Search Results (1,224)

With the accelerating global transition toward sustainable energy systems, power grids with a high share of renewable energy face increasing challenges due to volatility and uncertainty, necessitating advanced flexibility resource planning and stability optimization strategies. This paper presents a comprehensive distribution network planning framework that coordinates and integrates multiple types of flexibility resources through joint optimization and network reconfiguration to enhance system adaptability and operational resilience. A novel virtual network coupling modeling approach is proposed to address topological constraints during network reconfiguration, ensuring radial operation while allowing rapid topology adjustments to isolate faults and restore power supply. Furthermore, to mitigate the uncertainty and fault risks associated with extreme weather events, a CVaR-based risk quantification framework is incorporated into a bi-level optimization model, effectively balancing investment costs and operational risks under uncertainty. In this model, the upper-level planning stage optimizes the siting and sizing of flexibility resources, while the lower-level operational stage coordinates real-time dispatch strategies through demand response, energy storage operation, and dynamic network reconfiguration. Finally, a hybrid SA-PSO algorithm combined with conic programming is employed to enhance computational efficiency while ensuring high solution quality for practical system scales. Case study analyses demonstrate that, compared to single-resource configurations, the proposed coordinated planning of multiple flexibility resources can significantly reduce the total system cost and markedly improve system resilience under fault conditions. Full article

Modernizing irrigation systems (ISs) from traditional gravity methods to sprinkler and drip technologies has significantly improved water use efficiency. However, it has simultaneously increased electricity demand and operational costs. Integrating photovoltaic generators into ISs represents a promising solution, as solar energy availability typically aligns with peak irrigation periods. Despite this potential, photovoltaic pumping systems (PVPSs) often face reliability issues due to fluctuations in solar irradiance, resulting in frequent start/stop cycles and premature equipment wear. The IEC 62253 standard establishes procedures for evaluating PVPS performance but primarily addresses steady-state conditions, neglecting transient regimes. As the main contribution, the current paper proposes a non-intrusive, high-resolution monitoring system and a methodology to assess the performance of an isolated, high-power PVPS, considering also transient regimes. The system records critical electrical, hydraulic and environmental parameters every second, enabling in-depth analysis under various weather conditions. Two performance indicators, pumped volume efficiency and equivalent operating time, were used to evaluate the system’s performance. The results indicate that near-optimal performance is only achievable under clear sky conditions. Under the appearance of clouds, control strategies designed to protect the system reduce overall efficiency. The proposed methodology enables detailed performance diagnostics and supports the development of more robust PVPSs. Full article

This research considers a preliminary comparative technical evaluation of two micro-gas turbine (MGT) systems in combined heat and power (CHP) mode (100 kWe), aimed at supplying heat to a residential community of 15 average-sized buildings located in Central Europe over a year. Two systems were modelled in Ebsilon 15 software: a natural gas case (benchmark) and an ammonia-fueled case, both based on the same on-design parameters. Off-design simulations evaluated performance over variable ambient temperatures and loads. Idealized, unrecuperated cycles were adopted to isolate the thermodynamic impact of the fuel switch under complete combustion assumption. Under these assumptions, the study shows that the ammonia system produces more electrical energy and less excess heat, yielding marginally higher electrical efficiency and EUF (26.05% and 77.63%) than the natural gas system (24.59% and 77.55%), highlighting ammonia’s utilization potential in such a context. Future research should target validating ammonia combustion and emission profiles across the turbine load range, and updating the thermodynamic model with a recuperator and SCR accounting for realistic pressure losses. Full article

Modular multilevel converters (MMCs) are widely used in power-conversion applications, including distributed energy storage integration, because of their scalability, high efficiency, and reduced harmonic distortion. Integrating battery storage systems into MMC submodules using dual active bridge (DAB) converters provides electrical isolation and reduces voltage stress, harmonics, and common-mode issues. However, voltage fluctuations due to the battery state of charge can compromise the zero-voltage switching (ZVS) operation of a DAB and increase the reactive power circulation, leading to higher losses and reduced system performance. To address these challenges, this study investigated an active control strategy for submodule voltage regulation in an MMC with DAB-based battery integration. Assuming single-phase-shift modulation, two control strategies were evaluated. The first strategy regulated the DAB voltage on one side to match the battery voltage on the other, scaled by the high-frequency transformer turns ratio, which facilitated the ZVS operation and reduced the reactive power. The second strategy optimized this voltage to minimize the total power-conversion losses. The proposed control strategies improved the efficiency, particularly at low power levels, achieving several percentage points of improvement compared to maintaining a constant voltage. Full article

