Search Results (181)

This paper investigates the impact of renewable energy source (RES) integration on the Sicilian transmission network, considering the commissioning of new Mediterranean interconnections, namely the TUN-ITA and the Tyrrhenian Link. The expansion of transmission infrastructures and the increasing penetration of RES require an assessment of the Sicilian power system’s capability to accommodate high levels of power injection. This study was carried out in collaboration with the Italian transmission system operator Terna S.p.A. and the University of Palermo. It aims to evaluate the evolution of transmission line loading under future RES integration scenarios consistent with grid connection requests submitted to Terna and with national energy policy targets. The proposed methodology integrates micro-zonal assessments of wind and solar potential, estimation of capacity factors, development of RES capacity expansion scenarios, and steady-state power flow simulations. The simulations were performed using WinCreso^® software version 7.69 for three time horizons: 2028, 2029, and 2035. The results show the most congested transmission lines and the network areas most exposed to congestion. The analysis provides operational insights for prioritizing grid reinforcement measures and proposes a replicable methodological framework for other transmission system operators facing similar RES integration challenges. Full article

The increased penetration of renewable energy sources (RES) in the electrical grid has necessitated the concept of a Virtual Synchronous Generator (VSG) control which is used to make grid-connected power electronic converters behave as synchronous generators. While VSG controls are suitable for supporting the inertia of a microgrid, their use leads to grid instability in the event of a disturbance. This research addresses this limitation by integrating a fully connected Feedforward Neural Network (FCNN) into a VSG control to dynamically adjust the damping coefficient and inertia constant in real time. This approach could enhance system stability by reducing frequency and active power oscillations during grid disturbances, particularly during partial load rejection. To evaluate the effectiveness of the proposed method, a supervised learning-based FCNN was trained on VSG damping behavior under various grid disturbances. The trained model was then implemented in a simulation environment to regulate the VSG parameters dynamically. Simulation results show the neural network-based approach reduces high overshoots at the point of disturbance in active power and frequency oscillations; however, the VSG signal settles faster after the grid disturbance. These findings highlight the potential of machine learning in enhancing the stability of VSG-based microgrids, offering a computationally efficient solution for improving transient response and power-sharing performance. Full article

Wound healing in cosmetological and aesthetic surgery extends beyond tissue closure to achieving rapid regeneration, minimal scarring, and restoration of functional skin architecture. However, conventional wound care strategies inadequately regulate the complex wound microenvironment required for optimal cosmetic outcomes, leading to prolonged healing times and suboptimal aesthetic results, which can negatively impact patient satisfaction and increase the risk of complications. Phytochemicals exhibit multifunctional bioactivities, such as antioxidant, anti-inflammatory, antimicrobial, and pro-regenerative effects, but their clinical translation faces obstacles due to poor solubility, stability, and bioavailability. Nanotechnology-based delivery systems have emerged as a critical enabling strategy to overcome these limitations. This narrative review provides an updated, mechanistically integrated synthesis of phytochemical-loaded nanotherapeutics, including polymeric nanoparticles, nanohydrogels, nanofibers, and lipid- and vesicle-based systems, with a specific focus on their roles in modulating key wound-healing pathways, such as inflammation resolution, angiogenesis, collagen remodelling, and re-epithelialization. Evidence from preclinical studies consistently demonstrates that nano-enabled phytochemicals enhance therapeutic efficacy, improve skin penetration, and contribute to superior cosmetic outcomes, particularly by reducing fibrosis and scar formation. However, critical gaps remain, including limited high-quality clinical evidence, a lack of standardized formulation design, variability in reported outcomes, and unresolved concerns regarding long-term safety and regulatory translation. Taken together, the key insight of this review is that phytochemical-loaded nanotherapeutics represent a promising but still transitional strategy, biologically compelling at the preclinical level yet clinically under-validated. Bridging this gap requires rigorously designed clinical trials, quantitative outcome reporting, and balanced regulatory frameworks. Advancing these areas will be essential to translate nano-enabled phytochemicals from experimental systems into reliable, evidence-based solutions for cosmetological wound management. Full article

