Search Results (3,840)

The accelerating integration of electric vehicles (EVs) presents considerable operational challenges for distribution networks, particularly through aggravated voltage deviations and compromised protection coordination during periods of simultaneous charging. In response, this study introduces a novel protection-constrained Binary Evolutionary Algorithm (BEA) designed for expedited electric vehicle-oriented Distribution Network Reconfiguration (DNR) to enhance EV hosting capacity without necessitating costly infrastructure upgrades. The proposed framework uniquely embeds the inverse time–current characteristics of protective fuses—termed Protection Curve Consideration (PCC)—within the optimization process. By explicitly accounting for the thermal inertia of protection devices, the algorithm identifies reconfiguration strategies that uphold voltage stability under elevated EV transportation loading, including configurations typically deemed infeasible by conventional voltage-driven approaches. This selective coordination precludes unnecessary fuse operations, thereby preserving the continuity of electric vehicle charging services. Simulation results on a 16-bus radial distribution system, evaluated under four high-demand scenarios reflective of concentrated EV transportation charging, validate the efficacy of the BEA-PCC methodology. The approach achieves a maximum voltage deviation reduction of up to 15.2%, thereby enhancing power quality for all consumers. Moreover, compared to standard metaheuristic techniques, it reduces Energy Not Supplied (ENS) by 8% and switching operations by 20%, contributing to improved grid resilience and operational efficiency. These outcomes underscore the potential of BEA-PCC as an effective real-time control strategy for distribution system operators seeking to accommodate increasing electric vehicle penetration while safeguarding protection coordination and minimizing customer disruptions. Full article

This study presents the development and experimental evaluation of HygroCatch, a portable hybrid fog water harvesting prototype that integrates active and passive collection mechanisms. The device operates by combining fog droplet ionization in a high-voltage direct-current (HV DC) electrostatic field, thermoelectric cooling based on the Peltier effect, and mechanical deposition of droplets on vertical rods of symmetrical triads of electrodes. This hybrid approach enables adaptive operation across a wide range of fog liquid water content (LWC) conditions. The work establishes operating parameters for stable electrostatic ionization and evaluates the contribution of thermoelectric cooling to additional water harvesting. The results indicate that an operating voltage of 13–14 kV provides a stable ionization over a broad LWC range. The average fog water harvesting rate reached 3.15 kg/m²/h, with a maximum observed value of 4.44 kg/m²/h. On average, 56% of the collected water was obtained through HV DC ionization, 25% through Peltier-based thermoelectric cooling, and 19% through mechanical deposition on electrode grids under high LWC conditions. The total electrical power consumption of the device did not exceed 38.3 Wh/kg. The results demonstrate that a hybrid fog water harvesting strategy enables stable and efficient water collection under environmental conditions in which individual passive or active methods become ineffective. Full article

In this study, a post-processing algorithm was developed to mitigate the over-detection tendency of the Geo-KOMPSAT-2A fog detection algorithm (GK2A_FDA) by integrating surface observations, facilitated by the recent availability of high-resolution gridded surface analysis data. The method was optimized for six sub-algorithms (inland/coastal × daytime/nighttime/twilight) using an interpretable decision tree model with data from 2021 to 2023. The $RH$ (relative humidity) and $\mathsf{\Delta }FTs$ (clear-sky background minus fog-top brightness temperature) step defines detection boundaries in a two-dimensional decision space using joint false alarm-to-hit ratio and hit count distributions to effectively remove false-alarm-dominated regions with minimal impact on the probability of detection (POD). The post-processing steps were sequenced according to independently quantified accuracy gains ($RH$ and $\mathsf{\Delta }FTs$ >> $T_a$ > wind speed > solar zenith angle), with thresholds conservatively derived and seasonally optimized for South Korea. Following post-processing, the POD decreased only slightly (0.08–0.27%), while the false alarm ratio (FAR) and bias were reduced by 5.13–13.68% and 16.13–52.61%, respectively. Improvements were more pronounced during drier seasons than wet seasons; however, the residual high daytime bias (3.348–5.319) indicated the need for further GK2A_FDA refinement. This study demonstrated that integrating satellite and surface observations could effectively address the limitations of satellite-based fog detection. Full article

