Sandia-Japan Collaboration Related to New Mexico Smart Grid Projects.
Abstract not provided.
Ellis, Abraham↗
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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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Creation of Synthetic Electric Grids (SPP/MISO) Supporting PERFORM (Final Report)
Over the course of the project, two “realistic but not real” synthetic transmission-level grid models over the SPP-MISO and ERCOT footprints were created to provide more realistic data and increase the reliability and resiliency of the grids under a variety of scenarios. The synthetic ERCOT transmission grid is compatible with the distribution grid developed in collaboration with NREL. All generators are based on the EIA 860 data and a column with EIA plant code and Gen ID is added to generators of both grids so that they can be easily mapped. The improvements are also made to electric grids including N-1 contingencies with some remedial actions, improving the transmission lines to avoid lines in lakes, including an HVDC line to the SPP-MISO case, providing several generator parameters and their temporal constraints that were not included in EIA 860 form, generators’ cost curves, load offer curves, adding phase shifters and tap changers with impedance correction tables, adding reactive power control and partitioning the grids into active and reactive reserve zones and determine different types of the required reserve for each zone. Hourly load time series at the bus level were generated to create scenarios for solving power flow in different loading conditions. Weather measurement information and the models of renewable generators are used to directly include the impact of weather on the grids. Based on a variety of load and weather conditions the grids are improved to accommodate different conditions. The ERCOT 7k-bus grids were also modeled for the year 2030 with predicted improvements in renewable resources. The renewable generation model was also improved with historic weather data included. The impact of electric vehicles on the ERCOT grid is also modeled.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Generative AI for Power Grid Operations
Generative artificial intelligence (AI) has captured into the mainstream, demonstrating capabilities that once belonged solely to the realm of human cognition. From defeating world champions in complex games to generating human-quality text and images, Generative AI has proven its potential to revolutionize countless industries. The electric power grid is no exception. Generative AI's ability to process vast amounts of data rapidly, assist decision support and identify patterns could significantly enhance power grid operations. For example, Generative AI could improve state estimation where measurements are not available or integrate renewable energy sources more efficiently with probabilistic forecasting. The key contributions of this whitepaper are outlined below: (1) Comprehensive overview of Generative AI's applications in power grid operations: It highlights the opportunities in areas such as forecasting, state estimation, and demonstrating the potential for enhancing efficiency, reliability, and resilience. (2) Expanding Generative AI's impact through synergies with emerging technologies: The paper introduce NREL developed eGridGPT and explores how AI orchestration, multi-agent systems, and Digital Twins can collaborate to optimize grid operations, addressing the complexities of a decarbonized and electrified future. (3) In-depth analysis of challenges in implementing Generative AI: This includes considerations like data availability and quality, model validation, certification, and ethical concerns, ensuring responsible AI deployment. (4) Emphasizing human-AI collaboration: The whitepaper underscores the importance of trustworthy, transparency, and explainability in AI systems to promote seamless interaction between human operators and AI, ultimately improving decision-making. (5) Exploring future research and development: It identifies critical areas for further advancement to fully realize Generative AI's potential in power grid operations. This whitepaper serves as a valuable resource for researchers, practitioners, and policymakers looking to harness Generative AI for a more reliable, stable, and cost-effective power grid.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Technical Characterization and Benefit Evaluation of 5G-Enabled Grid Data Transport and Applications
