SEARCH · Search NASA

Results for “open source analysis platform”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10

All sources NASA NTRS DOE OSTI

DOE OSTI2022

AIMSim : An accessible cheminformatics platform for similarity operations on chemicals datasets

The recent advances in deep learning, generative modeling, and statistical learning have ushered in a renewed interest in traditional cheminformatics tools and methods. Quantifying molecular similarity is essential in molecular generative modeling, exploratory molecular synthesis campaigns, and drug-discovery applications to assess how new molecules differ from existing ones. Further, most tools target advanced users and lack general implementations accessible to the larger community. In this work, we introduce Artificial Intelligence Molecular Similarity (AIMSim), an accessible cheminformatics platform for performing similarity operations on collections of molecules called molecular datasets. AIMSim provides a unified platform to perform similarity-based tasks on molecular datasets, such as diversity quantification, outlier and novelty analysis, clustering, dimensionality reduction, and inter-molecular comparisons. AIMSim implements all major binary similarity metrics and molecular fingerprints and is provided as a Python package that includes support for command-line use as well as a Graphical User Interface for code-free utilization with fully interactive plots.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

CisSERS: Customizable In Silico Sequence Evaluation for Restriction Sites

High-throughput sequencing continues to produce an immense volume of information that is processed and assembled into mature sequence data. Here, data analysis tools are urgently needed that leverage the embedded DNA sequence polymorphisms and consequent changes to restriction sites or sequence motifs in a high-throughput manner to enable biological experimentation. Cis SERS was developed as a standalone open source tool to analyze sequence datasets and provide biologists with individual or comparative genome organization information in terms of presence and frequency of patterns or motifs such as restriction enzymes. Predicted agarose gel visualization of the custom analyses results was also integrated to enhance the usefulness of the software. Cis SERS offers several novel functionalities, such as handling of large and multiple datasets in parallel, multiple restriction enzyme site detection and custom motif detection features, which are seamlessly integrated with real time agarose gel visualization. Using a simple fasta-formatted file as input, Cis SERS utilizes the REBASE enzyme database. Results from Cis SERSenable the user to make decisions for designing genotyping by sequencing experiments, reduced representation sequencing, 3’UTR sequencing, and cleaved amplified polymorphic sequence (CAPS) molecular markers for large sample sets. Cis SERS is a java based graphical user interface built around a perl backbone. Several of the applications of Cis SERS including CAPS molecular marker development were successfully validated using wet-lab experimentation. Here, we present the tool Cis SERSand results from in-silico and corresponding wet-lab analyses demonstrating that Cis SERS is a technology platform solution that facilitates efficient data utilization in genomics and genetics studies.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Multifamily Building Stock Modeling: Cooperative Research and Development Final Report, CRADA Number CRD-17-00701

U.S. multifamily buildings house 35 million households, consuming 4 quads of source energy and spending $48 billion on utility bills every year. Almost all of these households live in urban areas, where cities are taking the lead on setting aggressive energy goals. Cities currently do not have the data and tools necessary to identify and target opportunities to save energy in their building stock. Building on the open-source, OpenStudio-based ResStock platform, this project will extend the publicly-available ResStock modeling capabilities to the multifamily sector, enabling Radiant Labs, and others, to partner with cities to market and strategically deploy cost-effective, energy efficiency (EE) upgrades directly to high-priority households. Radiant Labs has been successful in using ResStock's single-family capabilities to provide value to the City of Boulder, Colorado. However, lack of multifamily capabilities in ResStock is a roadblock for Radiant Labs working with New York City, San Francisco, Washington, D.C., and other cities that have expressed significant interest in working with them. In addition, other cities, companies, and utilities can use these open-source capabilities to grow their EE portfolios, thereby multiplying the impact of this project. This work also enables national-scale analysis of EE potential in multifamily buildings, which is of strong interest to a variety of stakeholders, including the U.S. Department of Energy (DOE) Office of Energy Policy and Systems Analysis (EPSA), DOE Weatherization Assistance Program (WAP), U.S. Department of Housing and Urban Development (HUD), and the Bonneville Power Administration (BPA).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

MOOSE ProbML: Parallelized probabilistic machine learning and uncertainty quantification for computational energy applications

