Distribution Of Stations Research Articles

Modeling and Optimization of NO2 Stations in the Smart City of Barcelona

The growing problem of nitrogen dioxide (NO2) pollution in urban environments is driving cities to adopt smart and sustainable approaches to address this challenge. To quantify and compare the effect of environmental policies, cities must be able to make informed decisions with real-time data that reflect the actual situation. Therefore, the objective of this work is threefold: The first is to study the behavior of the key performance indicator (KPI) of NO2 concentrations per station in Barcelona through exploratory analysis and clustering. The second is to predict NO2 concentration behavior, considering meteorological data. Lastly, a new distribution of current and new stations will be proposed using an optimization algorithm that maximizes the distance between them and covers the largest area of the city. As a result of this study, the importance of the location of measurement points and the need for better distribution in the city are highlighted. These new spatial distributions predict an 8% increase in NO2 concentrations. In conclusion, this study is a comprehensive tool for obtaining an accurate representation of NO2 concentrations in the city, contributing to informed decision-making, helping to improve air quality, and promoting a more sustainable urban environment.

Subterranean termites (Coptotermes formosanus [Blattodea: Rhinotermitidae]) colonies can readily intercept commercial inground bait stations placed at label-prescribed distance

Abstract The Formosan subterranean termite, Coptotermes formosanus Shiraki, is both an economically impactful pest and a successful invader. One method of subterranean termite control is baiting. According to the label, baits are installed surrounding the structure at a uniform interval distance of ≈3 m. However, homeowners and pest control professionals are often concerned that termites may bypass bait stations and have access to the structure. To address this concern, we experimentally duplicated field conditions using a large planar arena (3.6 m × 1.1 m) to study the optimal distribution of bait stations based on colony-wide foraging activity. We installed 2 bait stations 3 m apart as per label instructions and introduced C. formosanus colonies to allow them to explore the arena by tunneling through the sand. In this real-scale arena, all termite colonies intercepted a bait station in an average of 21 (± 8 SD) days. We assumed that termites could find bait faster if there were more bait stations by overlaying additional hypothetical baits closer than per label instruction, but the improvement was incremental, requiring 4 times more stations (0.45 m interval) to obtain a significant difference. We also revealed the characteristic behavior after intercepting bait stations, termites created a burst of tunnels that radiated from the bait station. These branching tunnels averaged 16 cm in length, suggesting immediate interceptions of additional auxiliary stations placed within 16 cm of an active station. These findings contribute to our understanding on how subterranean termites intercept inground bait stations.

Complicated thermo-chemical heterogeneity of the mantle transition zone beneath the Philippine Sea Plate revealed by SS precursors investigation

The Philippine Sea Plate (PSP), a region renowned for its intricate history of multi-stage subduction and back-arc extension, encounters difficulties in elucidating its deep seismic structure, primarily due to the sparse distribution of seismic stations. This study uses over 44,086 traces of SS precursors collected from >1,000 seismic stations over 10–20 years period, to image the mantle transition zone (MTZ) beneath the PSP. With the curvelet denoising technique, we provide high-resolution maps of the depths of the 410 km (D410) and 660 km (D660) discontinuities and the thickness of the MTZ. Notably, we demonstrate a 10–35 km MTZ thickening extending from the West Philippine Basin to the Shikoku Basin, which may be associated with the thermal effect and dehydration of stagnated slabs in the MTZ. Furthermore, the reflection gap and multiple reflectors were both observed in D410 and D660 beneath the Mariana Trench, which suggests that the vertically subducted Pacific plate transports substantial water and non-olivine components into the MTZ in this area. Additionally, a ∼10–25 km MTZ thinning with an abnormally shallow D660 has been observed beneath the Parece Vela Basin and Caroline Plate in the southern PSP, suggesting the potential existence of thermal upwelling from a secondary plume, which may be possibly the tree branch of the Caroline mantle plume in MTZ. Our results provide new seismological constraints on present and past mantle dynamics in and around the PSP.

