Search Results (782)

With the increasing penetration of distributed energy resources, distribution networks face elevated risks of power disruptions, which call for rapid and flexible emergency response mechanisms. There are not enough traditional emergency generator vehicles, and they are not highly adaptable when it comes to operations, which makes it hard to meet changing dispatching needs. Electric vehicles (EVs), on the other hand, can be used as distributed emergency resources that can be dispatched through vehicle-to-grid (V2G) interaction. Electric vehicle charging stations (EVCSs), on the other hand, are integrated energy storage units that use existing charging infrastructure to provide on-site grid support. To address this gap, this study proposes a comprehensive V2G-powered pre- and post-disaster coordination framework for enhancing distribution network resilience, with three core novelties: first, a refined individual EV model considering dual power and energy constraints is developed, and the Minkowski summation method is applied to accurately quantify the real-time aggregate regulation potential of EVCSs for the first time; second, a two-stage robust optimization model is formulated for pre-event strategic planning, which jointly optimizes EVCS participant selection and distribution network topology to address photo-voltaic (PV) power generation uncertainties; third, a multi-source collaborative dynamic scheduling model is constructed for post-disaster recovery, which explicitly incorporates the spatiotemporal dynamics of EVs and coordinates EVCSs, gas turbine generators (GTGs) and other resources for the first time. We carried out simulations on a modified IEEE 33-bus system with a 10 h extreme fault scenario. The results show that the proposed strategy raises the average critical load recovery ratio to 97.7% (2% higher than traditional deterministic optimization), lowers the total load shedding power by 0.2 MW and the load reduction cost by 19,797.63 CNY, and gives a net V2G power output of 3.42 MW (86.9% higher than the comparison strategy). The proposed V2G-enabled coordinated pre- and post-disaster fault recovery strategy significantly improves the resilience of distribution networks compared to traditional methods. This makes it easier and faster to recover from extreme disaster scenarios, with the overall load recovery rate reaching 91.8% and the critical load restoration rate staying above 85% throughout the recovery process. Full article

Peak-hour congestion in metro systems poses significant challenges to operational reliability and passenger experience. This study investigates a coordinated operational strategy that integrates passenger flow control, reservation-based entry, and skip-stop train operations to alleviate congestion in high-density metro corridors. A mathematical optimization model is formulated to jointly capture passenger demand, station crowding, and train capacity constraints, and is solved using an adaptive large neighborhood search algorithm. Numerical experiments based on a real-world metro line demonstrate that the proposed framework can effectively reduce passenger waiting time and improve the balance of passenger distribution across stations under peak-hour conditions. The results indicate that coordinating multiple operational measures yields better performance than applying individual strategies in isolation, highlighting the practical value of the proposed approach for congested metro systems. Full article

As a typical example of a high-density city, Macau’s medical resource allocation system, a key component of the city’s complex socio-technical system, suffers from significant spatial imbalances, which restricts the overall effectiveness of the medical service system. Based on the perspective of systems science theory, regards the allocation of medical resources as a dynamic system with multiple coupled factors. It comprehensively utilizes systems research methods such as POI data mining and space syntax analysis and employs techniques such as kernel density analysis and spatial structure coupling models to systematically evaluate the spatial structure, resource accessibility, and service balance of Macau’s medical service system. It found that (1) the Macau Peninsula has concentrated core medical resources, such as the Conde de São Januário Hospital (CHCSJ) and Kiang Wu Hospital, which form a core subsystem with high service saturation. Excessive concentration of resources has led to high concentration of a certain type of facility. (2) Taipa Island and the Cotai Reclamation Area have created an extended subsystem of medical resources along with urban development. However, the northern area does not have enough facilities, and its internal structure is not balanced. (3) Coloane Island has only basic health stations remaining, forming a marginal subsystem with scarce medical resources, which has a significant hierarchical gap with the core and extended subsystems. This spatial pattern of “saturated Macau peninsula, expanded Taipa Island, and sparse Coloane Island” is essentially a concrete manifestation of the imbalance between the medical resource allocation system and the urban spatial development system. Therefore, based on system optimization theory, it proposes constructing a multi-level, networked spatial system for medical facilities to promote the coordinated operation of various regional medical subsystems and achieve overall functional optimization and a balanced layout for Macau’s medical service system. This research analyzes the imbalance mechanism of high-density urban public service systems using systems science methods, providing not only a scientific basis for the precise optimization of Macau’s medical resource allocation system but also a practical reference for the planning and governance of similar high-density urban public service systems under a systems thinking framework. Full article

