolution.

ADVANCED PROPULSION SYSTEMS↗

Efficient Network Partitioning: Application for Decentralized State Estimation in Power Distribution Grids: Preprint

Increase in the proliferation of DERs requires real-time situational awareness for efficient grid operations. State estimation plays an important role for real time control and management of the power grid. As the sensing infrastructure grows, aggregating and handling high volumes of data at a centralized location is extremely difficult. To address this challenge, this paper first proposes a novel and efficient hierarchical spectral clustering-based network partition algorithm followed by a decentralized compressive sensing (DCS) based state estimation. The applicability of the proposed network partitioning algorithm is tested on IEEE-123 bus, IEEE-8500 node, and a 6204-node distribution network. The results shows that the proposed approach efficiently divides the network into multiple sub-networks with the minimum edge connections among the neighbors. Then, we perform DCS-based state estimation on the 6204-node distribution network after dividing the network into 18 optimal partitions. Simulation results show that DCS-based state estimation recovers the system states with high accuracy and low complexity.

ADMM↗

An Assessment of Deploying Advanced Pumped Storage Hydropower Technology in U.S. Electricity Markets

Pumped storage hydropower (PSH) is a type of hydropower technology where energy can be stored and generated by moving water between two reservoirs of differing elevations. In addition to providing 97% of the total utility-scale hydropower storage in the United States, PSH plants have operational characteristics - such as high ramp rates and the ability to provide reserves - that contribute to greater flexibility and reliability of the power grid. New PSH technologies can provide additional flexibility beyond existing, fixed-speed units. With the emergence of high levels of variable renewable energy resources (e.g., wind and solar), energy storage is expected to be crucial to the reliability and reliance of the power grid in a low-carbon future. At diurnal and longer durations, PSH plants have some of the lowest costs per unit of energy, have been proven to be reliable and efficient, are not cycle-limited, and typically have long lives, often exceeding 50 years. The work presented here focuses on a new generation of PSH: ternary PSH and quaternary PSH (together referred to as T/Q-PSH). Given recent experience in Europe, grid operators in the United States and elsewhere are increasingly focusing their attention on T/Q-PSH as a proven, financeable technology that can offer utility-scale, long-duration, fast-acting energy storage capabilities and grid services. T/Q-PSH differs from conventional PSH in that it can provide fast-response ancillary services during both generating and pumping operations. This capability together with fast mode switching times are key attributes that make T/Q-PSH technologies attractive for managing and stabilizing electricity systems with high amounts of variable renewable energy.

ENERGY PLANNING, POLICY, AND ECONOMY,HYDRO ENERGY↗

Online Detection of Power Grid Anomalies via Federated Learning

Data from sensors is critical for advanced applica- tions that support efficient, reliable, and resilient electric grid operations. Historically, data from phasor measurement units (PMU) has been utilized to develop a wide variety of wide area control and protection applications suitable for power grid control centers. However, until now, most of these could not be deployed for automated operations due to a set of data corruption challenges and uncertainty in the incoming data pipeline. In this paper, we address the problem of detecting different variety of anomalies that are evident in different high- speed power grid measurements. The paper discusses a workflow for handling problems with data acquisition and highlights some of the key findings suitable for anomaly detection in a centralized and distributed environment. The effectiveness of the proposed method was demonstrated with results utilizing realistic PMU datasets

Shinkle, Matthew W.↗

Machine Learning Solutions for a Stable Grid Recovery

Grid operating security studies are typically employed to establish operating boundaries, ensuring secure and stable operation for a range of operation under NERC guidelines. However, if these boundaries are severely violated, existing system security margins will be largely unknown, as would be a secure incremental dispatch path to higher security margins while continuing to serve load. As an alternative to the use of complex optimizations over dynamic conditions, this work employs the use of machine learning to identify a sequence of secure state transitions which place the grid in a higher degree of operating security with greater static and dynamic stability margins. Several reinforcement learning solution methods were developed using deep learning neural networks, including Deep Q-learning, Mu-Zero, and the continuous algorithms Proximal Reinforcement Learning, and Advantage Actor Critic Learning. The work is demonstrated on a power grid with three control dimensions but can be scaled in size and dimensionality, which is the subject of ongoing research.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Outdoor Test Bed Performance of a Power Line Sensor Using a Real-Time Event Simulator