Optimization and Application Analysis of the UAV "Last Mile" Mobile Terminal Distribution Station in Rural Area under the Background of E-Commerce

With the booming demands of global consumers, the grand total of international e-commercial procurement in 2024 will be expected to exceed 6.3 trillion USD. Under the pressure of huge orders and shipping lag, figuring out a cost-effective last mile delivery achieving real door-to-door service, especially in rural areas, has become one of the focuses in the international e-commercial market. To better solve this problem, this paper starts with the analysis of international e-commercial status and the feasibility of UAV mobile terminal station launch, taking good account of drone delivery route setting, time schedule, and costs spent to optimize the mobile station moving. Based on a series of experiments to iterate the Unmanned Aerial Vehicle (UAV) mobile terminal station delivery mode, it will provide some insights to the market about how the last mile delivery will develop in the next decade. This paper will focus on applying the hybrid ant colony algorithm to improve truck-drone collaborative last mile delivery route settings and figure out the best delivery route combo for cost saving.

Assessing the influence of climate change on multiple climate indices in Nepal using CMIP6 global climate models

Global climate models (GCMs) serve as essential tools for projecting future climate trends, but their coarse resolution limits localized impact assessments in sectors like hydrology, agriculture, and biodiversity. Observation data with a spatial resolution of a few kilometers are crucial for downscaling and bias-correcting GCMs at finer resolutions. However, Nepal's extreme topography and organizational challenges have led to uneven distribution of meteorological stations and inconsistent data quality. Moreover, CMIP6-based climate extremes projections for the entire country are currently unavailable. To tackle these challenges, we developed a comprehensive national database for Nepal, offering high-resolution historical and projected precipitation and temperature data analyzed through 25 climate extreme indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). Initially, observation grid data were prepared at a daily timescale with a spatial resolution of 0.05° × 0.05° for baseline period (1981–2010) using the Asian Precipitation High-Resolved Observational Data Integration Toward Evaluation (APHRODITE), the fifth generation of the European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), and available good quality observed climate data. This data was then utilized to downscale and bias-correct 18 CMIP6 GCMs for 2015–2100 under four SSPs (1–2.6, 2–4.5, 3–7.0, 5–8.5). Quantile mapping was employed for the bias correction of the CMIP6 GCMs. The performance of the multimodal ensemble (MME) indicated better Nash-Sutcliffe Efficiency (NSE), root mean square error ratio (RSR), and Percent Bias (PBIAS) of climate extreme indices for the historical period. A comparative analysis was conducted across Nepal's major geographic regions to account for spatial variability in regional climate systems. The finer-resolution dataset can be crucial to deepen our understanding of climate impacts, and climate change, and eventually informing the policy-making in Nepal. Moreover, the methodology can be effectively replicated in data-scarce developing nations to promote climate research and adaptation efforts.

A Deep Learning Framework for Satellite-Derived Surface PM2.5 Estimation: Enhancing Spatial Analysis in the United States

Abstract Monitoring fine particulate matter (PM2.5) is crucial for evaluating air quality and its effects on public health. However, the limited distribution of monitoring stations presents a challenge in accurately assessing air pollution, especially in areas distant from these stations. To address this challenge, our study introduces a two-step deep learning approach for estimating daily gap-free surface PM2.5 concentrations across the contiguous United States (CONUS) from 2018 to 2022, with a spatial resolution of 4 km. In the first phase, we employ a Depthwise-Partial Convolutional Neural Network (DW-PCNN) to fill gaps between surface PM2.5 stations, utilizing Aerosol Optical Depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the second phase, we integrate the PM2.5 grids imputed by the DW-PCNN with meteorological and anthropogenic variables into a Deep Convolutional Neural Network (Deep-CNN) to further enhance the accuracy of our estimation. This enables us to estimate gap-free surface PM2.5 concentrations accurately, evidenced by a Pearson’s correlation coefficient (R) of 0.92 and an Index of Agreement (IOA) of 0.96 in ten-fold cross-validation. We also introduce a grid-based method for calculating PM2.5 Design Values (DV), providing a continuous spatial representation of PM2.5 DV that enhances the traditional station-based approach. Our grid-based DV representations offer a comprehensive perspective on air quality, facilitating more detailed analysis. Furthermore, our model's ability to provide spatiotemporally consistent, gap-free PM2.5 data addresses the issue of missing values, supporting health impact research, policy formulation, and the accuracy of environmental assessments.