The adoption of photovoltaic (PV) energy in irrigation is rapidly increasing, supported by a range of available technologies. However, an agronomic perspective that could help overcome inherent limitations of PV systems remains absent. In fact, current irrigation design methods do not explicitly take into account the dynamic nature of PV power generation. While irrigation engineering conceptualises soil as a reservoir for plant-available water, it can also function as an energy reservoir, storing solar-derived energy in the form of soil moisture for subsequent crop use. Building on this concept, this study proposes an integrated framework for designing off-grid PV irrigation systems based on asymmetric irrigation sectors. The framework couples hydrological, agronomic, and energy components to synchronise solar energy generation with crop water requirements, thereby eliminating the need for intermediate energy storage. The methodology was applied to two case studies: a hedgerow olive orchard and an almond orchard in southern Portugal, both with drip irrigation. Results demonstrate that the asymmetric-sector design provides a technically feasible and low-complexity solution for integrating photovoltaic energy into irrigation systems. The conventional irrigation system required 1.42 kW of minimum pumping power for olive orchards and 1.32 kW for almond orchards. The dimensions of the main lines ranged from 97.8 mm for olive and 75 mm for almond orchards, while the flow rate of the emitter was 2.3 L h⁻¹ for olive and 3 L h⁻¹ for almond orchards. Although PV-compatible operation required hydraulic adjustments including increases in design flow rate (226–255%), pump power demand (87.5–241%), and pipe diameters (up to 120% in olive and 75% in almond), these adaptations enable irrigation systems to operate under the variability inherent to solar-based energy supply. This hydraulic oversizing leads to higher initial investment costs; however, this can be mitigated to a certain extent by diminished operating costs and complete energy autonomy from the electricity grid. Full article

The Xinqiao Cu-S polymetallic deposit is located in the Tongling ore concentration area of the Middle-Lower Yangtze River metallogenic belt. The orebodies consist of skarn orebodies and stratiform sulfide orebodies, but the genetic link between them remains controversial. In this study, magnetite was used as a proxy to systematically constrain the hydrothermal evolution from the intrusion to the contact zone and further to the stratiform orebodies. A representative drill hole (E603) was logged, and samples were systematically collected from the Jitou pluton outward to the contact zone. Composite samples from the 8–28 m interval were crushed and prepared as resin mounts for integrated TIMA automated mineralogy, BSE textural observation, and in situ LA-ICP-MS trace element analysis. Five types of magnetite (Mt1 to Mt5) were systematically identified. Mt1 occurs as inclusions within feldspar in the quartz monzodiorite. It exhibits typical magmatic magnetite characteristics and contains grid-like ilmenite exsolution, indicating crystallization during the late magmatic stage. Mt2 is distributed in the interstices of magmatic minerals, commonly showing hematitization and replacement of ilmenite exsolution lamellae by titanite. Its trace element geochemistry displays magmatic–hydrothermal transitional features. Mt3–Mt5 in the skarn and stratiform orebodies are paragenetic with retrograde alteration minerals (e.g., epidote, chlorite, and actinolite) and sulfides, and are characterized by low Ti, Al, and V contents and high Mg, Mn, and Sn contents, indicating a hydrothermal origin. From Mt3 to Mt5, (Ti + V) and (Al + Mn) decrease, while Zn and Mn increase, accompanied by a decrease in the (Si + Al)/(Mg + Mn) ratio. This reflects a trend of decreasing fluid temperature and progressively enhanced wall-rock buffering. The Mg-in-magnetite geothermometer yields relatively consistent results for Mt1–Mt3, but anomalously high temperatures for Mt4–Mt5. This suggests that the elevated Mg activity in the fluid, caused by reaction with carbonate wall rocks, can significantly influence the calculated temperatures. Therefore, this geothermometer should be used cautiously for magnetite in the outer skarn zone and interpreted in combination with other temperature constraints. The textures, paragenetic mineral assemblages, and trace element characteristics of magnetite collectively reveal a continuous mineralization process linking the skarn and stratiform orebodies at Xinqiao, providing robust mineralogical and geochemical evidence for the contribution of Yanshanian magmatic–hydrothermal activity to the stratiform mineralization. Full article

