Search Results (146)

The growing incorporation of distributed energy resources (DER) in power distribution grids, although pivotal to the energy transition, increases operational variability and amplifies the exposure to disturbances that can compromise resilience and the continuity of service during contingencies. Addressing these challenges requires both a shift toward flexible distribution architectures and the adoption of advanced power electronics interfacing systems. In this setting, this paper proposes a resilience-oriented strategy for medium-voltage (MV) distribution systems and clustered hybrid AC/DC microgrids interfaced through solid-state transformers (SSTs). When a fault occurs along an MV feeder segment, the affected microgrids naturally transition to islanded operation. However, once their local generation and storage become insufficient to sustain autonomous operation, the proposed framework reconfigures the power routing within the cluster by activating an emergency low-voltage DC (LVDC) power path that bypasses the faulted MV section. This mechanism enables controlled power sharing between microgrids during prolonged MV outages, ensuring the supply of priority loads without oversizing SSTs or reinforcing existing infrastructure. Experimental validation on a reduced-scale SST prototype demonstrates stable grid-forming and grid-following operation. The reliability of the proposed scheme is supported by both steady-state and transient experimental results, confirming accurate voltage regulation, balanced sinusoidal waveforms, and low current tracking errors. All tests were conducted at a switching frequency of 50 kHz, highlighting the robustness of the proposed architecture under dynamic operation. Full article

To enhance distribution network flexibility and economy under conditions involving a high penetration of distributed energy resources, this paper proposes a two-stage optimization method considering demand response (DR). The first stage establishes a marginal cost-based DR model using a “base compensation + increasing marginal cost” mechanism to curb irrational user behaviors, reducing peak-hour power purchase costs. The second stage develops a dynamic reconfiguration model minimizing network losses, voltage deviation, and switch operation costs. Solved by an Improved Grey Wolf Optimizer (IGWO), it incorporates a segmented voltage compensation mechanism quantifying user satisfaction through differentiated coefficients. The two stages operate in a coordinated framework where “temporal load optimization” informs “spatial topology reconfiguration”. Case results demonstrate that this coordinated approach significantly reduces power purchase costs, improves voltage quality, and minimizes network losses, providing an effective solution for efficient distribution network operation. Full article

With the increasing penetration of the distributed generation and the growing variability of loads, flexible microgrids (FMGs) require operational strategies that can adapt to seasonal changes while maintaining reliable performance. To overcome the limitations of fixed-interval partition updates, this paper proposes a threshold-triggered dynamic operation strategy for FMGs. A composite partition-updating index is formulated by integrating an operation optimization index, which reflects network loss and hybrid energy storage (HES) cost, with a seasonal load uniformity index, so that partition reconfiguration is triggered only when scenario transitions significantly deteriorate operating performance. By enhancing seasonal load uniformity across partitions, the proposed framework reflects a symmetry-oriented operation philosophy for FMGs. An HES model is further established to coordinate short-term energy storage (STES) and long-term energy storage (LTES) across multiple timescales. In conjunction with remotely controlled switches (RCSs), the proposed framework enables adaptive adjustment of FMG boundaries and source scheduling under diverse seasonal conditions. A case study on the IEEE 123-bus distribution system demonstrates that the proposed strategy effectively reduces power fluctuations and redundant switching operations, improves seasonal load uniformity, and enhances both the operational flexibility and economic efficiency of FMGs. Full article

Electromagnetic pumps are developed for industrial, medical and scientific applications, moving electrically conductive liquids and molten solder in electronics manufacturing using electromagnetism instead of mechanical parts. This study presents a comprehensive thermal analysis of an electromagnetic diaphragm pump, focusing on the influence of operating current, permanent magnet switching speed, and cooling conditions on pumping performance. The pump utilizes a flexible diaphragm embedded with a permanent neodymium magnet, which interacts with time-varying magnetic fields generated by electromagnets to drive fluid motion. Temperature monitoring is conducted using a waterproof DS18B20 sensor and an uncooled FLIR A325sc infrared camera, allowing accurate mapping of thermal distribution across the pump surface. Experimental results demonstrate that higher current and increased magnet switching speed lead to faster temperature rise, impacting the volume of fluid pumped. Incorporation of an automatic cooling fan effectively reduces coil temperature and stabilizes pump performance. Polynomial regression models describe the relationship between temperature, pumped liquid volume, and magnet switching speed, providing information to optimize pump operation and cooling strategies. The novel relationship between temperature and the volume of the pumped liquid is considered as a fourth-degree polynomial. In particular the model describes a quantitative evaluation of the effect of heating on pumping efficiency. An initial increase in temperature correlates with a higher pumped volume, but excessive heating leads to efficiency saturation or even decline. Indeed, mathematical dependencies are crucial in mechanical pump engineering for analyzing physical phenomena; this is achieved by using a mathematical equation to define how different physical variables are related to each other, enabling engineers to calculate performance and optimize the pump design. Full article

