Coupled transmission and distribution simulations to assess distributed generation response to power system faults
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This study performs a large quantity of transmission and distribution simulations for three regions in the Western Interconnection along with final transmission simulations informed by distribution simulation results. Results find that the type of ride-through implemented by distributed energy resources (DERs) is a significant factor to the overall power system response to transmission-level faults. This impact is particularly high for transmission-level faults that depress voltage levels for high numbers of substations (i.e., for cases that experience large regional voltage sags). Because of the programmed nature of the ride-through of IEEE 1547-compliant DERs, the potential for large reductions in real power injection is possible based on IEEE 1547 interpretation and specific implementation. In this study, we made a first attempt to determine the quantity of DERs affected by a variety of transmission-level faults as well as a granular response of distribution-simulated DERs to a few types of IEEE 1547-2003 interpretations and IEEE 1547-2018 voltage ride-through categories. The distribution-informed transmission studies show that the category of IEEE 1547 adherence, particularly with the new 2018 standard, can have a significant impact on the amount of DER-based generation lost following transmission-level fault events.
This is a survey paper about the state-of-the-art in large-scale network simulation. Networks for the smart grids are characterized by millions of sensor nodes exchanging information about the status of the grid. This information exchange must be realized reliably and efficiently due to the mission critical nature of the power grid. Hence, the applications and the network protocols developed for the smart grid need go through rigorous testing and analysis before deployment. Developers usually do not have access to such a large-scale network that can be used as a controlled test-bed; therefore, network simulation becomes an essential tool for testing. Network simulation is a well studied problem in the literature and there are various widely used network simulators. These simulators can be adopted for testing applications and protocols of the smart grid. Due to the scale of these networks, parallel/distributed simulations need to be conducted. Even though most network simulators support distributed simulations, generating a large-scale network model to simulate can still be a cumbersome task. In this survey, we describe a selection of commonly used network simulators and evaluate them with respect to the following features that can aid users in distributed simulations of large-scale networks: transparency of setting up distributed simulation, automated topology generation, information hiding, lightweight routing protocols, network error simulation, evaluation of the network model during simulation and trace analysis tools. As a complementary result, we identify two issues with network simulators that can be addressed with runtime steering methods.
This paper discusses the development of, approaches for, experiences with, and some results from a large-scale, high-performance-computer-based (HPC-based) co-simulation of electric power transmission and distribution systems using the Integrated Grid Modeling System (IGMS). IGMS was developed at the National Renewable Energy Laboratory (NREL) as a novel Independent System Operator (ISO)-to-appliance scale electric power system modeling platform that combines off-the-shelf tools to simultaneously model 100s to 1000s of distribution systems in co-simulation with detailed ISO markets, transmission power flows, and AGC-level reserve deployment. Lessons learned from the co-simulation architecture development are shared, along with a case study that explores the reactive power impacts of PV inverter voltage support on the bulk power system.
This software is based on a distribution function to represent a variable value over a time-step in time-series simulation of a photovoltaic or wind system, with application to other types of variable-output generator such as wave power. This invention offers important benefits over prior art. Prior art assumes a constant value for each parameter over the duration of each time-step. The form of the distribution function is determined by a maximum value (kW), a minimum value (kW) and energy (kWh) for each time step, and integration of this distribution function provides information such as energy in excess of some threshold (inverter capacity, interconnect capacity, on-site load, etc) and energy delivered under any such specified threshold. Results such as maximum power that can be exported from a distributed generation system are determined and very important for energy systems integration.
Capturing technical and economic impacts of solar photovoltaics (PV) and other distributed energy resources (DERs) on electric distribution systems can require high-time resolution (e.g. 1 minute), long-duration (e.g. 1 year) simulations. However, such simulations can be computationally prohibitive, particularly when including complex control schemes in quasi-steady-state time series (QSTS) simulation. Various approaches have been used in the literature to down select representative time segments (e.g. days), but typically these are best suited for lower time resolutions or consider only a single data stream (e.g. PV production) for selection. We present a statistical approach that combines stratified sampling and bootstrapping to select representative days while also providing a simple method to reassemble annual results. We describe the approach in the context of a recent study with a utility partner. This approach enables much faster QSTS analysis by simulating only a subset of days, while maintaining accurate annual estimates.
