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As renewable energy sources are integrated into power systems, the complexity of their management increases. For the analysis of modern power systems, clustering algorithms have become indispensable tools. The representation of an electric power transmission system involves conceptualizing it as a graph—a mathematical structure depicting a set of nodes and edges. In the context of power systems, these nodes correspond to buses, and the edges represent branches. Weighting may be assigned to the edges to denote the strength of their connections. In this paper, spectral clustering and hierarchical agglomerative clustering have been employed to analyze the Italian transmission network. Spectral clustering relies on the eigenvalues and eigenvectors derived from the Laplacian matrix associated with the network. The hierarchical agglomerative clustering, based on Thévenin impedance to define the distances, merges the two closest clusters at each step. The primary objective of this research is to identify distinct areas within the network based solely on its topology. In pursuit of this goal, admittances and Thévenin impedances have been employed as weights in the graph representation. By utilizing these approaches, the research aims at uncovering meaningful structures and patterns within the power transmission system based on the inherent connectivity and strength of the network elements. The techniques have been used on two real network models to demonstrate the performance of the algorithms with different grid configurations.

The secure operation of the transmission grid is of primary importance for any power system operator. However, the introduction of new technologies, market deregulation, and increasing levels of interconnectivity have made the analysis and assessment of power system security both more challenging and essential than ever. In this context, data-driven-based methodologies are being increasingly employed to classify and anticipate insecure future states, and make inferences on potential triggers of undesired operational conditions. This paper provides a comprehensive and systematic review of this fast-moving research area and covers data-driven-based methodologies deployed in both static and dynamic security assessment. Particular attention is paid to recent trends, such as the use of spatiotemporal feature selection algorithms and the increasing research activity in short-term voltage stability and frequency stability, which are not yet widely assessed as a collective in the existing literature.

The paper deals with the separation of power system into coherent areas; this is a relevant issue for managing the network in both normal operating conditions and during anomalous events. In particular, the attention is focused on partitioning the power system in such a way as to group together frequency signals, measured by means of phasor measurement units (PMU), exhibiting similar oscillatory behavior after the occurrence of a fault or disturbance. Unfortunately, the increasingly massive presence of renewable energy sources is undermining the clustering methods defined so far, requiring new solutions to the problem. To overcome the considered drawbacks, the authors propose hereinafter to ( ${i}$ ) improve the grouping capabilities of an iterative spectral clustering method thanks to the definition of new parameters for similarity estimation (Modified Bray Curtis index) and cluster thresholding (weighted Fiedler value) as well as (ii) enhance its robustness with respect to both measurement noise and uncertainty affecting the PMUs by means of a deep test procedure. To this aim, particular attention is paid in the design and assessment stage to the definition of both filtering algorithm and measurement parameters (e.g., the length of the analysis window). Once defined these parameters, the method is capable of 100% correctly separating transmission network sections oscillating with similar trends in a number of tests conducted on simulated and actual signals, so highlighting the promising performance of the method highlighting its reliability and efficacy in different test conditions.

This study presents the assumptions and strategies for the practical implementation of the dynamic mode decomposition approach in the wide-area monitoring system of the Italian transmission system operator, Terna. The procedure setup aims to detect poorly damped interarea oscillations of power systems. Dynamic mode decomposition is a data-driven technique that has gained increasing attention in different fields; the proposed implementation can both characterize the oscillatory modes and identify the most influenced areas. This study presents the results of its practical implementation and operational experience in power system monitoring. It focuses on the main characteristics and solutions identified to reliably monitor the interarea electromechanical modes of the interconnected European power system. Moreover, conditions to issue an appropriate alarm in case of critical operating conditions are described. The effectiveness of the proposed approach is validated by its application in three case studies: a critical oscillatory event and a short-circuit event that occurred in the Italian power system in the previous years, and a 15-min time interval of normal grid operation recorded in March 2021.

