Jorge De La Cruz

As power systems become increasingly user-centric and participatory, fairness is emerging as an important consideration in operational decision-making. However, justice-related aspects are often assessed only after system operation, limiting their role in scheduling strategies. An Operational Energy Justice (OEJ) model is proposed in this paper to incorporate equality in load-shedding distribution as a measurable and optimizable objective in multi-energy microgrid scheduling. A novel justice index based on load-shedding distribution among load points is introduced. The proposed model includes energy justice, economic cost, environmental impact, flexibility, and load shedding. In addition, a flexibility evaluation criterion is developed to assess the capability of the electric-thermal system to adapt to varying operating conditions. Three test systems are employed to validate the proposed approach and assess both effectiveness and scalability. The results demonstrate that incorporating the justice objective leads to an approximately 0.41% increase in operational cost compared with the corresponding fairness-unaware operating point, while simultaneously reducing total load shedding and substantially improving the equality of curtailment distribution. Furthermore, a controlled comparison with an identical total amount of load shedding shows that achieving a fairer distribution of curtailment requires only a 0.45% increase in operational cost. The findings confirm that the integration of justice into microgrid scheduling enables just, flexible, economical, and sustainable operation of the systems.

The rapid integration of renewable energy sources and the decentralization of power systems have positioned microgrids as essential for sustainable, resilient energy supply. However, their diverse operating conditions and complex topologies pose challenges for stability, protection, and autonomous control, particularly under fault conditions. This article surveys brain-inspired artificial intelligence (BIAI) models that enable self-healing functions in Microgrids (MGs). It covers structure-driven models, including convolutional, recurrent, and spiking neural networks, alongside behavior-driven approaches such as learning systems (reinforcement, transfer, attention mechanisms, and emotions). A special emphasis is placed on Brain Emotional Learning and its variation, BELBIC, which replicates emotion-driven neural mechanisms to provide adaptive, quick, and less complexity control suited to the nonlinear nature and uncertain behavior of MGs. Evidence reported in the literature suggests that BIAI approaches can improve fault detection, enhance restoration decisions, and support more resilient and adaptive self-healing strategies compared with conventional AI techniques. This review aims to assist researchers and practitioners in developing more robust, adaptive, autonomous self-healing MG architectures. It concludes by highlighting the open challenges and potential future research objectives to accelerate the adoption of BIAI in a dynamic energy environment.

[ASWRT] Sensor signal validation is essential for industrial automation, particularly in safety-critical environments. This paper proposes a training-free statistical framework with a hierarchical sliding-window strategy that sequentially performs sensor validation and anomaly detection. The approach combines signal stationarization with adaptive interquartile-range thresholds updated dynamically to handle non-stationary conditions. The framework is evaluated on the Tennessee Eastman Process (TEP) benchmark and univariate cases from the Skoltech Anomaly Benchmark (SKAB). In TEP, the proposed method effectively identifies abnormal operating regimes and outperforms matrix profile, k-Shape, and subsequence-level Isolation Forest baselines, achieving 99.8% precision and an F1-score of 74.3%. In SKAB, it achieves the best overall trade-off among the compared methods, reaching 84.6% precision, a 66.4% F1-score, and the lowest false positive rate. Owing to its low complexity and sensor-agnostic design, the framework is suitable for large-scale industrial monitoring without model training.

Alzheimer’s disease (AD) is the most prevalent form of dementia, and accessible biomarkers for early detection remain limited. This study introduces a biomimetic approach in which brain electrical activity is transformed into sound and vibration, emulating natural sensory encoding mechanisms. Resting-state EEG recordings from 36 AD patients and 29 healthy controls were averaged by group, directly sonified, and used to drive a membrane–laser system that projected dynamic vibrational patterns. This transformation mirrors how biological systems convert electrical signals into sensory representations, offering a novel bridge between neural dynamics and physical patterns. The resulting videos were processed through adaptive binarization, morphological filtering, and contour-based masking. Quantitative descriptors such as active area, spatial entropy, fractal dimension, and centroid dynamics were extracted, capturing group-specific differences. A Random Forest classifier trained on these features achieved an accuracy of 0.85 and an AUC of 0.93 in distinguishing AD from controls. These findings suggest that EEG sonification combined with vibrational projection provides not only a novel non-invasive biomarker candidate but also a biomimetic framework inspired by the brain’s own capacity to encode and represent complex signals.