The Earth’s lithosphere, the rigid outermost layer of the planet, is composed of numerous tectonic plates of varying sizes that move over the underlying asthenosphere. The motion and interaction of these plates give rise to a wide range of geodynamic processes. Accurate monitoring of these processes is essential for maintaining a stable, up-to-date, and reliable terrestrial reference frame. This study investigates the horizontal and vertical motions of the Antarctic Plate resulting from its interactions with adjacent plates. Tectonic plate movements can be determined using several space-geodetic techniques, including Global Navigation Satellite Systems (GNSS), Very Long Baseline Interferometry (VLBI), Satellite Laser Ranging (SLR), and Interferometric Synthetic Aperture Radar (InSAR). Among these methods, GNSS is currently the most widely used, as plate motions can be derived from continuous observations recorded at permanent stations and processed using scientific or commercial software. Within the scope of this research, GNSS data collected between 2020 and 2023 were processed using the GAMIT/GLOBK V.10.7 software package to estimate the coordinates and velocities of stations located on the Antarctic, South American, African, and Australian Plates in the ITRF14 reference frame. Furthermore, plate-fixed solutions were generated to analyze the relative motion of the Antarctic Plate with respect to neighboring plates. The results indicate that the Antarctic Plate moves at an average velocity of approximately 4–18 mm/year in the ITRF14 frame. The plate diverges from both the African and Australian Plates and exhibits predominantly strike-slip motion relative to the South American Plate. A comparison with existing global plate motion models demonstrates that the obtained velocities are consistent within 0–5 mm/year. Full article

Distributed hydrogen refueling stations enable the coupling of renewable generation, storage, and demand fulfillment; however, their performance depends on coordinated control under strict physical safety limits. Centralized controllers are often impractical due to privacy constraints and unreliable communication links, while unconstrained learning can reduce operating costs at the expense of unsafe pressure excursions. Therefore, this study evaluates safety-constrained coordination across multiple stations using federated learning-based distributed scheduling and benchmarks a non-federated Local Control baseline (local-only, no coordination). Using a feasibility-first rule with an acceptance threshold of $\tau =0.2$ on the pressure violation metric ($V_p\le 0.2$), the best feasible overall controller (Local Control) achieved a cost of 2131.83 with pressure violation $V_p=0.172$, representing a 37.22% reduction relative to a centralized reference cost of 3396.25. Federated training with Federated Averaging and a solar–wind mixing scheme produced the best feasible federated policy (cost 2423.72, $V_p=0.163$) with 866,688 transmitted bytes. Extensive simulations report cost, unmet demand, safety violations, and communication overhead, demonstrating that feasibility-first selection is essential because lower-cost policies can be unsafe (e.g., cost 1952.27 with $V_p=2.63$). Full article

This paper investigates the multi-objective optimization of the peak–valley difference, operating cost, and carbon emissions for urban logistics battery-swapping stations (BSSs) under photovoltaic uncertainty and stochastic demand. Unlike conventional plug-in charging, battery swapping decouples energy replenishment from the vehicle dwell time, enabling rapid service, but introducing discrete swap arrivals and power–inventory coupling challenges that continuous-load models cannot capture. A Stackelberg game-based framework models grid–BSS interactions, where the grid acts as the leader by setting time-of-use prices and BSSs respond by optimizing charging/discharging schedules. Carbon emissions are quantified using real-time carbon intensity data obtained from the Electricity Maps platform. The battery-swapping demand is modeled as a Poisson process, and a unified power–inventory coupling model captures the bidirectional dependence among PV generation, grid purchases, energy storage operations, and battery inventory dynamics, where the inventory feasibility constrains the power decisions. For multi-station coordination, an adaptive ADMM decomposes the problem into parallelizable sub-problems. Case studies of a 49-vehicle fleet across three BSSs in Qingdao, China, show that, compared with a no-optimization baseline, the proposed method reduces the peak–valley difference by approximately 21.6%, the operating cost by approximately 10.2%, and carbon emissions by approximately 15.7%. Compared with the single-objective counterparts, the multi-objective formulation further improves the peak–valley difference by approximately 26.9% and increases emission reduction by approximately 16.9%; paired t-tests on repeated runs indicate statistical significance (p < 0.05). The framework provides a scalable methodology for low-carbon BSS scheduling with explicit power–inventory coupling. Full article

