Search Results (91)

The global energy system is undergoing a significant transformation driven by rapid electrification, urbanization, and the emergence of new categories of electricity consumers. In particular, the increasing load density in low-voltage distribution networks within urban areas requires a reconsideration of conventional methodologies for the placement of transformer substations. Traditional planning approaches are often based on empirical service radii or static demand factors and therefore fail to adequately reflect the complexity of modern urban power systems. This study proposes a multi-objective optimization model for the optimal placement of transformer substations in 10/0.4 kV urban distribution networks. The proposed model simultaneously considers power losses, economic costs, and system reliability. In addition, the design load model is extended through the introduction of a comfort coefficient that captures additional electricity consumers typical of modern urban infrastructure, including HVAC systems, elevators, pumping systems, and electric vehicle charging stations. In contrast to traditional empirical approaches, the transformer service radius is modeled as a physical parameter determined by voltage drop limits, cable thermal constraints, and failure intensity. The optimization problem is solved using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Each candidate solution generated by the algorithm is validated through AC load-flow simulations performed in the DIgSILENT PowerFactory environment. The proposed methodology is evaluated using real data from a 0.48 km² urban area in the city of Tashkent. The results indicate that increasing the transformer service radius reduces capital investment costs but leads to higher power losses and longer interruption durations. According to the Pareto analysis, a service radius of approximately 300 m represents the optimal compromise between technical, economic, and reliability criteria for the studied area. The proposed methodology can serve as an effective tool for the scientifically grounded planning of urban power supply systems and for improving energy efficiency in modern distribution networks. Full article

The impact of electric vehicle (EV) charging stations on sustainable urban transitions has become a crucial research topic due to the rapid growth of the new energy industry. However, the uneven distribution of EV charging infrastructure currently presents a major challenge to urban spatial equity and sustainable development. This study focuses on the central metropolitan area of Wuhan, positioning EV charging stations as a key mediating variable to explore how green infrastructure interacts with the built environment to drive sustainable economic vitality. Utilizing multi-source big data, the XGBoost-SHAP model, and Bootstrap mediation tests, this research identifies the non-linear thresholds and mediation pathways involved. The findings reveal that: (1) distinct built environment factors exhibit clear non-linear relationships with commercial vitality; (2) EV charging stations act as critical mediators that spatially reinforce or balance built environment variables, exhibiting pronounced spatial heterogeneity; and (3) EV charging stations show a significant partial mediation effect, with building density wielding the strongest direct influence. This study provides vital scientific support for low-carbon urban planning and sustainable infrastructure deployment by uncovering the threshold characteristics and mediating mechanisms necessary to harmonize commercial vitality with environmental and urban sustainability. Full article

This study presents a comprehensive techno-economic and environmental evaluation of ten standalone solar-powered mobile charging station configurations for mini electric vehicles (MEVs) in Kuwait, simulated using HOMER Pro (v3.18.4). The configurations span DC–AC and pure DC-bus architectures, fixed and tracking photovoltaic (PV) systems, hybrid designs incorporating diesel generator backup, and fully renewable zero-emission systems. All configurations were evaluated under identical load demand (6460 kWh/year), solar resource, and economic assumptions derived from Kuwait’s desert climate at Al-Wafra farms (28°33′52.7″ N, 48°03′45.8″ E, annual average GHI = 5.49 kWh·m⁻²·day⁻¹). Performance was assessed using Net Present Cost (NPC), Levelised Cost of Energy (LCOE), annual PV energy production, CO₂ emissions, Energy Production Density (EPD), Renewable Fraction (RF), and the PV Energy Production-to-Load Ratio (PV-EPTLR). The results demonstrate that two-axis tracking on a DC-bank architecture without a generator (System 8) achieves the highest annual PV output of 13,635 kWh/year, representing a 36% increase over a fixed-tilt DC-bank system while eliminating 100% of operational CO₂ emissions. Among the hybrid configurations, vertical single-axis tracking on a DC-bank architecture with generator backup (System 6) yields the lowest lifecycle cost (NPC = USD 6271.8; LCOE = 0.0751 USD/kWh), representing a 57% reduction relative to the fixed-tilt DC–AC baseline. EPD analysis confirms that tracking-based systems improve structural energy efficiency by up to 36%, making them particularly suitable for mobile and weight-constrained deployments. The findings provide actionable guidance for deploying sustainable off-grid MEV charging infrastructure in regions with limited grid access, offering a scalable pathway toward zero-emission rural transportation in solar-rich arid environments. The study further provides a systematic comparison between DC–AC and pure DC-bank charging architectures under identical operating conditions. Full article