Structural organization of a fuzzy system for monitoring the condition of equipment and control of a gas distribution station

Objective. The objective of the study is to create an intelligent system for monitoring the condition of equipment and managing the operating modes of a gas distribution station (GDS) based on a logical-linguistic (fuzzy) model for making assessment and management decisions. Method. The theory of fuzzy sets and algorithms of L. Zade was used, which allows the use of qualitative concepts and allows the creation of logical-linguistic models of the controlled process on their basis. Result. The article provides a rationale for using an intelligent system for assessing the state of equipment and managing a gas distribution station (GDS) based on a logical-linguistic (fuzzy) model for representing the knowledge base for making assessment and management decisions. This approach provides developers of control systems for complex processes and/or objects with a clear and effective way to describe their dynamics. Conclusion. An intelligent system for assessing equipment conditions and managing the operating modes of the gas distribution station, in which the knowledge base is presented in the form of a logicallinguistic (fuzzy) model, allows for solutions to the specified problem based on the knowledge of an experienced expert (dispatcher), which will improve the quality of service and reduce the operating costs of the gas distribution station

Application of GRACE-based satellite estimates in the assessment of flood potential: A case study of Gangetic-Brahmaputra basin, India.

The high frequency of flood occurrences and the uneven distribution of hydrological stations make it difficult to monitor large-scale floods. Emergence of the Gravity Recovery and Climate Experiment (GRACE) satellite system sets up a new era of large-scale flood monitoring without much reliance on in situ hydrological observations. The GRACE-derived flood potential index (FPI) exhibits its ability to monitor major events of 2003, 2004, 2007, and 2008 over the Indo-Gangetic-Brahmaputra Basin (IGBB). Precipitation and soil moisture are the major influencing factors of flood. However, the response of potential flooding to such parameters is little known. Pearson's lag correlation analysis is used to examine the response of the GRACE-based FPI to precipitation and soil moisture over the study region comparing seasonal time series of the variables. Results exhibited a 2-month lagged response of FPI to precipitation in the Upper Gangetic Yamuna Chambal Basin (UGYCB) and the Lower Gangetic Basin (LGB) and 1-month lagged response in the Lower Brahmaputra Basin (LBB). With context to soil moisture, a 1-month lag is observed in the Gangetic basins, and no lag is observed in the LBB. Event wise analysis of the lags portrays slightly varying lags for different events; however, it provides a picture on the interaction between these variables. This study also assesses the agreement between FPI and satellite-based river discharge, i.e. Dartmouth Flood Observatory (DFO) discharge. A good correlation (> 0.60) between the two is observed. Threshold values of FPI are determined for the LBB due to its annual flood frequency. The nearly similar accuracy of threshold FPI, determined using DFO discharge, in monitoring floods and the predictive skill measure of FPI for LBB to the previous studies demonstrates the utility of satellite-based discharge in the quantification of threshold FPI values for different percentile floods.

Application of Single Neuron Adaptive PID Algorithm in Natural Gas Automatic Distribution Control System

Abstract Aiming at the most important control link of electric regulating valve in automatic distribution control system of natural gas station, this paper proposed to use single neuron adaptive Proportional-Integral-Derivative (SNA-PID) algorithm to make up for the problems of the traditional Proportional-Integral-Derivative (PID) algorithm, such as the difficulty of parameter setting and the inability to adapt to system interference. In order to compare the performance of the algorithm, the transfer function between the valve opening of the electric regulating valve on the branch of the gas transmission system of a natural gas station was taken as an example to simulate various practical application scenarios, and the control performance of the two algorithms was studied in detail. Finally, the practical engineering application and the performance verification of the algorithm were carried out in a natural gas distribution station in Jiangxi Province. The results showed that, compared with the PID algorithm, the SNA-PID algorithm can automatically adjust its own algorithm parameters, greatly reduce the number of valve operations during the adjustment process, and control the valve opening more smoothly, adjust the branch pressure more quickly and effectively, and also has a strong anti-interference ability. Therefore, SNA-PID algorithm is more suitable for the automatic distribution control system of natural gas station than the PID algorithm.

