Search Results (227)

Modern microgrids face critical challenges in maintaining stability and efficiency due to renewable energy intermittency and dynamic load demands. This paper proposes a novel real-time energy management framework that synergizes a bio-inspired T-Cell optimization algorithm with decentralized voltage-based droop control to address these challenges. A JADE-based multi-agent system (MAS) orchestrates coordination between the T-Cell optimizer and edge-level controllers, enabling scalable and fault-tolerant decision-making. The T-Cell algorithm, inspired by adaptive immune system dynamics, optimizes global power distribution through the MAS platform, while droop control ensures local voltage stability via autonomous adjustments by distributed energy resources (DERs). The framework is rigorously validated through Hardware-in-the-Loop (HIL) testing using OPAL-RT, which interfaces MATLAB/Simulink models with Raspberry Pi for real-time communication (MQTT/Modbus protocols). Experimental results demonstrate a 91% reduction in grid dependency, 70% mitigation of voltage fluctuations, and a 93% self-consumption rate, significantly enhancing power quality and resilience. By integrating centralized optimization with decentralized control through MAS coordination, the hybrid approach achieves scalable, self-organizing microgrid operation under variable generation and load conditions. This work advances the practical deployment of adaptive energy management systems, offering a robust solution for sustainable and resilient microgrids. Full article

Allergic diseases, particularly respiratory allergies like asthma and allergic rhinitis, are a growing public health concern influenced by environmental factors such as climate change and air pollution. The exposome framework enables a comprehensive assessment of how lifelong environmental exposures shape immune responses and allergic sensitization. Peru’s diverse ecosystems and climates provide a unique setting to investigate regional variations in allergic sensitization. This study characterized these patterns in five Peruvian regions with distinct climatic, urbanization, and socioeconomic characteristics. A total of 268 individuals from Lima, Piura, Tarapoto, Arequipa, and Tacna were analysed for allergen-specific IgE responses using a multiplex IgE detection system. The results revealed significant geographical differences in sensitization frequencies and serodominance profiles, based on descriptive statistics and supported by Chi-square comparative analysis. House dust mites were predominant in humid regions, while Arequipa exhibited higher sensitization to cat allergens. In Tacna, olive pollen showed notable prevalence alongside house dust mites. Tarapoto’s high humidity correlated with increased fungal and cockroach allergen sensitization. Notably, some allergens traditionally considered minor, such as Der p 5 and Der p 21, reached sensitization prevalences close to or exceeding 50% in certain regions. These findings provide the most detailed molecular characterization of allergic sensitization in Peru to date, highlighting the importance of region-specific allergy management strategies. Understanding environmental influences on allergic diseases can support more effective diagnostic, therapeutic, and preventive approaches tailored to diverse geographical contexts. Full article

The proactive involvement of photovoltaic (PV) smart inverters (PVSIs) in grid management facilitates voltage regulation and enhances the integration of distributed energy resources (DERs) within distribution networks. However, to fully exploit the capabilities of PVSIs, it is essential to achieve optimal control of their operations and effective coordination with voltage-regulating devices in the distribution network. This study developed a dual strategy approach to forecast the optimal setpoints of onload tap changers (OLTCs), PVSIs, and distribution static synchronous compensators (DSTATCOMs) to improve the voltage profiles in power distribution systems. The study began by running a centralized AC optimal power flow (CACOPF) and using the hourly PV output power and the load demand to determine the optimal active and reactive power of the PVSIs, the setpoint of the DSTATCOM, and the optimal tap setting of the OLTC. Furthermore, Machine Learning (ML) models were trained as controllers to determine the reactive-power setpoints for the PVSIs and DSTATCOMs as well as the optimal OLTC tap position required for voltage stability in the network. To assess the effectiveness of the method, comprehensive evaluations were carried out on a modified IEEE 33 bus with a high penetration of PV energy. The results showed that deep neural networks (DNNs) outperformed other ML models used to mimic the coordination method based on CACOPF. Furthermore, when the DNN-based controller was tested and compared with the optimizer approach under different loading and PV conditions, the DNN-based controller was found to outperform the optimizer in terms of computational time. This approach allows predictive control in power systems, helping system operators determine the action to be initiated under uncertain PV energy and loading conditions. The approach also addresses the computational inefficiency arising from contingencies in the power system that may occur when optimal power flow (OPF) is run multiple times. Full article

