Assessing Inverter-Based Resources Modeling Gaps in Commonly Used Short-Circuit Programs
Explore the source record for details and available documents.
Cheng, Zheyuan [Quanta Technology]↗
SEARCH · Search NASA
Results for “resource model”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Resource Planning Model: An Integrated Resource Planning and Dispatch Tool for Regional Electric Systems
This report introduces a new capacity expansion model, the Resource Planning Model (RPM), with high spatial and temporal resolution that can be used for mid- and long-term scenario planning of regional power systems. Although RPM can be adapted to any geographic region, the report describes an initial version of the model adapted for the power system in Colorado. It presents examples of scenario results from the first version of the model, including an example of a 30%-by-2020 renewable electricity penetration scenario.
24 POWER TRANSMISSION AND DISTRIBUTION↗
NREL's Resource Planning Model
The Resource Planning Model (RPM) is a capacity expansion model designed for a regional power system, such as a utility service territory, state, or balancing authority. Designed from the ground up to include nodal detail, hourly chronological dispatch, and high spatial resolution for renewable resources, recent work has focused on inclusion of storage and other flexible technologies of interest for future power systems with more variable generation (e.g., wind and solar). This slide deck gives an overview of RPM modeling methods, provides example outputs, and briefly summarizes completed analysis projects.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Resource utilization model for the algorithm to architecture mapping model
The analytical model for resource utilization and the variable node time and conditional node model for the enhanced ATAMM model for a real-time data flow architecture are presented in this research. The Algorithm To Architecture Mapping Model, ATAMM, is a Petri net based graph theoretic model developed at Old Dominion University, and is capable of modeling the execution of large-grained algorithms on a real-time data flow architecture. Using the resource utilization model, the resource envelope may be obtained directly from a given graph and, consequently, the maximum number of required resources may be evaluated. The node timing diagram for one iteration period may be obtained using the analytical resource envelope. The variable node time model, which describes the change in resource requirement for the execution of an algorithm under node time variation, is useful to expand the applicability of the ATAMM model to heterogeneous architectures. The model also describes a method of detecting the presence of resource limited mode and its subsequent prevention. Graphs with conditional nodes are shown to be reduced to equivalent graphs with time varying nodes and, subsequently, may be analyzed using the variable node time model to determine resource requirements. Case studies are performed on three graphs for the illustration of applicability of the analytical theories.
Stoughton, John W.↗
Description of a Website Resource for Turbulence Modeling Verification and Validation
The activities of the Turbulence Model Benchmarking Working Group - which is a subcommittee of the American Institute of Aeronautics and Astronautics (AIAA) Fluid Dynamics Technical Committee - are described. The group s main purpose is to establish a web-based repository for Reynolds-averaged Navier-Stokes turbulence model documentation, including verification and validation cases. This turbulence modeling resource has been established based on feedback from a survey on what is needed to achieve consistency and repeatability in turbulence model implementation and usage, and to document and disseminate information on new turbulence models or improvements to existing models. The various components of the website are described in detail: description of turbulence models, turbulence model readiness rating system, verification cases, validation cases, validation databases, and turbulence manufactured solutions. An outline of future plans of the working group is also provided.
Rumsey, Christopher L.↗
Uncertainty analysis of Resource Demand Model for Covid-19
As part of the Department of Energy response to the novel coronavirus pandemic of 2020, a modeling effort was sponsored by the DOE Office of Science. One task of this modeling effort at Sandia was to develop a model to predict medical resource needs given various patient arrival scenarios. Resources needed include personnel resources (nurses, ICU nurses, physicians, respiratory therapists), fixed resources (regular or ICU beds and ventilators), and consumable resources (masks, gowns, gloves, face shields, sedatives). This report documents the uncertainty analysis that was performed on the resource model. The uncertainty analysis involved sampling 26 input parameters to the model. The sampling was performed conditional on the patient arrival streams that also were inputs to the model. These patient arrival streams were derived from various epidemiology models and had a significant effect on the projected resource needs. In this report, we document the sampling approach, the parameter ranges used, and the computational workflow necessary to perform large-scale uncertainty studies for every county and state in the United States.
