Search Results (8,983)

The rapid integration of renewable energy into power systems has made voltage oscillations caused by the intermittency of wind and solar power a critical operational challenge. To mitigate these issues, this paper proposes a multi-mode coordinated reactive power control strategy to enhance voltage stability in renewable energy clusters. The approach integrates two key indicators: voltage sensitivity for steady-state regulation and an improved multi-renewable energy station short circuit ratio (MRSCR) that accounts for dynamic power interactions. Validation is conducted using a hardware-in-the-loop (HIL) platform combining real-time RMS-based simulation with physical controllers. Case studies on an offshore wind cluster demonstrate that the proposed method reduces voltage fluctuation amplitude more effectively than conventional automatic voltage control (AVC), successfully suppressing oscillations. The results confirm that the strategy exhibits stronger adaptability to varying grid conditions and offers a scalable solution for oscillation mitigation in large-scale renewable energy integration. Full article

Electric scooters and other micromobility innovations are becoming standard fare in urban transportation networks. Yet there are several obstacles that must be overcome, including concerns about users’ satisfaction and safety. This study aimed primarily at developing a user-centered methodological framework that combined different user-centered engineering tools such as voice of customers analysis, needs–metrics mapping, Pugh’s matrix and morphological design, strategic analysis approaches such as SWOT and PESTEL, and, a key innovation, the smart terrain-adaptive power management system (STAPMS), an AI-based feature that dynamically adjusts power output and regenerative braking based on Budapest’s varied topography and road conditions to improve energy efficiency and ride comfort. This innovative framework offers insights into redesign options aimed at enhancing customer satisfaction, product quality, and business growth. The proposed framework was validated on Lime electric scooters, particularly the S2 generation type. Three design concepts were generated and evaluated through a systematic approach to provide an optimal balance between users’ needs, technical performance, and strategic feasibility. The proposed user-centered framework shows significant potential to improve users’ satisfaction, enhanced usability, extended range, and increased market competitiveness, validating its viability for micromobility innovative solutions. The findings also demonstrate the necessity for systematic frameworks that link user experience with engineering design and can be generalized to other micromobility products. Full article

The integration of distributed renewable energy sources into distribution networks is a key approach to achieving sustainable and low-carbon power systems. However, high renewable penetration significantly increases the volatility and uncertainty of distribution systems, posing challenges to renewable energy accommodation and reliable operation. To address these challenges, active control of distribution networks is required, which in turn relies on accurate system states. In practice, the limited number and accuracy of measurement devices in distribution networks make dynamic state estimation a critical technology for sustainable distribution systems. In this paper, a novel dynamic state estimation method for sustainable distribution systems is proposed, incorporating spatiotemporal data correlation and adaptiveness to process and measurement noise. A CNN-BiGRU-Attention model is developed to reconstruct high-accuracy real-time pseudo-measurements, compensating for insufficient sensing infrastructure. Furthermore, a noise adaptive dynamic state estimation method is proposed based on an improved unscented Kalman filter. An amplitude modulation factor (AMF) is applied to track time-varying process noise, while an evaluation method based on robust Mahalanobis distance (RMD) is embedded to deal with non-Gaussian measurement noise. Finally, simulation studies on the IEEE 33-bus three-phase unbalanced distribution network demonstrate the effectiveness and robustness of the proposed method. Full article

Multi-unmanned aerial vehicle (UAV) systems are crucial for establishing resilient communication networks in disaster-stricken areas, but their limited energy and dynamic characteristics pose significant challenges for sustained and reliable service provision. Optimizing resource allocation in this situation is a complex sequential decision-making problem, which is naturally suitable for multi-agent reinforcement learning (MARL). However, the most advanced MARL methods (e.g., multi-agent proximal policy optimization (MAPPO)) often encounter difficulties in the “loosely coupled” multi-UAV environment due to their overly centralized evaluation mechanism, resulting in unclear credit assignment and inhibiting personalized optimization. To overcome this, we propose a novel hierarchical framework supported by MAPPO with decoupled critics (MAPPO-DC). Our framework employs an efficient clustering algorithm for user association in the upper layer, while MAPPO-DC is used in the lower layer to enable each UAV to learn customized trajectories and power control strategies. MAPPO-DC achieves a complex balance between global coordination and personalized exploration by redesigning the update rules of the critic network, allowing for precise and personalized credit assignment in a loosely coupled environment. In addition, we designed a composite reward function to guide the learning process towards the goal of proportional fairness. The simulation results show that our proposed MAPPO-DC outperforms existing baselines, including independent proximal policy optimization (IPPO) and standard MAPPO, in terms of communication performance and sample efficiency, validating the effectiveness of our tailored MARL architecture for the task. Through model robustness experiments, we have verified that our proposed MAPPO-DC still has certain advantages in strongly coupled environments. Full article

