Synthetic Grid Research Articles

Enhancing power grid resilience to winter storms via generator winterization with equity considerations

We develop two-stage stochastic programming models for generator winterization that enhance power grid resilience while incorporating social equity. The first stage in our models captures the investment decisions for generator winterization, and the second stage captures the operation of a degraded power grid, with the objective of minimizing load shed and social inequity. To incorporate equity into our models, we propose a concept called adverse effect probability that captures the disproportionate effects of power outages on communities with varying vulnerability levels. Grid operations are modeled using DC power flow, and equity is captured through mean or maximum adverse effects experienced by communities. We apply our models to a synthetic Texas power grid, using winter storm scenarios created from the generator outage data from the 2021 Texas winter storm. Our extensive numerical experiments show that more equitable outcomes, in the sense of reducing adverse effects experienced by vulnerable communities during power outages, are achievable with no impact on total load shed through investing in winterization of generators in different locations and capacities.

Progenitor and explosion properties of SN 2023ixf estimated based on a light-curve model grid of Type II supernovae

Abstract We estimate the progenitor and explosion properties of the nearby Type II SN 2023ixf using a synthetic model grid of Type II supernova light curves. By comparing the light curves of SN 2023ixf with the pre-existing grid of Type II supernovae containing about 228000 models with different combinations of the progenitor and explosion properties, we obtain the $\chi ^2$ value for every model and evaluate the properties of the models providing small values of $\chi ^2$. We found that the light-curve models with a progenitor zero-age main-sequence mass of $10\, {M}_\odot$, explosion energy of $(2\\!-\\!3) \times 10^{51}\:\mbox{erg}$, $^{56}\mbox{Ni}$ mass of 0.04–$0.06\, {M}_\odot$, mass-loss rate of $10^{-3}$–$10^{-2}\, {M}_\odot \:\mbox{yr}^{-1}$ with wind velocity of $10\:\mbox{km}\:\mbox{s}^{-1}$, and dense, confined circumstellar matter radius of $(6\\!-\\!10) \times 10^{14}\:\mbox{cm}$ match well to the observed light curves of SN 2023ixf. The photospheric velocity evolution of these models is also consistent with the observed velocity evolution. We note that the progenitor mass estimate could be affected by the adopted progenitor models. Although our parameter estimation is based on a pre-existing model grid and we do not perform any additional computations, the estimated parameters are consistent with those obtained by the detailed modeling of SN 2023ixf previously reported. This result shows that comparing the pre-existing model grid is a reasonable way to obtain a rough estimate for the properties of Type II supernovae. This simple way to estimate the properties of Type II supernovae will be essential in the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) era when thousands of Type II supernovae are expected to be discovered yearly.

An open data-based model for generating a synthetic low-voltage grid to estimate hosting capacity

This study develops and applies an open data-based reference electricity grid analysis (REGAL) model designed to create a synthetic representation of a low-voltage (LV) grid for a country-size geographic area. The model enables large-scale grid simulation in which new loads, such as electric vehicle charging, can be added to estimate their impacts on the current LV grid. The modeling is carried out in three steps: (1) generation of a synthetic LV grid; (2) addition of residential loads, including electric vehicle charging; and (3) evaluating if the grid capacity is exceeded. The grid is generated by selecting transformers and cables so that the system can fulfill the current demand while meeting national regulations and standards for distribution grids, all at the lowest total cost. This paper presents the results of calibration and validation against real-world data for the predicted electricity demands and synthetic grid generated by the model. Different calibration values were explored, and the accuracy of the estimations of grid capacities was calibrated using proprietary real-world data from grid operators. For a region with multiple grid cells, an average deviation from real-world data of ±10 % was achieved. For an average area of 1 km2, the error was 44.5 %, which means that the model is not suitable for analysis on this geographic level. However, the level of accuracy is deemed sufficient for initial estimations of hosting capacity for larger geographic areas, such as a region or a country, thereby enabling estimations of hosting capacity in new areas that lack publicly accessible grid capacities.