As part of a systematic study on the structures of the mixed halide isothiocyanates, Sn^IIHal(NCS), their single crystals were grown and structurally characterized. For Hal = F (1), the SnClF structure type was confirmed, while with Hal = Cl (2), Br (3), and I (4), there are three isostructural compounds of a new structure type, and for Hal = Cl (5), there is a second modification of a third structure type. These structure types have been described with respect to the composition and coordination geometry of the first, second, and van der Waals crust coordination spheres and their dependence on the halogen size and thiocyanate binding modes. With respect to the first coordination spheres, all three structure types constitute one-dimensional coordination polymers. In 1, “ladder”-type double chains result from μ₃-bridging fluorine atoms, and in 2–4, single-chains built up from μ₂-halogen atoms are pairwise “zipper”-like interconnected via κ²NS-bridging NCS ligands, which manage the halogen-linked chain assembly in the double chains of 5. Based on the octet rule, short atom distances are interpreted in terms of 2c-2e and various (symmetrical, quasi-symmetrical, and asymmetrical) kinds of 3c-4e bonds. Weak contacts, the topology of which suggests the extension of the latter bonding concept, are identified as electron-deficient, bifurcated tetrel bonds. Full article

The increasing frequency and severity of extreme weather events pose significant threats to power systems, particularly at the distribution level. The most detrimental consequence of such events is observed in critical loads due to high outage costs. As a result, there is a pressing need for utilities to invest in enhancing system resilience, which requires a comprehensive resilience investment framework and metrics to evaluate system performance. This paper proposes a distribution system resilience assessment framework to guide strategic investment decisions. The framework incorporates a mathematical model that estimates system restoration time after an extreme event, considering the criticality of loads, the interdependence of component failures and repair sequences, and the availability of repair crews. In addition, two new resilience metrics—disconnected load point hours (DLH) and normalized DLH (NDLH)—are introduced, which provide a more comprehensive view of system resilience by reflecting both vulnerability and the ability to withstand and recover from extreme events. Case studies are performed on a modified IEEE 69-bus test system utilizing the developed framework. The results evaluate the effectiveness of different resilience investment strategies, including infrastructure hardening, distributed energy resources management, and repair process coordination, in improving the system resilience for maintaining the critical loads and the overall distribution system. Full article

Power systems need to meet the ever-increasing demand for higher quality and reliability of electricity in distribution systems while remaining sustainable, secure, and economical. The globe is moving toward using renewable energy sources to provide electricity. An evaluation of the influence of artificial intelligence (AI) on the accomplishment of SDG7 (affordable and clean energy) is necessary in light of AI’s development and expanding impact across numerous sectors. Microgrids are gaining popularity due to their ability to facilitate distributed energy resources (DERs) and form critical client-centered integrated energy coordination. However, it is a difficult task to integrate, coordinate, and control multiple DERs while also managing the energy transition in this environment. To achieve low operational costs and high reliability, inverter control is critical in distributed generation (DG) microgrids, and the application of artificial neural networks (ANNs) is vital. In this paper, a power management strategy (PMS) based on Inverter Control and Artificial Neural Network (ICANN) technique is proposed for the control of DC–AC microgrids with PV-Wind hybrid systems. The proposed combined control strategy aims to improve power quality enhancement. ensuring access to affordable, reliable, sustainable, and modern energy for all. Additionally, a review of the rising role and application of AI in the use of renewable energy to achieve the SDGs is performed. MATLAB/SIMULINK is used for simulations in this study. The results from the measures of the inverter control, m, VL-L, and Vph_rms, reveal that the power generated from the hybrid microgrid is reliable and its performance is capable of providing power quality enhancement in microgrids through controlling the inverter side of the system. The technique produced satisfactory results and the PV/wind hybrid microgrid system revealed stability and outstanding performance. Full article