For battery-powered Smart Road components deployed in locations without access to the electrical grid, limited energy availability represents a major challenge to long-term autonomous operation. While photovoltaic panels are the most commonly used energy-harvesting solution, their effectiveness depends strongly on environmental and climatic conditions and may be insufficient in shaded areas or in highly dynamic road environments. Road infrastructure, however, inherently provides additional and largely underutilized energy sources, among which thermoelectric energy generated by temperature gradients within the road structure is particularly promising. This review addresses the problem of identifying viable alternatives or complements to photovoltaic energy harvesting by focusing on thermoelectric transducers as a potential power source for Smart Road applications. The objective of the article is to provide a comprehensive overview of the physical principles underlying thermoelectric transducers, the different architectures of thermoelectric modules, and their practical applicability in road transportation systems. Particular attention is devoted to implementation approaches that do not interfere with traffic flow or compromise road safety, as well as to existing applications of thermoelectric energy harvesting in transportation infrastructure. In addition, the review discusses the potential and limitations of concentrated thermoelectric transducers for increasing power density. By synthesizing current research results, this work evaluates the feasibility, advantages, and challenges of thermoelectric energy harvesting to extend the operational lifetime of autonomous Smart Road components and identifies directions for future research. Full article

Short-Term Residential Load Forecasting (STRLF) is a core task in smart grid dispatching and energy management, and its accuracy directly affects the economy and stability of power systems. Current mainstream methods still have limitations in addressing issues such as complex temporal patterns, strong stochasticity of load data, and insufficient model interpretability. To this end, this paper proposes an explainable and efficient forecasting framework named KAN+Transformer, which integrates Kolmogorov–Arnold Networks (KAN) with Transformers. The framework achieves performance breakthroughs through three innovative designs: constructing a Reversible Mixture of KAN Experts (RMoK) layer, which optimizes expert weight allocation using a load-balancing loss to enhance feature extraction capability while preserving model interpretability; designing an attention-guided cascading mechanism to dynamically fuse the local temporal patterns extracted by KAN with the global dependencies captured by the Transformer; and introducing a multi-objective loss function to explicitly model the periodicity and trend characteristics of load data. Experiments on four power benchmark datasets show that KAN+Transformer significantly outperforms advanced models such as Autoformer and Informer; ablation studies confirm that the KAN module and the specialized loss function bring accuracy improvements of 7.2% and 4.8%, respectively; visualization analysis further verifies the model’s decision-making interpretability through weight-feature correlation, providing a new paradigm for high-precision and explainable load forecasting in smart grids. Collectively, the results demonstrate our model’s superior capability in representing complex residential load dynamics and capturing both transient and stable consumption behaviors. By enabling more accurate, interpretable, and computationally efficient short-term load forecasting, the proposed KAN+Transformer framework provides effective support for demand-side management, renewable energy integration, and intelligent grid operation. As such, it contributes to improving energy utilization efficiency and enhancing the sustainability and resilience of modern power systems. Full article

Urban and peri-urban agriculture (UPA) has emerged as a critical strategy to address multidimensional urban challenges, including food insecurity, environmental degradation, and social inequality. Despite its potential benefits, UPA occupies a marginal position in municipal governance frameworks. Understanding how public policies and social influence mechanisms shape consumer behavior and producer viability requires a systems-thinking approach capable of capturing complex socio-economic-ecological interactions. Therefore, we developed an agent-based model (ABM) following the ODD + D protocol to simulate urban agriculture market dynamics, incorporating producer and consumer agents within a spatially explicit grid environment representing the urban landscape. We implemented three policy interventions and conducted six complementary experiments. Education campaigns achieved the highest local market share, demonstrating strict Pareto dominance over all subsidy-based strategies. Production subsidies yielded equivalent outcomes but at a fiscal cost, reducing producer income inequality (Gini). Stress tests revealed moderate resilience to production shocks. The findings demonstrate the power of agent-based modeling to uncover policy dynamics in complex urban food systems, providing actionable evidence for sustainable urban governance. Full article