This report summarizes the design and application of an outdoor power line sensor testbed (OPLST) with a real-time simulator and power meter to compare a potential transformer (PT) and current transformer (CT) versus an advanced line-post sensor. The OPLST was created to validate advanced medium-voltage (20/34.5 kV) outdoor power line sensors used in electrical distribution systems. Electrical utilities have installed metering and relay-protection transformers like PTs and CTs for several decades. The PTs/CTs are iron core measurement transformers based on the electromagnetic induction principle and provide reliable data in normal grid operation. However, new outdoor power line sensors (OPLS) using other technologies like voltage divider, Rogowski coil and optical principles have become available and may have favorable performance and costs compared to PTs/CTs. Therefore, the importance of testing these technologies with the PT/CT, to compare the measured phase voltage/current at different power grid scenarios is crucial to understand the performance of these new OPLS. For this study a G&W Electric Model CVS-36-O power line sensor was chosen as the OPLS to test with voltage/current signals. An OPAL-RT Technologies Model OP4510 real-time simulator and Schweitzer Engineering Laboratories Model SEL-735 power meter were installed with the 20/34.5 kV OPLST to compare the measured transient events collected from an advanced OPLS and the PT/CT. This system is installed at the Distributed Energy Communications and Control (DECC) lab, Oak Ridge National Laboratory (ORNL). The simulator generated different power grid scenarios (electrical faults, capacitor bank operation, service restoration, etc.), and its analog-output signals were connected to the voltage/current amplifiers that feed the 20/34.5 kV aerial cable loop through the PT/CT devices. Additional PT/CT devices were also wired with the medium voltage aerial cable loop to measure the phase current/voltage signals and server as references. After each test, common format for transient data exchange (COMTRADE) files were collected from the SEL-735 power meter and used to compare the performance of the OPLS with the PT/CT. The behavior of analog signals, harmonic components, total harmonic distortion and crest factors were assessed and found favorable for the G&W sensor as compared to the reference PT/CT.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Leveraging Existing Assets for Long Duration Energy Storage

Increased renewables penetration to electrical grid is necessary to reduce overall emissions from the electrical power generation sector. Nonetheless, its integration creates challenges to grid operators who must match the power being generated by intermittent renewables and other traditional energy sources with the demand from consumers, while ensuring the reliability and power quality for the entire system. Energy storage has been proposed as an alternative to natural gas peaking plants and a form to deliver excess renewable energy generation at times of peak demand. For energy storage to provide benefits to end customers (energy consumers), it must be reliable, efficient, and cost effective. The Illinois Sustainable Technology Center (ISTC), one of the surveys that integrate the Prairie Research Institute (PRI), aims to develop a Center for Energy Storage at Existing Assets (CESEA) at UIUC with the participation of Waste Pressure Corp and Ecotek Engineering USA LLC. CESEA will focus on LDES systems that can integrate to existing infrastructure in a manner that reduces the initial capital expenditure and demonstrates the ability to repurpose fossil assets that would otherwise become stranded, to serve the energy transition. CESEA aims to leverage UIUC’s unique facilities to validate LDES systems performance at a relevant operating environment. UIUC’s facilities include a 85-MW combined heat and power (CHP) power plant, two (2) solar PV plants totaling over 18 MWdc of installed capacity, an electrical grid along with a substation at transmission and distribution voltages, a 22-mile gas pipeline network operating at two pressure levels, along with steam and chilled water distribution networks. The new LDES systems will connect to the existing UIUC grid through a new test electrical station, which will have the capacity to accommodate additional connections to test new devices and technologies as part of future CESEA R&D activities. The test electrical station will contain meters, instrumentation, and controls to accurately capture data and allow optimization of control algorithms. CESEA will initially focus on technologies that: i) utilize existing equipment or facilities to perform at least one of the process steps in LDES (charging, storage, or discharging), ii) leverage mature or commercially available components or controls, iii) show potential for cost-leadership in 10+ hour storage at a commercial scale. Initial technologies that were identified to meet these criteria include Compressed Gas Energy Storage (CGES), and TES. CGES stores electricity by raising the pressure of a compressible gas inside a control volume and converting the stored energy to electricity via expansion-generation. CGES is a generalization of CAES that covers any working gas (not just air). A successful CGES demo will help to circumvent many challenges faced by CAES (long development times due to site prospecting, high cost of compression and storage, heat recovery management, etc.) by: 1) utilizing existing infrastructure (compressors, pipelines, underground storage or pressure vessels) used in the transportation and storage of industrial gases for LDES charging and storage; 2) deploying over sites already-developed for industrial applications with minor additional work; 3) leveraging the price structure of commercial industrial gas to cover the costs of electricity used during charging. A previous DOE-sponsored conceptual study (DE-FE-0032018) estimated the levelized cost of energy of a 1.1 MW / 17 MWh CGES system at $0.08/kWh, with a commercial 10x scale system cost estimated at <$0.04/kWh (Giardinella, 2022). The pilot-sized system was estimated to avoid up to 2693 tons of CO2/year.