High penetration of renewable energy sources (RES) in power systems introduces substantial source-load uncertainty and flexibility challenges, leading to misalignments between economic optimization and environmental sustainability. An edge-side electricity-carbon coordinated hybrid trading mechanism was proposed to enhance flexibility in microgrid clusters. A three-layer time-varying carbon emission factor (CEF) model is developed to quantify negative emissions as tradable Chinese Certified Emission Reductions (CCERs). An endogenous economic equilibrium point enables dynamic switching between Incentive-Based Demand Response during high-carbon periods and Price-Based Demand Response during low-carbon periods, based on marginal profit comparisons. A Wasserstein distance-based distributionally robust CVaR (WDR-CVaR) strategy constructs a data-driven ambiguity set to optimize decisions under worst-case distributional shifts in edge-side data. Simulations on a modified IEEE 33-bus system show that the mechanism increases the Multi-Energy Aggregator’s (MEA) expected profit by 12.3%, reduces carbon emissions by 17.6%, with WDR-CVaR demonstrating superior out-of-sample performance compared to sample average approximation methods. The approach internalizes environmental values through carbon-electricity coupling and edge intelligence, providing a resilient framework for low-carbon distribution network operations. Full article

The capacity of traditional distribution networks is limited. After large-scale distributed power sources are connected, it is difficult to consume them at the same voltage level, which can lead to transformer reverse overloading and voltage limit violations. Although the unified power flow controller (UPFC) excels in flexible power flow regulation and power quality optimization, existing research on it is mostly focused on the transmission grid, focusing on device topology, power flow control, etc. Fault protection is also targeted at high-voltage and ultra-high-voltage domains and only covers a single overvoltage or overcurrent fault. Research on the protection of the unified power flow controller in a distribution network (D-UPFC) remains scarce. A key challenge is the absence of fault protection schemes that are compatible with the unified power flow controller in a distribution network, which cannot meet the requirements of the distribution network for monitoring and protecting multiple fault types, rapid response, and equipment economy. This paper first designs a protection device centered on the distribution thyristor bypass switch (D-TBS), completes the thyristor selection and transient energy extraction, optimizes the overvoltage protection loop parameter, then builds a three-level coordinated protection architecture, and, finally, verifies through functional and system tests. The results show that the thyristor control unit trigger is reliable and the total overvoltage response delay is 1.08 μs. In the case of a three-phase short-circuit fault in a 600 kVA/10 kV system, the distribution thyristor bypass switch can rapidly reduce the secondary voltage of the series transformer, suppress transient overcurrent, achieve isolation protection of the main equipment, provide a reliable guarantee for the engineering application of the distribution network unified power flow controller, and expand its distribution network application scenarios. Full article

The advent of sixth-generation (6G) communications envisions a paradigm of ubiquitous intelligence and seamless physical–digital fusion, demanding unprecedented performance from the optical transport infrastructure. Achieving terabit-per-second capacities, microsecond latency, and nanosecond synchronisation precision requires a convergent, flexible, open, and AI-native x-Haul architecture that integrates communication with distributed edge computing. This study conducts a systematic literature review of recent advances, challenges, and enabling optical technologies for intelligent and autonomous 6G networks. Using the PRISMA methodology, it analyses sources from IEEE, ACM, and major international conferences, complemented by standards from ITU-T, 3GPP, and O-RAN. The review examines key optical domains including Coherent PON (CPON), Spatial Division Multiplexing (SDM), Hollow-Core Fibre (HCF), Free-Space Optics (FSO), Photonic Integrated Circuits (PICs), and reconfigurable optical switching, together with intelligent management driven by SDN, NFV, and Artificial Intelligence/Machine Learning (AI/ML). The findings reveal that achieving 6G transport targets will require synergistic integration of multiple optical technologies, AI-based orchestration, and nanosecond-level synchronisation through Precision Time Protocol (PTP) over fibre. However, challenges persist regarding scalability, cost, energy efficiency, and global standardisation. Overcoming these barriers will demand strategic R&D investment, open and programmable architectures, early AI-native integration, and sustainability-oriented network design to make optical fibre a key enabler of the intelligent and autonomous 6G ecosystem. Full article