The neutron survival probability (and related quantities including probabilities of extinction and initiation) is a central element of the broader stochastic theory of neutron populations and finds application in fields including reactor start-up, analysis of reactor power bursts and criticality accidents, and safeguards. In a full neutron transport formulation, the equation governing the single-neutron survival probability is a backward or adjoint-like integro-partial differential equation with the added complexity of being highly nonlinear. Analogous formulations of this equation exist in the context of many approximate theories of neutron transport, with the point kinetics formulation having received significant theoretical attention since the 1940s. This work continues this tradition by providing a novel analysis of the single-neutron survival probability equation using the tools of boundary layer theory. The analysis reveals that the “fully dynamic” solution of the single-neutron survival probability equation—and some key probability distributions derived from it—may be cast as a singular perturbation around the underlying quasi-static single-neutron probability of initiation. In this perturbation solution, the expansion parameter is the ratio of the neutron generation time to a macroscopic time scale characterizing the overall system evolution; this interpretation illuminates some of the fundamental structural aspects of neutron survival phenomena. Full article

The integration of renewable energy sources into modern power systems requires advanced control strategies to maintain stability, reliability, and efficiency. This paper presents a comprehensive review of the application of artificial intelligence techniques, including machine learning, deep learning, and reinforcement learning, in improving droop control for renewable energy integration. These artificial intelligence-based methods address key challenges such as frequency and voltage regulation, power sharing, and grid compliance under conditions of high renewable penetration. Machine learning approaches, such as support vector machines, are used to optimize droop parameters for dynamic grid conditions, while deep learning models, including recurrent neural networks, capture complex system dynamics to enhance the stability of distributed energy systems. Reinforcement learning algorithms enable adaptive, autonomous control, improving multi-objective optimization within microgrids. In addition, emerging directions such as transfer learning and real-time data analytics are explored for their potential to enhance scalability and resilience. Overall, this review synthesizes recent advances to demonstrate the growing impact of artificial intelligence in droop control and outlines future pathways toward more intelligent and sustainable power systems. Full article

Focal adhesions (FAs) are critical multi-protein complexes regulating cell adhesion, migration, and survival, and their dysregulation contributes to cancer metastasis and vascular diseases. Despite extensive research on FA formation, little is known about FA turnover, particularly its regulation by autophagy. This study introduces a novel tandem fluorescence reporter capable of tracking the entire FA-phagy flux, from autophagosome formation to lysosomal degradation. The reporter, based on a red–green fluorescence system with a lysosome-specific cleavage site, integrates seamlessly into endogenous focal adhesion complexes, demonstrating sensitivity and specificity to autophagy stimuli. Validated in multiple cell lines, the tool revealed dynamic FA-phagy responses to starvation-induced autophagy and the involvement of autophagy regulators such as mTOR and ATG genes. This versatile reporter provides a powerful tool for investigating FA-phagy mechanisms, with significant implications for cancer biology and vascular research. Full article

The LLC resonant converter constitutes a high-order switching system characterized by multiple operational modes and region-dependent switching sequences. This complexity poses significant challenges to system modeling and dynamic analysis. Furthermore, its inherent high-order nonlinearity tends to induce detrimental nonlinear phenomena, including bifurcation and chaos, which are particularly undesirable in power electronic systems that demand the utmost priority for stability and reliability. To address these concerns, this work focuses on investigating the dynamic behaviors and stability of LLC resonant converter control systems. This study aims to elucidate the origins and evolution of these nonlinear characteristics, thereby facilitating the design of higher-performance power electronic systems. First, a continuous-time model of the closed-loop controlled LLC resonant converter system was established using the sigmoid function modeling method. This model allows direct application of continuous system theory to analyze dynamic behavior, significantly reducing analytical complexity. Second, the system’s bifurcation characteristics and stability were comprehensively investigated through Floquet theory, bifurcation diagrams, and Lyapunov exponent spectra. Results reveal that PFM-controlled LLC resonant converters exhibit rich nonlinear dynamics under variations in key parameters. Experiments successfully captured the observed nonlinear phenomena, validating the evolution of system dynamics and stability. This work provides a novel perspective for stability analysis and parameter design in multi-resonant converter systems. Full article

