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Results for “universal power supply”

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 19 records

Modeling Framework for Data Center

This chapter highlights the critical need for advanced modeling of data centers due to their rapidly increasing energy consumption and impact on grid reliability. Driven by the demand for AI applications, data centers are projected to consume a significant portion of US energy by 2028, putting stress on an already challenged power grid. The chapter emphasizes the importance of "fast" time-scale models to understand the dynamic interactions between data centers and the grid, especially given the rapid power fluctuations of AI workloads. It outlines a modeling framework that includes both offline and real-time EMT domain simulations, detailing the necessary representations for various components like utility interfaces, transformers, IT loads, UPS, cooling loads, Battery Energy Storage Systems (BESS), generators, protection systems, and higher-level control systems. While standard simulation tools like PSCAD offer basic models, custom development is often required to accurately capture the unique and fast-changing behaviors of modern data centers. The chapter also discusses key metrics and test cases for validating these models, focusing on transient load responses, protection relay coordination, and demand flexibility. Finally, it addresses the challenges of modeling large-scale data centers, such as computational complexity and the trade-off between model fidelity and practicality, suggesting hybrid modeling approaches as a solution. The overarching goal is to create a robust framework that helps assess data center impacts on grid stability, identify vulnerabilities, and inform the development of standards for reliable integration of these large loads into the bulk power system.

25 ENERGY STORAGE↗

Optimal operation of multi-plant steam district heating systems for enhanced efficiency and sustainability

Despite their crucial role in supplying heat and power to universities, industries, and healthcare facilities, many steam-based district heating systems rely on outdated control methods. Among these, multi-central plant districts are particularly challenging due to the complexities of coordinating multiple plants, optimizing load distributions, and managing system downtime. In response, new operational strategies are developed to enhance the efficiency and sustainability of steam districts while utilizing existing resources. These strategies include reducing plant operational pressure without compromising the reliable supply to buildings and optimizing load allocation across multiple plants. The load allocation considers boiler part-load efficiency, runtime, network losses, and building pressure set points, and is compared with traditional multi-boiler controls. To support this exploration, new dynamic Modelica models are developed. In addition, methods to reduce modeling complexities are incorporated, enhancing their suitability for practical applications. A holistic district-wide analysis using a real university case study demonstrates a 4.7% fuel savings by lowering boiler operational pressure from 900 kPa to 600 kPa, along with a 13.3% reduction in condensation losses across the distribution network. Furthermore, the load allocation approach results in a 13.1% reduction in fuel consumption during peak winter periods and 15.3% during shoulder periods, with corresponding decreases in carbon emissions and fuel costs. This approach can also save maintenance costs by reducing the boiler runtime by 49.6%. In conclusion, this research underscores the benefits of retrofitting aging steam district heating systems, offering immediate operational improvements by enhancing efficiency, meeting regulatory compliance, and extending infrastructure lifespans while delaying costly overhauls.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Commissioning of the Power Supplies and Coils of the SMART Tokamak

The small aspect ratio tokamak (SMART) is a spherical tokamak (ST) that offers unique capabilities for studying the potential of negative triangularity. It has been designed, constructed, and is currently being operated by the Plasma Science Fusion Technology (PSFT) Laboratory at the University of Seville. SMART has a total of 21 coils, organized into seven independent circuits and driven by five modular power supplies (PS). The PS operation relies on switching converter technology based on IGBT and supercapacitors (SCs). The PS delivers predefined current waveforms to the copper coil system consisting of the central solenoid (CS), 12 toroidal field (TF) coils, three series pairs of poloidal field (PF) coils, and two independent PF coils. This study details the commissioning and validation of the PS toroidal and solenoid coils, as well as the assembly of coils in SMART. The maximum rated current and slope were measured, along with the series impedance of the coils, the output current ripple, and the noise levels. The internal parameters of the SCs were measured, and optimized current profiles were proposed to enhance overall performance. A comparison has been made between the theoretical values and the experimental results, providing insight into the performance of the PS and areas for improvement.

