Modern electric power systems have an increasingly complex structure due to rise in power demand and integration of diverse energy sources. Monitoring these large-scale systems, which relies on efficient state estimation (SE), represents a challenging computational task and requires efficient simulation tools for power system steady-state analyses. Motivated by this observation, we propose JuliaGrid, an open-source framework written in the Julia programming language, designed for high performance execution across multiple platforms. The framework implements observability analysis, weighted least-squares and least-absolute value estimators, bad data analysis, and various algorithms related to phasor measurements. To complete power system analysis, the framework includes power flow and optimal power flow, enabling measurement generation for the SE routines. Leveraging computationally efficient algorithms, JuliaGrid solves large-scale systems across all SE routines with competitive execution times compared to other open-source frameworks. These capabilities are validated through simulations on power systems with 10000, 20000 and 70000 buses.

In this paper, we analyze examples of research institutes that stand out in scientific excellence and social impact. We define key practices for evaluating research results, economic conditions, and the selection of specific research topics. Special focus is placed on small countries and the field of artificial intelligence. The aim is to identify components that enable institutes to achieve a high level of innovation, self-sustainability, and social benefits.

The development of more complex inverter-based resources (IBRs) control is becoming essential as a result of the growing share of renewable energy sources in power systems. Given the diverse range of control schemes, grid operators are typically provided with black-box models of IBRs from various equipment manufacturers. As such, they are integrated into simulation models of the entire power system for analysis, and due to their nature, they can only be simulated in the time domain. Other system analysis approaches, like eigenvalue analysis, cannot be applied, making the comprehensive analysis of defined systems more challenging. This work introduces an approach for identification of three-phase IBR models for grid-forming and grid-following inverters using Hammerstein-Wiener models. To this end, we define a simulation framework for the identification process, and select suitable evaluation metrics for the results. Finally, we evaluate the approach on generic grid-forming and grid-following inverter models showing good identification results.

The continuous rise of multimedia entertainment has led to an increased demand for delivering outstanding user experience of multimedia content. However, modeling user-perceived Quality of Experience (QoE) is a challenging task, resulting in efforts for better understanding and measurement of user-perceived QoE. To evaluate user QoE, subjective quality assessment, where people watch and grade videos, and objective quality assessment in which videos are graded using one or many objective metrics are conducted. While there is a plethora of video databases available for subjective and objective video quality assessment, these videos are artificially infused with various temporal and spatial impairments. Videos being assessed are artificially distorted with startup delay, bitrate changes, and stalls due to rebuffering events. To conduct a more credible quality assessment, a reproduction of original user experiences while watching different types of streams on different types and quality of networks is needed. To aid current efforts in bridging the gap between the mapping of objective video QoE metrics to user experience, we developed DashReStreamer, an open source framework for re-creating adaptively streamed video in real networks. The framework takes inputs in the form of video logs captured by the client in a non-regulated setting, along with an .mpd file or a YouTube URL. The ultimate result is a video sequence that encompasses all the data extracted from the video log. DashReStreamer also calculates popular video quality metrics like PSNR, SSIM, MS-SSIM, and VMAF. Finally, DashReStreamer allows creating impaired video sequences from the popular streaming platform YouTube. As a demonstration of framework usage, we created a database of 332 realistic video clips, based on video logs collected from real mobile and wireless networks. Every video clip is supplemented with bandwidth trace and video logs used in its creation and also with objective metrics calculation reports. In addition to dataset, we performed subjective evaluation of video content, assessing its effect on overall user QoE. We believe that this dataset and framework will allow the research community to better understand the impacts of video QoE dynamics.

Rising shares of renewable generation raises uncertainty and thus the number of possible power flow scenarios in the power system, which in turn increases the possibility for unforeseen contingencies, such as power line or generator failures and their combinations. Therefore, operators cannot longer rely only on operational experience to deal with every contingency. Our proposed method involves identifying the most effective countermeasures to minimize the impact of contingencies on the power system. We take into account various options, such as load shedding, adjusting phase-shifting transformer angles, and injecting active power using fast elements. The proposed approach considers primary control of generators and its limitations in order to compensate for power imbalances in the system. The problem is formulated as a mixed-integer linear optimization problem, employing DC power flow equations. The applicability of the approach is evaluated on the IEEE 39-bus system, and the scalability of the approach is shown on five systems with up to 6470 buses.

Data-driven state estimation (SE) is becoming increasingly important in modern power systems, as it allows for more efficient analysis of system behaviour using real-time measurement data. This paper thoroughly evaluates a phasor measurement unit-only state estimator based on graph neural networks (GNNs) applied over factor graphs. To assess the sample efficiency of the GNN model, we perform multiple training experiments on various training set sizes. Additionally, to evaluate the scalability of the GNN model, we conduct experiments on power systems of various sizes. Our results show that the GNN-based state estimator exhibits high accuracy and efficient use of data. Additionally, it demonstrated scalability in terms of both memory usage and inference time, making it a promising solution for data-driven SE in modern power systems.