This paper introduces a reinforced learning-based supervisory control architecture that oversees multiple Recursive Least Squares (RLS) based self-tuning pump controllers and determines when each loop is permitted to adapt its gains. The supervisor learns adaptation policies that minimize interaction between loops while preserving responsiveness to changing hydraulic conditions. A two-loop pump station simulation is used to evaluate performance under product changes and transient flow disturbances. The results show that the supervisory layer reduces the number of simultaneous adaptation events by over 70%, leading to a 32% lower pressure-tracking error and 45% fewer gain-induced oscillations compared to conventional independent adaptive control. The reinforcement learning policy converges within 15 training episodes, resulting in stable adaptation scheduling and seamless transitions. The key novelty of this work lies in introducing decentralized reinforcement-learning-based coordination for adaptive pump control, enabling supervisory decision-making that actively prevents interference between controllers during transients. This approach provides a scalable and lightweight solution for coordinating multi-loop pump stations, enhancing robustness and operational performance in real-world pipeline systems. Full article

Large-scale hydropower development provides substantial socio-economic and energy benefits but simultaneously introduces complex ecological and environmental challenges that require comprehensive scientific assessment. This study systematically evaluates the effects of the leading reservoir (Longpan hydropower station, referring to the uppermost and principal flow-regulating dam in the cascade) in the middle reaches of the Jinsha River’s operation on the water environment of the mainstream Yangtze River, China, with the aim of clarifying its water quality responses and supporting evidence-based basin management. Based on an analysis of the current water quality conditions of the Yangtze River and a comparative review of the operational experience of the Three Gorges Reservoir, this research explores the mechanisms through which large reservoirs alter hydrological and ecological processes. These mechanisms include reduced flow velocity, prolonged water residence time, weakened pollutant dispersion, and increased risk of algal blooms in tributaries. To quantitatively assess these impacts, an improved river dilution–mixing model was developed and applied to simulate the water quality response during the dry season (February–April) under different discharge scenarios. Key downstream monitoring sections were examined. The modeling results indicate that the operation of the Leading reservoir can moderately reduce dry-season concentrations of key pollutants (e.g., total phosphorus, permanganate index) at downstream sections by approximately 2–5% on average, with spatially heterogeneous effects. Although the overall improvement magnitude remains limited, the combined effects of sediment deposition and in situ degradation may yield more pronounced real-world benefits. The findings underscore the importance of optimizing the regulatory function of the Longpan Reservoir through coordinated operation within the cascade reservoir system. It is recommended to integrate water resource allocation, water quality management, and aquatic ecosystem protection, alongside enhanced pollution control and ecological restoration in key zones. The methodology and findings provide a referenced framework for assessing the water-environmental implications of large-scale reservoir regulation in other major river systems. Full article

This paper addresses the critical challenges of multi-stakeholder interest coordination and low-carbon operation in modern power systems, specifically focusing on the interaction among an Integrated Energy System (IES), Electric Vehicle Charging Stations (EVCS), and Load Aggregators (LA). To tackle these challenges, we propose a novel data-model driven optimization framework. A bi-level model is established, where the upper-level IES acts as the leader, and the lower-level EVCS and LA serve as followers. At the core of our approach is an integrated dynamic pricing mechanism that synergistically combines EVCS operational schedules, carbon emission signals, and load demand response. This mechanism, enhanced by predictive insights from historical data, effectively guides lower-level entities to participate in the upper-level IES’s optimization, thereby aligning individual benefits with system-wide low-carbon goals. The resulting bi-level problem is solved iteratively using CPLEX, with the optimal equilibrium selected via a joint optimality formula. The proposed methodology is validated on a multi-stakeholder case study. Results demonstrate that our AI-enhanced dynamic pricing and dispatch model not only effectively balances the interests of all parties but also significantly improves the system’s low-carbon economic performance, showcasing the potential of integrating physical models with data-driven insights for future energy system management. Full article