Here, this paper presents the development and demonstration of massively parallel probabilistic machine learning (ML) and uncertainty quantification (UQ) capabilities within the Multiphysics Object-Oriented Simulation Environment (MOOSE), an open-source computational platform for parallel finite element and finite volume analyses. In addressing the computational expense and uncertainties inherent in complex multiphysics simulations, this paper integrates Gaussian process (GP) variants, active learning, Bayesian inverse UQ, adaptive forward UQ, Bayesian optimization, evolutionary optimization, and Markov chain Monte Carlo (MCMC) within MOOSE. It also elaborates on the interaction among key MOOSE systems — Sampler, MultiApp, Reporter, and Surrogate — in enabling these capabilities. The modularity offered by these systems enables development of a multitude of probabilistic ML and UQ algorithms in MOOSE. Example code demonstrations include parallel active learning and parallel Bayesian inference via active learning. The impact of these developments is illustrated through five applications relevant to computational energy applications: UQ of nuclear fuel fission product release, using parallel active learning Bayesian inference; very rare events analysis in nuclear microreactors using active learning; advanced manufacturing process modeling using multi-output GPs (MOGPs) and dimensionality reduction; fluid flow using deep GPs (DGPs); and tritium transport model parameter optimization for fusion energy, using batch Bayesian optimization. These capabilities are part of the MOOSE framework.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Control And Optimization Modular Modeling Application For Nuclear Deployment

The purpose of the COMMAND code is to provide a flexible, scalable tool for use in developing, integrating, and testing the technologies necessary for achieving autonomous operations of advanced nuclear reactors. The code enables users to efficiently implement custom simulations and experiments by combining key methods from different software modules. These modules are focused on: modeling and simulation tools, such as nuclear simulation tools used for high-fidelity modeling (e.g., Reactor Excursion and Leak Analysis Program [RELAP5-3D] and Monte Carlo N-Particle [MCNP]); machine learning and optimization tools (e.g., anomaly detection and data-driven modeling techniques); advanced control in its digital, high-performance, and supervisory control forms (e.g., proportional integral derivative (PID) control and model predictive control (MPC); and integration with hardware through industrial communication protocols. To ensure flexibility and scalability, COMMAND was designed to be both modular—the software “pieces” all inherit from generic building blocks and can be combined and connected to create complicated simulations—and high performing—designed for parallel processing, enabling simulations and experiments to take advantage of multi-core computers, servers, and nodes. The code is written in the Python programming language due to the language's popularity, active community, and open-source and cross-platform nature. Maintaining consistency with other simulation tools used within the nuclear energy community, users implement simulations and experiments through text input files, which define components, parameters, connections, etc., through lines of text. Given that COMMAND is written in Python, these input files are native Python scripts, and so use the standard Python structure and formatting. This also enables users to take advantage of Python's extensive package library to develop custom capabilities for their specific use cases.

Faber, Jacob [Idaho National Laboratory (INL), Ida↗

DOE OSTI2018

A Transparent Translation from Legacy System Model into Common Information Model

Advances in the smart grid are forcing utilities toward better monitoring, control, and analysis of distribution systems, and require extensive cyber-based, intelligent systems and applications to realize various functionalities. The ability of systems, or components within systems, to interact and exchange services or information with each other is the key to the success of smart grid technologies, and it requires efficient information exchanging and data sharing infrastructure. The Common Information Model (CIM) is a standard that allows different applications to exchange information about an electrical system, and it has become a widely accepted solution for information exchange among different platforms and applications. However, most existing legacy systems were developed using their own languages rather than CIM. Integrating such legacy systems is a challenge for utilities, and the appropriate use of the integrated legacy systems is even more intricate. Thus, this paper describes an approach and open-source tool developed to translate legacy system models into CIM format. The developed tool was tested for a commercial distribution management system and simulation results have proved its effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2014

Reference Model 2 Full Scale Geometry (RM2: River Current Turbine)