Dynamic response analysis of pipelines in gas distribution stations under seismic loads

Seismic loads in earthquake-prone regions significantly affect the structural integrity of pipelines at urban natural gas distribution stations, compromising safety. This study analyzes the dynamic response of pipelines in gas distribution stations under seismic loading and proposes effective measures to mitigate stress concentration. A numerical model of the pipeline in the gas distribution station, based on actual cases, was established. The material’s constitutive relationship was derived from tensile tests. The seismic response spectrum, obtained from ground motion data, was introduced to analyze the pipeline’s mechanical behavior. The dynamic response analysis identified critical stress concentration locations in the pipeline under operational conditions. Based on displacement analysis, the study recommended seismic measures. Results indicated that under seismic loading, higher stress values occurred primarily in pipeline sections with intricate flow pattern variations. The manifold connection was the most vulnerable point, with its average stress being 3.37 times that of the system’s average stress. The proposed seismic measures effectively reduced stress concentration at these critical points, mitigating the earthquake’s impact on the gas distribution station. This study establishes a theoretical framework for improving the seismic design and maintenance of natural gas distribution stations, enhancing their seismic resilience and operational safety.

Using GIS Spatial Analysis for Examination the Utility of the Suggested Suitability Energy Farms Over Kirkuk City

Global electricity demand is rising, prompting increased attention to renewable energy sources like solar power. This research focuses on identifying suitable areas for power lines connecting solar farms to distribution stations in Kirkuk City. Data types, including urban areas, industrial zones, farms, water bodies, and archaeological sites, were analyzed using GIS-based spatial tools. The study highlights accessible and inaccessible regions of Kirkuk's electricity transmission infrastructure, providing insights for decision-makers. The analysis categorizes areas as either benefitted or unbenefited in the context of solar farm suitability. An important consideration is the need for additional electrical distribution stations to accommodate energy transfer from these solar farms.

Optimization scheduling strategy for flexible interconnected distribution network considering transformer offline

Based on the flexible interconnection technology with intelligent soft open point (SOP), new opportunities have arisen to address issues such as complex power flow and uneven load rates in existing distribution networks. However, most existing research focuses on load balancing strategies and N-1 fault tolerance, without fully considering the impact of flexible access and offline of distribution transformers on the losses in the distribution network. Therefore, this paper analyzes the impact of structural adjustments to the distribution transformer interconnection system on the comprehensive losses of the distribution network, and concludes that under low-load conditions, the offline of lightly loaded transformers optimizes the comprehensive losses of the distribution network. However, the offline of lightly loaded transformers leads to an increase in the remaining transformer load rates in the distribution station areas. To address this shortfall, this paper proposes an optimization scheduling strategy for flexible interconnected distribution networks considering transformer offline, by simultaneously utilizing shared energy storage on the DC side to balance transformer load rates and minimize losses. Finally, case studies based on an IEEE 18-node interconnected system are conducted to validate the proposed approach under two different scenarios. The results demonstrate that the proposed control strategy outperforms existing strategies by optimizing the comprehensive losses of the system, thereby enhancing its operational economy and reliability.

Research on modeling method for digitizing distribution substation areas based on big data from smart meters

With the wide application of smart meters, real-time data collection in power grid operation can be carried out in real time, which provides big data support for the digital modeling of distribution station areas. At the same time, the digital transformation of the distribution area can be promoted through the digital modeling of the distribution area. In this situation, how to use the big data of smart meters to achieve the digital modeling of the distribution area needs to be further studied. Firstly, this paper briefly introduces the connotation of digitalization in the distribution station area. At the same time, aiming at the digitalization of the distribution station area, it analyzes the digitalization of station features, user features, new energy features, network parameters and operation features, and obtains the digitalization model. Finally, on the basis of the above, select an application scenario to introduce the application of digitalization modeling. The research results can provide a reference for the combination of big data application of smart meters and digitalization of the distribution area, and help the digital transformation of the distribution area.

