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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Demand Driven Energy Management for PIPO Auxiliary Power Supply Architecture

In this paper, a demand driven energy management (DDEM) technique is implemented on modular parallel input parallel output DC/DC auxiliary power supply (APS) structure. In this method, modules are brought in/out of the operation making them operate under high loading conditions, avoiding low efficiency under light load. Such a method finds its application in power conservative systems with variable load. Implementation of DDEM and the energy saved is shown using APS comprised of two 15W commercially off-the-shelf DC-DC modules, with 100–1000 VDC input and 24 V output voltage. Along with the DDEM, the lifetime of the power stage devices is controlled minimizing their junction temperature swing through control of cooling system. This control method uses digital twin as interface to virtualize the power devices loading operation/ health condition. Accordingly, no extra sensor or closed loop control required to measure the junction temperature of the devices or feedback the losses to the cooling flow.

Azadeh, Yalda↗
DOE OSTI2019

Optimum Planning for Inductively Charged On-Demand Automated Electric Shuttles at Greenville, South Carolina

Wireless charging technology presents perfect fits for autonomous electric vehicles for realizing a fully automated system (vehicle and charger). This paper presents a planning optimization analysis for a pilot project of in-route wireless charging infrastructure serving fixed-route on-demand shared automated electric shuttles (SAESs) at Greenville, South Carolina, USA. A single-objective non-linear integer planning optimization problem is formulated. A comprehensive cost function representing the inductively charged SAESs is developed, considering road construction, power electronics and materials, traction battery, and installation costs. The optimization problem is solved to determine the best combination of the system key design parameters (number and allocations of wireless chargers, charging power level, track length and on-board battery capacity) that show the most cost-effective solution and allow the SAESs realizing charge sustaining operation. The planning platform incorporates representative simulated traffic data (drive cycles and routes) for four SAESs at Greenville project using the Simulation of Urban Mobility (SUMO) tool. These data are fed to a vehicle powertrain model and a wireless charger power model to predict the battery power, energy and state-of-charge (SOC) profiles, which are provided to the search algorithm to assess the design objectives under specific constraints. The results indicated that implementing wireless charging at a few designated stops for fixed-route SAESs with proper design allows the vehicles realizing charge sustaining operation, infinite range and zero recharge downtime, with a significant reduction in the on-board battery (36 % ) and road coverage (69%), at minimum cost.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Preparing an on-Demand Cloud Processing Workflow for NISAR Ecosystems Science Products

In preparation for the NISAR launch and data collection in 2024, the NISAR Project Science Team is building workflows for each Science Team discipline (Ecosystems, Cryosphere, and Solid Earth). This abstract focuses on the Ecosystem disciplines and the development of on-demand cloud-processing workflows for wetlands inundation, forest biomass, agricultural active crop area, and forest disturbance. The workflow simulates NISAR data using UAVSAR or ALOS-2 Single Look Complex data, which are processed to Level 2 geocoded polarimetric covariance matrix products using InSAR Scientific Computing Environment 3.0 software and to Level 3 science products using the Algorithm Theoretical Basis Documents. In this presentation, we describe these workflows and efforts to improve efficiency and data accessibility by using a cloud processing system. We present preliminary sample products from each Ecosystem discipline: inundation, forest biomass, crop area, and forest disturbance.

Christensen, Alexandra↗
DOE OSTI2021

Inertia Emulation Control using Demand Response via 5G Communications

Building energy equipment is moving rapidly towards Internet of Things (IoT)-driven devices to provide consumer connectivity and device management. These device-level interfaces along with 5G communications will be leveraged to develop control architectures to engage a large number of monitoring and control devices and provide real-time and reliable energy services. Emerging 5G networks have high potential to provide the communication technology for demand response, with fast transfer speed, high reliability, and high number of connections. Guaranteed inertial response to limit frequency fluctuations is one of the main challenges in modern power systems due to the increased penetration of renewable generation, and it is largely affected by communication delays and packet losses. This paper analyzes inertial response and rate of change of frequency in a power system model with inverter-interfaced air conditioners. The control loop considers time delays and packet losses to show the need to switch to 5G networks in future smart grids.

Morovati, Samaneh↗
DOE OSTI2015

Frequency Responsive Demand in U.S. Western Power System Model

Frequency control from the demand side presents a novel and viable way to complement control by traditional generators. This paper presents simulation tests of a previously developed load controller in a large interconnected system model. The controller was implemented as a user defined model in PowerWorld software. WECC system model was used. The simulation tests include comparison of situation with and without control, sensitivities to controller location, control gain parameters, and type of end-use loads. The performance is measured in terms of frequency and intertie power flow deviations after a large generator outage.