This paper introduces a reverse demand response scheme that uses electric vehicles in an isolated microgrid system, aiming to solve the renewable energy curtailment issue. We focus on an off-grid system where the system operator faces a stabilization problem due to surplus energy production, while electric vehicles seek to charge energy at a lower price. In our system model, the operator determines the incentive to encourage more charging facilities and electric vehicles to participate in the reverse demand response program. Charging facilities, acting as brokers, use a portion of these incentives to further encourage electric vehicle engagement. Electric vehicles follow the decisions made by the broker and system operator to determine their charging strategy within the system. Consequently, charging energy and incentives are allocated to the electric vehicles in proportion to their decisions. The paper investigates the economic benefits of individual participants and the contribution of power stabilization by implementing a hierarchical decision-making heterogeneous multi-leaders multi-followers Stackelberg game. By demonstrating the existence of a unique Nash Equilibrium, we show the effectiveness of the proposed model in an isolated microgrid environment. Full article

Research on reliability control for enhancing power systems under random loads holds significant and undeniable importance in maintaining system stability, performance, and safety. The primary challenge lies in determining the reliability index while optimizing system parameters. To effectively address this challenge, we developed a novel intelligent algorithm and conducted an optimal reliability assessment for a Negative Stiffness Device (NSD) seismic isolation structure incorporating fractional-order damping. This algorithm combines the Gaussian Radial Basis Function Neural Network (GRBFNN) with the Particle Swarm Optimization (PSO) algorithm. It takes the reliability function with unknown parameters as the objective function, while using the Backward Kolmogorov (BK) equation, which governs the reliability function and is accompanied by boundary and initial conditions, as the constraint condition. During the operation of this algorithm, the neural network is employed to solve the BK equation, thereby deriving the fitness function in each iteration of the PSO algorithm. Then the PSO algorithm is utilized to obtain the optimal parameters. The unique advantage of this algorithm is its ability to simultaneously achieve the optimization of implicit objectives and the solution of time-dependent BK equations.To evaluate the performance of the proposed algorithm, this study compared it with the algorithm combines the GRBFNN with Genetic Algorithm (GA-GRBFNN)across multiple dimensions, including performance and operational efficiency. The effectiveness of the proposed algorithm has been validated through numerical comparisons and Monte Carlo simulations. The control strategy presented in this paper provides a solid theoretical foundation for improving the reliability performance of mechanical engineering systems and demonstrates significant potential for practical applications. Full article

Permanent magnetic couplings provide critical advantages for deep-sea systems through static-sealed, contactless power transmission. However, conventional metallic isolation sleeves incur significant eddy current losses, limiting efficiency and high-speed operation. Limited torque capacities fail to meet the operational demands of harsh marine environments. This study presents a novel permanent magnet coupling featuring a ceramic isolation sleeve engineered for deep-sea cryogenic ammonia submersible pumps. The ceramic sleeve eliminates eddy current losses and provides exceptional corrosion resistance in acidic/alkaline environments. To withstand 3.5 MPa hydrostatic pressure, a 6-mm-thick sleeve necessitates a 10 mm operational air gap, challenging magnetic circuit efficiency. To address this limitation, an improved 3D magnetic equivalent circuit (MEC) model was developed that explicitly accounts for flux leakage and axial end-effects, enabling the accurate characterization of large air gap fields. Leveraging this model, a Taguchi method-based optimization framework was implemented by balancing key parameters to maximize the torque density. This co-design strategy achieved a 21% increase in torque density, enabling higher torque transfer per unit volume. Experimental validation demonstrated a maximum torque of 920 Nm, with stable performance under simulated deep-sea conditions. This design establishes a new paradigm for high-power leak-free transmission in corrosive, high-pressure marine environments, advancing applications from deep-sea propulsion to offshore energy systems. Full article