Fiducial Reference Measurement for Greenhouse Gases (FRM4GHG)

The Total Carbon Column Observing Network (TCCON) and the Infrared Working Group of the Network for the Detection of Atmospheric Composition Change (NDACC-IRWG) are two ground-based networks that provide the retrieved concentrations of up to 30 atmospheric trace gases, using solar absorption spectrometry. Both networks provide reference measurements for the validation of satellites and models. TCCON concentrates on long-lived greenhouse gases (GHGs) for carbon cycle studies and validation. The number of sites is limited, and the geographical coverage is uneven, covering mainly Europe and the USA. A better distribution of stations is desired to improve the representativeness of the data for various atmospheric conditions and surface conditions and to cover a large latitudinal distribution. The two successive Fiducial Reference Measurements for Greenhouse Gases European Space Agency projects (FRM4GHG and FRM4GHG2) aim at the assessment of several low-cost portable instruments for precise measurements of GHGs to complement the existing ground-based sites. Several types of low spectral resolution Fourier transform infrared (FTIR) spectrometers manufactured by Bruker, namely an EM27/SUN, a Vertex70, a fiber-coupled IRCube, and a Laser Heterodyne spectro-Radiometer (LHR) developed by UK Rutherford Appleton Laboratory are the participating instruments to achieve the Fiducial Reference Measurements (FRMs) status. Intensive side-by-side measurements were performed using all four instruments next to the Bruker IFS 125HR high spectral resolution FTIR, performing measurements in the NIR (TCCON configuration) and MIR (NDACC configuration) spectral range. The remote sensing measurements were complemented by AirCore launches, which provided in situ vertical profiles of target gases traceable to the World Meteorological Organization (WMO) reference scale. The results of the intercomparisons are shown and discussed. Except for the EM27/SUN, all other instruments, including the reference TCCON spectrometer, needed modifications during the campaign period. The EM27/SUN and the Vertex70 provided stable and precise measurements of the target gases during the campaign with quantified small biases. As part of the FRM4GHG project, one EM27/SUN is now used as a travel standard for the verification of column-integrated GHG measurements. The extension of the Vertex70 to the MIR provides the opportunity to retrieve additional concentrations of N2O, CH4, HCHO, and OCS. These MIR data products are comparable to the retrieval results from the high-resolution IFS 125HR spectrometer as operated by the NDACC. Our studies show the potential for such types of spectrometers to be used as a travel standard for the MIR species. An enclosure system with a compact solar tracker and meteorological station has been developed to house the low spectral resolution portable FTIR systems for performing solar absorption measurements. This helps the spectrometers to be mobile and enables autonomous operation, which will help to complement the TCCON and NDACC networks by extending the observational capabilities at new sites for the observation of GHGs and additional air quality gases. The development of the retrieval software allows comparable processing of the Vertex70 type of spectra as the EM27/SUN ones, therefore bringing them under the umbrella of the COllaborative Carbon Column Observing Network (COCCON). A self-assessment following the CEOS-FRM Maturity Matrix shows that the COCCON is able to provide GHG data products of FRM quality and can be used for either short-term campaigns or long-term measurements to complement the high-resolution FTIR networks.

Hydrological model calibration in data-deficient basins using satellite altimetry and a hydrodynamic model