This report summarizes the Year 1 work of Pacific Northwest National Laboratory’s (PNNL’s) 5G Fabricated Resource and Asset Management Encompassment for energy infrastructure (Energy FRAME) project funded by the Department of Energy Office of Science’s Advanced Scientific Computing Research Program. 5G is a breakthrough technology that enables a fully mobile and connected society, and a 5G-enabled digital continuum will be one of the critical foundations for a clean energy economy and grid modernization. In collaboration with PNNL’s Advanced Wireless Communication team and Center for Advanced Technology Evaluation team, the project team has been evaluating the system performance of 5G testbeds in the PNNL 5G Innovation Studio, and has formulated a co-simulation test case of power system transmission, distribution, and communication (T&D&C) networks considering 5G technology and high penetration of distributed energy resources. The methodology developed in the 5G Energy FRAME project can be customized to fit different future grid scenarios to evaluate multiple (dynamic) configurations (computing, sensing, communication, environment) for different stakeholders. In summary, our main technical highlights in project Year 1 are as follows: 1) Technical characterization of 5G standalone architectures, 2) Formulation of co-simulation test case of T&D&C networks embedded with 5G, 3) Initial benefit evaluation of 5G communication platform for grid use cases, and 4) Additional extended discussions on edge computing, artificial intelligence and machine learning, and high-performance computing and cloud computing adoptions. In addition, a collection of system performance data is shared through the publicly available weblink, https://www.pnnl.gov/projects/5g-energy-frame/publications
24 POWER TRANSMISSION AND DISTRIBUTION↗
Implementing multi-settlement decentralized electricity market design for transactive communities with imperfect communication
Recent advances in information and communication technologies and smart metering, provides strategic opportunities for ``prosumers" to reform their conventional energy practices towards more consumer-centric economies. From an operational perspective, managing power distribution networks is becoming more difficult with such active grid-edge systems providing limited to no visibility or control. Transactive Energy (TE) has been emerging as a key enabler towards effectively and efficiently integrating prosumers into competitive electricity markets. This work presents a transactive implementation of community-centric markets. A co-simulation framework is developed for evaluating the proposed market structure with high-fidelity models. Case studies on the IEEE-123 node test system demonstrate that community-centric transactive markets can enable communities of prosumers to operate collaboratively as grid-edge systems. The potential benefits of implementing community-centric TE systems are also illustrated.
Mukherjee, Monish↗
Transactive Implementation of Decentralized Electricity Market for Grid-Edge Systems
The electricity landscape is evolving towards more decentralized approaches due to the proliferation of distributed energy resources and the participation of increasingly smart consumers and producers (prosumers). Recent advances in information and communication technologies and smart metering, provides strategic opportunities for “prosumers” to reform their conventional energy practices towards more consumer-centric economies. From an operational perspective, managing power distribution networks is becoming more difficult with such active grid-edge systems providing limited to no visibility or control. Transactive Energy (TE) has been emerging as a key enabler towards effectively and efficiently integrating prosumers into competitive electricity markets. This work presents a transactive implementation of community-centric markets. A co-simulation framework is developed for evaluating the proposed market structure with high-fidelity models. Case studies on the IEEE-123 node test system demonstrate that community-centric transactive markets can enable communities of prosumers to operate collaboratively as grid-edge systems. The potential benefits of implementing community-centric TE systems are also illustrated.
transactive, microgrids, centralized market operat↗
Implementation of Feldman-Cousins Corrections and Oscillation Calculations in the HPC Environment for the NOvA Experiment
Analysis of neutrino oscillation data involves a combination of complex fitting procedures and statistical correction techniques that are used to determine the full three-flavor PMNS parameters and constraint contours. These techniques rely on computationally intensive “multi-universe” stochastic modeling. The process of calculating these contours and corrections can dominate final stages of the data analysis and become a bottleneck for examining the ef-fect of systematic variations on the final results. As part of the DOE SciDAC-4 sponsored research program, we present a new implementation of a neutrino oscillation fitting and framework to carry out calculations of Feldman-Cousins corrections. This implementation is specifically designed and optimized to operate on modern High-Performance Computing (HPC) facilities. We present the performance of the system in calculating allowed regions for the NOvA experiment based on 8.9 x 1020 and 6.9 x 1020 protons-on-target (POT) neutrino and antineutrino datasets, and compare the performance of this new implementation run at the NERSC supercomputing facility with that from methods used previously by the NOvA collaboration running on grid computing facilities.