As a decentralized energy management paradigm, micro-energy grid (MEG) clusters enable synergistic operation of heterogeneous distributed energy assets, particularly through multi-energy vector coupling mechanisms that enhance distributed energy resource (DER) utilization efficiency in next-generation power networks. While individual MEGs demonstrate limited capability in responding to upper-grid demands using surplus energy after fulfilling local supply/demand balance, coordinated cluster operation significantly enhances system-wide flexibility. This paper proposes a two-stage robust optimization model that systematically addresses both the synergistic complementarity of multi-MEG systems and renewable energy uncertainty. First, the basic operation structure of MEG, including distributed generation, cogeneration units, and other devices, is established, and the operation mode of the MEG cluster responding to host utility grid flexibly is proposed. Then, aiming to reduce operation expenses, an optimal self-scheduling plan is generated by establishing a MEG scheduling optimization model; on this basis, the flexibility response capability of the MEG is measured. Finally, to tackle the uncertainty issue of wind and photovoltaic power generation, the two-stage robust theory is employed, and the scheduling optimization model of MEG cluster flexibility response to the host utility grid is constructed. A southern MEG cluster is chosen for simulation to test the model and method’s effectiveness. Results indicate that the MEG cluster’s flexible response mechanism can utilize individual MEGs’ excess power generation to meet the host utility grid’s dispatching needs, thereby significantly lowering the host utility grid’s dispatching costs. Full article

During this period, as distributed energy resources (DERs) are crucial for meeting energy needs and renewable technology advances rapidly, photovoltaic (PV)-powered energy islands (EIs) requiring a constant energy supply have emerged. EIs represent a significant milestone in the global energy transformation towards clean and sustainable energy production. They play a vital role in the future energy infrastructure, offering both environmental and economic benefits. In this context, reliance on information and communication technologies for system management raises concerns regarding the cybersecurity vulnerabilities of PV-supported EIs. In other words, since EIs transmit power through power converters—integral cyber-physical components of these systems—they are uniquely susceptible to cyber-attacks. To tackle this vulnerability, a cyber-attack detection scheme using a supervised machine learning (SML) model is proposed. The initial goal is to ensure the transfer and maintenance of energy demands without power loss for critical loads by detecting cyber-attacks to establish a defense mechanism. Two distinct artificial neural network (ANN) structures are implemented to identify cyber threats and support subsequent power demand, resulting in a complementary approach. The findings reveal the model’s effectiveness, demonstrating high accuracy (e.g., a cross-entropy loss of 12.842 × 10⁻⁴ for ANN-I with a 99.98% F1 score and an MSE of 1.0934 × 10⁻⁷ for ANN-II). Therefore, this work aims to open the fundamental way for addressing this issue, particularly concerning hijacking attacks and false data injection (FDI) cyber-attacks on PV-powered EIs. The success of this model and its outcomes confirm the effectiveness of the proposed approach method. Full article

The increasing power consumption reflects technological and industrial growth, but meeting this demand with conventional fossil-fuel-based plants is challenging. Microgrids address this issue by integrating renewable energy-based Distributed Energy Resources (DERs) and Energy Storage Systems (ESS). Efficient Microgrid operation requires a power management system to balance supply and demand, reduce costs, and ensure load prioritization. This paper presents a wide area measurement (WAMS)-based Centralized Power Management System (CPMS) for AC microgrids in both Islanded and Grid-Connected modes. The modified IEEE 13-bus system is utilized as a microgrid test system by integrating DERs and ESS. WAMS significantly enhances intra-microgrid communication by offering real-time, high-resolution monitoring of electrical parameters, surpassing the limitations of traditional SCADA-based monitoring systems. In grid-connected mode, the proposed CPMS effectively manages dynamic grid tariffs, generation variability in DERs, and state-of-charge (SoC) variations in the ESS while ensuring uninterrupted load supply. In islanded mode, a load prioritization scheme is employed to dynamically disconnect and restore loads to enhance the extent of load coverage across consumer categories. The inclusion of diverse load categories, such as domestic, industrial, commercial, etc., enhances the practical applicability of the CPMS in real-world power systems. The effectiveness of the proposed CPMS is validated through multiple case studies conducted in Simulink/MATLAB. Full article