Large basins with insufficient hydrological station distribution and lacking sufficient meteorological data, such as water level and discharge, pose significant challenges in developing reliable hydrological models. These models are crucial for addressing water resource-related challenges such as climate change (e.g., floods and droughts) and human-induced activities (e.g., dams) impacts. Recent advancements in satellite altimetry offer an efficient alternative by providing water level data; however, integrating this data into hydrological models remains an ongoing challenge. Here we demonstrate that a properly developed hydrodynamic model can effectively translate the satellite-observed water levels at various river cross-sections (hereafter referred to as virtual stations (VSs)) into the required discharge data, facilitating reliable hydrological modeling for data-deficient basins. Our results, based on the assessment of water level data derived from a hydrodynamic model and satellite altimetry, showed high consistency (NSE > 0.96) in the Lancang-Mekong River (LMR) basin as a case study. We found that the distribution of stations along the mainstream, in terms of their number and distance from each other, can significantly impact the performance of hydrological modeling processes. The results revealed that when a large stretch of the river is not monitored by a station, a large uncertainty occurs in runoff generation, at least for that segment of the river. However, the inclusion of virtual stations in the modeling process improved the overall performance, indicating the advantages of satellite altimetry data in better modeling of data-deficient basins. Our findings revealed that virtual stations can be effectively utilized in physically based and spatially distributed hydrological models to develop a sub-basin modeling approach that addresses the regional contribution of sub-basins to downstream river flow. The integrated modeling framework holds potential for hydrological analysis and water resources management for basins lacking sufficient gauging data and provides insight into how the distribution of stations can influence the calibration of hydrological models.

Near-Surface Air Temperature Estimation Based on an Improved Conditional Generative Adversarial Network.

To address the issue of missing near-surface air temperature data caused by the uneven distribution of ground meteorological observation stations, we propose a method for near-surface air temperature estimation based on an improved conditional generative adversarial network (CGAN) framework. Leveraging the all-weather coverage advantage of Fengyun meteorological satellites, Fengyun-4A (FY-4A) satellite remote sensing data are utilized as conditional guiding information for the CGAN, helping to direct and constrain the near-surface air temperature estimation process. In the proposed network model of the method based on the conditional generative adversarial network structure, the generator combining a self-attention mechanism and cascaded residual blocks is designed with U-Net as the backbone, which extracts implicit feature information and suppresses the irrelevant information in the Fengyun satellite data. Furthermore, a discriminator with multi-level and multi-scale spatial feature fusion is constructed to enhance the network's perception of details and the global structure, enabling accurate air temperature estimation. The experimental results demonstrate that, compared with Attention U-Net, Pix2pix, and other deep learning models, the method presents significant improvements of 68.75% and 10.53%, respectively in the root mean square error (RMSE) and Pearson's correlation coefficient (CC). These results indicate the superior performance of the proposed model for near-surface air temperature estimation.

Electricity Generation at Gas Distribution Stations from Gas Surplus Pressure Energy

At gas distribution stations (GDSs), the process of throttling (pressure reduction) of natural gas occurs on gas pressure regulators without generating useful energy. If the gas expansion process is created in a turbine, to the shaft where an electric generator is connected, then electricity can be obtained. At the same time, the recycling of secondary energy resources is provided, which is an important component in the efficient use of natural resources. The obtained electric power can be supplied to the external power grid and/or used for the GDS’s own needs. The process of generating electricity at the GDS from gas overpressure energy is an environmentally friendly, energy-saving technology that ensures an uninterrupted, autonomous operation of the GDS in the absence of an external energy supply. The power needs of a GDS with regard to electricity are relatively small (5 ÷ 20 kW). Expansion in throttling devices or turbine flow paths leads to gas cooling with a possible hydrate formation. It is prevented via gas preheating or vortex expansion equipment that keeps the further gas temperature at a necessary level. Turbogenerators can be created on the basis of vortex expansion turbomachines, which have many advantages compared to turbomachines of other types. This article studies how gas pressure (outlet: gas distribution station) and gas preheating (inlet: vortex expansion machine) influence turbogenerator parameters. Nine turbogenerator variants for the power needs of gas distribution stations have been assessed.