Space weather events triggered by solar activity impact critical technologies like the Global Navigation Satellite System (GNSS) by causing atmospheric imbalances that alter ionospheric electron density. This study investigates the upper atmosphere response to the severe geomagnetic storms of October 2024, focusing on the coupling and compositional exchange between the ionosphere and thermosphere. Data were analysed from two mid-latitude African stations, Rabat (RABT) and Hermanus (HNUS), using GNSS-Total Electron Content (TEC) measurements alongside thermospheric circulation observations from NASA-GOLD and solar wind indices from OMNIWeb. The October 2024 storm, which reached a minimum Dst of −333 nT, drove a negative ionospheric storm phase marked by TEC depletions exceeding 50 TECU. This response was driven by storm-time thermospheric upwelling of N₂-rich air, which lowered the O/N₂ ratio and accelerated plasma loss via charge-exchange reactions. Furthermore, a distinct hemispheric asymmetry was observed, as the equatorward thermospheric circulation in the Northern Hemisphere arrived before that of the Southern Hemisphere. Direct post-processing of the Earth-Centred Earth-Fixed (ECEF) coordinates using RTKLIB single-point position revealed that, while positioning accuracy significantly degraded at HNUS with errors increasing by up to 270%, it counterintuitively improved at RABT, where errors reached their minimum during the main and early recovery phases of the storm. These findings highlight that the technological impact of severe space weather is determined not just by storm magnitude but by the specific sign and spatial structure of the regional ionospheric response. Full article

The growing use of electric vehicles (EVs) requires fast charging solutions capable of delivering high power levels with greater efficiency and less impact on the power grid. This article presents the design and simulation of a Level 3 fast direct current (DC) charger for electric vehicles based on an LLC resonant DC-DC converter. The proposed architecture incorporates an isolated LLC resonant converter, selected for its soft switching capability, low switching losses, and reduced electromagnetic interference (EMI). The main contribution of this work is the design and simulation of a 50 kW LLC resonant converter developed specifically for a Level 3 DC fast charger for electric vehicles, a power level that, to the authors’ knowledge, has not been previously described in the current scientific literature using this topology. For the proposed converter, it has been proposed to use commercially available wide bandgap (WBG) semiconductor devices specifically made of silicon carbide (SiC). This allows for high switching frequency operation, lower conduction and switching losses, and higher power density. The key design parameters, component selection, and operating principles are analyzed in detail. Simulation results demonstrate high conversion efficiency, reduced switching stress, and stable operation under fast charging conditions, validating the suitability of the LLC topology for high-power electric vehicle charging applications. The proposed system offers a scalable and efficient solution that can contribute to the development of compact, grid-compatible DC fast charging stations, supporting the growing demand for electromobility infrastructure. Full article