This paper investigates the effect of Environmental, Social and Governance (ESG) scores and Credit Default Swap (CDS) spreads on the profitability of Halkbank, one of the biggest state-owned banks in Türkiye, an emerging economy. To this end, we employ Non-linear Autoregressive Distributed Lag (NARDL) and Markov Switching Regression (MSR) methods, taking into account non-linear market risks, using Halkbank’s quarterly data consisting of 63 observations for the period 2009Q1–2024Q3. Moreover, to prevent multicollinearity, we aggregate banking-specific and macroeconomic indicators into a single composite index using Principal Component Analysis (PCA). Our MSR findings suggest that ESG scores and CDS spreads negatively affect bank profitability and that these effects are particularly pronounced during periods of high market volatility. Similarly, NARDL findings suggest that ESG scores have asymmetric effects on bank performance, with both positive and negative changes in ESG performance having a negative impact on profitability, and moreover, negative changes have a more negative impact on profitability. This means that the bank’s sustainability initiatives may be costly and negatively affect profitability in the short run, but these effects will be more negative if initiatives deteriorate. Our findings emphasize the need for banks to adopt a gradual ESG approach that enables them to increase their capacity without compromising financial stability and for regulatory structures to have a flexible and sophisticated risk management framework capable of rapidly adapting to different market conditions. Therefore, our study provides valuable insights to sector managers and policymakers regarding the financial implications of sustainability approaches. Full article

To address the issues of uneven transformer loading and underutilized capacity in conventional distribution areas interconnected by bus-tie switches, this paper proposes a flexible mutual-aid control strategy based on the distributed power flow controller (DPFC). An equivalent model of the interconnected transformer area system is established, and the phasor relationships between the DPFC’s injected voltage amplitude, phase angle and transmitted power are analytically derived. On this basis, a capacity-ratio-oriented mutual-aid control strategy is developed, enabling the DPFC to dynamically distribute active power according to the rated capacities of interconnected transformers. A simulation model is built in PSCAD/EMTDC to validate the proposed method. The results demonstrate that the strategy achieves full power mutual aid among transformer areas, reducing the heavy-load transformer utilization from 76.67% to 30.56%, thereby realizing rational capacity allocation and improving the operational reliability and efficiency of the distribution network. Full article

The cloud manufacturing (CMfg) platform serves as a centralized hub for allocating and scheduling tasks to distributed resources. It features a concrete two-agent model that addresses real-world industrial needs: the first agent handles long-term flexible tasks, while the second agent manages urgent short-term tasks, both sharing a common due date. The second agent employs multitasking scheduling, which allows for the flexible suspension and switching of tasks. This paper addresses a novel scheduling problem aimed at minimizing the total weighted completion time of the first agent’s jobs while guaranteeing the second agent’s due date. For single-machine cases, a polynomial algorithm provides an efficient baseline; for parallel machines, an exact branch-and-price approach is developed, where the polynomial method informs the pricing problem and structural properties accelerate convergence. Computational results demonstrate significant improvements: the branch-and-price solves large-sized instances (up to 40 jobs) within 7200 s, outperforming CPLEX, which fails to find solutions for instances with more than 15 jobs. This approach is scalable for industrial cloud manufacturing applications, such as automotive parts production, and is capable of handling both design validation and quality inspection tasks. Full article

Renewable energy sources provide environmental and economic benefits by replacing conventional energy sources. In Korea, photovoltaic (PV) systems are increasingly deployed in apartment complexes and residential buildings. In self-consumption PV systems, surplus generation exceeding local demand often leads to a reverse power flow. This phenomenon becomes more frequent in microgrid environments where multiple distributed energy resources are interconnected. Accordingly, inverter control strategies based on generation forecasting have emerged as critical challenges. In this paper, we propose an on-device artificial intelligence model for inverter control that integrates net power forecasting with time-series neural networks. Two novel forecasting methods were proposed and introduced: Prediction-to-Prediction (P–P) and Net-Power Prediction (N–P). Various neural network models were trained and evaluated using multiple performance metrics. A novel threshold adjustment mechanism based on the mean absolute error was designed for inverter control. The control scenarios were analyzed by comparing the actual power losses with the forecast-based power losses, and the energy savings were quantified by adjusting the correction factor. The proposed forecasting methods achieved a reduction of approximately 40–70% in energy losses compared with the actual loss levels. The threshold adjustment strategy enhances flexibility in balancing the number of on/off switching events and the power loss, contributing to improved energy efficiency and system stability. Full article