CycLight: Learning traffic signal cooperation with a cycle-level strategy

This study introduces CycLight, a novel cycle-level deep reinforcement learning (RL) approach for network-level adaptive traffic signal control (NATSC) systems. Traditional RL-based step-by-step traffic signal controllers, while precise and responsive, demand high computational resources for frequent data exchanges and can increase traffic accident risks due to disordered phase switching. Different from the step-by-step control strategy aforementioned, the proposed CycLight adopts a cycle-level strategy, optimizing cycle length and splits simultaneously using Parameterized Deep Q-Networks (PDQN) algorithm. This cycle-level approach effectively reduces the computational burden associated with frequent data communication, meanwhile enhancing the practicality and safety of real-world applications. Moreover, a decentralized framework is formulated for multi-agent cooperation, while attention mechanism is integrated to accurately assess the impact of the surroundings on the current intersection. CycLight is tested in a large synthetic traffic grid using the microscopic traffic simulation tool, Simulation of Urban MObility (SUMO). Experimental results not only demonstrate the superiority of CycLight over other state-of-the-art approaches but also showcase its robustness against information transmission delays.

Generating Synthetic Power Grids Using Exponential Random Graph Models

Synthetic power grids enable real-world energy system simulations and are crucial for algorithm testing, resilience assessment, and policy formulation. We propose a novel method for the generation of synthetic transmission power grids using exponential random graph (ERG) models. Our two main contributions are (1) the formulation of an ERG model tailored specifically for capturing the topological nuances of power grids and (2) a general procedure for estimating the parameters of such a model conditioned on working with connected graphs. From a modeling perspective, we identify edge counts per bus type and k-triangles as crucial topological characteristics for synthetic power-grid generation. From a technical perspective, we develop a rigorous methodology to estimate the parameters of an ERG constrained to the space of connected graphs. The proposed model is flexible, easy to implement, and successfully captures the desired topological properties of power grids. Published by the American Physical Society 2024

The role of data-driven methods in power system security assessment from aggregated grid data

Data-driven techniques have been considered as an enabling technology for reducing the computational burden of both static and dynamic power system security analysis. Anyway, the studies reported in the literature mainly focused on inferring from historical data the mapping between the bus variables before and after a certain contingencies set, while, to the best of the Author's knowledge, limited contributions have been devoted to try and classify the power system security state by processing aggregated grid data. This is a relevant issue to address for a Transmission System Operator since it could allow a sensible decrease in the computational burden and, considering that aggregated grid data can be reliably predicted from several hours to one day ahead, it may enable the evolution of security assessment to security forecasting. In trying and filling this research gap, this paper explores the role of machine learning and feature selection algorithms. A realistic case study involving 2 years of synthetic grid data simulated on the Italian power system model against future potential operational scenarios characterized by a high share of renewables is presented and discussed to identify the most promising computing paradigms, analyzing the criticality of tuning the feature selection and classifier algorithms.

Disaster impact prediction in the power grid using artificial intelligence based on Texas synthetic grid data replication

Power grids are endangered yearly by disasters that could well affect our livelihood. This paper investigates an AI (Artificial Intelligence) solution to understand better and ultimately predict disasters using the data from previously observed disasters and artificially generated data. The data is fed to the AI in the form of resiliency calculations, which are handled by another sub-program and then go through stages of linear regression models, normal curving models, and decision tree models. Afterward, the main body assigns a value percentile to each of the predictions and reaches a final prediction that is reliable, fast, and accurate. The AI then reviews its decision and checks it with the actual data to see if the forecast is accurate and refines itself according to the newly gathered data. This Algorithm has massive potential implications for decision-making while facing a disaster. It can also help test out different approaches to the problems in a safe environment before the actual procedure initiates, saving time and reducing costs. This paper is an extension to this lab’s effort to simulate a real-life disaster and grid via disaster data and multi-layer analysis to predict the steps of disaster and the grid’s responses. The raw data (generator’s generations, load’s amount, lines current, etc.) is refined through the resiliency equations introduced by this lab to be evaluated via the algorithm.