Power system dynamics, power system control↗
DOE OSTI2017

Voltage-Load Sensitivity Matrix Based Demand Response for Voltage Control in High Solar Penetration Distribution Feeders

This paper presents a voltage-load sensitivity matrix (VLSM) based voltage control method to deploy demand response resources for controlling voltage in high solar penetration distribution feeders. The IEEE 123-bus system in OpenDSS is used for testing the performance of the preliminary VLSM-based voltage control approach. A load disaggregation process is applied to disaggregate the total load profile at the feeder head to each load nodes along the feeder so that loads are modeled at residential house level. Measured solar generation profiles are used in the simulation to model the impact of solar power on distribution feeder voltage profiles. Different case studies involving various PV penetration levels and installation locations have been performed. Simulation results show that the VLSM algorithm performance meets the voltage control requirements and is an effective voltage control strategy.

distribution system modeling↗
DOE OSTI2017

A Study of the Impact of Peak Demand on Increasing Vulnerability of Cascading Failures to Extreme Contingency Events

The vulnerability of large power systems to cascading failures and major blackouts has become evident since the Northeast blackout in 1965. Based on analyses of the series of cascading blackouts in the past decade, the research community realized the urgent need to develop better methods, tools, and practices for performing cascading-outage analysis and for evaluating mitigations that are easily accessible by utility planning engineers. PNNL has developed the Dynamic Contingency Analysis Tool (DCAT) as an open-platform and publicly available methodology to help develop applications that aim to improve the capabilities of power planning engineers to assess the impact and likelihood of extreme contingencies and potential cascading events across their systems and interconnections. DCAT analysis will help identify potential vulnerabilities and allow study of mitigation solutions to reduce the risk of cascading outages in technically sound and effective ways. Using the DCAT capability, we examined the impacts of various load conditions to identify situations in which the power grid may encounter cascading outages that could lead to potential blackouts. This paper describes the usefulness of the DCAT tool and how it helps to understand potential impacts of load demand on cascading failures on the power system.

indexes↗
DOE OSTI2016

Hardware-in-the-Loop Co-simulation of Distribution Grid for Demand Response

In modern power systems, co-simulation is proposed as an enabler for analyzing the interactions between disparate systems. This paper introduces the co-simulation platform Virtual Grid Integration Laboratory (VirGIL) including Hardware-in-the-Loop testing, and demonstrates its potential to assess demand response strategies. VirGIL is based on a modular architecture using the Functional Mock-up Interface industrial standard to integrate new simulators. VirGIL combines state-of-the-art simulators in power systems, communications, buildings, and control. In this work, VirGIL is extended with a Hardware-in-the-Loop component to control the ventilation system of a real 12-story building in Denmark. VirGIL capabilities are illustrated in three scenarios: load following, primary reserves and load following aggregation. Experimental results show that the system can track one minute changing signals and it can provide primary reserves for up-regulation. Furthermore, the potential of aggregating several ventilation systems is evaluated considering the impact at distribution grid level and the communications protocol effect.

Rotger-Griful, Sergi↗
DOE OSTI2020

Time-of-Use and Demand Charge Battery Controller Using Stochastic Model Predictive Control

Stationary batteries in residential and commercial buildings are often used to smooth customer load profiles and to lower customer electricity bills. Controllers for these battery systems should account for customer energy consumption, rate structures, and high internal battery temperatures, which can lead to reduced performance over the battery lifetime. It is important to consider the uncertainty in forecasting energy consumption and temperature, especially for customers with highly variable and uncertain loads. We propose a novel battery controller using stochastic model predictive control that accounts for these uncertainties and can handle complex rate structures, including demand charges. We show that the controller performs better than standard model predictive control when there is significant uncertainty in the forecast. We also show improvements in the performance with more accurate forecasts and with a more aggressive control strategy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Planning Optimization for Inductively Charged On-demand Automated Electric Shuttles Project at Greenville, South Carolina

Wireless charging technology presents an ideal fit for autonomous electric vehicles for realizing a fully automated system (vehicle and charger). This paper presents a planning optimization analysis for a pilot project of in-route wireless charging infrastructure serving fixed-route on-demand shared automated electric shuttles (SAESs) at Greenville, South Carolina, USA. A single-objective non-linear mixed integer system planning optimization problem is formulated. A comprehensive cost model representing the overall inductively charged SAESs system is developed, considering road construction, power electronics and materials, traction battery, and installation costs. The optimization problem is solved to determine the best combination of the system key design parameters (number and allocations of wireless chargers, charging power level, track length and on-board battery capacity) that show the most cost-effective solution and allow the SAESs to achieve charge-sustaining operation. The planning platform incorporates representative simulated traffic data (driving speed and routes) for four SAESs in the Greenville using the Simulation of Urban Mobility tool. These data are fed to a vehicle powertrain model and a wireless charger power model to predict the battery power, energy and state-of-charge profiles, which are provided to the search algorithm to assess the design objectives under specific constraints. Finally, the results indicate that implementing high-power (100 kW) wireless charger at a few designated stops for fixed-route SAESs with the proper track length allows the vehicles to realize charge-sustaining operation, infinite range, and zero recharge downtime, with a significant reduction in the on-board battery (36%) and road coverage (69%), at minimum cost.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Transactive Control of Commercial Buildings for Demand Response