The electrification of cities, and especially the increase in electric vehicles (EVs), is driving the need to expand the public network of charging points in a sustainable manner. Unlike traditional ‘Greenfield’ planning approaches that assume an empty scenario, this work proposes a non-greenfield planning strategy that explicitly integrates pre-existing charging infrastructure, thereby reflecting real-world urban conditions. To address this, a two-phase hybrid optimization framework is presented, combining a Genetic Algorithm (GA) with a weighted K-Means clustering technique, performing its calculations while taking into account and preserving the existing stations during the optimization phases. In the GA phase, different candidate solutions are generated and evaluated through a customized fitness function, designed to maximize population demand coverage while penalizing excessive or redundant station installations; the individual’s encoding and the design of genetic operators have been modified in such a way that already-installed stations remain fixed throughout the GA optimization. In the second phase, the weighted K-Means algorithm refines the position of the new stations, considering the existing ones as fixed centroids and optimizing the placement of the new points, based on population density. Two case studies show that while the pre-existing network induces a slight increase in the total number of stations compared to a theoretical optimal greenfield solution, the proposed method significantly reduces the number of new installations required to achieve comparable coverage. The proposed method provides urban planners and decision-makers with realistic, sustainable and cost-effective growth plans that are fully compatible with current infrastructure and aligned with long-term urban sustainability goals. Full article

With the advancement of digital hydropower stations, the requirements of real-time, high-precision industrial soft measurement of key power equipment operating status are attracting more and more attention. However, it is difficult to transfer energy to the monitoring sensor in strong electromagnetic environments. In this paper, a high-efficiency, high-power-density magnetic field energy harvester is proposed for monitoring sensors in hydropower stations, which captures the energy from the magnetic flux leakage of a hydroelectric generating set. Efficient magnetic energy capture is achieved by modeling material properties and optimizing the receiver’s magnetic core parameters via a Genetic Algorithm. The theoretical analysis of charging characteristics is given, and a Maximum Power Point Tracking (MPPT) control circuit is proposed, realizing high-efficiency energy conversion. Finally, an experimental planet is built. Under 70–130 Gs power-frequency magnetic fields, the system delivers 2.8–5.1 V open-circuit voltage, 66 mW maximum load power, and 6.5 mW/cm³ power density. Full article

This paper describes the benchmark dataset for the electric vehicle routing problem with time windows. It is designed to facilitate the large-scale and reproducible evaluation of routing approaches under diverse charging scenarios. It is an extension of the Homberger 1000-customer vehicle-routing benchmark dataset through the incorporation of computationally derived charging-station data. For the 60 base instances included in the dataset, charging-station locations are randomly generated within the customer-coordinate bounds, and two variants are provided, resulting in 120 benchmark problems used in the validation and baseline analyses. A normalized local customer-density score is derived for each station. It is used to determine charging rates and log-normal parameters for prices and waiting times. Two variants are included in the dataset. Variant A maintains the original customer time-window constraints, while Variant B relaxes customer due dates based on the distance from the depot, subject to the depot closing time. The dataset is complemented by instance files, station attributes, parameters, and scripts. It also includes the results of feasibility tests, baseline solver tests, difficulty analyses, and sensitivity tests. These results show that the benchmark includes both easier and harder instance classes under different charging settings. Overall, the dataset is intended to support its use as a reproducible benchmark. Full article

This paper has the goal of exploring the potential of electromagnetic propulsion systems based on tethers to create artificial Sun-synchronous orbits for remote sensing satellites, as well as performing station-keeping maneuvers and de-orbiting of the satellite after the end of its useful life. To create artificial Sun-synchronous orbits, the force is applied to keep the longitude of the ascending node with the same angular velocity of the apparent motion of the Sun around the Earth, which is the definition of a Sun-synchronous orbit. These orbits are very important for remote sensing satellites, because in these orbits the satellite passes by a given point at the same time, helping in analyzing the data collected. The use of electrodynamic tethers can extend the regions of Sun-synchronous orbits, both in terms of inclination and semi-major axis. To perform the de-orbiting of the satellite, the same tether can apply a force in the opposite direction of the motion of the satellite, so reducing its energy and decreasing the semi-major axis until the satellite crashes into the atmosphere of the Earth. This is very important to avoid increasing the presence of space debris in space, a very serious problem nowadays. For the station-keeping maneuvers, we just need to use the appropriate control laws, from time to time, to correct any errors in the Keplerian elements. A significant advantage of employing an electrodynamic tether over traditional thrusters is that it does not require consumption of fuel. The study assumes that a current can flow in both directions through the tether, so interacting with the magnetic field of the Earth to create the Lorentz force. The possibility of using electrodynamic tethers with autonomous charge generation, to avoid dependence on plasma densities and other external factors, is considered. The results presented here help in space and planetary science, since they give more options for remote sensing satellites, which are a key element in planetary science. Full article