Tensile fracture in asphalt involves complex mechanical responses and component migration. This study employs molecular dynamics (MD) simulations with the COPMASS II force field to investigate water intrusion at the asphalt–aggregate interface and subsequent tensile cracking at the nanoscale. To evaluate moisture damage, a ternary interface model was constructed using a specific distribution of water molecules at a target density. Results indicate that thickness significantly enhances moisture resistance; specifically, the asphalt film in the thinnest model (AS1) was penetrated by water molecules, leading to localized interfacial failure. Further uniaxial tensile simulations at a loading rate of 0.01 Å/psreveal that as film thickness increases (AS1 to AS4), the peak stress rises from 103.2 to 113.8 MPa, and the fracture energy increases from 136 to 747 kcal/mol. Based on the density redistribution of SARA fractions, component migration is divided into three stages: structural relaxation, resin-driven de-peptization, and polar component re-aggregation. Finally, the Asphaltene Index (I_A) is proposed as a predictive indicator, showing that cracks consistently initiate in regions with minimum I_A values. These findings provide quantitative insights into the molecular mechanisms underlying asphalt durability. Full article

The increasing penetration of renewable energy sources (RES) introduces significant variability and instability in modern power systems, creating a growing need for advanced and coordinated energy storage solutions. However, a key unresolved challenge remains the integrated modeling and optimal sizing of hybrid energy storage systems (ESS) that combine technologies with different temporal characteristics under high RES penetration. This study addresses this challenge by developing a unified techno-economic and physical–mathematical framework for hybrid ESS integrating lithium-ion (Li-ion), vanadium redox flow batteries (VRFB), and hydrogen (H₂) technologies. Unlike conventional approaches that treat storage technologies independently or use simplified hybrid representations, the proposed framework jointly considers dynamic energy balance, degradation-aware lifecycle behavior, and multi-criteria cost optimization. The model was implemented using Python 3.10-based simulation tools and evaluated under renewable penetration scenarios of 30%, 50%, and 70%. The results indicate that increasing RES penetration leads to higher power fluctuations, reaching ±15–20% at 50% RES and ±20–25% at 70% RES. The optimized hybrid system achieves an overall efficiency of up to 92%, reduces total system cost to approximately 450 USD/kWh, and extends operational lifetime to 25 years, demonstrating a balanced techno-economic performance compared to standalone storage technologies. The proposed framework addresses this gap by coupling dynamic energy balance analysis with degradation-aware techno-economic optimization, enabling coordinated allocation of storage functions across short-, medium-, and long-duration timescales. In this way, the study not only evaluates hybrid storage performance, but also provides a practical decision-support framework for renewable-dominated power systems, particularly in the context of Kazakhstan’s energy transition. Full article

This paper proposes a continuous inertia estimation framework for transmission-level power systems with high renewable energy penetration, using a battery energy storage system (BESS) as a controllable small-signal power injection source. The proposed framework integrates BESS-based active power injection, a two-stage signal-smoothing scheme, and a rate-of-change-of-frequency (RoCoF)-based estimation mechanism to enable continuous inertia estimation without relying on major disturbance events. With noise-robust processing and moving-window analysis, the framework can reliably track inertia variations under noisy measurement conditions and diverse operating scenarios. The framework is validated on the IEEE 39-bus system under renewable energy source (RES) penetration levels of 0%, 10%, 20%, and 30%. The estimation error remains within ±3.5% across all scenarios, ranging from 1.26% at 0% RES penetration to 3.43% at 30% penetration. In addition, the estimated inertia closely follows the theoretical decrease from 3.20 s to 2.22 s as RES penetration increases. These results demonstrate the accuracy and robustness of the proposed framework for continuous inertia monitoring in low-inertia power systems with high-RES penetration. Full article