Transactive control is a type of distributed control strategy that uses market mechanism to engage self-interested responsive loads to achieve power balance in the electrical power grid. In this paper, we propose a transactive control approach of commercial building Heating, Ventilation, and Air- Conditioning (HVAC) systems for demand response. We first describe the system models, and identify their model parameters using data collected from Systems Engineering Building (SEB) located on our Pacific Northwest National Laboratory (PNNL) campus. We next present a transactive control market structure for commercial building HVAC system, and describe its agent bidding and market clearing strategies. Several case studies are performed in a simulation environment using Building Control Virtual Test Bed (BCVTB) and calibrated SEB EnergyPlus model. We show that the proposed transactive control approach is very effective at peak clipping, load shifting, and strategic conservation for commercial building HVAC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Optimizing Service Restoration in Distribution Systems With Uncertain Repair Time and Demand

This paper proposes a novel method to co-optimize the distribution system operation and repair crew routing for outage restoration after extreme weather events. A two-stage stochastic mixed integer linear program is developed. The first stage is to dispatch the repair crews to the damaged components. The second stage is distribution system restoration using distributed generators, and reconfiguration. We consider demand uncertainty in terms of a truncated normal forecast error distribution, and model the uncertainty of the repair time using a lognormal distribution. A new decomposition approach, combined with the progressive hedging algorithm, is developed for solving large-scale outage management problems in an effective and timely manner. Furthermore, the proposed method is validated on modified IEEE 34- and 8500-bus distribution test systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

A Hierarchical VLSM-Based Demand Response Strategy for Coordinative Voltage Control between Transmission and Distribution Systems

This paper presents a two-stage hierarchical voltage-load sensitivity matrix (VLSM)-based demand response (DR) algorithm for regulating the voltage at distribution feeders while fulfilling the transmission system DR requests. This allows distributed energy resources be aggregated to provide transmission-level services without causing operational issues in the distribution grids. At the beginning of each operation interval, a transmission system controller will issue DR commands to distribution system controllers. In the first stage, the distribution system controller uses a VLSM-based dispatch algorithm to dispatch the available DR resources on the distribution feeder with an objective to minimize the total voltage deviations at the lowest cost. Then, power flow studies are conducted assuming that the DR commands have been executed. If voltage violations are detected, a second-stage VLSM-based DR dispatch will be performed to remove those violations. After that, the upper and lower DR limits calculated for the next operation interval are sent back to the transmission system controller so the transmission optimization algorithm can use them as operational constraints to make subsequent decisions. The DR resources include smart photovoltaic (PV) inverters that can curtail real power and provide reactive power support, controllable loads and capacitor banks. In conclusion, the IEEE 123-bus test system with 5-minute PV and load data is used to evaluate the performance of the algorithm. In conclusion, simulation results show that the proposed algorithm can fulfill the transmission-level DR requests while maintaining the voltage in the distribution system within limits.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Agent-Based Simulation of Price-Demand Dynamics in Multi-Service Charging Station

As the adoption of electric vehicles and hydrogen fuel-cell vehicles grows, understanding how dynamic pricing strategies influence charging and refueling behaviors becomes crucial for optimizing local energy markets. This paper proposes a simulation-based analysis of a hydrogen-electricity integrated charging station that serves both types of vehicles. A multi-agent simulation framework is developed to model the interactions between vehicles and the station, incorporating price- and delay-sensitive behaviors in decision-making. The station can dynamically adjust energy prices, while vehicles optimize their charging or refueling choices based on their utility values. A series of sensitivity analyses are conducted to evaluate how electricity pricing, infrastructure capacity, and waiting behavior impact station performance. Results highlight that moderate electricity prices maximize user participation without sacrificing profit, infrastructure should be right-sized to demand to avoid over- or underutilization, and delay-toleration also affects service outcomes, which may reach the maximum service coverage at the threshold of 45 minutes.

Wang, Xudong [University of Tennessee, Knoxville (↗
DOE OSTI2016

Consumption-Based Conservation Targeting: Linking Biodiversity Loss to Upstream Demand through a Global Wildlife Footprint: Global wildlife footprint

Although most conservation efforts address the direct, local causes of biodiversity loss, effective long-term conservation will require complementary efforts to reduce the upstream economic pressures, such as demands for food and forest products, which ultimately drive these downstream losses. Here, we present a wildlife footprint analysis that links global losses of wild birds to consumer purchases across 57 economic sectors in 129 regions. The United States, India, China, and Brazil have the largest regional wildlife footprints, while per-person footprints are highest in Mongolia, Australia, Botswana, and the United Arab Emirates. A US$100 purchase of bovine meat or rice products occupies approximately 0.1 km 2 of wild bird ranges, displacing 1–2 individual birds, for 1 year. Globally significant importer regions, including Japan, the United Kingdom, Germany, Italy, and France, have large footprints that drive wildlife losses elsewhere in the world and represent important targets for consumption-focused conservation attention.

