José Ángel Barrios

The transition toward active and converter-dominated distribution networks is increasing the need for Digital Real-Time Simulation (DRTS) platforms capable of supporting the validation, operation, and planning of modern power systems with high penetrations of Distributed Energy Resources (DERs). This review aims to critically examine the technological and methodological barriers that continue to limit the evolution of DRTS from a validation tool toward an operational cyber–physical infrastructure for future intelligent power systems. Particular attention is devoted to computational scalability, communication latency, Hardware-in-the-Loop (HIL), model conversion, proprietary “black-box” devices, digital twins, and the computational strategies required to preserve deterministic real-time execution. The reviewed literature indicates that maintaining real-time determinism while preserving high-fidelity electromagnetic transient (EMT) models remains one of the principal technological challenges for future DRTS platforms. The analysis further shows that many of the current limitations associated with digital twins, grid-forming technologies, and large-scale industrial deployment originate not from isolated technological deficiencies, but from the interaction among computational, communication, interoperability, synchronization, and model-management constraints. The evidence indicates that overcoming these limitations requires coordinated advances in computational architectures, communication infrastructures, model automation, interoperability, and cyber–physical integration rather than isolated hardware improvements. Overall, this review argues that the future impact of DRTS will depend not only on improvements in simulation performance, but also on its evolution into an interoperable and experimentally oriented cyber–physical infrastructure capable of supporting phenomenological analysis and the next generation of intelligent, resilient, and autonomous power systems.

An AI-based methodology was developed for estimating the state-of-health (SOH) of lithium-ion batteries based on secondary operational data and benchmarked with ANN, SVM, RF, and BiLSTM models. The proposed framework was evaluated by using tolerance-based accuracy, Bland–Altman agreement analysis, residual autocorrelation diagnostics, and Cartesian Taylor diagram comparison. The BiLSTM model was the best among the tested models for SOH prediction, with the least prediction error, best agreement with the reference SOH values, and near-white-noise residual behavior. The framework was further extended to Remaining Useful Life (RUL) prediction, where the BiLSTM model showed the most consistent overall performance. We also propose a residual-based anomaly detection as a potential extension of the battery monitoring framework. However, a quantitative evaluation of anomaly detection is out of scope in this study due to the lack of labeled anomaly data in the CALCE dataset. The proposed framework is validated by complementary statistical diagnostics, providing a robust and practical framework for non-intrusive battery health monitoring.

This paper presents a sizing and optimization methodology for LCL filters tailored to high-capacity modular power systems. The approach prioritizes the strategic selection of the resonance frequency, an asymmetric inductance design, and strict harmonic current limits. The methodology is validated through a case study simulation of a 126 MW photovoltaic plant in a region of Mexico, analyzing its 2.34 MW inverter architecture. The simulations show that precise capacitor sizing for reactive power management, combined with a passive resistive damping strategy, ensures compliance with grid interconnection standards (IEEE 1547) and power quality standards (IEC 61000). This approach simplifies practical implementation by eliminating the need for complex active damping control algorithms. Additionally, dynamic decoupling is validated through time-domain step responses, and frequency-domain sensitivity analysis confirms robust stability margins even under ±20% variations in passive parameters. Ultimately, the system achieves voltage total harmonic distortion (THD) levels below 0.18%, demonstrating a scalable solution for maintaining grid stability.

The problem of the optimal siting and placement of static compensates (STATCOMs) in power systems is addressed in this paper from an exact mathematical optimization point of view. A mixed-integer nonlinear programming model to present the problem was developed with the aim of minimizing the annual operating costs of the power system, which is the sum of the costs of the energy losses and of the installation of the STATCOMs. The optimization model has constraints regarding the active and reactive power balance equations and those associated with the devices’ capabilities, among others. To characterize the electrical behavior of the power system, different load profiles such as residential, industrial, and commercial are considered for a period of 24 h of operation. The solution of the proposed model is reached with the general algebraic modeling system optimization package. The numerical results indicate the positive effect of the dynamic reactive power injections in the power systems on annual operating cost reduction. A Pareto front was built to present the multi-objective behavior of the studied problem when compared to investment and operative costs. The complete numerical validations are made in the IEEE 24-, IEEE 33-, and IEEE 69-bus systems, respectively.

Entry temperature estimation is a major concern for finishing mill set-up in hot strip mills. Variations in the incoming bar conditions, frequent product changes and measurement uncertainties may cause erroneous estimation, and hence, an incorrect mill set-up causing a faulty bar head-end. In earlier works, several varieties of neuro-fuzzy systems have been tested due to their adaptation capabilities. In order to test the combination of the simplicity offered by Takagi–Sugeno–Kang systems (also known as Sugeno systems) and the modeling power of type-2 fuzzy, in this work, hybrid-learning type-2 Sugeno fuzzy systems are evaluated and compared with the results presented earlier. Systems with both empirically and fuzzy c-means-generated rules as well as purely fuzzy systems and grey-box models are tested. Experimental data were collected from a real-life mill; datasets for rule-generation, training, and validation were randomly drawn. Two of the grey-box models presented here reach 100% of bars with 20 °C or less prediction error, while two of the purely fuzzy systems improved performance with respect to purely fuzzy systems presented elsewhere, however it was only a slight improvement.