This paper deals with the simulation and the experimental confirmation of electromagnetic events that could interfere with the successful formation of the restoration path during the power system restoration procedure. The studied phenomena are more relevant for bulk power systems characterized by a low short circuit power as the restoration backbone. In particular, two case studies have been simulated and analyzed: one related to a transformer energization during the formation of the restoration path, and the other one occurred after the de-energization of some transmission lines and one autotransformer belonging to the restoration path. From the simulation results, it emerged that such events are related to the resonant effects between the supplying transformer and the restored network. Such resonances could have negative effects on the restoration if they are not effectively managed. In order to evaluate the impact of such phenomena in real networks, the measurement recordings of on-field tests were compared with the simulation results. It is worth noting that the performed analyses require the knowledge of several parameters that were not always available in practice. Hence, the exact magnitude of the described resonant phenomena was not easy to foresee for the restoration of real networks. The performed comparison confirms the preliminary simulation results and highlights that detailed electromagnetic models are particularly important to support the power system restoration management, in particular the planning of recovery procedures.

Large-scale deployment of renewable energy sources in power systems is basically motivated by two universally recognized challenges: the need to reduce as far as possible the environmental impact of the massive increase of energy request and the dependency on fossil-fuel. Renewable energy sources are interfaced to the network by means of interfacing power converters which inherently exhibit zero inertia differently from the conventional synchronous generators. This matter jointly to the high level of time variability of the renewable resources involve dramatically frequency changes, recurrent frequency oscillations and high variability of frequency profile in general. The need of a fast estimation of time variability of the power system inertia arises at the aim of predicting critical conditions. Based on the analysis of some actual data of the Italian Transmission Network, in this paper the authors propose an autoregressive model which is able to describe the dynamic evolution of the power system inertia. More specifically, the inertia is modeled as the sum of a periodic component and a noise stochastic process distributed according a non-Gaussian model. The numerical results reported in the last part of the paper, demonstrating the efficiency and precision of estimation of inertia, allow justifying the assumptions of the above modeling.

Battery participation in the service of power system frequency regulation is universally recognized as a viable means for counteracting the dramatic impact of the increasing utilization of renewable energy sources. One of the most complex aspects, in both the planning and operation stage, is the adequate characterization of the dynamic variation of the state of charge of the battery in view of lifetime preservation as well as the adequate participation in the regulation task. Since the power system frequency, which is the input of the battery regulation service, is inherently of a stochastic nature, it is easy to argue that the most proper methodology for addressing this complex issue is that of the theory of stochastic processes. In the first part of the paper, a preliminary characterization of the power system frequency is presented by showing that with an optimal degree of approximation it can be regarded as an Ornstein–Uhlenbeck process. Some considerations for guaranteeing desirable performances of the control strategy are performed by assuming that the battery-regulating power depending on the frequency can be described by means of a Wiener process. In the second part of the paper, more realistically, the regulating power due to power system changes is described as an Ornstein–Uhlenbeck or an exponential shot noise process driven by a homogeneous Poisson process depending on the frequency response features requested of the battery. Because of that, the battery state of charge is modeled as the output of a dynamic filter having this exponential shot noise process as input and its characterization constitutes the central role for the correct characterization of the battery life. Numerical simulations are carried out for demonstrating the goodness and the applicability of the proposed probabilistic approach.

Northern electric grid of Italy was involved in a black start mock drill. This test took place on November 2016 in order to check the coordination and efficiency of all the operational staffs and the automatic regulators involved in the case of a real blackout scenario. This paper presents the results of a research carried out in collaboration with Terna Rete Italia (the Italian Transmission System Operator), in order to find strengths and weaknesses and eventually to upgrade the existing restoration plan. Starting from the measures coming from generating plants and substations bars, the model of frequency regulation system for both the participating hydro and thermal power plants is developed. This work gives the system operator an essential tool to understand many complex dynamics and phenomena occurred during the test. At the same time, a robust model simulating the system behavior with different grid configurations is presented: it gives the opportunity to enhance both the classical power system control theory and the black start practices.