Conventional methods for generating knock-out or knock-in mammalian cell models using CRISPR-Cas9 genome editing often require tedious single-cell clone selection and expansion. In this study, we develop and optimise rapid and robust strategies to engineer homozygous fluorescent reporter knock-in cell pools with precise genome editing, circumventing clonal variability inherent to traditional approaches. To reduce false-positive cells associated with random integration, we optimise the design of donor DNA by removing the start codon of the fluorescent reporter and incorporating a self-cleaving T2A peptide system. Using fluorescence-assisted cell sorting (FACS), we efficiently identify and isolate the desired homozygous fluorescent knock-in clones, establishing stable cell pools that preserve parental cell line heterogeneity and faithfully reflect endogenous transcriptional regulation of the target gene. We evaluate the knock-in efficiency and rate of undesired random integration in the electroporation method with either a dual-plasmid system (sgRNA and donor DNA in two separate vectors) or a single-plasmid system (sgRNA and donor DNA combined in one vector). We further demonstrate that coupling our single-plasmid construct with an integrase-deficient lentivirus vector (IDLV) packaging system efficiently generates fluorescent knock-in reporter cell pools, offering flexibility between electroporation and lentivirus transduction methods. Notably, compared to the electroporation methods, the IDLV system significantly minimises random integration. Moreover, the resulting reporter cell lines are compatible with most of the available genome-wide sgRNA libraries, enabling unbiased CRISPR screens to identify key transcriptional regulators of a gene of interest. Overall, our methodologies provide a powerful genetic tool for rapid and robust generation of fluorescent reporter knock-in cell pools with precise genome editing by CRISPR-Cas9 for various research purposes. Full article

In the process of the work of a coal power station is formed ash and slag, which, along with process water, are deposited in the dumps. Coal ash waste dumps significantly degrade the surrounding environment due to their unprotected surfaces, which are highly susceptible to wind and water erosion. This results in the dispersion of contaminants into adjacent ecosystems. Pollutants migrate into terrestrial and aquatic systems, compromising soil quality and water resources, and posing documented risks to the environment and human health. Primary succession on the coal ash dumps of the Apatity thermal power plant (Murmansk Region, NW Russia) was initiated by cyanobacterial colonization. We studied cyanobacterial communities inhabiting three spoil sites that varied in time since decommissioning. These sites are characterized by exceptionally high concentrations of calcium and magnesium oxides—levels approximately double those found in the region’s natural soils. A total of 18 cyanobacterial taxa were identified in disposal sites. Morphological analysis of visible surface crusts revealed 16 distinct species. Furthermore, 24 cyanobacterial strains representing 11 species were successfully isolated into unialgal culture and tested with a molecular genetic approach to confirm their identification from 16S rRNA. Three species were determined with molecular evidence. Cyanobacterial colonization of coal fly ash disposal sites begins immediately after deposition. Primary communities initially exhibit low species diversity (four taxa) and do not form a continuous ground cover in the early years. However, as succession progresses—illustrated by observations from a 30-year-old deposit—spontaneous surface revegetation occurs, accompanied by a marked increase in cyanobacterial diversity, reaching 12 species. Full article

The research demonstrates, through simulation and laboratory validation, the development of a low-voltage DC-link (LVDC) back-to-back converter system that enables multi-frequency power transfer. The system operates in two distinct modes, which include a three-phase grid-connected converter transferring fundamental and 5th and 7th harmonic power to a three-phase residential inverter supplying a clean 50 Hz load and another mode that uses a DC–DC buck–boost converter to integrate a battery storage unit for single-phase load supply. The system allows independent control of each harmonic component and maintains a clean sinusoidal voltage at the load side through DC-link isolation. The LVDC link functions as a frequency-selective barrier to suppress non-standard harmonic signals on the load side, effectively isolating the multi-frequency power grid from standard-frequency household loads. The proposed solution fills the gap between the multi-frequency power systems and the single-frequency loads because it allows the transfer of total multi-frequency grid power to the traditional household loads with pure fundamental frequency. Experimental results and simulation outcomes demonstrate that the system achieves high efficiency, robust harmonic isolation, and dynamic adaptability when load conditions change. Full article

This paper presents a standalone system that utilizes a capacitive isolated series resonant converter using an extremum-seeking control algorithm to extract the maximum power from PV panels. While resonant converters have been used for battery charging applications, series resonant converters that utilize capacitive galvanic isolation have not been sufficiently explored, and their design considerations for battery charging have not been established. In addition, extremum-seeking control algorithms have been explored for maximum power point tracking using PWM converters, but not using PFM converters such as resonant converters. This paper lays out the advantages of using an extremum-seeking-based control algorithm with resonant converters, specifically series resonant converters, and it presents simulation results of a 200 W standalone battery charging system to validate the stated benefits. Full article