To address the challenges of insufficient frequency regulation capability of individual stations, poor collaborative economic performance, and unfair benefit allocation caused by fluctuations in photovoltaic (PV) output and variations in electric vehicle (EV) connectivity during vehicle-to-grid (V2G) interactions under high-penetration PV integration, this paper proposes a coordinated optimal operation strategy for peer-to-peer (P2P) energy sharing and joint frequency regulation among multiple electric vehicle charging stations (EVCSs). First, a collaborative framework for P2P energy sharing and joint frequency regulation among EVCSs is constructed to describe the operational mechanism of inter-station energy mutual support and coordinated response to frequency regulation signals. Subsequently, an aggregate model of the dispatchable potential for EV clusters within each station is established based on Minkowski Summation (M-sum), characterizing the charging and discharging power boundaries and frequency regulation potential of the EV clusters. Meanwhile, distributionally robust chance constraints (DRCC) based on the Kullback–Leibler (KL) divergence are introduced to handle the uncertainty of PV power generation within the EVCS. On this basis, a dynamic frequency regulation output model for EV clusters and a multi-station P2P energy sharing model are designed, with the optimization objective of minimizing the total operating cost. Finally, to quantify the differential contributions of each EVCS in the collaborative operation, an asymmetric Nash bargaining benefit allocation mechanism is proposed, which incorporates a comprehensive contribution index considering both energy sharing and joint frequency regulation, The model is solved in a distributed manner using the alternating direction method of multipliers (ADMM). Simulation results demonstrate that, compared to non-cooperative operation, the frequency regulation completeness rates of the EVCSs after cooperation increase by 5.7%, 5.2%, and 4.4%, respectively; meanwhile, the total operating cost drops from CNY 16,187.61 under non-cooperative operation to CNY 15,997.47, achieving a reduction of 1.18%. The proposed strategy not only meets grid frequency regulation demands but also enhances the economic efficiency of multi-station collaborative operation and the fairness of benefit distribution. Full article

SSRMS refers to a Space Station Remote Manipulator System. The robotic arm of the Wentian module can complete tasks such as supporting astronauts’ extravehicular activities, installing and maintaining payloads, and inspecting the space station. The seven-joint SSRMS manipulator is critical for space missions. This study aims to build its kinematic model via screw theory. It simplifies SSRMS to right-angle rods, defines joint screw axes, twist coordinates, and initial pose matrix. Using the PoE (Product of Exponentials) formula, the 7-DOF forward kinematics equation is derived. In addition, it derives fixed joint angle for inverse kinematics, including analytical solutions and numerical solutions. It elaborates analytical solutions for fixing joints 1/7 and 2/6 and numerical solutions for fixing joints 3/4/5, solves all joint angles via kinematic decoupling, and addresses special cases. Experiments with China’s space station small arm parameters show the probability of meeting the accuracy threshold ${10}^{-4}$ is 99.79%, verifying model effectiveness, while noting singularity-related weak solving areas. This provides a reliable basis for subsequent inverse kinematics optimization. Full article

Accurate real-time positioning of Unmanned Aerial Vehicles (UAVs) is critical for navigation and mapping but remains challenging in complex environments due to signal blockages and multipath effects. This study presents a comparative framework for real-time error prediction of the Global Navigation Satellite System (GNSS), evaluating two machine learning models (Random Forest and XGBoost) and a deep learning model (Long Short-Term Memory network) against an Extended Kalman Filter baseline. A high-precision total station provides ground-truth coordinates, enabling the derivation of positioning error labels from synchronized GNSS raw data. Among the evaluated models, the tree-based XGBoost model achieves a significantly lower Mean Squared Error (MSE) and a considerably higher Coefficient of Determination ($R^2$) score than other models in predicting positioning deviations. The high-accuracy error predictions from the optimal model establish the core of a software-only solution for positioning integrity. The framework demonstrates that reliable, real-time error estimates can be derived directly from observation data, providing the essential input required for future compensation systems without necessitating additional hardware. Full article