This paper presents a thorough examination of the technical requirements for a new Distribution System Operation (DSO) scheme in Colombia. This study contextualizes these requirements to consider local particularities by looking at national and international standards and models relevant to DSO. This study aims to align the technical requirements to the DSO technologies that offer the greatest advantages (real-time data readings to automate commercial cycle, suspension and reconnection of the service, improving reliability and quality of power supply, and environmental benefits) and the fewest implementation obstacles. Today, an electrical operator can become more proactive by integrating technologies such as advanced metering infrastructure (AMI), distributed energy resources (DER), microgrids, and advanced distribution automation (ADA). This study will provide a structured framework for the implementation of a cutting-edge DSO technology in order to assist Colombia’s energy sector in becoming more dynamic and efficient with a smarter and more active electricity distribution system.

This paper compares concentrated and distributed reactive power compensation to improve the power factor at the point of common connection (PCC) of an industrial electrical system (IES) with harmonics. The electrical system under study has a low power factor, voltage variation, and harmonics caused by motors operating at low loads and powered by variable-speed drives. The designed compensation system mitigates harmonics and reduces electrical losses with the shortest payback period. Four solutions were compared, considering concentrated and distributed compensation with capacitor banks and harmonic filters. Although the cost of investment in concentrated compensation is lower than that of distributed compensation, a higher reduction in electrical losses and a lower payback period are obtained with distributed compensation. Although the lowest payback period was obtained with distributed compensation with capacitor banks (0.4 years), it is not recommended in the presence of harmonics because the effects of current harmonics significantly reduce the useful life of these elements. For this reason, distributed compensation with harmonic filters obtained a payback period of 0.6 years, and it was proposed as the best solution. These results should be considered in projects aimed at power factor compensation in IESs with harmonics. The concentrated compensation of the capacitor bank at the PCC is proposed because of the lower investment cost and ease of installation. However, the advantages of distributed compensation with harmonic filters have not been evaluated. An energy efficiency approach is used to analyze the impact of the location methods of the power factor compensation equipment on the electrical losses of the IES.

This article introduces a new approach for validating directional overcurrent protection schemes in ring-topology electrical distribution systems with distributed energy resources (DERs). The proposed protection scheme incorporates overcurrent and directional functions and addresses DER-induced challenges such as variable short circuit levels. This study employs real-time and offline simulations to evaluate the performance of the protection coordination scheme using a digital twin under DER-supplied loads. The utilization of digital twins offers the possibility to simulate different scenarios, providing real-time responses to dynamic changes and allowing for informed decision-making in response to disturbances or faults. This study aims to present a new approach to validate the performance of the proposed protection scheme when the load is entirely supplied by DERs, highlighting issues such as false trips and protection system blindness resulting from changes in short circuit currents. The results show a breakdown in the coordination of the protection scheme during the fault conditions, demonstrating the effectiveness of digital twins in validating the protection scheme’s performance. Performing an analysis in the electromagnetic transient (EMT) domain improves the validation and refines the results.

Microgrids (MGs) are building blocks of smart power systems formed by integrating local power generation resources, energy storage systems (ESSs), and power-consuming units. While MGs offer many benefits, including increased resilience and flexibility, there remains a need for improved control and protection techniques that can ensure their performance and automatic restoration in response to dynamic operating conditions and failure events. Recently, researchers have explored model-free emotional learning adaptive strategies based on the emotional response of human brains to control MGs. These model-free control strategies are well-suited for handling the complexity, nonlinearity, and uncertainty present in MGs and offer several advantages over traditional approaches. This article provides an overview of different emotional learning techniques applied to MG control and protection, their challenges, and future trends. In addition, we draw parallels between the hierarchical control architecture (HCA) of MGs and the emotional learning process in the human brain, discussing their operational strategies and key areas of research. Finally, the future implementations of brain emotional learning (BEL) in the control and protection of MGs are discussed, and concluding remarks on the potential of this approach are provided.