Abstract An experimental Quantum Key Distribution (QKD) implementation requires advanced costly hardware, unavailable in most research environments, making protocol testing and performance evaluation complicated. This has been a major motivation for the development of QKD simulation frameworks, to allow researchers to obtain insight before proceeding into practical implementations. Several simulators have been introduced over the recent years. However, only four are publicly available, only one of which models equipment imperfections. Currently, no open‐source simulator includes all following capabilities: channel attenuation modelling, equipment imperfections and effects on key rates, estimation of elapsed time during classical and quantum‐ channel processes, use of truly random binary sequences for qubits and measurement bases, shared ‐bit fraction customization. In this paper, we present NuQKD, an open‐ source modular, intuitive simulator, featuring all the above capabilities. NuQKD establishes communication between two computer terminals, accepts custom user inputs (iterations, raw key size, attacker interception rate etc.) and evaluates the sifted key, Quantum Bit Error Rate (QBER), elapsed communication time, and more. NuQKD capabilities include optical fiber and free ‐space simulation, modeling of equipment/channel imperfections, bitstrings from True Random Number Generators, decoy‐state protocol support, and automated evaluation of performance metrics. We expect NuQKD to enable convenient and accurate representation of actual experimental conditions.
This presentation discusses the next-generation analysis framework for full-scale transmission and distribution modeling that supports millions of highly distributed energy resources, and also discusses future directions for transmission and distribution.
This data package is associated with the publication “On the Transferability of Residence Time Distributions in Two 10-km Long River Sections with Similar Hydromorphic Units” submitted to the Journal of Hydrology (Bao et al. 2024).Quantifying hydrologic exchange fluxes (HEFs) at the stream-groundwater interface, along with their residence time distributions (RTDs) in the subsurface, is crucial for managing water quality and ecosystem health in dynamic river corridors. However, directly simulating high-spatial resolution HEFs and RTDs can be a time-consuming process, particularly for watershed-scale modeling. Efficient surrogate models that link RTDs to hydromorphic units (HUs) may serve as alternatives for simulating RTDs in large-scale models. One common concern with these surrogate models, however, is the transferability of the relationship between the RTDs and HUs from one river corridor to another. To address this, we evaluated the HEFs and the resulting RTD-HU relationships for two 10-kilometer-long river corridors along the Columbia River, using a one-way coupled three-dimensional transient surface-subsurface water transport modeling framework that we previously developed. Applying this framework to the two river corridors with similar HUs allows for quantitative comparisons of HEFs and RTDs using both statistical tests and machine learning classification models. This data package includes the model inputs files and the simulation results data. This data package contains 10 folders. The modeling simulation results data are in the folders 100H_pt_data and 300area_pt_data, for the study domain Hanford 100H and 300 area respectively. The remaining eight folders contain the scripts and data to generate the manuscript figures. The file-level metadata file (Bao_2024_Residence_Time_Distribution _flmd.csv) includes a list of all files contained in this data package and descriptions for each. The data dictionary file (Bao_2024_Residence_Time_Distribution _dd.csv) includes column header definitions and units of all tabular files.
The goal of this project was to develop a tool for facilitating simulation, validation and discovery of multiscale dynamical processes in microbial ecosystems. This led to the development of an open-source software platform for Computation Of Microbial Ecosystems in Time and Space (COMETS). COMETS performs spatially distributed time-dependent flux balance based simulations of microbial metabolism. Our plan involved building the software platform itself, calibrating and testing it through comparison with experimental data, and integrating simulations and experiments to address important open questions on the evolution and dynamics of cross-feeding interactions between microbial species.
To help bridge the gap between traditional power system engineering instruction and emerging industry needs, a new set of classroom and research tools are needed. Real-time simulation tools emulating power system control room software present an opportunity to introduce students to the array of operational considerations, technical challenges, and decision-making associated with power system operations. The GridAPPS-D platform is proposed to support coursework and academic research as it provides an open-source platform for simulation, application development, and software integration. The GridAPPS-D platform, simulation capabilities, development environment, and interactive training are discussed in the context of lessons-learned from implementation for undergraduate and graduate students in the US and India. A series of potential GridAPPS-D supported academic capabilities are introduced.
Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.
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Electric grid transformation with the proliferation of distributed energy resources (DER), such as solar photovoltaic (PV), wind, advanced energy storage technologies, and electric vehicles, and the growing use of communication technologies in both transmission and distribution systems are increasing the need to capture the interactions among these systems. Advanced modeling, control, and simulation tools that can perform co-simulation of electric power systems with other domains become indispensable to accurately model these interactions. To address this need, we have developed a codebase that integrates an electric power distribution system simulator, DER models, and DER controls. The codebase is based on OpenDSS, a distribution system simulator, and an existing open source co-simulation framework called HELICS. The contribution and uniqueness of the proposed codebase is that it tailors the generic HELICS framework specifically for distribution grid-related applications. The codebase includes an OpenDSS wrapper that controls the simulation, implements advanced DER controls, and extracts power flows, voltages, and other power system element information from the the distribution network modeled in OpenDSS. Sample HELICS federates, including a federate for OpenDSS, are provided that can communicate messages through the HELICS interface. Sample federates can be modified and additional HELICS federates can be added by the user depending on their use case requirements. SEE ALSO: https://github.com/NREL/dss-cosim