Investigation of the 2011 Yingjiang, Yunnan, China Ms. 5.8 Earthquake Sequence: Seismic Migration, Seismogenic Mechanism, and Hazard Implication

On March 10, 2011, an Ms. 5.8 earthquake struck Yingjiang City, western Yunnan, China, causing destructive damage. Due to the very sparse distribution of seismic stations on the southwestern border of China, its seismogenic structure and mechanism remain controversial. In this study, with the aid of machine-learning-based detection and location workflow and template matching technique, we detect 10,356 events ranging from December 1, 2010, to April 30, 2011. The high-precision earthquake catalog shows that the foreshocks initiated in the extensional stepover connecting the northeast and middle segments of the Dayingjiang fault and then bilaterally extended northeast and southwest, with migration fronts that can be simulated by fluid diffusion model with diffusivities of 0.8 m2/s and 0.19 m2/s, respectively. The mainshock occurred at the southwest end of the foreshock sequence and then probably activated the northwest-trending blind fault. In addition, we determine the full moment tensor solutions for the mainshock, six large foreshocks, and one aftershock, with magnitudes ranging from 3.03 to 5.80, in which the mainshock was characterized by an obvious negative isotropic (ISO) component. The static Coulomb failure stress change caused by five Mw ≥ 4.0 foreshocks on the mainshock fault plane is ∼24 kPa, reaching the typical static triggering threshold. Therefore, we suggest that both the fluid diffusion and stress perturbation contribute to triggering the mainshock. This study advances our understanding of the spatiotemporal evolution, seismogenic mechanism, and hazard implication for the Yingjiang Ms. 5.8 earthquake and provides additional evidence of natural fluid-triggered seismicity in western Yunnan.

A Novel Areal Maintenance Strategy for Large-Scale Distributed Photovoltaic Maintenance

A smart grid is designed to enable the massive deployment and efficient use of distributed energy resources, including distributed photovoltaics (DPV). Due to the large number, wide distribution, and insufficient monitoring information of DPV stations, the pressure to maintain them has increased rapidly. Furthermore, based on reports in the relevant literature, there is still a lack of efficient large-scale maintenance strategies for DPV stations at present, leading to the high maintenance costs and overall low efficiency of DPV stations. Therefore, this paper proposes a maintenance period decision model and an areal maintenance strategy. The implementation steps of the method are as follows: firstly, based on the reliability model and dust accumulation model of the DPV components, the maintenance period decision model is established for different numbers of DPV stations and different driving distances; secondly, the optimal maintenance period is determined by using the Monte Carlo method to calculate the average economic benefits of daily maintenance during different periods; then, an areal maintenance strategy is proposed to classify all the DPV stations into different areas optimally, where each area is maintained to reach the overall economic optimum for the DPV stations; finally, the validity and rationality of this strategy are verified with the case study of the DPV poverty alleviation project in Badong County, Hubei Province. The results indicate that compared with an independent maintenance strategy, the proposed strategy can decrease the maintenance cost by 10.38% per year, which will help promote the construction of the smart grid and the development of sustainable cities. The results prove that the method proposed in this paper can effectively reduce maintenance costs and improve maintenance efficiency.

High-Resolution Monitored Data Analysis of EV Public Charging Stations for Modelled Grid Impact Validation

As electric vehicle adoption grows, understanding the impact of electric vehicle charging on electricity grids becomes increasingly important. Accurate grid impact modelling requires high-quality charging infrastructure data. This study examined the electric vehicle recharging infrastructure and usage patterns in a region of the Italian Alps over a three-year period from 2021 to 2023. The primary objectives were to analyze the growth and distribution of electric vehicle charging stations, assess energy consumption, and evaluate charging behaviours across various recharging points. The research involved collecting empirical data from 411,800 recharging sessions and simulated data using the emobpy tool to model energy consumption and charging behavior. Key findings reveal a substantial increase in the number of recharging points, from 673 in 2021 to 970 in 2023, with the total energy delivered increasing from 938 MWh in 2021 to 4133 MWh in 2023. The data showed distinct temporal trends: AC points were primarily used during the day, while DC points saw higher usage during morning and late afternoon peaks, aligning with travelling times. The study’s validation of simulation results against empirical data emphasized the importance of high-quality input for accurate grid impact assessments. These findings suggest the necessity for strategic placement of recharging infrastructure and provide practical insights for policymakers, urban planners, and utility companies to support sustainable electric vehicle integration.