Sousa, Alexandre↗
Assured Resource Sharing in Ad-Hoc Collaboration
The project seeks an innovative framework to enable users to access and selectively share resources in distributed environments, enhancing the scalability of information sharing. We have investigated secure sharing & assurance approaches for ad-hoc collaboration, focused on Grids, Clouds, and ad-hoc network environments.
97 MATHEMATICS AND COMPUTING↗
Bringing Federated Identity to Grid Computing
The Fermi National Accelerator Laboratory (FNAL) is facing the challenge of providing scientific data access and grid submission to scientific collaborations that span the globe but are hosted at FNAL. Users in these collaborations are currently required to register as an FNAL user and obtain FNAL credentials to access grid resources to perform their scientific computations. These requirements burden researchers with managing additional authentication credentials, and put additional load on FNAL for managing user identities. Our design integrates the existing InCommon federated identity infrastructure, CILogon Basic CA, and MyProxy with the FNAL grid submission system to provide secure access for users from diverse experiments and collab orations without requiring each user to have authentication credentials from FNAL. The design automates the handling of certificates so users do not need to manage them manually. Although the initial implementation is for FNAL's grid submission system, the design and the core of the implementation are general and could be applied to other distributed computing systems.
Teheran, Jeny↗
Grid Interaction Technical Team Roadmap
The mission of the Grid Interaction Technical Team (GITT) is to support a transition scenario to large scale grid-connected vehicle charging with transformational technology, proof of concept and information dissemination. The GITT facilitates technical coordination and collaboration between vehicle-grid connectivity and communication activities among U.S. DRIVE government and industry partners.
EV smart grid↗
Leveraging Industry Research to Educate a Future Electric Grid Workforce
GridEd is a collaborative educational initiative consisting of the Electric Power Research Institute, seven (7) Partner universities (Arizona State University, Clarkson University, Georgia Institute of Technology, Portland State University, University of California–Riverside, University of North Carolina-Charlotte, and University of Puerto Rico Mayaguez), and participating utility and industry sponsors. This educational initiative focuses on developing and training the next generation of power engineers so they can help shape the electric grid of the future by anticipating and fulfilling the needs of changing electric industry requirements. GridEd is leveraging electric industry research to educate a future electric grid workforce by empowering new and continuing education students, not only to become competent and well-informed engineers, but also to participate and influence major technological, social, and policy decisions that address critical global challenges.
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Leveraging Industry Research to Educate a Future Electric Grid Workforce
GridEd is a collaborative educational initiative consisting of the Electric Power Research Institute, seven (7) Partner universities (Arizona State University, Clarkson University, Georgia Institute of Technology, Portland State University, University of California–Riverside, University of North Carolina-Charlotte, and University of Puerto Rico Mayaguez), and participating utility and industry sponsors. This educational initiative focuses on developing and training the next generation of power engineers so they can help shape the electric grid of the future by anticipating and fulfilling the needs of changing electric industry requirements. GridEd is leveraging electric industry research to educate a future electric grid workforce by empowering new and continuing education students, not only to become competent and well-informed engineers, but also to participate and influence major technological, social, and policy decisions that address critical global challenges.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Developing Generic Dynamic Models for the 2030 Eastern Interconnection Grid
The Eastern Interconnection Planning Collaborative (EIPC) has built three major power flow cases for the 2030 Eastern Interconnection (EI) based on various levels of energy/environmental policy conditions, technology advances, and load growth. Using the power flow cases, this report documents the process of developing the generic 2030 dynamic models using typical dynamic parameters. The constructed model was validated indirectly using the synchronized phasor measurements by removing the wind generation temporarily.
