An integrated transmission and distribution test system for evaluation of transactive energy designs
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The science focal plane of the LSST camera is made up of 21 fully autonomous 144 Mpixel imager units designated raft tower modules (RTM). These imagers incorporate nine 4K x 4K fully-depleted CCDs and 144 channels of readout electronics, including a dedicated CMOS video processing ASIC and components that provide CCD biasing and clocking, video digitization, thermal stabilization, and a high degree of monitoring and telemetry. Here, the RTM achieves its performance goals for readout speed, read noise, linearity, and crosstalk with a power budget of less than 400mW/channel. Series production is underway on the first units and the production will run until 2018. We present the RTM final design, tests of the integrated signal chain, and performance results for the fully-integrated module with pre-production CCDs.
In fiscal year 2016 an Engineering Evaluation of an integrated off-gas system was conducted. This study resulted in a report entitled Engineering Evaluation of an Integrated Off-Gas Treatment System for Used Nuclear Fuel Reprocessing Facilities (Jubin et al. 2016a), from here referred to as the “Engineering Evaluation.” This study focused on the capture and retention of the volatile radionuclides ( 3 H, 14 C, 85 Kr, and 129 I), selected semi-volatile radionuclides, specifically 106 Ru, and chemical species like NO and NO 2 generated during the dissolution of the used nuclear fuel. The study examined the design of the combined head-end off-gas streams and the vessel off-gas stream. The study drew upon the available literature to conduct the equipment sizing and sorbent usage. A number of assumptions were required to complete this analysis and major gaps in the available data were identified that, if resolved, could increase the fidelity of the engineering design.
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This document introduces Version 2 of the Dynamic Contingency Analysis Tool (DCAT) software package developed by Pacific Northwest National Laboratory and serves as a guide for using this package to conduct cascading failure simulations for the electric power system. The DCAT is an open-platform and publicly available software; it is intended to help develop applications that aim to improve the capabilities of power system planning engineers to assess the impact and likelihood of extreme contingencies and potential cascading events across their systems and interconnections. Outputs from the DCAT will support finding mitigating actions to reduce the risk of cascading outages in technically sound and effective ways. This manual provides detailed instructions on how to use this tool on multicore Windows workstations or servers. In the public version of DCAT, several improvements have been made over the previous version: (1) it was modularized; (2) a graphical user interface (GUI) was developed; (3) a new version of the MPjobs parallel processing module was incorporated; and (4) a batch-processing function was added. The features of the DCAT GUI are described, including some examples of how to use the GUI to perform simulations. A full explanation of the DCAT methodology is available in the Phase 1 report.
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Bulk power systems operations, in both simulation and practice, are often built on the principle of minimizing production costs, which assumes to first order that demand is fixed and that the only degrees of freedom for balancing generation and load are supply-side unit commitment and dispatch decisions. The purpose of this report is to interrogate the value of welfare-maximizing markets that not only physically balance demand and supply but also ensure that market outcomes provide consumer value by directly maximizing welfare, defined as the value of consumption minus production costs.
The report documents melter and off-gas performance results obtained on the DM1200 during processing of LAW Sub-Envelope Cl feed. The principal objectives of the DM1200 melter testing were to demonstrate suitable processing and production rates for LAW Cl feed; characterize the glass product for elemental composition, the presence of organic compounds, and TCLP response for metals; and measure destruction and removal efficiencies (DREs) for organic compounds across the melter and off-gas treatment components. The feed was spiked with organic compounds for part of the testing duration. Data were collected for both process engineering needs and to support regulatory activities. The data needed for regulatory support were collected using EPA methods and independent data analyses and data checking were performed.
2023 Final presentation – Summer Italian internship
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GridSTAGE (Spatio-Temporal Adversarial scenario GEneration) is a framework for the simulation of adversarial scenarios and the generation of multivariate spatio-temporal data in cyber-physical systems. GridSTAGE is developed based on Matlab and leverages Power System Toolbox (PST) where the evolution of the power network is governed by nonlinear differential equations. Using GridSTAGE, one can create several event scenarios that correspond to several operating states of the power network by enabling or disabling any of the following: faults, AGC control, PSS control, exciter control, load changes, generation changes, and different types of cyber-attacks. Standard IEEE bus system data is used to define the power system environment. GridSTAGE emulates the data from PMU and SCADA sensors. The rate of frequency and location of the sensors can be adjusted as well. Detailed instructions on generating data scenarios with different system topologies, attack characteristics, load characteristics, sensor configuration, control parameters are available in the Github repository - https://github.com/pnnl/GridSTAGE. There is no existing adversarial data-generation framework that can incorporate several attack characteristics and yield adversarial PMU data. The GridSTAGE framework currently supports simulation of False Data Injection attacks (such as a ramp, step, random, trapezoidal, multiplicative, replay, freezing) and Denial of Service attacks (such as time-delay, packet-loss) on PMU data. Furthermore, it supports generating spatio-temporal time-series data corresponding to several random load changes across the network or corresponding to several generation changes. A Koopman mode decomposition (KMD) based algorithm to detect and identify the false data attacks in real-time is proposed in https://ieeexplore.ieee.org/document/9303022. Machine learning-based predictive models are developed to capture the dynamics of the underlying power system with a high level of accuracy under various operating conditions for IEEE 68 bus system. The corresponding machine learning models are available at https://github.com/pnnl/grid_prediction.
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