Moving toward sustainable energy in small-scale dairies is an indispensable requirement and a significant challenge in developing countries. This study investigates a solar-powered refrigeration system with heat recovery designed to address the energy challenges faced by small-scale dairy farmers in off-grid areas of developing nations. It presents a novel solar-powered refrigeration system with integrated heat recovery, experimentally optimized to simultaneously deliver heating and cooling while valorizing waste heat and synergistically integrating solar energy to establish a decentralized and energy-autonomous milk preservation system for off-grid applications. The proposed system successfully recovers an average of 55% of the heat rejected by the condenser, thereby delivering more than 1000 W of usable thermal energy necessary for milk pasteurization. The experimental findings showed a coefficient of performance of 4.7, representing a 43% improvement over conventional systems, and achieved a Carnot efficiency of 42%. In addition, the system yields an annual energy savings of 3650 kWh and reduces carbon emissions by 971 kg per year for a 50 L unit. These findings underscore the system’s substantial potential to enhance energy efficiency, promote sustainability, reduce spoilage, improve incomes, mitigate carbon emissions, and enhance local milk preservation capabilities within small-scale dairy operations, minimizing reliance on diesel or firewood, particularly in regions that are distant from access to grid energy. Full article

Nighttime light (NTL), a crucial indicator of human activity intensity, has not been systematically analyzed for its interactive mechanisms with air pollution and climate change. This study first investigates the spatiotemporal evolution of China’s total nighttime light (TNTL) and average nighttime light (ANTL), alongside key indicators of meteorological parameters and air pollution, at the grid scale from 2000 to 2023. We then employ prefecture-level city data and a geographically and temporally weighted regression (GTWR) model to quantify the spatiotemporally heterogeneous associations of temperature (TMP), precipitation (PRE), fine particulate matter (PM_2.5), ozone (O₃), land use (LUL), topography, and socioeconomic factors with NTL. The results indicate that (1) China’s NTL exhibits a significant overall upward trend, with areas of increase or significant increase comprising 92.04% of the total study area. TNTL growth demonstrates regional heterogeneity, expanding by a factor of 4.91 in East China and 2.65 in Northeast China; (2) meteorological and air pollution indicators display spatiotemporal non-stationarity, with the synergistic effect between O₃ and PRE being the strongest; (3) among NTL drivers, LUL contributes most significantly (0.44), followed by TMP (0.14) > PM_2.5 (−0.33 × 10⁻¹) > O₃ (0.17 × 10⁻¹) > PRE (−0.33 × 10⁻⁶); (4) TMP and PRE may primarily influence NTL by altering ecological conditions and nighttime activity patterns. TMP shows a strong positive correlation with NTL in the junction zone of South, East, and Central China, whereas PRE predominantly exerts a negative influence; (5) air pollution exhibits distinct spatiotemporal effects: high PM_2.5 and O₃ generally correspond to lower NTL, though positive correlations persist in some areas due to industrial structures, highlighting the need for integrated policies that balance air quality management with sustainable urban planning; (6) the 2013 “Air Pollution Prevention and Control Action Plan” significantly strengthened the negative correlation between PM_2.5 and NTL in North China. However, O₃ concentrations increased by 28.9% after 2017, underscoring the challenge of coordinating VOC and NOx controls for long-term atmospheric sustainability. Full article

This study proposes a variable-scale optimization strategy for land-levelling path planning to overcome the limitations of conventional traversal-based operations, including poor coordination, insufficient planning, low operational efficiency, and the computational burden associated with large datasets and constrained earthmoving capacity. For large-scale inter-regional earthwork balancing, an improved ant colony optimization (IACO) algorithm is developed to generate efficient region to region transfer routes. After verifying that inter-regional earthwork balance satisfies the levelling requirement, a field-wide fine-levelling plan is produced at the grid scale using a hybrid method that integrates an improved A* search with ant colony optimization (FIA*ACO). The proposed framework is evaluated through simulation and field experiments using measurement-based indicators, including the maximum elevation difference and the proportion of points within ±5 cm of the target elevation. Field results show that IACO-based inter-regional planning increases the ±5 cm compliant proportion by 14.18 percentage points and reduces the maximum elevation difference by 0.079 m. Subsequent FIA*ACO-based fine-gridded planning further improves the ±5 cm compliant proportion by 20.82 percentage points and decreases the maximum elevation difference by 0.311 m. Overall, the results demonstrate that inter-regional planning rapidly expands the area meeting levelling standards, while grid-level refinement further enhances levelling quality, validating the effectiveness of the proposed variable-scale strategy for land-levelling path planning. Full article