54 ENVIRONMENTAL SCIENCES↗
High Speed Medium Voltage CHP System with Advanced Grid Support
The project was a collaboration between Clemson University and TECO Westinghouse Motor Company. The goal of the project was to develop a medium voltage commercial grid-tied system with advanced grid support functions. Current state-of-the-art in medium voltage, multi-megawatt power electronic systems do not support advanced grid functions, traditionally due to power electronic limitations and required applications. The developed system is capable of interacting with the power system at the distribution level to efficiently integrate gas turbine generation systems or any other ac generation system with up to 20MWe of power production capacity. This project demonstrated a high level of technical readiness of the 1MW, 500Hz, 15000 RPM high frequency Combined Heat and Power (CHP) generator and electric machine system, utilizing advanced grid support functions required by IEEE Std. 1547-2018.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
IN31A-1734 Development and Evaluation of a Gridded CrIS/ATMS Visualization for Operational Forecasting
A collaborative effort between SPoRT, CIMSS, CIRA, GINA, and NOAA has produced a unique gridded visualization of real-time CrIS/ATMS sounding products. This product uses the NUCAPS retrieval algorithm and polar2grid software to generate plan-view and cross-section visualization for forecast challenges associated with cold air aloft and convective potential. Forecasters at select partner offices have been able to view the Gridded NUCAPS products in AWIPS alongside other operational data products with generally favorable feedback.
aviation↗
A Cooperative Multi-Agent Deep Reinforcement Learning Framework for Real-Time Residential Load Scheduling
Internet-of-Things (IoT) enabled monitoring and control capabilities are enabling increasing numbers of household users with controllable loads to actively participate in smart grid energy management. Realizing an efficient real-time energy management system that takes advantage of these developments requires novel techniques for managing the increased complexity of the control action space in resolving multiple challenges such as the uncertainty in energy prices and renewable energy output along with the need to satisy physical grid constraints such as transformer capacity. Addressing these challenges, we develop a multi-household energy management framework for residential units connected to the same transformer and containing DERs such as PV, ESS and controllable loads. The goal of our framework is to schedule controllable household appliances and ESS such that the cost of procuring electricity from the utility over a horizon is minimized while physical grid constraints are satisfied at each scheduling step. Traditional energy management frameworks either perform global optimization to satisfy grid constraints but suffer from high computational complexity (for example Integer Program, Mixed Integer Programming frameworks and centralized reinforcement learning) or perform decentralized real-time energy management without satisfying global grid constraints (for example multi-agent reinforcement learning with no cooperation). In contrast, we propose a cooperative multiagent reinforcement learning (MARL) framework that i) operates in real-time, and ii) performs explicit collaboration to satisfy global grid constraints. The novelty in our framework is two fold. Firstly, our framework trains multiple independent learners (IL) for each household in parallel using historical data and performs real-time inferencing of control actions using the most recent system state. Secondly, our framework contains a low complexity knapsack based cooperation agent which combines the outputs of ILs to minimize cost while satisfying grid constraints. Simulation results show that our cooperative MARL approach achieves significant cost improvement over centralized reinforcement learning and day-ahead planning baselines. Moreover, our approach strictly satisfies physical constraints with no apriori knowledge of system dynamics while the baseline approaches have occasional violations. We also measure the training and inference time by ranging the number of households from 1 to 25. Results show that our cooperative MARL approach scales best among various approaches.
14 SOLAR ENERGY↗
Domain modeling and grid generation for multi-block structured grids with application to aerodynamic and hydrodynamic configurations
About five years ago, a joint development was started of a flow simulation system for engine-airframe integration studies on propeller as well as jet aircraft. The initial system was based on the Euler equations and made operational for industrial aerodynamic design work. The system consists of three major components: a domain modeller, for the graphical interactive subdivision of flow domains into an unstructured collection of blocks; a grid generator, for the graphical interactive computation of structured grids in blocks; and a flow solver, for the computation of flows on multi-block grids. The industrial partners of the collaboration and NLR have demonstrated that the domain modeller, grid generator and flow solver can be applied to simulate Euler flows around complete aircraft, including propulsion system simulation. Extension to Navier-Stokes flows is in progress. Delft Hydraulics has shown that both the domain modeller and grid generator can also be applied successfully for hydrodynamic configurations. An overview is given about the main aspects of both domain modelling and grid generation.
Spekreijse, S. P.↗
International Collaboration on the Evaluation and Certification of Interoperable Grid-Support Converters.
Abstract not provided.
Johnson, Jay Tillay↗