Impact of Static Urban Traffic Flow-Based Traffic Weighted Multi-Maps Routing Strategies on Pollutant Emissions

Addressing urban traffic congestion is a pressing issue requiring efficient solutions that need to be analyzed regarding travel time and pollutant emissions. The traffic weighted multi-maps (TWM) method has been proposed as an efficient mechanism for congestion mitigation that enables differential traffic routing and path diversity by strategically distributing different network views (maps) to the drivers. Previous works have focused on TWM generation by creating optimal edge weights, but the complexity exponentially increases with the network size and traffic group diversity. This work describes how congestion and emissions can be addressed using TWM maps based on the k-shortest paths for the traffic flows (instead of individuals) that are optimally assigned and distributed to the components of the traffic flow. The map allocation strategies optimal TWM (OTV), optimal TWM per path flow with linear constraints (LCTV), and its variant unconstrained optimal TWM per path flow (UCTV) are described. They use maps generated from the k-shortest paths of the traffic flows (kSP-TWM). The heuristic solution obtained is compared with the theoretical static traffic assignment estimation baseline with different configurations, regarding congestion reduction, total travel time enhancement, and pollutant emissions. Experiments are developed using a synthetic traffic grid network scenario with a mesoscopic simulation. They show that the solution provided is adequate for its proximity to the theoretical equilibrium solutions and can generate minimum emissions patterns. The presented solution opens new possibilities for further congestion and pollutant management studies and seamless integration with existing traffic management frameworks.

Modified Erdős–Rényi Random Graph Model for Generating Synthetic Power Grids

Modified Erdős–Rényi Random Graph Model for Generating Synthetic Power Grids

ZPEKTR: A code for spectral synthesis of fast-rotating stars

Estimating the physical states of the surfaces of fast-rotating stars is challenging due to several intrinsic processes, which include radiative flux inhomogeneities on the photosphere induced by rotation and circumstellar signatures in their spectra. The analysis of their spectra ultimately requires the use of synthetic grids of spectra accounting for all these physical processes. In this paper, we present the `von ZeiPEl's code for gravity darKening specTRal synthesis' (ZPEKTR) code, which is designed to perform the spectral synthesis of fast-rotating stars, accounting for gravity darkening, limb-darkening effects in the continuum and geometrical deformation induced by fast rotation. We consider colatitudinal temperature and surface-gravity variations, assuming both the classical prescription developed by von Zeipel and the new formulation by Espinosa-Lara. The code runs either with a rectangular or a triangular mesh on the stellar surface. We compare the temperature and gravitational distribution as a function of the stellar latitude arising from both models. The line profiles of 4388, 4471, 4922, and 6678 produced with both formalisms are compared at three different rotation rates and illustrate differences in shape and central intensity. We also illustrate the fittings of 31 line spectra of classical Be stars averaged from the Be Stars Observation Survey (BeSOS) database and make a comparison among their apparent physical parameters and ages determined from plane-parallel non-local thermodynamical equilibrium (non-LTE) models and parameters determined from classical von Zeipel models, finding a displacement of more evolved objects towards the zero-age main sequence. We also compare the distributions of projected rotation velocities of these objects obtained with and without the inclusion of gravity-darkening effects with ZPEKTR. We observe a shift of the histogram of rotation velocities calculated accounting for effects of gravity darkening concerning rotation velocities obtained through the fittings with classical plane-parallel non-LTE models. We show that models that do not account for gravity darkening can underestimate the rotation velocity, because the stellar latitudes that contribute the higher velocities are those in the equator with the least radiative flux. We envisage near-future improvements to the code, such as the inclusion of differential rotation and treatment of tidal forces in binary stellar systems.

Predicting dynamic stability from static features in power grid models using machine learning.