The lack of a reliable national sampling frame poses a major challenge for conducting representative population and household surveys, particularly in developing countries affected by displacement and rapid territorial change. This study addresses this gap by developing Armenia’s first digitized national sampling frame, where accessible survey frames are severely limited. We introduce an innovative pre-EA tool to semi-automatically construct the digital sampling frame using publicly available datasets. Compared with traditional approaches, this method outperforms in several ways: it enables rapid, semi-automated frame construction, minimizes resource requirements, eliminates geometric errors associated with manual digitization, and produces pre-census EAs (pre-EAs) that both nest within administrative boundaries and align with visible ground features. The approach also integrates gridded population data to reflect recent urbanization and migration, generating pre-census EAs and urban–rural classifications suitable for national surveys. The sampling frame was successfully applied in the World Bank’s “Listening to Armenia” survey. Overall, the study demonstrates that automated, data-driven approaches can efficiently produce accurate, scalable, and adaptable national sampling frames, offering potential utility in other countries facing similar constraints. Full article

Technological advances such as Vehicle-to-Grid (V2G) have the potential to support renewable energy integration and grid stability, but large-scale deployment depends on users’ willingness to participate, particularly in public charging environments. While prior research has examined V2G from technical feasibility and system-level perspectives, everyday public settings remain unexplored. This study investigates electric vehicle (EV) users’ willingness to engage in V2G services in public spaces, with a focus on incentives, expectations, and how participation aligns with existing routines and parking conditions. A mixed-method approach was applied, combining a survey of 544 car users with two waves of user-centered interviews. The survey data were analyzed using factor analysis and linear regression models, while the interview data were thematically analyzed. The results show that users’ evaluations of V2G are shaped by sustainability expectations, perceived efficiency, and uncertainties, and preferences for public V2G participation are strongly influenced by convenience, clarity of the offer, and perceived control. Home charging practices emerged as a key reference point shaping expectations of public V2G services. Across both methods, simple and transparent incentives, such as reduced charging or parking costs, were consistently preferred over more complex reward models, including point-based systems or dynamic energy trading. Concerns related to control over trips, battery degradation, trust in service providers, and added complexity remain important barriers to participation. The findings highlight the need for user-centered and socio-technical design of public V2G services that align with users’ everyday routines, parking conditions, and expectations to support broader adoption beyond the home context. Full article

This study presents an integrated simulation framework that combines agent-based transport modeling with probabilistic load-flow analysis to quantify power system loading of long-haul heavy-duty electrification. The approach is applied to a case study considering fully electrified road freight in the Skåne region in Sweden, using high-resolution transport demand data and the actual power grid model used by the grid owner in the study area. The synthetic freight population covers the full long-haul truck segment intersecting Skåne. Both public en-route fast charging and end-of-trip depot charging are considered. The analysis reveals two fundamentally different charging demand profiles: a heavily fluctuating profile for public en-route charging, accounting on average for 82% of the total daily charging energy, and a stable profile for end-of-trip depot charging, covering on average the remaining 18%. The latter is achieved through a Linear Programming (LP) optimization model that flattens the load by scheduling charging across depot stay windows. These profiles serve as inputs to a probabilistic load-flow simulation that computes loading distributions for substation transformers. The simulation results show that in 4 of the 43 primary substations studied, the maximum transformer loading exceeds 100% following the introduction of truck charging, with peak loading at the most affected substation rising from 99% to 159%. This stress is primarily caused by the public charging demand, which peaks from late morning to noon, aligning with the early stages of logistics operations. However, there is no clear correlation between the magnitude of the truck charging load and the impact on transformer loading, since this is also highly dependent on local grid conditions. These findings highlight the value of integrated transport-energy simulations for planning resilient infrastructure and guiding targeted grid reinforcements. Full article