Magnetically coupled resonant wireless power transfer (MC-WPT) systems with multi-relay coupling structures can significantly extend the transmission distance. However, system performance is highly sensitive to the spatial positions and coupling conditions of the relay coils. Any misalignment can alter the energy transfer path, causing shifts in the optimal operating frequency and reductions in efficiency. This makes conventional single-frequency or static-tuning strategies unsuitable for handling complex variations in coupling states. To address this issue, this paper investigates a three-relay MC-WPT system and proposes an adaptive frequency control and energy routing method that combines clustering and Q-learning for scenarios with severe coil misalignment. First, a physical model based on coupled-mode theory is established to describe the relationships among coupling coefficients, operating frequency, and transmission efficiency. High-dimensional coupling state data are then collected under different relay coil misalignment conditions. Next, principal component analysis (PCA) and clustering algorithms are used to extract representative coupling patterns and identify the system’s optimal efficiency points, forming an offline database that includes mappings of optimal frequencies. Furthermore, Q-learning is introduced to enable adaptive frequency control through online state recognition. Finally, under severe coil misalignment, frequency retuning of non-misaligned coils is applied to actively shield misaligned coils and reconstruct the energy transfer path. Simulation and experimental results show that the proposed method can achieve real-time frequency control and dynamic energy routing in multi-relay MC-WPT systems without additional hardware. The system transmission efficiency is significantly improved under all relay misalignment scenarios, effectively addressing the optimal frequency shift problem in multi-relay coupling structures and providing a new approach for intelligent and efficient MC-WPT systems under complex coupling conditions. Full article

This paper proposes a novel, dual-output, hybrid-clamped, quasi-five-level inverter (DO-HC-FLI) topology, capable of providing two independent AC voltage outputs with adjustable frequency and amplitude. Derived from a dual-output, active, neutral-point-clamped, three-level inverter, the proposed topology introduces three additional switches per phase to create dynamic switching paths. This expands the available range of DC-side voltage outputs and significantly improves the utilization rate of the DC–link voltage. Additionally, by adopting an asymmetric DC–link voltage configuration, the output line voltage levels of the conventional four-level inverter are increased to a number comparable to that of a five-level inverter. The front-end stage employs a hybrid series-parallel architecture, integrating dual Buck circuits with DC power sources. This configuration supplies the subsequent inverter stage with DC voltage levels at an optimal asymmetric ratio. In conjunction with a dual-output space vector pulse width modulation (SVPWM) strategy, the proposed system can collaboratively optimize the output voltage level characteristics of the inverter stage. Furthermore, a comprehensive analysis and comparison with other multilevel inverters are presented to demonstrate the superiority of the proposed topology. Finally, simulations and experiments are conducted to validate the effectiveness and feasibility of the proposed topology and modulation strategy. Full article

Droop control is an effective power regulation method for islanded microgrids to cope with fluctuations in renewable energy and loads. However, its power coordination performance is easily affected by the line impedance. When virtual impedance is introduced to enhance impedance matching, fixed values struggle to adapt flexibly to varying grid conditions. To address this specific limitation, this paper proposes a novel power coordination control strategy based on real-time line impedance identification. The method first analyzes the power distribution principle and equilibrium conditions under droop control. Crucially, it then establishes a dynamic virtual impedance regulation mechanism. By continuously identifying the actual line impedance, the proposed strategy dynamically adjusts the virtual impedance, thereby reshaping the inverter’s output impedance in real-time to match the grid conditions. This approach directly enhances the inverter’s adaptability to impedance variations, which is the core challenge in robust power coordination. Simulation results demonstrate that, compared to methods using fixed virtual impedance, the proposed strategy significantly improves power-sharing accuracy and system robustness under uncertainties such as fluctuating line impedance and load changes. Full article

Optical Wireless Power Transmission (OWPT) holds a significant position for enabling cable-free energy delivery in long-distance, high-energy, and mobile scenarios. However, ensuring human and equipment safety under high-power laser exposure remains a critical challenge. This study reports a vision-based OWPT safety system that implements the principle of automatic emission control (AEC)—dynamically modulating laser emission in real time to prevent hazardous exposure. While camera-based OWPT safety systems have been proposed in the concept, there are extremely limited working implementations to date. Moreover, existing systems struggle with response speed and single-object assumptions. To address these gaps, this research presents a low-latency safety architecture based on a customized deep learning-based object detection framework, a dedicated OWPT dataset, and a multi-threaded control stack. The research also introduces a real-time risk factor (RF) metric that evaluates proximity and velocity for each detected intrusion object (IO), enabling dynamic prioritization among multiple threats. The system achieves a minimum response latency of 14 ms (average 29 ms) and maintains reliable performance in complex multi-object scenarios. This work establishes a new benchmark for OWPT safety system and contributes a scalable reference for future development. Full article