Contains the Reference Model 2 (RM2) full scale geometry files of the River Current Turbine, developed by the Reference Model Project (RMP). These full scale geometry files are saved as SolidWorks assembly, IGS, X_T, and STEP files, and require a CAD program to view. This data was generated upon completion of the project on September 30, 2014. The Reference Model Project (RMP), sponsored by the U.S. Department of Energy (DOE), was a partnered effort to develop open-source MHK point designs as reference models (RMs) to benchmark MHK technology performance and costs, and an open-source methodology for design and analysis of MHK technologies, including models for estimating their capital costs, operational costs, and levelized costs of energy. The point designs also served as open-source test articles for university researchers and commercial technology developers. The RMP project team, led by Sandia National Laboratories (SNL), included a partnership between DOE, three national laboratories, including the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), and Oak Ridge National Laboratory (ORNL), the Applied Research Laboratory of Penn State University, and Re Vision Consulting. Reference Model 2 (RM2) is a variable speed dual-rotor cross-flow river turbine that is deployed at the waters surface. It was designed for deployment at a reference site modeled after a reach in the Mississippi River near Baton Rouge, Louisiana. The rotors are anchored to a two-pontoon vessel platform. Surface deployment of the turbine minimizes the handling requirements during deployment and recovery and reduces overall costs for all O&M activities, including allowing for easy access to the power conversion chain (PCC). The design (two rotors per platform) also reduces the environmental footprint and associated environmental compliance costs.

16 TIDAL AND WAVE POWER↗

DOE OSTI2018

A Transparent Translation from Legacy System Model into Common Information Model: Preprint

Advance in smart grid is forcing utilities towards better monitoring, control and analysis of distribution systems, and requires extensive cyber-based intelligent systems and applications to realize various functionalities. The ability of systems, or components within systems, to interact and exchange services or information with each other is the key to the success of smart grid technologies, and it requires efficient information exchanging and data sharing infrastructure. The Common Information Model (CIM) is a standard that allows different applications to exchange information about an electrical system, and it has become a widely accepted solution for information exchange among different platforms and applications. However, most existing legacy systems are not developed using CIM, but using their own languages. Integrating such legacy systems is a challenge for utilities, and the appropriate utilization of the integrated legacy systems is even more intricate. Thus, this paper has developed an approach and open-source tool in order to translate legacy system models into CIM format. The developed tool is tested for a commercial distribution management system and simulation results have proved its effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Solventless, Ambient-Pressure Production of Bio-Based Lactones Over Earth-Abundant, Mixed Metal Oxide Catalysts for Circular Polyesters

Transitioning to a circular plastics economy will require use of renewable feedstocks, energy-efficient processes, and closed-loop recyclable polymers, such as polyesters. A key challenge lies in sustainably sourcing monomers used to make recyclable polyesters. This work presents a catalytic platform utilizing earth-abundant Cu(x)Ca(1-x)O mixed metal oxides for the oxidative dehydrocyclization of bio-based diols to lactones, which are advantaged for energy-efficient ring-opening polymerization. Operating below 200 degrees C, at ambient pressure, and without solvent, the process uses air as the sole oxidant, achieving high yields of lactones across a broad substrate scope of C4-8 diols in the liquid phase. The oxidative dehydrocyclization reaction is thermodynamically downhill due to water formation and energy-efficient compared to incumbent, non-redox pathways utilized in fossil carbon-based industrial processes for lactone production. Mechanistic studies reveal facile redox cycling of Cu2+-O(Ca2+)-Cu2+ interfacial sites unique to the developed catalyst. Techno-economic analysis and life cycle assessment estimate 40% lower energy demand and 15% lower GHG intensity per mass of butyrolactone produced compared to the fossil carbon-based route. Liquid-phase oxidative dehydrocyclization offers a promising approach for scalable lactone production from renewable, bio-based diols to enable circular polyesters.

36 MATERIALS SCIENCE↗

DOE OSTI2014

Reference Model 2 Scaled Geometry (RM2: River Current Turbine)