59 BASIC BIOLOGICAL SCIENCES↗
Payload accommodation and development planning tools - A Desktop Resource Leveling Model (DRLM)
The Desktop Resource Leveling Model (DRLM) has been developed as a tool to rapidly structure and manipulate accommodation, schedule, and funding profiles for any kind of experiments, payloads, facilities, and flight systems or other project hardware. The model creates detailed databases describing 'end item' parameters, such as mass, volume, power requirements or costs and schedules for payload, subsystem, or flight system elements. It automatically spreads costs by calendar quarters and sums costs or accommodation parameters by total project, payload, facility, payload launch, or program phase. Final results can be saved or printed out, automatically documenting all assumptions, inputs, and defaults.
Hilchey, John D.↗
Resource Tracking Model Updates and Trade Studies
The Resource Tracking Model has been updated to capture system manager and project manager inputs. Both the Trick/General Use Nodal Network Solver Resource Tracking Model (RTM) simulator and the RTM mass balance spreadsheet have been revised to address inputs from system managers and to refine the way mass balance is illustrated. The revisions to the RTM included the addition of a Plasma Pyrolysis Assembly (PPA) to recover hydrogen from Sabatier Reactor methane, which was vented in the prior version of the RTM. The effect of the PPA on the overall balance of resources in an exploration vehicle is illustrated in the increased recycle of vehicle oxygen. Case studies have been run to show the relative effect of performance changes on vehicle resources.
Chambliss, Joe↗
Quantifying Uncertainties in Modeling Wind Resource Data from Different PBL Schemes in the WRF Model: A Case Study Over the Puerto Rico Region
This study examines the modeling uncertainty of wind resource data stemming from the use of various planetary boundary layer (PBL) parameterizations available in the Weather Research and Forecasting (WRF) model. WRF-based wind simulations spanning 20 years at 3-km resolution using 11 different PBL schemes are used to objectively investigate the uncertainty in modeling wind speed for land-based wind (LBW) and offshore wind (OSW) locations in Puerto Rico. The uncertainty in the wind modeling for the 20-year dataset is quantified using the spread index (SI) and standard deviation (SD). For virtual LBW and OSW sites, the SI and SD values are analyzed as calculated across various spatial and temporal scales. Because the PBL's atmospheric stability conditions can be characterized into two dominant categories, the study focuses on analyzing the SI and SD for daytime (mainly unstable PBL conditions) and nighttime (mainly stable PBL conditions). For wind shear (10 m-200 m) at the OSW and LBW sites, WRF-based numerical experiments indicate the following SI (or SD) ranges: 39%-94% (0.74 m/s-1.44 m/s) during the daytime for OSW, 50%-75% (0.68 m/s-1.19 m/s) during the daytime for LBW, 37%-60% (0.73 m/s-1.12 m/s) during the nighttime for OSW, and 57%-143 % (0.65 m/s-1.43 m/s) during the nighttime for LBW. While a high SI is observed when modeling LBW during the nighttime, there are notable modeling uncertainties during the daytime on the leeward side of the orographic barriers for Puerto Rico.
17 WIND ENERGY↗
Resource Tracking Model Updates and Trade Studies
The Resource tracking model has been updated to capture system manager and project manager inputs. Both the Trick/GUNNS RTM simulator and the RTM mass balance spreadsheet have been revised to address inputs from system managers and to refine the way mass balance is illustrated. The revisions to the RTM included addition of a Plasma Pyrolysis Assembly (PPA) to recover hydrogen from Sabatier reactor methane which was vented in the prior version of the RTM. The effect of the PPA on the overall balance of resources in an exploration vehicle is illustrated in the increased recycle of vehicle oxygen. Additionally simulation of EVAs conducted from the exploration module was added. Since the focus of the exploration module is to provide a habitat during deep space operations the EVA simulation approach to EVA is based on ISS EVA protocol and processes. Case studies have been run to show the relative effect of performance changes on vehicle resources.