Biodiversity & Conservation↗
DOE OSTI2016

SU-F-P-13: NRG Oncology Medical Physics Manpower Survey Quantifying Support Demands for Multi Institutional Clinical Trials

Purpose: A survey was taken by NRG Oncology to assess Full Time Equivalent (FTE) contributions to multi institutional clinical trials by medical physicists.No current quantification of physicists’ efforts in FTE units associated with clinical trials is available. The complexity of multi-institutional trials increases with new technologies and techniques. Proper staffing may directly impact the quality of trial data and outcomes. The demands on physics time supporting clinical trials needs to be assessed. Methods: The NRG Oncology Medical Physicist Subcommittee created a sixteen question survey to obtain this FTE data. IROC Houston distributed the survey to their list of 1802 contact physicists. Results: After three weeks, 363 responded (20.1% response). 187 (51.5%) institutions reporting external beam participation were processed. There was a wide range in number of protocols active and supported at each institution. Of the 187 clinics, 134 (71.7%) participate in 0 to 10 trials, 28 (15%) in 11 to 20 trials, 10 (5.3%) in 21 to 30 trials, 9 (4.8%) had 40 to 75 trials. On average, physicist spent 2.7 hours (SD: 6.0) per week supervising or interacting with clinical trial staff. 1.25 hours (SD: 3.37), 1.83 hours (SD: 4.13), and 0.64 hours(SD: 1.13) per week were spent on patient simulation, reviewing treatment plans, and maintaining a DICOM server, respectively. For all protocol credentialing activities, physicist spent an average of 37.05 hours (SD: 96.94) yearly. To support dosimetrists, clinicians, and therapists, physicist spend on average 2.07 hours (SD: 3.52) per week just reading protocols. Physicist attended clinical trial meetings for on average 1.13 hours (SD: 1.85) per month. Conclusion: Responding physicists spend a nontrivial amount of time: 8.8 hours per week (0.22 FTE) supporting, on average, 9 active multi-institutional clinical trials.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Computational Basis for On-Demand Production of Diversified Therapeutic Phage Cocktails

New therapies are necessary to combat increasingly antibiotic-resistant bacterial pathogens. We have developed a technology platform of computational, molecular biology, and microbiology tools which together enable on-demand production of phages that target virtually any given bacterial isolate. Two complementary computational tools that identify and precisely map prophages and other integrative genetic elements in bacterial genomes are used to identify prophage-laden bacteria that are close relatives of the target strain. Phage genomes are engineered to disable lysogeny, through use of long amplicon PCR and Gibson assembly. Finally, the engineered phage genomes are introduced into host bacteria for phage production. As an initial demonstration, we used this approach to produce a phage cocktail against the opportunistic pathogen Pseudomonas aeruginosa PAO1. Two prophageladen P. aeruginosa strains closely related to PAO1 were identified, ATCC 39324 and ATCC 27853. Deep sequencing revealed that mitomycin C treatment of these strains induced seven phages that grow on P. aeruginosa PAO1. The most diverse five phages were engineered for nonlysogeny by deleting the integrase gene (int), which is readily identifiable and typically conveniently located at one end of the prophage. The Δint phages, individually and in cocktails, killed P. aeruginosa PAO1 in liquid culture as well as in a waxworm (Galleria mellonella) model of infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

DReAM: Demand Response Architecture for Multi-level District Heating and Cooling Networks

In this paper, we exploit the inherent hierarchy of heat exchangers in District Heating and Cooling (DHC) networks and propose DReAM, a novel Demand Response (DR) architecture for Multi-level DHC networks. DReAM serves to economize system operation while still respecting comfort requirements of individual consumers. Contrary to many present day DR schemes that work on a consumer level granularity, DReAM works at a level of hierarchy above buildings, i.e. substations that supply heat to a group of buildings. This improves the overall DR scalability and reduce the computational complexity. In the first step of the proposed approach, mathematical models of individual substations and their downstream networks are abstracted into appropriately constructed low-complexity structural forms. In the second step, this abstracted information is employed by the utility to perform DR optimization that determines the optimal heat inflow to individual substations rather than buildings, in order to achieve the targeted objectives across the network. We validate the proposed DReAM framework through experimental results under different scenarios on a test network.

Bhattacharya, Saptarshi↗