Contains the Reference Model 2 (RM2) scaled scale geometry files of the River Current Turbine, developed by the Reference Model Project (RMP). These scaled geometry files are saved as SolidWorks assembly, IGS, and STEP files, and require a CAD program to view. The scaled RM2 device was tested at the Saint Anthony Falls Laboratory (SAFL) at the University of Minnesota flume. The scale of the geometries included in this submission are at a 1:15 scale compared to the full scale geometry. This data was generated upon completion of the project on September 30, 2014. The Reference Model Project (RMP), sponsored by the U.S. Department of Energy (DOE), was a partnered effort to develop open-source MHK point designs as reference models (RMs) to benchmark MHK technology performance and costs, and an open-source methodology for design and analysis of MHK technologies, including models for estimating their capital costs, operational costs, and levelized costs of energy. The point designs also served as open-source test articles for university researchers and commercial technology developers. The RMP project team, led by Sandia National Laboratories (SNL), included a partnership between DOE, three national laboratories, including the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), and Oak Ridge National Laboratory (ORNL), the Applied Research Laboratory of Penn State University, and Re Vision Consulting. Reference Model 2 (RM2) is a variable speed dual-rotor cross-flow river turbine that is deployed at the water?s surface. It was designed for deployment at a reference site modeled after a reach in the Mississippi River near Baton Rouge, Louisiana. The rotors are anchored to a two-pontoon vessel platform. Surface deployment of the turbine minimizes the handling requirements during deployment and recovery and reduces overall costs for all O&M activities, including allowing for easy access to the power conversion chain (PCC). The design (two rotors per platform) also reduces the environmental footprint and associated environmental compliance costs.

16 TIDAL AND WAVE POWER↗

DOE OSTI2014

Reference Model 2 Cost Breakdown (RM2: River Current Turbine)

Contains the Reference Model 2 (RM2) spreadsheets with the cost breakdown structure (CBS) for the levelized cost of energy (LCOE) calculations for a single RM2 device and multiple unit arrays. These spreadsheets are contained within an XLSX file and a spreadsheet editor such as Microsoft Excel is needed to open the file. This data was generated upon completion of the project on September 30, 2014. The Reference Model Project (RMP), sponsored by the U.S. Department of Energy (DOE), was a partnered effort to develop open-source MHK point designs as reference models (RMs) to benchmark MHK technology performance and costs, and an open-source methodology for design and analysis of MHK technologies, including models for estimating their capital costs, operational costs, and levelized costs of energy. The point designs also served as open-source test articles for university researchers and commercial technology developers. The RMP project team, led by Sandia National Laboratories (SNL), included a partnership between DOE, three national laboratories, including the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), and Oak Ridge National Laboratory (ORNL), the Applied Research Laboratory of Penn State University, and Re Vision Consulting. Reference Model 2 (RM2) is a variable speed dual-rotor cross-flow river turbine that is deployed at the water?s surface. It was designed for deployment at a reference site modeled after a reach in the Mississippi River near Baton Rouge, Louisiana. The rotors are anchored to a two-pontoon vessel platform. Surface deployment of the turbine minimizes the handling requirements during deployment and recovery and reduces overall costs for all O&M activities, including allowing for easy access to the power conversion chain (PCC). The design (two rotors per platform) also reduces the environmental footprint and associated environmental compliance costs.

16 TIDAL AND WAVE POWER↗

DOE OSTI2019

Energy Modeling in Urban Districts: Forecast of Multi-Sector Energy Use and GHG Emissions

This document reports the findings of the energy use and greenhouse gases (GHG) emissions model loosely based on three districts in the San Francisco Bay Area, District A, District B, and District C. Modeling platforms exist for city energy benchmarking, inventorying, and GHG emissions forecasting and planning. However, the wide variety and features of today’s tools, their focus on a sub-set or snapshot data from various energy generation and consumption sectors, and the fact that many of them are not open data models, create sub-optimal environments for the energy analysis districts are seeking to conduct. Hence, we have developed a new software tool with customized data and dynamic visualization; DEPICT (Decision-support and Emissions Prediction Interactive Cities Tool) to obtain energy and emissions forecasts at different stages of the districts’ build-out by varying selected design parameters. This report presents the methodology and framework we have developed to estimate whole district emissions and details the results by district, with the objective of finding insights into the main sources of emissions, and the available levers to reduce them efficiently. This document details the impact of each emissions mitigation measure investigated, along with the assumption and models that were used to reach these values

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2014

EXAMINING THE ROLE AND RESEARCH CHALLENGES OF SOCIAL MEDIA AS A TOOL FOR NONPROLIFERATION AND ARMS CONTROL TREATY VERIFICATION