With the increasing adoption of electric vehicles (EV), congestion at charging stations during peak hours has become a prominent issue, imposing significant pressure on station scheduling. Furthermore, the large-scale integration of photovoltaics (PV) introduces dual uncertainties in both generation and load, negatively impacting grid voltage. To tackle the above problems, a strategy for demand response balancing and congestion alleviation of charging stations under grid–road network partition mapping is proposed in this paper. Firstly, a user demand response capability assessment method based on the Fogg Behavior Model is proposed to evaluate the demand response potential of individual users in each zone. The results are aggregated to obtain the demand response participation capability of each zone, thereby realizing capability-based allocation and achieving demand response balancing. Secondly, the road network is divided into several zones and mapped to the power grid, and a two-layer cross-zone collaborative autonomy model is established. The upper layer aims to alleviate inter-zone congestion and balance inter-station power, taking into account the grid voltage level. A tripartite benefit model involving the power grid, charging stations and users is constructed, and an inter-zone mutual-aid model for the upper layer is established and solved optimally. The lower layer establishes an intra-zone self-consistency model, which subdivides different functional zone types within the road network zone, allocates and accommodates the cross-zone power from the upper-layer output inside the zone, and synchronously performs intra-zone cross-zone judgment to avoid congestion at charging stations. Simulation verification is carried out on the IEEE 33-bus system. The results show that the proposed method can effectively alleviate the congestion of charging stations, the balance degree among all zones is increased by 43.58%, and the power grid voltage quality is improved by about 38%. This study offers feasible guidance for exploring large-scale planned participation of electric vehicles in power system demand response. Full article

Voltage sags pose severe risks to sensitive equipment in modern industries, requiring power quality monitoring equipment to possess fast and accurate sag detection capabilities. The traditional second-order generalized integrator (SOGI) will have oscillation phenomena in the case of DC offset, low-frequency harmonics, and high-frequency impulse noise. This study introduces a strong detection algorithm that combines Adaptive Composite Morphological Filtering (ACMF) with an improved Mixed Second- and Third-Order Generalized Integrator (MSTOGI). First, the ACMF pre-filtering module dynamically adjusts the scale of composite structuring elements through periodic parameter optimization, effectively filtering high-frequency random impulses while preserving the sharp transitions of abrupt voltage changes. Second, MSTOGI eliminates DC offset, and optimizes the gain coefficient to achieve the best dynamic response speed. Ultimately, a cascaded notch filter (CNF) module focuses on and removes even-order harmonic ripples caused by the synchronous reference frame transformation. Simulation results indicate that under severe grid conditions involving multiple composite distortions, the proposed architecture reduces the sag detection time to within 1.0 ms under typical operating conditions, with steady-state phase errors strictly controlled within a ±2° range. This method provides a reliable solution for DVR and UPS. Full article

The transportation sector’s decarbonization represents one of the most critical challenges in achieving global climate targets. This study presents a comprehensive analysis of an integrated renewable energy system that produces green hydrogen through a hybrid solar photovoltaic (PV) and wind power configuration. The proposed system combines a 1.2 MWp solar array with 800 kW wind turbines, feeding a 1 MW proton exchange membrane (PEM) electrolyzer for hydrogen production. The hydrogen is subsequently compressed, stored at 350 (for trucks and buses) and 700 bar (for cars), and then utilized either directly for fuel cell electric vehicles (FCEVs) or reconverted to electricity via a 250 kW stationary PEM fuel cell to support electric vehicle (EV) charging infrastructure. Through detailed techno-economic simulation using HOMER Pro and MATLAB/Simulink 2022a, we demonstrate that the hybrid configuration achieves a 71% electrolyzer capacity factor, producing 55.8 tonnes of hydrogen annually with a levelized cost of 5.82 €/kg. The system ensures over 60 h of grid-independent operation while reducing CO₂ emissions by 1656 tones annually compared to conventional grid-powered alternatives. Results indicate that hybrid renewable hydrogen systems can provide economically viable solutions for sustainable mobility infrastructure, with projected cost reductions making them competitive with fossil fuel alternatives by 2030. Full article

The increasing share of wind power generation has intensified the occurrence of curtailment events in power systems worldwide, mainly driven by transmission constraints, operational limitations, and imbalances between generation and demand. In the Brazilian context, this phenomenon has become more pronounced since 2023, highlighting structural challenges of the Brazilian Interconnected Power System and the need for reliable methodologies to estimate curtailed wind generation. This study presents a methodology to estimate wind power potential during curtailment events, aiming to support forecasting models and the economic compensation of affected generating agents. The proposed approach integrates measured power generation data, technical information of wind farms, and anemometric measurements from SCADA systems, combining data filtering and consistency procedures, gap-filling based on spatial correlation among wind farms, and regression models supported by statistical and computational techniques for wind-to-power conversion. The methodology was applied to more than 1000 wind farms connected to the Brazilian transmission grid and achieved accuracy levels above 95% on a semi-hourly basis and exceeding 99% for annual aggregations. Full article