γ-photon tomography, which leverages the high penetration and electrical neutrality of high-energy γ-photons, offers a promising non-contact approach for industrial flow field monitoring. However, γ-photon flow-field images are inherently grayscale and exhibit probabilistic statistical imaging characteristics, leading to color banding artifacts when processed by mainstream colorization algorithms like DeOldify, which compromise structural continuity and visual consistency. To address this issue, this paper proposes a Structure Enhancement Colorization Network (SECN) model for γ-photon flow-field image colorization. A U-Net + GAN framework is employed, with ResNet101 as the generator backbone. It integrates structure-aware enhancement and multi-scale attention modules, while the discriminator incorporates enhanced blocks for improved boundary and texture discrimination. By adaptively fusing global–local features across channel and spatial dimensions, the SECN model effectively suppresses color banding artifacts and enhances structural consistency. To validate the effectiveness of the proposed algorithm, two CFD-simulated γ-photon flow-field image colorization scenarios—namely a large-scale vortex wake and a horizontal wake—are used as evaluation targets. In terms of image quality metrics, the proposed colorization algorithm achieves PSNR, SSIM, FID, and MAE values of 32.5831, 0.8612, 17.8514, and 0.0191, respectively, corresponding to improvements over DeOldify of 4.54%, 2.82%, 5.18%, and 11.16%. When considering information entropy, the proposed colorization algorithm achieves an average entropy value of 4.0257, marking a 4.44% increase compared to DeOldify’s 3.8543, demonstrating superior information preservation and reduced uncertainty in reconstructing complex probabilistic structures. Furthermore, from the perspective of parameter inversion, the temperature inversion MAPE is 7.60%, which is a significant reduction of 18.42% compared to that of DeOldify. Full article

In the scenario where renewable energy sources (RESs) are connected to the power system (PS) through a flexible high-voltage direct current (HVDC) transmission system, their output becomes highly intermittent and volatile due to meteorological factors like wind direction and speed. This variability poses significant challenges to the real-time power balance and control of the PS. To address the uncertainties in system operation and the challenges of RES consumption, this paper proposes an artificial intelligence (AI) algorithm-driven collaborative optimization strategy for virtual power plants (VPPs) considering RESs transmitted by flexible HVDC. Firstly, a self-attention mechanism and multiple gated structures are integrated into a long short-term memory (LSTM) deep learning model. This enhancement improves the model’s ability to capture multi-timescale characteristics of RESs, increasing forecasting accuracy and robustness. Based on these forecasts, a total cost optimization model for VPP operation is developed, which includes high penalty costs for wind and solar curtailment. By embedding economic constraints that prioritize RESs usage, the model can reduce waste caused by traditional cost-driven scheduling. Additionally, to solve the high-dimensional nonlinear optimization problem in VPP scheduling, an improved population-based incremental learning (PBIL) algorithm is introduced. It incorporates an elite retention strategy and an adaptive mutation operator to boost global search efficiency and convergence speed. Simulations based on an VPP incorporating typical offshore wind and solar RESs transmitted via flexible HVDC demonstrate that the improved LSTM reduces MAPE by 7.14% for wind and 4.27% for PV compared to classical LSTM, and the proposed method achieves the lowest curtailment rates (wind 10.74%, PV 10.23%) and total cost (43,752 RMB), outperforming GA, PSO, and GW by 10–18% in cost reduction. Simulation results show that the proposed strategy enhances RESs consumption while maintaining system economy under flexible HVDC transmission. This work offers theoretical and practical insights for optimizing PS with high RES penetration and supports the low-carbon transition of new-type PS. Full article

Background: Crohn’s disease complicated by intra-abdominal abscesses often requires surgery. Percutaneous drainage may prevent surgery, but optimal post-drainage management is unclear. We aimed to analyze the long-term outcomes of Crohn’s disease with intra-abdominal abscesses after intervention. Methods: Patients with penetrating Crohn’s disease and a single intra-abdominal abscess were enrolled in this multicenter, international, retrospective study after the detection of the abscess (baseline), with a minimum follow-up of 12 months. Those requiring urgent bowel resection were excluded. Patients were grouped by elective surgical need after successful (catheter insertion with effective drainage) percutaneous drainage (controls: no pre-resection drainage). The primary outcome was abscess recurrence. We also assessed stoma rate, post-procedural complications, hospitalizations, advanced treatment need, postoperative luminal recurrence, and need for re-drainage. Results: We studied 157 patients with Crohn’s disease (9 countries; males: 58%, median age: 32.4 [interquartile range: 25–39 years]); 89/157 underwent percutaneous drainage (median follow-up: 95.9 weeks [interquartile range: 58–104]). Abscess recurrence did not differ by drainage (p = 0.221). Abscess size was associated with advanced-treatment initiation (Odds ratio: 0.978; 95% confidence interval: 0.960–0.997, p = 0.023) and postoperative luminal recurrence (Odds ratio: 1.044, 95% confidence interval: 1.012–1.078, p = 0.007). Time to resection was longer after drainage, and ROC analysis raised predictive value for re-drainage (16.6 weeks post-drainage; AUC = 0.82, 95% confidence interval: 0.73–0.92). Patients without drainage had more post-procedural complications. Conclusions: Abscess size should guide management. Delayed resection may increase re-drainage odds, whereas surgery alone may have higher complication rates. Percutaneous drainage can safely postpone resection. Full article