A reliable supply with electric power is vital for our society. Transmission line failures are among the biggest threats for power grid stability as they may lead to a splitting of the grid into mutual asynchronous fragments. New conceptual methods are needed to assess system stability that complement existing simulation models. In this article, we propose a combination of network science metrics and machine learning models to predict the risk of desynchronization events. Network science provides metrics for essential properties of transmission lines such as their redundancy or centrality. Machine learning models perform inherent feature selection and, thus, reveal key factors that determine network robustness and vulnerability. As a case study, we train and test such models on simulated data from several synthetic test grids. We find that the integrated models are capable of predicting desynchronization events after line failures with an average precision greater than 0.996 when averaging over all datasets. Learning transfer between different datasets is generally possible, at a slight loss of prediction performance. Our results suggest that power grid desynchronization is essentially governed by only a few network metrics that quantify the networks' ability to reroute the flow without creating exceedingly high static line loadings.

Transfer Learning for Transient Stability Predictions in Modern Power Systems Under Enduring Topological Changes

The topological configuration of a bulk power grid is often altered by network investment upgrades, forecasted disasters and random faults, as well as planned operator-triggered transmission line maintenance and controlled switching actions. Such topological variations can drastically change the measurement data distribution from phasor measurement units (PMUs), which may in turn compromise the accuracy of the artificial intelligence (AI)-aided monitoring and control applications using the measurements. For instance, data-driven transient stability assessment (TSA) models that were trained with static network topologies may no longer be accurate for monitoring power grid stability as the network topology changes. Not only would the number of possible topology changes be too vast to train all possible scenarios, but also the training process will render computationally intensive. This paper proposes a model-based transfer learning (TL) approach that integrates a convolutional neural network and a long short-term memory network (ConvLSTM), to efficiently train a new stability prediction model that predicts the system operating states (SOSs) and identify critical generators (CGs) in case of instability when the system undergoes enduring topological changes. Numerical analyses on three test cases including the IEEE 39-bus test system, the IEEE 118-bus test system, and the large-scale 2000-bus synthetic power grid in the state of Texas verify the efficiency of the proposed approach and highlight benefits in training time and accuracy, when compared to the state-of-the-art alternatives. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —As the national power grid goes through transitions towards digitalization and modernization with emerging technologies, maintaining its reliability and resiliency against environmental stressors and cyber attacks remains an urgent need. The power system topology is expected to change more frequently, sometimes to accommodate the proliferation of heterogeneous distributed energy resources and tackle their intermittence, sometimes event-driven due to disruptive events (e.g., faults), and sometimes operator-triggered for maintenance activities and responsive control to return the system back to its normal operating condition. This article is motivated by the need for an efficient and computationally-attractive approach for online situational awareness and real-time transient stability monitoring and assessment of the power grid under an enduring topological change, where the main goal is to identify the power system operating states (SOSs) and critical generators (CGs) in case of instability. Instead of training a new model for each topological change, this paper proposes an adaptive power system transient stability assessment (TSA) method that uses transfer learning (TL) which in return reduces the training time yet with less data than a newly-trained model for each topology change scenario.

Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operations

Day-ahead operation involves a complex and computationally intensive optimization process to determine the generator commitment schedule and dispatch. The optimization process is a mixed-integer linear program (MILP) also known as security-constrained unit commitment (SCUC). Independent system operators (ISOs) run SCUC daily and require state-of-the-art algorithms to speed up the process. Existing patterns in historical information can be leveraged for model reduction of SCUC, which can provide significant time savings. In this paper, machine learning (ML) based classification approaches, namely logistic regression, neural networks, random forest and K-nearest neighbor, were studied for model reduction of SCUC. The ML was then aided with a feasibility layer (FL) and post-process technique to ensure high quality solutions. The proposed approach is validated on several test systems namely, IEEE 24-Bus system, IEEE-73 Bus system, IEEE 118-Bus system, South-Carolina (SC) synthetic grid 500-Bus system, and Polish 2383-Bus system. Moreover, model reduction of a stochastic SCUC (SSCUC) was demonstrated utilizing a modified IEEE 24-Bus system with renewable generation. Simulation results demonstrate a high training accuracy to identify commitment schedule while FL and post-process ensure ML predictions do not lead to infeasible solutions with minimal loss in solution quality.