The integration of Soft Open Points (SOPs) into distribution networks has been an essential method for enhancing operational flexibility and efficiency. But simultaneous optimization of network reconfiguration and SOP scheduling constitutes a difficult mixed-integer nonlinear programming (MINLP) problem that is likely to suffer from premature convergence with standard metaheuristic solvers, particularly in large power networks. This paper proposes a novel hybrid algorithm, hTLBO–CO, which synergistically integrates the exploitative capability of Teaching–Learning-Based Optimization (TLBO) with the explorative capability of the Cheetah Optimizer (CO). One of the notable contributions of our framework is an in-depth problem formulation that enables SOP locations on both tie and sectionalizing switches with an efficient constraint-handling scheme, preserving topo-logical feasibility through a minimum spanning tree repair scheme. The evolved hTLBO–CO algorithm is systematically validated across IEEE 33-, 69-, and 119-bus test feeders with differential operational scenarios. Results indicate consistent dominance over established metaheuristics (TLBO, CO, PSO, JAYA), showing significant efficiency improvement in power loss minimization, voltage profile enhancement, and convergence rate. Remarkably, in a situation with a large-scale 119-bus power grid, hTLBO–CO registered a significant 50.30% loss reduction in the single-objective reconfiguration-only scheme, beating existing state-of-the-art approaches by over 15 percentage points. These findings, further substantiated by comprehensive statistical and multi-objective analyses, confirm the proposed framework’s superiority, robustness, and scalability, establishing hTLBO–CO as a robust computational tool for the advanced optimization of future distribution networks. Full article

The traditional DC distribution grid is evolving into a meshed structure to create additional energy exchange paths and integrate the rapidly growing renewable energy sources. However, existing converter stations lack sufficient power flow controllability, necessitating the development of multiport power electronic transformers to address potential power flow congestion and high loss issues. This paper proposes a compact multi-terminal modular-multilevel-converter-based power electronic transformer (M³C-PET). This device enables flexible power flow regulation of the connected feeders through adopting two small-capacity power flow control modules (PFCMs). The simple structure and reduced switching count make the proposed PET more competitive and prominent and more cost-effective. Furthermore, this paper elaborates on the operational principle of the proposed device and presents a multilayer power balancing control strategy along with a power flow control scheme. These control strategies are designed based on the internal and external energy distribution mechanism of the proposed PET. The feasibility and effectiveness of the proposed topology and control schemes are rigorously validated through both a MATLAB/Simulink simulation model and a scaled-down experimental prototype. Full article

To meet the demand for energy-efficient and high-performance computing in resource-limited sensor edge applications, this paper presents a reconfigurable memristor-based computing-in-memory circuit for Content-Addressable Memory (CAM). The scheme exploits the analog multi-level resistance characteristics of memristors to enable parallel multi-bit processing, overcoming the constraints of traditional binary computing and significantly improving storage density and computational efficiency. Furthermore, by employing dynamic adjustment of the mapping between input signals and reference voltages, the circuit supports dynamic switching between exact and approximate CAM modes, substantially enhancing functional flexibility. Experimental results demonstrate that the 32 × 36 memristor array based on a TiN/TiOx/HfO₂/TiN structure exhibits eight stable and distinguishable resistance states with excellent retention characteristics. In large-scale array simulations, the minimum voltage separation between state-representing waveforms exceeds 6.5 mV, ensuring reliable discrimination by the readout circuit. This work provides an efficient and scalable hardware solution for intelligent edge computing in next-generation sensor networks, particularly suitable for real-time biometric recognition, distributed sensor data fusion, and lightweight artificial intelligence inference, effectively reducing system dependence on cloud communication and overall power consumption. Full article

Amid the global energy transition, rapidly expanding wind and solar installations challenge power grids with variability and uncertainty. We propose an adaptive framework for renewable energy accommodation assessment under high-dimensional uncertainties, integrating three innovations: (1) Response Surface Methodology (RSM) is adopted for the first time to construct a closed-form polynomial of renewable energy accommodation in terms of resource hours, load, installed capacity, and transmission limits, enabling millisecond-level evaluation; (2) LASSO-regularized RSM suppresses high-dimensional overfitting by automatically selecting key interaction terms while preserving interpretability; (3) a Bayesian kernel density extension yields full posterior distributions and confidence intervals for renewable energy accommodation in small-sample scenarios, quantifying risk. A case study on a renewable-rich grid in Northwest China validates the framework: two-factor response surface models achieve R² > 90% with < 0.5% mean absolute error across ten random historical cases; LASSO regression keeps errors below 1.5% in multidimensional space; Bayesian density intervals encompass all observed values. The framework flexibly switches between deterministic, sparse, or probabilistic modes according to data availability, offering efficient and reliable decision support for generation-transmission planning and market clearing under multidimensional uncertainty. Full article