Indigenous students remain significantly underrepresented in medical education, contributing to persistent health inequities in their communities. Systemic barriers, including cultural isolation, inadequate resources, and biased curricula, hinder their success. But what if generative artificial intelligence (GAI) could be the game-changer? This scoping review explores the potential of generative artificial intelligence (GAI) in making medical education more inclusive and supportive for Indigenous students through a comprehensive analysis of existing literature. From AI-powered engagement platforms to personalised learning systems and immersive simulations, GAI can be harnessed to bridge the gap. While GAI holds promise, challenges like biased datasets and limited access to technology must be addressed. To unlock GAI’s potential, we recommend faculty development, expansion of digital infrastructure, and Indigenous-led AI design. By carefully harnessing GAI, medical schools can take a crucial step towards creating a more diverse and equitable healthcare workforce, ultimately improving health outcomes for Indigenous communities. Full article

With the rapid development of heterogeneous satellite networks integrating geostationary earth orbit (GEO) and low earth orbit (LEO) satellite systems, along with the significant growth in the number of satellite users, it is essential to consider frequency compatibility and coexistence between GEO and LEO systems, as well as to design effective system resource allocation strategies to achieve efficient utilization of system resources. However, existing beam-hopping (BH) resource allocation algorithms in LEO systems primarily focus on beam scheduling within a single time slot, lacking unified beam management across the entire BH cycle, resulting in low beam-resource utilization. Moreover, existing algorithms often employ iterative optimization across multiple resource dimensions, leading to high computational complexity and imposing stringent requirements on satellite on-board processing capabilities. In this paper, we propose a BH-based beam scheduling and resource allocation framework. The proposed framework first employs geographic isolation to protect the GEO system from the interference of the LEO system and subsequently optimizes beam partitioning over the entire BH cycle, time-slot beam scheduling, and frequency and power resource allocation for users within the LEO system. The proposed scheme achieves frequency coexistence between the GEO and LEO satellite systems and performs joint optimization of system resources across four dimensions—time, space, frequency, and power—with reduced complexity and a progressive optimization framework. Simulation results demonstrate that the proposed framework achieves effective suppression of both intra-system and inter-system interference via geographic isolation, while enabling globally efficient and dynamic beam scheduling across the entire BH cycle. Furthermore, by integrating the user-level frequency and power allocation algorithm, the scheme significantly enhances the total system throughput. The proposed progressive optimization framework offers a promising direction for achieving globally optimal and computationally tractable resource management in future satellite networks. Full article

Accurate short-term forecasting of photovoltaic power generation is vital for the operational stability of isolated energy systems, especially in regions with increasing renewable energy penetration. This study presents a novel AI-based forecasting framework applied to the island of Cyprus. Using machine learning methods, particularly regression trees, the proposed approach evaluates the impact of key environmental variables on PV performance, with an emphasis on atmospheric dust transport and air composition variability. A distinguishing feature of this work is the integration of cross-continental dust events and diverse atmospheric parameters into a structured forecasting model. A new clustering methodology is introduced to classify these inputs and analyze their correlation with PV output, enabling improved feature selection for model training. Importantly, all input parameters are sourced from publicly accessible, internet-based platforms, facilitating wide reproducibility and operational application. The obtained results demonstrate that incorporating dust deposition and air composition features significantly enhances forecasting accuracy, particularly during severe dust episodes. This research not only fills a notable gap in the PV forecasting literature but also provides a scalable model for other dust-prone regions transitioning to high levels of solar energy integration. Full article

This study compares and analyzes statistical, machine learning, and deep learning outlier-detection methods on real power-usage data from the metal, food, and chemical industries to propose the optimal model for improving energy-consumption efficiency. In the metal industry, a Z-Score-based statistical approach with threshold optimization was used; in the food industry, a hybrid model combining K-Means, Isolation Forest, and Autoencoder was designed; and in the chemical industry, the DBA K-Means algorithm (Dynamic Time Warping Barycenter Averaging) was employed. Experimental results show that the Isolation Forest–Autoencoder hybrid delivers the best overall performance, and that DBA K-Means excels at detecting seasonal outliers, demonstrating the efficacy of these algorithms for smart energy-management systems and carbon-neutral infrastructure Full article