In the past, providing an online and real-time response to cyber-physical attacks in large-scale power microgrids was considered a fundamental challenge by operators and managers of power distribution networks. To address this issue, an innovative framework is proposed in this paper, enabling real-time responsiveness to cyberattacks while focusing on the techno-economic energy management of large-scale power microgrids. This framework leverages the large change sensitivity (LCS) method to receive immediate updates to the system's optimal state under disturbances, eliminating the need for the full recalculation of power flow equations. This significantly reduces computational complexity and enhances real-time adaptability compared to traditional approaches. Additionally, this framework optimizes operational points, including resource generation and network reconfiguration, by simultaneously considering technical, economic, and reliability parameters-a comprehensive integration often overlooked in recent studies. Performance evaluation on large-scale systems, such as IEEE 33-bus, 69-bus, and 118-bus networks, demonstrates that the proposed method achieves optimization in less than 2 s, ensuring superior computational efficiency, scalability, and resilience. The results highlight significant improvements over state-of-the-art methods, establishing the proposed framework as a robust solution for real-time, cost-effective, and resilient energy management in large-scale power microgrids under cyber-physical disturbances.

This paper implements low-cost GaN-based three-phase voltage and current sources to validate power protecting relays. This proposal develops a voltage generator that uses three single-phase full-bridge converters equipped with an LC filter and an isolating transformer. It also includes a current generator using three single-phase half-bridge converters furnished with an LCL filter. Both voltage and current testing sources are assembled employing GaN transistor technology, which enables operation at high switching frequencies and passive filters with low-value components. Experimental tests are conducted to confirm and validate the operation and dynamic performance of voltage and current testing sources. Finally, the performance of the voltage and current signal generator is assessed by testing a commercial protection system (SEL-451) through directional current function. This is achieved by emulating a fault condition, where three voltage and current signals are generated to trigger the trip signal after surpassing the relay threshold.

This paper proposes an innovative transient stability index (TSI) designed to enhance the real-time assessment of power system stability. The TSI integrates a corrected kinetic energy approach with a modified equal area criterion, offering a novel methodology for evaluating transient stability margins in power systems. Unlike traditional methods, the proposed TSI operates without relying on post-fault data, making it particularly suitable for online applications. A structure-preserving model is utilized to represent the power network, accounting for key factors such as controller behavior during transient events. Additionally, a new statistical classification method is introduced to efficiently determine the individual contribution of generators to the overall system stability. The effectiveness of the proposed approach is validated through comprehensive case studies on IEEE 9-bus and IEEE 39-bus systems. The simulation results confirm that the proposed method provides accurate, real-time insights into the transient stability margins of power systems, demonstrating its practical advantages in both analysis and operation.

Failures of Induction Motors (IMs) can lead to unscheduled downtime and interruption in industry processes. This paper concentrates on the detection of the stator's inter-turn faults which are one of the most frequent causes of failures in IMs. The proposed detection method is based on a similarity index that uses the current waveform. To be more specific, the proposed algorithm presents a full-cycle sliding-window-based index based on cosine similarity that only uses current signals for detection of the stator's inter-turn faults. The proposed index cuts the phase difference before/after the disturbance and, as a result, it only depends on the size variations of the current waveform. The proposed method is technically unaffected by non-fault transient conditions including voltage imbalance, voltage sag, voltage swell, and heavy load changes. The performance of the proposed method is validated with numerous simulated scenarios and has good accuracy and speed of convergence.

Abstract This article proposes a holistic and improved methodology for the inductors’ design in power electronic circuits operating at high frequencies. This methodology includes an analytical design, a finite element modeling of the electromagnetic behavior with JMAG software, a simulation analysis in a Simulink environment, and experimental verification of the effectiveness of inductor design in a laboratory-scale step-up boost power converter prototype. The holistic approach allowed us to achieve all target specifications such as maximum power (300 W), minimum voltage (60 V), inductance (1.77 mH), and maximum current ripple (1 A). It guarantees the losses’ minimization in core and copper (21.47 W), and marginal errors below 4% between the analytical, simulated, and experimental values.

The increasing integration of variable renewable energy resources through power electronics has brought about substantial changes in the structure and dynamics of modern power systems. In response to these transformations, there has been a surge in the development of tools and algorithms leveraging real-time computational power to enhance system operation and stability. Data-driven methods have emerged as practical approaches for extracting reliable representations from non-linear system data, enabling the identification of dynamics and system parameters essential for analysing stability and ensuring reliable operation. This study provides a comprehensive review of recent contributions in the literature concerning the application of data-driven identification, analysis, and control methods in various aspects of power system operation. Specifically, the focus is on frequency support, power oscillation detection, and damping, which play crucial roles in maintaining grid stability. By discussing the challenges posed by parametric uncertainties, load and source variability, and reduced system inertia, this review sheds light on the opportunities for future research endeavours.

This chapter presents the behavior analysis of the regional electric market (REM) of Central America between 2019 and 2020. Its objective is to assess the impact of the pandemics and natural disasters that occurred in this period on the dealings of the REM. The results were obtained through the statistical analysis of a database the regional operating entity provided. The first part of the study focuses on the injections and withdrawals from the regional transmission network. The second part of the study deals with the foregoing average price in different link nodes and regions throughout the market. In summary, the dealings in the regional electric market fell by 8.2%. The average price fell by 43.9%. Most countries in the market maintained their position as buyers or sellers but in smaller quantities. The contract market became smaller, and the opportunity market grew, both with reference to 2019.

In the realm of energy infrastructure planning, Generation Expansion Planning (GEP) stands as a critical component, aimed at determining the optimal generation unit capacities, resource combinations, locations, and commissioning schedules. In parallel, Transmission Network Expansion Planning (TNEP) is intended to identify the future requirements for transmission infrastructure, ensuring an efficient and reliable energy supply. While GEP and TNEP share a common goal of equipping generation and transmission networks to meet future demands, traditional approaches tend to address these aspects separately or sequentially, neglecting their inherent interdependence. To address this limitation, co-optimization models have been developed to integrate Generation and Transmission Network Expansion Planning (GTNEP) in a unified approach. In this work, a comprehensive review of the existing literature on GTNEP models is conducted. The objective of this review is to classify and identify gaps and opportunities for further research, spanning both centralized and liberalized electrical markets.

The growing demand for electric power and the need for an energy transition that contributes to the reduction of global greenhouse gas emissions have driven the development of various energy generation, storage, and offset technologies. These technologies are known as distributed energy resources. Their integration into distribution power systems not only contributes to improving operating aspects, but also allows supplying electricity to areas that do not have access to large-scale power systems. Therefore, the integration and management of these resources has become a topic of interest, and several studies seek to optimize their impact on technical, economic, and environmental aspects. However, this optimization poses specific challenges related to the type and number of variables related to the operation of a distribution power system. This review article aims to describe the main challenges posed by three-phase AC three-phase distribution power systems under scenarios involving the integration of distributed energy resources. In addition, it presents some approaches proposed by different authors to improve the technical, economic, and environmental aspects of power grids. It can be stated that the strategies presented in the literature fail to consider scenarios that simultaneously integrate different types of technologies and optimize them while following a multi-objective approach and considering three-phase systems in a context of variable generation and demand. Therefore, future work in this field should address these aspects in a holistic manner, taking into account the computation efforts and processing times required by intelligent algorithms.