Multi-Objective Traffic Signal Control Using Network-Wide Agent Coordinated Reinforcement Learning

The optimization of intersection signal control can improve traffic efficiency, reduce congestion degree, and improve traffic safety. Aiming at implementing the coordinated adaptive traffic signal control (ATSC) across large-scale arterial network, multi-agent reinforcement learning (MARL) has been widely concerned and lucubrated. Nevertheless, the existing MARL-based ATSC studies suffers from several limitations: (1) While most existing researches focused on the mobility performance of controlled corridor, there calls for a methodology that aims at combine multi-objective performance on traffic safety, efficiency, and network coordination simultaneously; (2) Most methods ignore the correlations between multiple agents, nor considers the spatial-temporal dependencies among the corelated neighboring intersections due to high communications requirements, which can hardly be achieved in real adaptive coordination control. To overcome the aforementioned difficulties, a multi-objective reinforcement learning model (NACRL) for network-wide coordinated signal control is proposed. Firstly, to enforce a coordinated network control with safety and efficiency considerations, a reward mechanism inspecting both traffic safety and traffic efficiency indicators was designed to achieve ideal performance in terms of mobility, safety and smooth. Secondly, the proposed NACRL conducted a centralized training-decentralized execution framework, this overcomes the critical limitation of data transmission in the field implementation while explicitly analyzing the traffic state over the entire network instead of examining each isolated intersection. Last but not least, the proposed algorithm utilized the attention mechanism to dynamically capture the sophisticated spatial-temporal dependencies over the complex arterial network, which aids the better coordinated control over multi-agents deployed at the intersections across the corridor. To testify the effeteness of the proposed algorithm, extensive experiments were implemented in both large-scale synthetic traffic grid and real-world arterial network. The experiment demonstrated that the proposed NACRL algorithm outperforms other state-of-the-art baselines with simultaneously improved performance in terms of traffic safety, traffic efficiency and network coordination, as well as improved algorithm convergence and interpretability.

Toward dynamic stability assessment of power grid topologies using graph neural networks.

To mitigate climate change, the share of renewable energies in power production needs to be increased. Renewables introduce new challenges to power grids regarding the dynamic stability due to decentralization, reduced inertia, and volatility in production. Since dynamic stability simulations are intractable and exceedingly expensive for large grids, graph neural networks (GNNs) are a promising method to reduce the computational effort of analyzing the dynamic stability of power grids. As a testbed for GNN models, we generate new, large datasets of dynamic stability of synthetic power grids and provide them as an open-source resource to the research community. We find that GNNs are surprisingly effective at predicting the highly non-linear targets from topological information only. For the first time, performance that is suitable for practical use cases is achieved. Furthermore, we demonstrate the ability of these models to accurately identify particular vulnerable nodes in power grids, so-called troublemakers. Last, we find that GNNs trained on small grids generate accurate predictions on a large synthetic model of the Texan power grid, which illustrates the potential for real-world applications.

Sampling the Faraday rotation sky of TNG50: imprint of the magnetized circumgalactic medium around Milky Way-like galaxies

ABSTRACT Faraday rotation measure (RM) is arguably the most practical observational tracer of magnetic fields in the diffuse circumgalactic medium (CGM). We sample synthetic Faraday rotation skies of Milky Way-like galaxies in TNG50 of the IllustrisTNG project by placing an observer inside the galaxies at a solar circle-like position. Our synthetic RM grids emulate specifications of current and upcoming surveys; the NRAO VLA Sky Survey (NVSS), the Polarisation Sky Survey of the Universe’s Magnetism (POSSUM), and a future Square Kilometre Array (SKA1-mid) polarization survey. It has been suggested that magnetic fields regulate the survival of high-velocity clouds. However, there is only a small number of observational detections of magnetized clouds thus far. In the first part of the paper, we test conditions for the detection of magnetized circumgalactic clouds. Based on the synthetic RM samplings of clouds in the simulations, we predict upcoming polarimetric surveys will open opportunities for the detection of even low-mass and distant clouds. In the second part of the paper, we investigate the imprint of the CGM in the all-sky RM distribution. We test whether the RM variation produced by the CGM is correlated with global galaxy properties, such as distance to a satellite, specific star formation rate, neutral hydrogen covering fraction, and accretion rate to the supermassive black hole. We argue that the observed fluctuation in the RM measurements on scales less than 1○, which has been considered an indication of intergalactic magnetic fields, might in fact incorporate a significant contribution of the Milky Way CGM.