Power electronics↗

MADWEC Techno-Economic Analysis: Cooperative Research and Development Final Report

The objective of this project was for the facility to conduct a techno-economic assessment of the Maximal Asymmetric Drag Wave Energy Converter (MADWEC), developed by the University of Massachusetts Dartmouth (UMass Dartmouth), used for powering remote monitoring and AUV charging systems compared to other existing power supply options. The assessment estimates capital expenditures (CapEx), operational expenditures (OpEx), and power performance for 18 scenarios with the purpose of identifying key cost drivers, comparing total system cost, and comparing the power performance of the power supply options in terms of required installed capacity and estimated theoretical annual energy performance. The scenarios include two end-uses: (1) AUV charging and (2) offshore remote monitoring); three power sources: (1) MADWEC), (2) photovoltaic (PV) solar buoy, (3) and traditional battery swapping); and three locations; (1) nearshore, (2) far-offshore, and (3) high-latitude). In addition, other project goals included developing high level installation, operation, and maintenance plans for each scenario.

16 TIDAL AND WAVE POWER↗

Reestablishing larval connectivity in an estuarine landscape: the importance of shoreline and subtidal oysters ( Crassostrea virginica ) in a comprehensive oyster restoration program

The decline of oyster reefs in estuaries has resulted from a combination of chronic and acute disturbances. The loss has resulted in decreased yield for the oyster fishery as well as a decline in ecological benefits that has led to increased efforts to restore oyster reefs. The need for scientific guidance in accomplishing these restoration goals has become even more pressing in the northcentral Gulf of Mexico in the wake of injury to oyster reefs resulting from the Deepwater Horizon oil spill. Restoration of both the shallow, marsh-fringing oyster aggregations and the deeper subtidal oyster reefs is necessary. Historically, fringing oysters have been overlooked in the oyster habitat landscape because of their limited commercial value. Here, we use a biophysical transport model to examine the transport and settlement of oyster larvae in known oyster reefs along the coast of the northcentral Gulf of Mexico. The modeling demonstrated that the majority of oyster larvae settle within the embayment (>98%) or sub-basin (>65%) of their origin. Additionally, the model demonstrated the importance of fringing oysters as a source of larvae to re-seed other fringing oysters along marsh edges as well as subtidal oyster reefs. We conclude that networks of reefs, including both fringing oyster habitat and subtidal oyster reefs within sub-basins, are necessary to provide resilience to the population at the sub-basin level. Finally, we conclude that fringing oyster habitat may serve as an archipelago-like network to enhance larval supply and connectivity for oysters throughout the mesosaline portions of estuaries.

ADCIRC↗

Pilot Heavy-Duty Electric Vehicle Deployment for Anchorage, Alaska, Municipal Solid Waste Collection

Through a grant awarded by the U.S. Department of Energy, the Municipality of Anchorage initiated a pilot program in their Solid Waste Services (SWS) department to add heavy-duty electric trucks to its vehicle fleet. The project involves the purchase and deployment of a Peterbilt 220EV electric box truck and two Peterbilt 520EV heavy-duty electric refuse trucks. The Alaska Center for Energy and Power at the University of Alaska Fairbanks performed data analysis. Data collected include telemetry data from both types of electric trucks, charging data from the 520EV telemetry data and a Level 2 charger, and facility-level electric use data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

ChargePoint Level 2 Charging Data

This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Pilot Heavy-Duty Electric Vehicle Deployment for Anchorage, Alaska, Municipal Solid Waste Collection

Through a grant awarded by the U.S. Department of Energy, the Municipality of Anchorage initiated a pilot program in their Solid Waste Services (SWS) department to add heavy-duty electric trucks to its vehicle fleet. The project involves the purchase and deployment of a Peterbilt 220EV electric box truck and two Peterbilt 520EV heavy-duty electric refuse trucks. The Alaska Center for Energy and Power at the University of Alaska Fairbanks performed data analysis. Data collected include telemetry data from both types of electric trucks, charging data from the 520EV telemetry data and a Level 2 charger, and facility-level electric use data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

ChargePoint Level 2 Charging Data

This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

ChargePoint Level 2 Charging Data

This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

ChargePoint Level 2 Charging Data

This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

ChargePoint Level 2 Charging Data

This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

ChargePoint Level 2 Charging Data

This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

ChargePoint Level 2 Charging Data

This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

ChargePoint Level 2 Charging Data

This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

ChargePoint Level 2 Charging Data

This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

ChargePoint Level 2 Charging Data

This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

ChargePoint Level 2 Charging Data

This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