This paper introduces a novel metric, termed the Generalized Fiedler Vector (GFV), to evaluate the dynamic connectivity in power systems. The proposed metric leverages the network connectivity, represented by the system Laplacian matrix, together with the nodal inertia distribution, following a formulation previously developed by the first author. By capturing the interplay between system topology and dynamic properties, the GFV provides valuable insights for the optimal siting of stochastic generation to mitigate its impact on local and system-wide frequency variability. The effectiveness of the proposed approach is demonstrated through Monte Carlo simulations performed on the IEEE 68-bus test system.

This paper proposes a novel formulation of effective regional inertia that explicitly accounts for both system topology and the spatial distribution of inertia. Unlike traditional approaches that model a region as an aggregated machine with an equivalent inertia, the proposed metric provides a topology-aware representation. The methodology builds on an analytical framework that extends classical slow coherency theory to address network partitioning and regional frequency stability. Based on these partitions, we develop a systematic procedure to evaluate the effective inertia of each region, enabling a more accurate interpretation of local inertial contributions, including those from virtual inertia provided by inverter-based resources (IBRs). Case studies on the IEEE 39-bus and 68-bus systems demonstrate that the integration of inertial devices does not uniformly improve system frequency response, underscoring the importance of the proposed metric for effective regional inertia assessment.

This paper introduces a novel formulation to evaluate the local synchronization of power system devices, namely Synchronization Energy (SE). The formulation is derived based on the complex frequency concept and the Teager Energy Operator applied to the complex power. This formulation offers valuable insights into the relationship between complex frequency of voltage and current of the device and its stationary operation. Based on this relationship we derive the conditions for a novel definition of local synchronization of power system devices. Through various case studies, the paper demonstrates how SE can effectively assess local synchronization under diverse operating conditions.

This paper introduces an open-source research platform for monitoring the Mexican interconnected power grid, allowing real-time processing and information extraction of the grid’s dynamic condition. Moreover, the platform is a Python-based development that embeds different ringdown and clustering analytics tools. In the case of ringdown analysis, the modal information can be extracted using some of the most known algorithms, i.e., Prony analysis, eigensystem realization algorithm (ERA), and matrix pencil (MP). For clustering analysis, the coherent behaviour of generator and non-generator buses is provided by applying recent state-of-the-art techniques such as affinity propagation, K-means, hierarchical agglomerative clustering, and typicality data analysis. The results of up to 93 PMUs show that this open-source platform suits researchers’ and engineers’ power system dynamic analysis requirements.

The increasing use of power converters in radial networks has raised significant challenges regarding system stability. This paper addresses these challenges through a comparative analysis of control strategies for Grid-Following (GFL) and Grid-Forming (GFM) converters, both with a nominal capacity of 5 kW, using state-space modeling (SSM) to evaluate their performance in weak and strong grid conditions. The study examines how line impedance variations impact system stability, providing a detailed analysis of each converter response to changing grid conditions, from strong to weak grids. The accuracy of the theoretical models is validated through real-time testing in Control Hardware-in-the-Loop (CHIL), ensuring that the models accurately reflect real-world dynamics. This research offers valuable insights for enhancing the stability of power converters in modern grid applications.

The increasing complexity of power systems, driven by the decentralization of generation and the growing demand for operational reliability, has reinforced the importance of Wide-Area Monitoring Systems (WAMS) for dynamic system analysis. Among WAMS applications, event detection and classification are essential for enabling timely and accurate responses to disturbances. However, event classification methodologies face significant challenges, such as the occurrence of multiple and sequential events, disturbance overlap, and a high degree of similarity among different classes. This work proposes a multi-label classification methodology based on Convolutional Neural Networks (CNNs), applied to events detected using a two-level robust approach consisting of the Discrete Wavelet Transform-based spectral analysis, followed by a deep neural network (DNN) strategy that prevents false alarms. The main contributions include: (i) the construction of a multi-label dataset comprising 11,205 real events from the Brazilian Interconnected Power System (BIPS), covering Line Tripping (LnT), Electromechanical Oscillations (EO), Loss of Load (LoL) and Generation Tripping (GT); (ii) the development of a classification model based solely on real synchrophasor data; and (iii) its experimental validation during six months of online operation in the BIPS, including common challenges associated with Phasor Measurement Unit (PMU) data, such as noise, missing data, and synchronization errors. The model achieved a Hamming loss of 0.082, correctly classifying 91.8% of the events during deployment. These results demonstrate the effectiveness and robustness of the proposed approach in operational contexts, contributing to faster and more reliable decision-making in power system control centers.

This paper presents the application of a methodology for estimating the inertia of generator groups of the Brazilian Interconnected Power System (BIPS) using phasor measurement unit (PMU) data acquired at 230 kV transmission substations. The methodology has been applied using synchrophasor data collected during disturbances in a Brazilian subsystem with two hydroelectric power plants (HPPs), as well as two interconnections with the rest of the BIPS, composed of three 230 kV transmission lines and a back-to-back converter station. Simulated and real data have been analyzed to assess the limitations and to identify required improvements toward a practical application of the perturbation-based methodology in the estimation of the inertia of real systems. A by-product of the application of the methodology is the obtaining of a second-order dynamic equivalent, useful for model-based online applications.