Chambliss, Joe↗
Implications of Model Structure and Detail for Utility Planning: Scenario Case Studies Using the Resource Planning Model
In this report, we analyze the impacts of model configuration and detail in capacity expansion models, computational tools used by utility planners looking to find the least cost option for planning the system and by researchers or policy makers attempting to understand the effects of various policy implementations. The present analysis focuses on the importance of model configurations — particularly those related to capacity credit, dispatch modeling, and transmission modeling — to the construction of scenario futures. Our analysis is primarily directed toward advanced tools used for utility planning and is focused on those impacts that are most relevant to decisions with respect to future renewable capacity deployment. To serve this purpose, we develop and employ the NREL Resource Planning Model to conduct a case study analysis that explores 12 separate capacity expansion scenarios of the Western Interconnection through 2030.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Implications of Model Structure and Detail for Utility Planning. Scenario Case Studies using the Resource Planning Model
We examine how model investment decisions change under different model configurations and assumptions related to renewable capacity credit, the inclusion or exclusion of operating reserves, dispatch period sampling, transmission power flow modeling, renewable spur line costs, and the ability of a planning region to import and export power. For all modeled scenarios, we find that under market conditions where new renewable deployment is predominantly driven by renewable portfolio standards, model representations of wind and solar capacity credit and interactions between balancing areas are most influential in avoiding model investments in excess thermal capacity. We also compare computation time between configurations to evaluate tradeoffs between computational burden and model accuracy. From this analysis, we find that certain advanced dispatch representations (e.g., DC optimal power flow) can have dramatic adverse effects on computation time but can be largely inconsequential to model investment outcomes, at least at the renewable penetration levels modeled. Finally, we find that certain underappreciated aspects of new capacity investment decisions and model representations thereof, such as spur lines for new renewable capacity, can influence model outcomes particularly in the renewable technology and location chosen by the model. Though this analysis is not comprehensive and results are specific to the model region, input assumptions, and optimization-modeling framework employed, the findings are intended to provide a guide for model improvement opportunities.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Assessing the reliability of medical resource demand models in the context of COVID-19
Abstract Background Numerous medical resource demand models have been created as tools for governments or hospitals, aiming to predict the need for crucial resources like ventilators, hospital beds, personal protective equipment (PPE), and diagnostic kits during crises such as the COVID-19 pandemic. However, the reliability of these demand models remains uncertain. Methods Demand models typically consist of two main components: hospital use epidemiological models that predict hospitalizations or daily admissions, and a demand calculator that translates the outputs of the epidemiological model into predictions for resource usage. We conducted separate analyses to evaluate each of these components. In the first analysis, we validated various hospital use epidemiological models using a recent validation framework designed for epidemiological models. This allowed us to quantify the accuracy of the models in predicting critical aspects such as the date and magnitude of local COVID-19 peaks, among other factors. In the second analysis, we evaluated a range of demand calculators for ventilators, medical gowns, and COVID-19 test kits. To achieve this, we decoupled these demand calculators from the underlying epidemiological models and provided ground truth data for their inputs. This approach enabled a direct comparison of the demand calculators, comparing them against each other and actual usage data when available. The code is available athttps://doi.org/10.5281/zenodo.13712387. Results Performance varied greatly across the epidemiological models, with greater variability in COVID-19 hospital use predictions than for COVID-19 deaths as analyzed previously. Some models did not have any peaks. Among those that did, the models under-estimated date of peak approximately as often as they over-estimated, but were more likely to under-estimate magnitude of peak, with typical relative errors around 50%. Regarding demand calculator predictions, there was significant variability, including five-fold differences in predictions for gown models. Validation against actual or surrogate usage data illustrated the potential value of demand models while demonstrating their limitations. Conclusions The emerging field of demand modeling holds promise in averting medical resource shortages during future public health emergencies. However, achieving this potential necessitates focused efforts on standardization, transparency, and rigorous model validation before placing reliance on demand models in critical public health decision-making.