This paper presents a comprehensive optimization framework for modern distribution systems, integrating distribution system reconfiguration (DSR), soft open point (SOP) operation, photovoltaic (PV) allocation, and energy storage system (ESS) management to minimize daily active power losses. The proposed approach employs a novel hybrid metaheuristic algorithm, the Cheetah-Grey Wolf Optimizer (CGWO), which synergizes the global exploration capabilities of the Cheetah Optimizer (CO) with the local exploitation strengths of Grey Wolf Optimization (GWO). The optimization model addresses time-varying loads, renewable generation profiles, and dynamic network topology while rigorously enforcing operational constraints, including radiality, voltage limits, ESS state-of-charge dynamics, and SOP capacity. Simulations on a 33-bus distribution system demonstrate the effectiveness of the framework across eight case studies, with the full DER integration case (DSR + PV + ESS + SOP) achieving a 67.2% reduction in energy losses compared to the base configuration. By combining the global exploration of CO with the local exploitation of GWO, the hybrid CGWO algorithm outperforms traditional techniques (such as PSO and GWO) and avoids premature convergence while preserving computational efficiency—two major drawbacks of standalone metaheuristics. Comparative analysis highlights CGWO’s superiority over standalone algorithms, yielding the lowest energy losses (997.41 kWh), balanced ESS utilization, and stable voltage profiles. The results underscore the transformative potential of coordinated DER optimization in enhancing grid efficiency and reliability. Full article

(1) Background: Respiratory allergens, particularly ragweed (RW) pollen and house dust mites (HDMs), are major triggers of respiratory inflammation and allergic diseases. This study investigated the impact of single- versus combined-allergen exposure on the barrier function of normal human bronchial epithelial (NHBE) cells cultured at the air–liquid interface (ALI). (2) Methods: NHBE cells were exposed to RW pollen extract (200 µg/mL), HDM extract (200 µg/mL) and their combination at varying concentrations (200 µg/mL, 100 µg/mL, 50 µg/mL, 25 µg/mL). Additional groups included a mixture of Amb a 1, Amb a 11 and Amb a 12 (100 mg/mL) and combinations of Der p 1 with the ragweed allergens (50 mg/mL, 100 µg/mL). Transepithelial electrical resistance (TEER) was recorded over 72 hours to assess barrier integrity, and immunofluorescence (IF) staining for zonula occludens-1 (ZO-1) was performed to evaluate tight junction alterations. (3) Results: TEER measurements showed a significant reduction in epithelial barrier integrity following allergen exposure, with the most pronounced disruption observed with the combined exposure to RW and HDM groups. IF staining confirmed extensive tight junction damage, highlighting their synergistic impact. (4) Conclusions: These findings emphasize the importance of assessing cumulative allergen effects, as combined exposure may exacerbate epithelial dysfunction and represent a key aspect in the management of allergic rhinitis and asthma. Full article