Traditional arms control treaty verification activities typically involve a combination of technical measurements via physical and chemical sensors, state declarations, political agreements, and on-site inspections involving international subject matter experts. However, the ubiquity of the internet, and the electronic sharing of data that it enables, has made available a wealth of open source information with the potential to benefit verification efforts. Open source information is already being used by organizations such as the International Atomic Energy Agency to support the verification of state-declared information, prepare inspectors for in-field activities, and to maintain situational awareness . The recent explosion in social media use has opened new doors to exploring the attitudes, moods, and activities around a given topic. Social media platforms, such as Twitter, Facebook, and YouTube, offer an opportunity for individuals, as well as institutions, to participate in a global conversation at minimal cost. Social media data can also provide a more data-rich environment, with text data being augmented with images, videos, and location data. The research described in this paper investigates the utility of applying social media signatures as potential arms control and nonproliferation treaty verification tools and technologies, as determined through a series of case studies. The treaty relevant events that these case studies touch upon include detection of undeclared facilities or activities, determination of unknown events recorded by the International Monitoring System (IMS), and the global media response to the occurrence of an Indian missile launch. The case studies examine how social media can be used to fill an information gap and provide additional confidence to a verification activity. The case studies represent, either directly or through a proxy, instances where social media information may be available that could potentially augment the evaluation of an event. The goal of this paper is to instigate a discussion within the verification community as to where and how social media can be effectively utilized to complement and enhance traditional treaty verification efforts. In addition, this paper seeks to identify areas of future research and development necessary to adapt social media analytic tools and techniques, and to form the seed for social media analytics to aid and inform arms control and nonproliferation policymakers and analysts. While social media analysis (as well as open source analysis as a whole) will not ever be able to replace traditional arms control verification measures, they do supply unique signatures that can augment existing analysis.

nonproliferation, arms control, social media↗

DOE OSTI2023

Large Language Models (LLMs) for Energy Systems Research

The integration of Large Language Models (LLMs) in energy systems research promises transformative results, as demonstrated in this work, particularly in the realms of information retrieval and legal document analysis. We have developed a chat-based interface, specifically designed to query an extensive corpus of technical reports from the National Renewable Energy Laboratory (NREL). This interface capitalizes on the natural language processing capabilities of LLMs, providing future consumers of NREL research with a user-friendly platform to access and extract valuable information from technical documents, thus enhancing the dissemination of research to the public. In addition to information retrieval, we have employed LLMs to extract renewable energy siting ordinances from a variety of legal documents, a task traditionally driven by significant human labor. This automated extraction not only supports the ongoing development of the high-impact NREL siting ordinance database but also ensures the database's accuracy and comprehensiveness. Crucially, we have augmented the performance of LLMs through the integration of a decision tree framework, resulting in a substantial improvement in extraction accuracy. Comparative analysis with manual efforts has shown that this approach not only rivals but also significantly surpasses human accuracy, heralding increased reliability in legal document analysis for energy systems research. To democratize access to these advancements and foster collaborative research, we introduce the "Energy Language Model" (ELM), an open-source software package. ELM encapsulates the methodologies and tools developed in this work, providing researchers and practitioners with a robust toolkit to conduct similar analyses within their respective domains. Through these contributions, this work underscores the immense potential of LLMs in revolutionizing energy systems research, improving accuracy, efficiency, and accessibility in the field.

automation↗

DOE OSTI2023

FORCE Integration with DRAFT and IDAES

Integrated energy systems (IES) combine, in mutually beneficial ways, power from variable renewable energy sources and nuclear power plants (NPP) to produce multiple commodities and improve economic viability under uncertain market conditions. Technical and economic analysis of IES requires modeling of complex processes with software models having enough fidelity to capture real-world dynamics while still being capable of running using reasonable computing resources. The open-source Framework for Optimization of Resources and Economics (FORCE) tool suite, developed at Idaho National Laboratory (INL), has enabled comprehensive modeling and simulation of IES. The capabilities within FORCE include grid portfolio optimization through the Holistic Energy Resource Optimization Network (HERON) and the transient process model analysis library HYBRID, among others. Recent developments for the FORCE toolset have centered on strengthening the flexibility and modularity to link with external models and other available software to enhance IES simulations. Adding versatility to the FORCE toolset improves capability resulting in better techno-economic simulation of nuclear and IES components (both in potential higher fidelity and accuracy to expected performance after deployment). It also helps leverage existing work in the IES field and improve efficiency in national code development. This report focuses on two such endeavors: integration of the Dynamic Reliability Analysis Framework Tool (DRAFT) and the Design Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES) software packages into FORCE.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Advanced simulation capability for environmental management, integrated tool-sets and simulator to enhance public communication - 15186