As the penetration rate of renewable energy continues to rise, the equivalent inertia of power systems has significantly decreased, leading to a marked degradation in frequency stability support capabilities. Under conditions of high renewable energy penetration, the question of how to effectively enhance grid frequency support capacity has become a critical research topic in the field of power system operation and control. This paper first systematically analyzes the impact of key control parameters on the frequency dynamic response of power systems. It investigates the intrinsic relationship between these parameters and system frequency stability through both analytical frequency response modeling and time-domain simulation analysis. A frequency stability margin metric is constructed based on the grid frequency response process to quantify the system’s frequency stability performance. Building upon this foundation, an improved ResNet-based frequency stability margin prediction model is established to enable rapid estimation of the frequency stability margin. Furthermore, Bayesian optimization is introduced to optimize frequency control parameters, thereby enhancing system frequency stability. Case studies conducted on the simulation system CSEE-FS with insufficient frequency support capability demonstrate that the proposed method effectively increases the frequency stability margin and significantly improves the system’s frequency response performance. Full article

The increasing use of integrated renewable energy sources (RESs) is undoubtedly reshaping the structure of power systems. In such conditions, achieving energy efficiency and sustainability requires the development and integration of digital solutions to manage energy flows and assets optimization. This paper aims to provide a comprehensive review of the successful integration of artificial intelligence (AI) in the energy sector, particularly in relation to the high penetration of renewable energy. The paper presents trends and potential scenarios in the digitalization of energy, along with the associated challenges. It analyzes particular applications of AI tools in strategic areas of the energy sector. Five key areas of the energy sector are identified where AI tools are applied: forecasting electricity generation from RES; forecasting demand and price fluctuations on the electricity spot market; the real-time management of energy flows and assets in active microgrids; and data processing and analyzing, and general industrial direction. The article also attempts to summarize the current status, goals, key areas, and activities in the irreversible transformation of power structures into digital intelligent ones. This digital transformation is a gradual process with consecutive steps. To improve understanding and clarity, the authors present a three-phase roadmap of AI adoption. To develop an adequate AI integration strategy, it is necessary to understand the technologies, algorithms, hierarchical structure, and connections within this structure. Accordingly, the article presents a taxonomy of the hierarchical structure of AI. The subsequent step involves the sequential construction of a digitalization model. Here, the authors consider it necessary to present a 4-layer structure model of AI energy democracy. Finally, through a comparative analysis of different types of intelligent applications for energy problem solving, guidelines are provided for successful decision making in compliance with the specified harmonized standards and protocols. Full article

The increasing share of variable renewable energy sources (RES) in power systems leads to growing challenges related to grid balancing and the management of periodic electricity surpluses. One potential pathway for utilizing these surpluses is their conversion into synthetic fuels via Power-to-X technologies. This study analyzes the technical and economic potential of surplus renewable electricity utilization for the production of green hydrogen and synthetic fuels, using Poland and Spain as representative case studies of power systems with low and high RES penetration, respectively. An original methodology based on hourly power system data was developed to identify technically feasible surplus electricity volumes, accounting for changes in renewable and conventional generation, minimum renewable energy share thresholds, and a minimum two-hour continuous operation requirement. The analysis quantifies both instantaneous and usable surplus energy on an annual basis and evaluates the resulting capacity factors of Power-to-X installations. The results show that the annual usable surplus energy amounts to approximately 886 GWh in Poland and 2329 GWh in Spain, corresponding to maximum capacity factors of about 27% and 50%, respectively. Based on these surpluses and assuming low-cost electricity during surplus periods (10 EUR/MWh), the levelized cost of green hydrogen was estimated at 4.1 EUR/kg in Poland and 2.18 EUR/kg in Spain. The resulting production costs of green methanol reach approximately 739 EUR/Mg for Poland and 378 EUR/Mg for Spain after accounting for avoided CO₂ emissions. The findings indicate that surplus-based Power-to-X systems can play a meaningful role in integrating high shares of renewable energy, particularly in power systems with high and stable RES penetration. However, their contribution remains strongly constrained by surplus availability, temporal continuity, and system-specific characteristics. Full article