Medical Informatics↗
Modeling Uncertainty in Large Natural Resource Allocation Problems
The productivity of the world's natural resources is critically dependent on a variety of highly uncertain factors, which obscure individual investors and governments that seek to make long-term, sometimes irreversible investments in their exploration and utilization. These dynamic considerations are poorly represented in disaggregated resource models, as incorporating uncertainty into large-dimensional problems presents a challenging computational task. This study introduces a novel numerical method to solve large-scale dynamic stochastic natural resource allocation problems that cannot be addressed by conventional methods. The method is illustrated with an application focusing on the allocation of global land resource use under stochastic crop yields due to adverse climate impacts and limits on further technological progress. For the same model parameters, the range of land conversion is considerably smaller for the dynamic stochastic model as compared to deterministic scenario analysis. The scenario analysis can thus significantly overstate the magnitude of expected land conversion under uncertain crop yields.
Cai, Yongyang↗
Development of a Water Recovery System Resource Tracking Model
A simulation model has been developed to track water resources in an exploration vehicle using Regenerative Life Support (RLS) systems. The Resource Tracking Model (RTM) integrates the functions of all the vehicle components that affect the processing and recovery of water during simulated missions. The approach used in developing the RTM enables its use as part of a complete vehicle simulation for real time mission studies. Performance data for the components in the RTM is focused on water processing. The data provided to the model has been based on the most recent information available regarding the technology of the component. This paper will describe the process of defining the RLS system to be modeled, the way the modeling environment was selected, and how the model has been implemented. Results showing how the RLS components exchange water are provided in a set of test cases.
Chambliss, Joe↗
The Grid Value of Ocean Current Energy in Florida: Preprint
Ocean current technology has been proposed as a potential contributor to Florida's energy portfolio. There has been limited investigation of how this energy would be valued when integrated into the Florida electrical grid. This study assesses three future grid scenarios to evaluate the impact of adding ocean current to each. NREL's capacity expansion model, Resource Planning Model, is used to identify the least-cost generation mix through 2050, with and without ocean current. The first scenario, Business as Usual, Base case assuming current policies, ocean current does not replace fossil-based technologies. In the second scenario, we allow solar and storage to have lower costs than the first scenario which allows ocean current to retire gas earlier and more variable generation technologies to be deployed. In the third scenario, the Florida carbon constraint 95 by 2050 from 2020 levels case, ocean current can play a bigger role in decarbonization than the two other cases when coupled with other technologies.
capacity expansion model↗
Current State, Challenges, and Opportunities in Genome-Scale Resource Allocation Models: A Mathematical Perspective
Stoichiometric genome-scale metabolic models (generally abbreviated GSM, GSMM, or GEM) have had many applications in exploring phenotypes and guiding metabolic engineering interventions. Nevertheless, these models and predictions thereof can become limited as they do not directly account for protein cost, enzyme kinetics, and cell surface or volume proteome limitations. Lack of such mechanistic detail could lead to overly optimistic predictions and engineered strains. Initial efforts to correct these deficiencies were by the application of precursor tools for GSMs, such as flux balance analysis with molecular crowding. In the past decade, several frameworks have been introduced to incorporate proteome-related limitations using a genome-scale stoichiometric model as the reconstruction basis, which herein are called resource allocation models (RAMs). This review provides a broad overview of representative or commonly used existing RAM frameworks. This review discusses increasingly complex models, beginning with stoichiometric models to precursor to RAM frameworks to existing RAM frameworks. RAM frameworks are broadly divided into two categories: coarse-grained and fine-grained, with different strengths and challenges. Discussion includes pinpointing their utility, data needs, highlighting framework strengths and limitations, and appropriateness to various research endeavors, largely through contrasting their mathematical frameworks. Finally, promising future applications of RAMs are discussed.
59 BASIC BIOLOGICAL SCIENCES↗