To address the lagging construction and irrational spatial distribution of current electric vehicle (EV) charging infrastructure, scientific location and capacity planning has emerged as a critical research focus in transportation electrification. Through a systematic review of domestic and international literature, this paper analyzes the evolution of charging station planning models from single economic indicators to multi-objective frameworks incorporating grid constraints, carbon emission benefits, and user behavior. Research indicates that while geometric spatial partitioning and swarm intelligence algorithms are widely utilized, existing methods face limitations in handling iterative spatial service area matching and overcoming the premature convergence of standard Particle Swarm Optimization (PSO). Consequently, this study proposes an integrated technical route utilizing Voronoi diagrams to adaptively partition service areas based on demand density, and constructing a comprehensive model encompassing construction and maintenance costs, environmental costs, and generalized user costs. To solve this highly complex spatial allocation problem, a Catfish Particle Swarm Optimization (CPSO) algorithm is employed as an efficient computational tool. Ultimately, this approach aims to provide practical, quantitative decision support for urban EV charging network planning by balancing the conflicting interests of operators, users, and the power grid within a comprehensive ‘Total Social Cost’ framework. Full article

Accurate forecasting of citywide electric vehicle (EV) charging load is critical for alleviating station-level congestion, improving energy dispatching, and supporting the stability of intelligent transportation systems. However, large-scale EV charging networks exhibit complex and heterogeneous spatio-temporal dependencies, and existing approaches often struggle to scale with increasing station density or long forecasting horizons. To address these challenges, we develop a modular spatio-temporal prediction framework that decouples temporal sequence modeling from spatial dependency learning under an encoder–decoder paradigm. For temporal representation, we introduce a global aggregation mechanism that compresses multi-station time-series signals into a shared latent context, enabling efficient modeling of long-range interactions while mitigating the computational burden of cross-channel correlation learning. For spatial representation, we design a dynamic multi-scale attention module that integrates graph topology with data-driven neighbor selection, allowing the model to adaptively capture both localized charging dynamics and broader regional propagation patterns. In addition, a cross-step transition bridge and a gated fusion unit are incorporated to improve stability in multi-horizon forecasting. The cross-step transition bridge maps historical information to future time steps, reducing error propagation. The gated fusion unit adaptively merges the temporal and spatial features, dynamically adjusting their contributions based on the forecast horizon, ensuring effective balance between the two and enhancing prediction accuracy across multiple time steps. Extensive experiments on a real-world dataset of 18,061 charging piles in Shenzhen demonstrate that the proposed framework achieves superior performance over state-of-the-art baselines in terms of MAE, RMSE, and MAPE. Ablation and sensitivity analyses verify the effectiveness of each module, while efficiency evaluations indicate significantly reduced computational overhead compared with existing attention-based spatio-temporal models. Full article

The rapid development of electromobility increases the need for fast, accessible and robust charging stations devoted to EVs (electric vehicles). Planning a network of such stations poses new challenges—amongst others, a power supply that may power such chargers. One major concept is to utilise wind energy as a power source. The paper analyses meteorological data gathered since 2001 in several stations across Poland to achieve quantitative indexes, which summarise (a) wind power density (WPD) as a metric of energy amount, (b) long-term (multiannual) time trends of amount of energy, (c) short-term stability (and thus predictability) of the wind power. The indexes that cover the abovementioned factors allow the authors to answer the research questions, where the local wind conditions allow the authors to consider the integration of a wind powerplant and a network of EV chargers. Additionally, we investigated locations where the amount of available energy is sufficient, but the variability of wind power impedes its practical exploitation. In such cases, the power system may be extended by an energy storage system that acts as a buffer, smoothing power fluctuations and thereby improving the robustness and reliability of downstream charging systems. Full article