In this paper, the impact of inverter-based resources (IBRs) on the frequency dynamics of the Brazilian Interconnected Power System (BIPS) is evaluated. A measurement-based framework is proposed to assess the impact of IBR penetration on the system-wide and regional/local frequency dynamics. The analysis leverages data from a low-voltage wide area monitoring system (WAMS) and publicly available historical generation records from the Brazilian System Operator. A methodology is introduced to extract local frequency fluctuations across regions using a variational mode decomposition (VMD) approach. The findings reveal a continuous degradation in the system-wide frequency and local frequency variations, underscoring the need for enhanced regional monitoring and evaluation metrics to maintain frequency stability in large-scale interconnected systems.

The increasing deployment of Intelligent Electronic Devices (IEDs) for obtaining synchrophasors and recording oscillographies in Electric Power Systems (EPS) has enhanced system operation, enabling monitoring, control, and protection applications. However, the growing number of IEDs makes managing the large flow of data (from different sources and of different natures) a challenge for the system operator. This article presents an infrastructure and proposes a solution, based on the convergence of synchrophasor and oscillography data, for the analysis of disturbances in an EPS. The infrastructure includes an event detection module based on power system frequency data, a module that captures oscillographies recorded by the IEDs, and a module that identifies events with high systemic impact (based on predefined criteria) and retrieves the corresponding data, focusing the system operator's analysis effort on disturbances of greater interest.

This paper summarizes recent advancements on spatio-temporal data-driven and machine learning methods for static and dynamic security assessment, and their particular use cases. It is a collective effort of different research groups members of the IEEE Working Group on Big Data Analytics for Transmission Systems, to provide transmission system operators (TSOs) with innovative tools and ideas for their potential implementation. The algorithms presented here are classified as non-training and training approaches, namely spatio-temporal and machine learning based, considering as input time series from time domain simulations, and or synchrophasor data from wide-area monitoring systems. The efficacy of these algorithms is then evaluated in different IEEE benchmark models and using real system measurements from different countries.

This paper presents a framework for the static and dynamic evaluation of the Brazilian Interconnected Power System (BIPS), considering multiple operating points and contingency scenarios. The simulations are carried out using software programs that are well-accepted in the Brazilian power system industry. The evaluation of the results uses defined indexes and is divided into i) static voltage violations and ii) transient and frequency stability evaluation. The results show the capacity of the proposed framework to generate, simulate, and process a large amount of data, extracting relevant information about BIPS static and dynamic performance.

The expansion of variable generation has driven a transition toward a 100% non-fossil power system. New system needs are challenging system stability and suggesting the need for a redesign of the ancillary service (AS) markets. This paper presents a comprehensive and broad review for industrial practitioners and academic researchers regarding the challenges and potential solutions to accommodate high shares of variable renewable energy (VRE) generation levels. We detail the main drivers enabling the energy transition and facilitating the provision of ASs. A systematic review of the United States and European AS markets is conducted. We clearly organize the main ASs in a standard taxonomy, identifying current practices and initiatives to support the increasing VRE share. Furthermore, we envision the future of modern AS markets, proposing potential solutions for some remaining fundamental technical and market design challenges.

This paper proposes the FTFT for the real-time monitoring of modern power grids through dynamic harmonic estimations. The FTFT results from the DTTFT implemented with the FFT, instead of its filter bank implementation. In FTFT, the harmonic Taylor-Fourier coefficients are obtained in batch, and from these complex coefficients, estimates of frequency, amplitude, and RoCoF are obtained. The FTFT is embedded in two low-cost platforms, one consisting of a digital signal processor (DSP) coded using C programming language, and a development PMU (D-PMU); where a computationally efficient calculation of the FTFT is coded in both platforms. Both platforms process instantaneous voltage and current signals, rendering phasor and harmonic estimates as if they were extracted by the impulse response of a set of FIR filters and their first and second differentiators of the DTTFT. The phasor and harmonic estimations are accomplished using a sliding window that updates sample by sample, instantaneously providing amplitude, phase, frequency, and RoCoF estimations at the fundamental and harmonic frequencies with a high reporting rate. Experimental results confirm the proposition's performance, precision, and effectiveness under steady-state conditions, sudden changes, and harmonic analysis.