The rapid integration of renewable energy sources (RES), particularly wind, together with fluctuating demand, has introduced significant uncertainty into power system operation, challenging traditional approaches for estimating Transmission Reliability Margin (TRM) and Available Transfer Capability (ATC). This paper proposes a fully adaptive TRM estimation framework that leverages rolling-window statistical analysis of net-load forecast errors to capture real-time uncertainty fluctuations. By continuously updating both the confidence factor and window length based on evolving forecast-error statistics, the method adapts to changing grid conditions. The framework is validated on the IEEE 30-bus system with 80 MW wind (42.3% penetration) and assessed for scalability on the IEEE 118-bus system (40.1% wind penetration). Comparative analysis against static TRM, fixed-confidence rolling-window, and Monte Carlo Simulation (MCS)-based methods shows that the proposed approach achieves 88.0% reliability coverage (vs. 81.8% for static TRM) while providing enhanced transfer capability for 31.5% of the operational day (7.5 h). Relative to MCS, it yields a 20.1% lower mean TRM and a 2.5% higher mean ATC, with an adaptation ratio of 18.8:1. Scalability assessment confirms preserved adaptation (12.4:1) with sub-linear computational scaling (1.82 ms to 3.61 ms for a 3.93× network size increase), enabling 1 min updates interval. Full article

The integration of high-penetration renewable energy sources (RESs) and electric vehicles (EVs) increases the risk of voltage fluctuations in distribution networks. Traditional static partitioning strategies struggle to handle the intermittency of wind turbine (WT) and photovoltaic (PV) generation, as well as the spatiotemporal randomness of EV loads. Furthermore, existing scheduling methods typically optimize EV active power or reactive compensation independently, missing opportunities for synergistic regulation. The main novelty of this paper lies in proposing a spatiotemporally coupled voltage-stability optimization framework. This framework, based on an hourly updated electrical distance matrix that accounts for RES uncertainty and EV spatiotemporal transfer characteristics, enables hourly dynamic network partitioning. Simultaneously, coordinated active–reactive optimization control of EVs is achieved by regulating the power factor angle of three-phase six-pulse bidirectional chargers. The framework is embedded within a hierarchical model predictive control (MPC) architecture, where the upper layer performs hourly dynamic partition updates and the lower layer executes a five-minute rolling dispatch for EVs. Simulations conducted on a modified IEEE 33-bus system demonstrate that, compared to uncoordinated charging, the proposed method reduces total daily network losses by 4991.3 kW, corresponding to a decrease of 3.9%. Furthermore, it markedly shrinks the low-voltage area and generally raises node voltages throughout the day. The method effectively enhances voltage uniformity, reduces network losses, and improves renewable energy accommodation capability. Full article

Greenhouse gas emissions are a primary contributor to climate change and the observed rise in global temperatures. To reduce these emissions, renewable energy sources (RESs) must replace fossil fuels in power generation. Because of the mismatch between production and demand, the increase in RES is limited. To address this phenomenon, the addition of renewable energy generation should be accompanied by storage systems. In this paper, the island of Crete is examined for various renewable energy generations and storage capacities using the PowerWorld Simulator software. Four main scenarios are studied in which the installed renewable energy generation is increased to reach substation limits. For every scenario, different renewable energy generation mixes are considered between wind farms and photovoltaics. Furthermore, for all sub-scenarios, different storage capacities are considered, ranging from 1.6 GWh to 12.8 GWh. This study proves that storage systems are mandatory to increase renewable energy penetration. In certain scenarios, a battery energy storage system can further increase renewable energy penetration from 6.15% to 28.07%. Although the battery energy storage system enhanced renewable penetration, increasing transmission line capacities should also be considered regarding the scenario. Full article