The operation targets of the distribution management system (DMS) have become increasingly diverse and complex. This complexity stems from the integration of distributed energy resources (DERs) to achieve carbon neutrality, alongside the introduction of new facilities such as electric vehicle charging stations and IoT sensors into the power distribution network. As the distribution system diversifies, there has been a growing need for interoperability to address these challenges effectively. Numerous DMS applications rely on topology processing (TP) for analyzing and managing power network structure, and the demand for nodes to process the connectivity has increased with the addition of new operational equipment. The speed of TP decreases as the number of nodes managed by a single DMS increases. Consequently, the operational reliability of TP-based DMS applications declines due to a decrease in the performance of TP. This paper proposes a new framework leveraging ontology databases (ODBs) to improve the performance of TP in DMS under an interoperability environment. The presented framework identifies shortcomings of traditional DMS utilizing relational databases (RDBs) and proposes a remedy by employing an ODB framework to achieve faster TP performance based on the common information model (CIM) for ensuring interoperability between components within the DMS. To validate the efficacy of the proposed method, various case studies were conducted on the DMS managing the headquarters level actual distribution network of the Republic of Korea to compare the TP performance in the case of an RDB and an ODB applied to the DMS. Results of case studies demonstrate that the proposed CIM-oriented ODB framework for the DMS guarantees much faster TP speed than the one with an RDB. Full article

This paper presents a model predictive control (MPC)-based energy management system (EMS) for optimizing cooperative operation of networked microgrids (MGs). While the isolated operation of individual MGs limits system-wide optimization, the proposed approach enhances both stability and efficiency through integrated control. The system employs mixed-integer quadratic constrained programming (MIQCP) to model complex operational characteristics of MGs, facilitating the optimization of interactions among distributed energy resources (DERs) and power exchange within the MG network. The effectiveness of the proposed method was validated through a series of case studies. First, the performance of the algorithm was evaluated under various weather conditions. Second, its robustness against prediction errors was tested by comparing scenarios with and without disturbance prediction. Finally, the cooperative operation of MGs was compared with the independent operation of a single MG to analyze the impact of the cooperative approach on performance improvement. Quantitatively, integrating predictions reduced operating costs by 19.23% compared to the case without predictions, while increasing costs by approximately 3.7% compared to perfect predictions. Additionally, cooperative MG operation resulted in an average 46.18% reduction in external resource usage compared to independent operation. These results were verified through simulations conducted on a modified version of the IEEE 33-bus test feeder. Full article

Healthcare professionals in pediatric oncology are at a high risk of burnout. Art therapy is being increasingly recognized as a potential tool for reducing stress and improving emotional well-being. The Art-Out pilot project aimed at nursing staff was initiated in a pediatric oncology unit. The staff members participating in the project were guided in a team-building course integrated with art and clay therapy, aiming to reduce burnout levels, improve emotional climate, and strengthen resilience. Methods: Burnout levels were assessed through the Maslach Burnout Inventory (MBI), alexithymia was measured with the Toronto Alexithymia Scale (TAS-20), and emotional regulation difficulties were evaluated through the Difficulties in Emotion Regulation Scale (DERS); these tests were assessed before (T0) and after (T1) the team-building course (Art-Out project). Results: Data analysis showed a significant reduction in burnout, alexithymia, and emotional dysregulation, highlighting the positive impact of this approach in improving team dynamics and emotional management. Conclusions: Our study confirms the high risk of burnout, alexithymia, and emotional dysregulation among pediatric oncology healthcare workers, underscoring the need for targeted interventions to prevent and mitigate these risks. Full article

Conventional droop control (a decentralized method to regulate power sharing by adjusting voltage–current slopes) in DC microgrids faces challenges in balancing precise current distribution, bus voltage regulation, and communication pressure, especially in distributed energy management scenarios. To address these limitations, this paper proposes an adaptive control strategy combining three layers: (1) Primary control achieves power sharing and voltage stabilization via U-I droop characteristics for distributed energy resources (DERs); (2) Secondary control corrects voltage deviations and droop coefficient imbalances through multi-agent consensus algorithms, ensuring global equilibrium; (3) Event-triggered consensus control minimizes communication pressure via a novel protocol with time-varying coupling weights and a hybrid trigger function combining state variables and time-decaying terms rigorously proven to exclude Zeno behavior (i.e., infinite triggering in finite time) using Lyapunov stability theory. Full article