25 ENERGY STORAGE↗

Improving the Power Outage Resilience of Buildings with Solar PV through the Use of Battery Systems and EV Energy Storage

Buildings with solar photovoltaic (PV) generation and a stationary battery energy storage system (BESS) may self-sustain an uninterrupted full-level electricity supply during power outages. The duration of off-grid operation is dependent on the time of the power fault and the capabilities of the home energy management system (HEMS). In this paper, building resilience is quantified by analyzing the self-sustainment duration for all possible power outages throughout an entire year. An evaluation method is proposed and exercised on a reference house in California climate zone 9 for which the detailed electricity usage is simulated using the EnergyPlus software. The influence of factors such as energy use behavioral patterns, energy storage capacity from the BESS, and an electric vehicle (EV) battery on the building resilience is evaluated. Varying combinations of energy storage and controllable loads are studied for optimally improved resilience based on user preferences. It is shown that for the target home and region with a solar PV system of 7.2 kW, a BESS with a capacity of 11 kWh, and an EV with a battery of 80 kWh permanently connected to the home, off-grid self-sustained full operation is guaranteed for at least 72 h.

14 SOLAR ENERGY↗

Efficient Network Partitioning: Application for Decentralized State Estimation in Power Distribution Grids

Increase in the proliferation of distributed energy resources require real-time situational awareness for efficient grid operations. State estimation plays an important role for the real-time control and management of the power grid. As the sensing infrastructure grows, aggregating and handling high volumes of data at a centralized location is extremely difficult. To address this challenge, this paper first proposes a novel and efficient hier-archical spectral clustering-based network partitioning algorithm followed by a decentralized compressive sensing (DCS)-based state estimation. The applicability of the proposed network partitioning algorithm is tested on an IEEE 123-bus network, an IEEE 8,500-node system, and a 6,000+ node distribution network. The results shows that the proposed approach efficiently divides the network into multiple sub-networks with the minimum number of edge connections among the neighbors. Then, we perform DCS-based state estimation on the 6,000+ node distribution network after dividing the network into 18 optimal partitions. Simulation results show that the DCS-based state estimation recovers the system states with high accuracy and low complexity.

alternating direction method of multipliers↗

Tapless Voltage Regulating Transformer. Final Report

Voltage regulation is a common practice in power grid operation. The conventional voltage regulation methods have been facing ever increasing challenges as the penetration of renewable generations deepens in the grid. The intermittent nature of the renewable sources may result in sudden rise or drop of power generation, possibly breaking the generation-load balance. A lack of adequate voltage control not only affects the power quality but is detrimental to the system operation with the potential for large economic losses. Most conventional voltage regulation devices rely on mechanical tap changers to impose the regulation in a step-wise manner. Although sufficient for normal operations, the mechanical tap changers can be easily worn out in the challenging situations created by renewables. In this project, a novel technology called tapless voltage regulating transformer (TAREX) was proposed and investigated. It has the potential to excel at addressing the new voltage regulation challenges and replace the conventional regulating transformers. TAREX is based on the concept of the saturable-core reactor (SCR). The voltage of a TAREX can be smoothly and continuously regulated in a reasonable range by using a low rating power electronics based controller. It uses control of the transformer core magnetization to regulate the voltage on the windings, and thus eliminates the mechanical tap changer. This project is to prove the principle of TAREX, understand the voltage regulation mechanism, and preliminarily understand its impacts on the power systems through modeling, prototyping, and testing.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Grid-Forming Inverter-Based Resource Research Landscape: Understanding the Key Assets for Renewable-Rich Power Systems

The shift to net zero energy systems has changed the face of our power grid. Traditional large-scale synchronous generators found inside coal and natural gas plants are being replaced with inverter-based resource (IBR) technologies. This transition to an IBR-dominant power grid introduces new characteristics, altering how our grid operates. Therefore, the role of IBRs has expanded, requiring them to provide a range of essential services to keep our grid reliable, resilient, and secure.

energy management↗

A Robust Event Diagnostics Platform: Integrating Tensor Analytics and Machine Learning into Real-time Grid Monitoring

The objective of this project is to develop a robust event diagnostics (RED) platform by integrating state-of-the-art tensor analytics and machine learning into real-time grid monitoring. The proposed platform can effectively analyze and discover the information hiding within the provided PMU data for effective real-time grid monitoring. The proposed RED platform provides a set of robust diagnostics tools for grid operation and management, including 1) data quality assessment, 2) data completion, 3) event detection, and 4) robust event classification. All the functionalities of the RED platform can help the operator to make informed decisions and respond in a timely manner. The developed RED platform will serve as an innovative advisory tool to reliably identify key events and discover new insights about the events and grid characteristics in the PMU data, and contribute to the efficient, safe, reliable operation and design of the nation’s electric system.

24 POWER TRANSMISSION AND DISTRIBUTION↗