We consider the problem of maximum-likelihood estimation in linear models represented by factor graphs and solved via the Gaussian belief propagation algorithm. Motivated by massive Internet of Things (IoT) networks and edge computing, we set the above problem in a clustered scenario, where the factor graph is divided into clusters and assigned for processing in a distributed fashion across a number of edge computing nodes. For these scenarios, we show that an alternating Gaussian belief propagation (AGBP) algorithm that alternates between inter- and intracluster iterations, demonstrates superior performance in terms of convergence properties compared to the existing solutions in the literature. We present a comprehensive framework and introduce appropriate metrics to analyze the AGBP algorithm across a wide range of linear models characterized by symmetric and nonsymmetric, square, and rectangular matrices. We extend the analysis to the case of dynamic linear models by introducing the dynamic arrival of new data over time. Using a combination of analytical and extensive numerical results, we show the efficiency and scalability of the AGBP algorithm, making it a suitable solution for large-scale inference in massive IoT networks.

Nonlinear state estimation (SE), with the goal of estimating complex bus voltages based on all types of measurements available in the power system, is usually solved using the iterative Gauss-Newton (GN) method. The nonlinear SE presents some difficulties when considering inputs from both phasor measurement units and supervisory control and data acquisition system. These include numerical instabilities, convergence time depending on the starting point of the iterative method, and the quadratic computational complexity of a single iteration regarding the number of state variables. This paper introduces an original graph neural network based SE implementation over the augmented factor graph of the nonlinear power system SE, capable of incorporating measurements on both branches and buses, as well as both phasor and legacy measurements. The proposed regression model has linear computational complexity during the inference time once trained, with a possibility of distributed implementation. Since the method is noniterative and non-matrix-based, it is resilient to the problems that the GN solver is prone to. Aside from prediction accuracy on the test set, the proposed model demonstrates robustness when simulating cyber attacks and unobservable scenarios due to communication irregularities. In those cases, prediction errors are sustained locally, with no effect on the rest of the power system's results.

Fifth-Generation (5G) networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for Machine Learning (ML)/Artificial Intelligence (AI) functions, 5G networks are expected to enable novel data-centric Smart Grid (SG) services. In this paper, we explore how data-driven SG services could be integrated with ML/AI-enabled 5G networks in a symbiotic relationship. We focus on the State Estimation (SE) function as a key element of the energy management system and focus on two main questions. Firstly, in a tutorial fashion, we present an overview on how distributed SE can be integrated with the elements of the 5G core network and radio access network architecture. Secondly, we present and compare two powerful distributed SE methods based on: i) graphical models and belief propagation, and ii) graph neural networks. We discuss their performance and capability to support a near real-time distributed SE via 5G network, taking into account communication delays.

—The power system state estimation (SE) algorithm estimates the complex bus voltages based on the available set of measurements. Because phasor measurement units (PMUs) are becoming more widely employed in transmission power systems, a fast SE solver capable of exploiting PMUs’ high sample rates is required. To accomplish this, we present a method for training a model based on graph neural networks (GNNs) to learn estimates from PMU voltage and current measurements, which, once it is trained, has a linear computational complexity with respect to the number of nodes in the power system. We propose an original GNN implementation over the power system’s factor graph to simplify the incorporation of various types and numbers of measurements both on power system buses and branches. Fur-thermore, we augment the factor graph to improve the robustness of GNN predictions. Training and test examples were generated by randomly sampling sets of power system measurements and annotated with the exact solutions of linear SE with PMUs. The numerical results demonstrate that the GNN model provides an accurate approximation of the SE solutions. Additionally, errors caused by PMU malfunctions or the communication failures that make the SE problem unobservable have a local effect and do not deteriorate the results in the rest of the power system.

Multimedia streaming over the Internet (live and on demand) is the cornerstone of modern Internet carrying more than 60% of all traffic. With such high demand, delivering outstanding user experience is a crucial and challenging task. To evaluate user Quality of Experience (QoE) many researchers deploy subjective quality assessments where participants watch and rate videos artificially infused with various temporal and spatial impairments. To aid current efforts in bridging the gap between the mapping of objective video QoE metrics to user experience, we developed DashReStreamer, an open-source framework for re-creating adaptively streamed video in real networks. DashReStreamer utilises a log created by a HTTP adaptive streaming (HAS) algorithm run in an uncontrolled environment (i.e., wired or wireless networks), encoding visual changes and stall events in one video file. These videos are applicable for subjective QoE evaluation mimicking realistic network conditions. To supplement DashReStreamer, we re-create 234 realistic video clips, based on video logs collected from real mobile and wireless networks. In addition our dataset contains both video logs with all decisions made by the HAS algorithm and network bandwidth profile illustrating throughput distribution. We believe this dataset and framework will permit other researchers in their pursuit for the final frontier in understanding the impact of video QoE dynamics.

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of high sampling rates of PMUs. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.

Estimating the system state is a non-trivial task given a large set of measurements, fuelling the ongoing research attempts to find efficient, scalable and fast state estimation (SE) algorithms. The centralised SE becomes impractical for large-scale systems, particularly if the measurements are spatially distributed across wide geographical areas. Dividing the large-scale systems into clusters (i.e., subsystems) and distributing the computation across clusters, solves the constraints of a centralised based approach. In such scenarios, using distributed SE methods brings many advantages over the centralised approaches. In this paper, we propose a novel distributed method to solve the linear SE model by combining local solutions obtained by applying weighted least-squares (WLS) of the given subsystems with the Gaussian belief propagation (GBP) algorithm. The proposed method is based on the factor graph operating without a central coordinator, where subsystems exchange only “beliefs”, thus preserving the privacy of the measurement data and state variables. Further, we propose an approach to speed-up evaluation of the local solutions upon arrival of new information to the subsystem. Finally, the proposed algorithm reaches the accuracy of the centralised WLS solution in a few iterations and outperforms the vanilla GBP algorithm with respect to its convergence properties.