With the global trend towards “carbon neutrality,” the use of electric vehicles is becoming increasingly widespread, leading to new impacts on urban spaces. In the process of allocating resources for urban charging stations, there are widespread issues such as a singular planning approach and inadequate adaptation to actual travel demands. Therefore, this study adopts a method of integrating multi-source data to optimize the planning and layout of public electric vehicle charging facilities in Macau, striving to achieve breakthroughs in theoretical methods and key technologies. The study obtained a determination coefficient of R² = 0.43 through quantitative analysis, which is within a reasonable range of fitting spatial syntax and charging facility layout. This indicates that there is a moderate positive correlation between the distribution of charging facilities and core indicators such as road network integration and accessibility—about 43% of layout differences can be explained by spatial syntax indicators, and the remaining 57% of differences reserve space for optimizing multiple factors such as population density and parking lot distribution. On this basis, this study compares the layout experience of medium to high-density cities such as Hong Kong and Singapore, and combines the common characteristics of old parishes on Macau Island and new urban areas on outlying islands to explore innovative sustainable development technology paths that are suitable for Macau. This study not only summarizes the key factors and optimization breakthroughs that affect the spatial distribution of charging facilities in Macau, providing basic data and methodological strategies for charging facility planning, but also helps Macau save energy and reduce emissions, build a green city through layout optimization, provide practical reference for the development of land reclamation areas, and provide reference for carbon neutrality and smart city construction in the Guangdong Hong Kong Macau Greater Bay Area. Full article

Fuel cell electric vehicles (FCEVs) rely on a battery system to manage transient load demands and to recover braking energy. In recent years, hybrid topologies that also integrate supercapacitors have gained considerable attention, since they can improve system efficiency, driving dynamics, and component lifetime. Supercapacitors, thanks to their much higher power density compared to conventional batteries, are particularly promising for adoption in FCEVs. Most studies in the literature, however, evaluate these architectures under standardized homologation driving cycles. While such cycles provide a common benchmark for comparison, they generally exhibit less energy-intensive profiles and therefore do not fully capture the real operating demands of a vehicle. For this reason, the present work investigates the use of batteries and supercapacitors in FCEVs under an actual urban driving mission, where the route includes an experimentally measured altitude profile. This approach allows for a more realistic assessment of energy requirements. Furthermore, the analysis carried out in this study considers different powertrain configurations: the exclusive use of a battery, the sole use of a supercapacitor, and a hybrid combination of both systems. These scenarios are evaluated both for an FCEV that can only be refueled with hydrogen and for a plug-in hybrid version of the vehicle that can also recharge its battery from an external charging station. Full article

This paper addresses the critical challenge of deploying a minimum number of wireless charging pads (WCPs) in obstacle-rich, large-scale Wireless Rechargeable Sensor Networks (WRSNs) to sustain drone operations. We assume a single base station, stationary sensors, convex polygonal obstacles that drones must avoid, and that both the base station and WCPs provide unlimited energy. To solve this, we propose the Outside-to-Inside Onion-Peeling (OIOP) strategy, a novel two-stage algorithm that prioritizes the coverage of the most remote sensors first and then refines the deployment by removing redundant pads while strictly adhering to obstacle constraints. Simulation results demonstrate OIOP’s superior efficiency: it reduces the number of required pads by approximately 10.83% ± 1.30% and 12.16% ± 1.59% compared to state-of-the-art methods (SMC and MC) and achieves execution times that are 58.02% ± 2.44% and 72.09% ± 2.88% faster, respectively. The algorithm also exhibits remarkable robustness, showing the smallest performance degradation as obstacle density increases. Full article