A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data

Power systems are prone to a variety of events (e.g. line trips and generation loss) and real-time identification of such events is crucial in terms of situational awareness, reliability, and security. Using measurements from multiple synchrophasors, i.e., phasor measurement units (PMUs), we propose to identify events by extracting features based on modal dynamics. We combine such traditional physics-based feature extraction methods with machine learning to distinguish different event types. Including all measurement channels at each PMU allows exploiting diverse features but also requires learning classification models over a high-dimensional space. To address this issue, various feature selection methods are implemented to choose the best subset of features. Using the obtained subset of features, we investigate the performance of two well-known classification models, namely, logistic regression (LR) and support vector machines (SVM) to identify generation loss and line trip events in two datasets. The first dataset is obtained from simulated generation loss and line trip events in the Texas 2000-bus synthetic grid. The second is a proprietary dataset with labeled events obtained from a large utility in the USA involving measurements from nearly 500 PMUs. Our results indicate that the proposed framework is promising for identifying the two types of events.

A framework for synthetic power system dynamics.

We present a modular framework for generating synthetic power grids that consider the heterogeneity of real power grid dynamics but remain simple and tractable. This enables the generation of large sets of synthetic grids for a wide range of applications. For the first time, our synthetic model also includes the major drivers of fluctuations on short-time scales and a set of validators that ensure the resulting system dynamics are plausible. The synthetic grids generated are robust and show good synchronization under all evaluated scenarios, as should be expected for realistic power grids. A software package that includes an efficient Julia implementation of the framework is released as a companion to the paper.

From Delaunay triangulation to topological data analysis: generation of more realistic synthetic power grid networks

Abstract Assessing novel methods for increasing power system resilience against cyber-physical hazards requires real power grid data or high-quality synthetic data. However, for security reasons, even basic connection information for real power grid data are not publicly available. We develop a randomised model for generating realistic synthetic power networks based on the Delaunay triangulation and demonstrate that it captures important features of real power networks. To validate our model, we introduce a new metric for network similarity based on topological data analysis. We demonstrate the utility of our approach in application to IEEE test cases and European power networks. We identify the model parameters for two IEEE test cases and two European power grid networks and compare the properties of the generated networks with their corresponding benchmark networks.

Leveraging Power Grid Topology in Machine Learning Assisted Optimal Power Flow

Machine learning assisted optimal power flow (OPF) aims to reduce the computational complexity of these non-linear and non-convex constrained optimization problems by consigning expensive (online) optimization to offline training. The majority of work in this area typically employs fully connected neural networks (FCNN). However, recently convolutional (CNN) and graph (GNN) neural networks have also been investigated, in effort to exploit topological information within the power grid. Although promising results have been obtained, there lacks a systematic comparison between these architectures throughout literature. Accordingly, we introduce a concise framework for generalizing methods for machine learning assisted OPF and assess the performance of a variety of FCNN, CNN and GNN models for two fundamental approaches in this domain: regression (predicting optimal generator set-points) and classification (predicting the active set of constraints). For several synthetic power grids with interconnected utilities, we show that locality properties between feature and target variables are scarce and subsequently demonstrate marginal utility of applying CNN and GNN architectures compared to FCNN for a fixed grid topology. However, with variable topology (for instance, modeling transmission line contingency), GNN models are able to straightforwardly take the change of topological information into account and outperform both FCNN and CNN models.