Time synchronization and positioning of bistatic radar transceivers is required to coordinate and meaningfully merge the measurements made between them. It simultaneously allows the radar transceivers to change their position throughout time. Despite their acknowledged vulnerabilities, Global Navigation Satellite Systems (GNSSs) are the preferred source for Positioning, Navigation and Timing (PNT) services. Because of these vulnerabilities however, research on possible signal sources to obtain alternative positioning, navigation and timing (A-PNT) is of interest. This present work proposes the use of a narrow-band transponder installed on a geostationary (GEO) satellite to be used as one anchor for a future time transfer. A channel calibration is made between the transceiver station and the chosen satellite. Diverse models are used to estimate the channel effects throughout the signal propagation path, estimate the time delay, and correct the measurements, accordingly. The available channel bandwidth on the proposed satellite is 2.7 kHz, limiting the accuracy of the time measurements. After integration of multiple pulses, a time accuracy of approximately 1 μs is obtained. The range measurements are compared against satellite positions propagated from publicly available two-line element sets (TLEs). The obtained results suggest that, after calibration, the expected accuracy and a good repeatability is obtained. Thus, making the QO-100 satellite a suitable anchor for the proposed technique. Full article

Against the backdrop of the ongoing advancement of China’s dual-carbon goals and the coordinated strategy for ecological protection and high-quality development in the Yellow River Basin (YRB), it is important to clarify the spatiotemporal dynamics of air pollution in the densely populated urban agglomerations of the mid–lower YRB. Using station-based daily observations from 2015 to 2024, this study examines six major air pollutants (PM_2.5, PM₁₀, CO, NO₂, O₃ and SO₂) across the Shandong Peninsula, Central Plains, and Guanzhong Plain urban agglomerations. Sen’s slope estimator and the Mann–Kendall test are applied to quantify long-term trends, while partial correlation analysis and the GeoDetector model are used to diagnose pollutant co-variations and the drivers of spatial heterogeneity. Results indicate that while PM_2.5, PM₁₀, NO₂, SO₂, and CO concentrations significantly decreased, O₃ exhibited a statistically significant upward trend (Z = 2.32, p = 0.02), particularly with pronounced summer maxima. PM_2.5 shows clear seasonal variation, with elevated levels during winter and reduced levels during summer. Marked spatial contrasts are also observed: elevated particulate matter and CO are concentrated in the northern part of the Central Plains, while higher O₃ levels are more evident in coastal areas, particularly within the Shandong Peninsula urban agglomeration. In terms of inter-pollutant relationships, particulate matter and CO are positively associated with SO₂, whereas O₃ is negatively correlated with NO₂. GeoDetector results further suggest that air temperature, wind speed, and topography are the key factors associated with the spatial differentiation of pollutant levels; notably, the interaction between wind speed and temperature provides the greatest explanatory power, with effects that vary seasonally. These findings provide a scientific basis for region-specific air-pollution control and for advancing the co-benefits of carbon reduction and pollution mitigation in the YRB. Full article

Rapid electrification of road transport and growing shares of variable renewable generation are pushing urban low-voltage feeders toward their operating limits. Uncoordinated electric vehicle (EV) charging can create transformer overloads, voltage violations, and unfair delays, while most existing smart charging schemes either ignore distribution network constraints or treat fairness and risk in an ad hoc way. This paper proposes a city-scale hierarchical scheduling framework that coordinates EV charging and vehicle-to-grid (V2G) services under renewable variability. In the upper layer, a LinDistFlow-based optimal power flow computes feeder-constrained power envelopes and shadow prices over a rolling horizon, capturing transformer and voltage limits under photovoltaic (PV) uncertainty. In the lower layer, each station solves a queue-aware receding-horizon optimization that allocates charging/V2G set points across plugs using α-fair and lexicographic objectives, with conditional value-at-risk (CVaR) constraints on waiting times and state-of-charge (SoC) shortfalls. A digital twin of a medium-sized city with 24 stations (238 plugs) on five feeders and PV shares between 25% and 55% is used for evaluation. Compared with uncoordinated charging and myopic baselines, the proposed scheduler reduces feeder peak loading and PV curtailment while improving user experience and equity: average waits and 90% CVaR of waits are lowered, the Gini coefficient of waiting times drops (e.g., from 0.31 to 0.22), and SoC shortfalls are significantly reduced, all while respecting voltage limits. Each receding-horizon step executes in under 30 s on commodity hardware, indicating that the framework is practical for real-time deployment in city-scale smart charging platforms. Full article