The inertia displacement in power grids, caused by the massive integration of renewable energy resources (RES) and responsive loads, has become one of the biggest challenges to operating and controlling power systems. Locating the centre of inertia (COI) of a region can help identify this inertia displacement. This work proposes a novel fully data-driven disturbance-based methodology to estimate both the COI and the inertia of a region, where the variation of the COI due to RES and load contribution are considered. The methodology uses a recursive form of the typicality-based data analysis (TDA) to find the pilot-bus (TDAp). The TDA methodology approximates the multi-modal distribution of active power and frequency measurements by the typicality property, to detect the COI of each region and the corresponding pilot-bus. A composite metric of correlation and cosine similarities of active power and frequency measurements is used to approximate the measurements’ unknown distribution. The window of measurements necessary for detection is attained by the convergence of the typicality variance. The frequency of the detected pilot-bus of the region and tie-lines active powers deviations are used by an auto-regressive moving average exogenous input (ARMAX) approach to estimate the inertia of a regional equivalent machine. The methodology is capable of identifying RES and load contribution, since the pilot-bus detection through the TDAp is sensible to the COI displacement by these equipments, unlike traditional methods that only consider an average of synchronous machines’ inertia and some heuristics for load contribution. The proposed methodology is tested by using the IEEE 68-bus benchmark test system, an adapted version with aggregated dynamical loads, and also with RES participation through type-3 wind generators, corroborating the effectiveness of the proposal.

To ensure the continuous operation of a microgrid, proactive planning is essential, especially when contemplating possible dynamic events that alter the scheduled scenarios for the islanded operation or during its transition from connected to islanded mode. This paper introduces an innovative mathematical programming model for the AC optimal power flow (AC-OPF) with dynamic security constraints (DSCs), considering two scenarios: the islanded operation and the transition. This model uses positive-sequence equations to represent the inverter-based resources (IBRs) for grid-forming and grid-following roles, along with a fourth-order synchronous generator model equipped with excitation and frequency control systems. They assess the dynamic response to specific events such as short-circuits, decreases in PV generation, increases in load demand during the islanded operation, and the transition itself. The DSCs are applied to dispatchable distributed energy resources (DERs), which react to variations in the microgrid, considering generation and opportunity costs to minimize the discrepancy between planned and secure operating points. The mathematical programming model is implemented using AMPL, and solutions are obtained through the nonlinear optimization solver IPOPT. The tests are conducted in an adapted version of the microgrid being developed at the University of Campinas that includes a synchronous generator, photovoltaic (PV) generation, and a battery energy storage system (BESS). Results demonstrate the model’s effectiveness in adjusting generation dispatch to withstand defined events and optimizing generation resources, even when limits are not reached.

One of the main features of microgrids is their ability to operate in grid-connected and islanded modes. In each mode of operation, distributed energy resources (DERs) can be managed through grid forming or grid following control strategies. A microgrid may experience significant voltage and frequency fluctuations during unintentional isolation events. This papers proposes an adaptation of the control strategy known as Virtual Synchornous Machine with the aims to smooth the transition to islanded mode without depending on any external command signal for islanding detection. The proposed control strategy is applied to a 1 MW, 1.2 MWh Battery Energy Storage System that will act as the grid former in an 11.9 kV microgrid currently being deployed at the State University of Campinas (Brazil). Hardware-in-the-loop simulations result of the system with the control strategy implemented in a digital signal processor (DSP) are presented for validation.

As the integration of inverted-based resources (IBR) continues to grow within power systems, the complexity of analyzing numerous potential future scenarios and operational points escalates. This paper introduces a modified index for evaluating voltage performance in large-scale power systems across multiple operating points. This approach allows for the swift evaluation of voltage profile in specific areas, at different voltage levels, distinguishing between over-voltage and under-voltage problems. The entire Brazilian Interconnected Power System (BIPS) is considered as a case study. The proposed index successfully identifies critical locations, providing a precise characterization of voltage violations, and it could be employed to assist system planners and system operators in fast scanning bus voltages across multiple cases.

Islanded microgrids, characterized by limited energy generation capacity and low inertia due to renewable sources based on power electronic converters, face significant challenges. These challenges include significant frequency and voltage deviations that can lead to microgrid collapse. This work proposes an enhanced approach to the data-driven control algorithm using the traditional multivariable (MIMO) Virtual Reference Feedback Tuning (VRFT) technique applied to power electronic converter sources. This method focuses on optimizing proportional and integral control with a reduced set of measured data samples to follow the dynamic performance prescription defined by the designer. The effectiveness of the data-driven control feasibility is certified through control hardware-in-the-loop (CHIL) simulation.

The high penetration of inverter-based generation imposes the necessity to include an inertia requirement in the unit commitment (UC) formulation. Additionally, fair compensation must be provided to incentivize the participation of capable suppliers. Being a discrete service of inherent nonconvex nature, the inertial response (IR) is particularly challenging to pricing. We define explicit marginal IR prices and analyze their economic value by suitably extending three UC pricing methods. The first one is based on a primal-dual formulation that ensures a revenue-adequate condition, while the second and third ones are the convex-hull pricing and an approximation proposed in the literature. For the considered instances, the computed IR prices succeed in signaling the inertia scarcity. As such, the proposed mechanisms ensure IR payments at a level that incentivizes units to support system security.