The U.S. Department of Energy (US DOE) Office of Environmental Management (EM), Office of Soil and Groundwater Remediation, is supporting multi-laboratory development of the Advanced Simulation Capability for Environmental Management (ASCEM). ASCEM is an open source and modular computing framework that incorporates new advances and tools for predicting contaminant fate and transport in natural and engineered systems. ASCEM facilitates transparent, efficient and integrated approaches to modeling and site characterization, and provides robust and standardized assessments of performance and risk for EM cleanup and closure activities. Site subsurface simulation modeling and performance assessments are complex and involve sensitivity evaluations and uncertainty quantification to better understand the risks at a site. The ASCEM tool-set was designed in close collaboration with the sites and other potential end-users (both the public and regulators) to allow better communication and understanding of these complex modeling efforts. ASCEM's integrated tool-sets provide a user interface and tools for managing work-flows, including conceptual model development, management of data and metadata for model input, sensitivity analysis, model calibration and uncertainty analysis, model execution on diverse computational platforms, and processing of model output, including visualization. The hierarchical and modular design of ASCEM HPC simulator Amanzi easily lends itself to translating this conceptual model of a site into a numerical model that can easily be spatially and temporally discretized to run on the HPC simulator. Together this integrated work-flow allows for a seamless and integrated analysis tracking all provenance information and modeling uncertainties. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

ProvLight: Efficient Workflow Provenance Capture on the Edge-to-Cloud Continuum

Modern scientific workflows require hybrid infrastructures combining numerous decentralized resources on the IoT/Edge interconnected to Cloud/HPC systems (aka the Computing Continuum) to enable their optimized execution. Understanding and optimizing the performance of such complex Edge-to-Cloud workflows is challenging. Capturing the provenance of key performance indicators, with their related data and processes, may assist in understanding and optimizing workflow executions. However, the capture overhead can be prohibitive, particularly in resource-constrained devices, such as the ones on the IoT/Edge.To address this challenge, based on a performance analysis of existing systems, we propose ProvLight, a tool to enable efficient provenance capture on the IoT/Edge. We leverage simplified data models, data compression and grouping, and lightweight transmission protocols to reduce overheads. We further integrate ProvLight into the E2Clab framework to enable workflow provenance capture across the Edge-to-Cloud Continuum. This integration makes E2Clab a promising platform for the performance optimization of applications through reproducible experiments.We validate ProvLight at a large scale with synthetic workloads on 64 real-life IoT/Edge devices in the FIT IoT LAB testbed. Evaluations show that ProvLight outperforms state-of-the-art systems like ProvLake and DfAnalyzer in resource-constrained devices. ProvLight is 26—37x faster to capture and transmit provenance data; uses 5—7x less CPU; 2x less memory; transmits 2x less data; and consumes 2—2.5x less energy. ProvLight [1] and E2Clab [2] are available as open-source tools.

Rosendo, Daniel↗

DOE OSTI2020

STEPS: A Portable Numerical Simulation Toolkit for Electrical Power System Dynamic Studies

Numerical simulation is the key technique for large scale power system analysis. Redistribution of global renewable power via international interconnections requires new simulation tools to study the interconnected systems with different nominal frequencies as a whole. In this paper we introduce an open source simulation toolkit for electrical power systems (STEPS) which is hosted at Github. Its kernel is coded in C++ with major functions of power flow and electro-mechanical dynamic simulation. Flexible options are provided and configurable to improve power flow solution and dynamic simulation. Common devices and models are supported in STEPS for AC/DC hybrid system studies. Studies of interconnected systems with different nominal frequencies is supported in STEPS for research of international interconnection. Application program interfaces are provided and wrapped with Python to enable high-level interfaces for general applications. STEPS is thread safe and parallel computation is supported in both kernel and script levels to accelerate simulation. It is portable and works on Windows and GNU/Linux platforms. Cases from small to large scale systems are thoroughly tested to validate the toolkit with commercial packages as benchmarks.

42 ENGINEERING↗