Energy Utilization Efficiency Research Articles

Competitive Elimination Improved Differential Evolution for Wind Farm Layout Optimization Problems

The wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency under different wind conditions by optimizing the spatial layout of wind turbines to fully mitigate energy losses caused by wake effects. Some high-performance continuous optimization methods, such as differential evolution (DE) variants, exhibit limited performance when directly applied due to WFLOP’s discrete nature. Therefore, metaheuristic algorithms with inherent discrete characteristics like genetic algorithms (GAs) and particle swarm optimization (PSO) have been extensively developed into current state-of-the-art WFLOP optimizers. In this paper, we propose a novel DE optimizer based on a genetic learning-guided competitive elimination mechanism called CEDE. By designing specialized genetic learning and competitive elimination mechanisms, we effectively address the issue of DE variants failing in the WFLOP due to a lack of discrete optimization characteristics. This method retains the adaptive parameter adjustment capability of advanced DE variants and actively enhances population diversity during convergence through the proposed mechanism, preventing premature convergence caused by non-adaptiveness. Experimental results show that under 10 complex wind field conditions, CEDE significantly outperforms six state-of-the-art WFLOP optimizers, improving the upper limit of power generation efficiency while demonstrating robustness and effectiveness. Additionally, our experiments introduce more realistic wind condition data to enhance WFLOP modeling.

Wake model selection in offshore wind energy: balancing efficiency and cost in Indian offshore wind farms

Abstract This study addresses the critical need for efficient offshore wind energy utilization in India, focusing on the impact of different wake models on turbine performance and financial viability. By evaluating models such as TurbOPark and Deep Array Wake Loss (DAWL), we examined their effectiveness in predicting wake losses and optimizing turbine layouts in offshore subzones. The findings reveal that higher wind farm capacity densities lead to significant differences in performance across models. The TurbOPark model predicts the highest array losses, resulting in the lowest capac1ity utilization factors (CUF) and highest Levelized Cost of Energy (LCoE), reflecting its conservative nature. In contrast, the Modified Park and Eddy Viscosity models consistently estimate lower array losses, leading to lower LCoE and reduced financial burdens on the government, particularly when LCoE is fixed. These results underscore the importance of selecting appropriate wake models that balance cost efficiency with accurate performance predictions. The study highlights the need for refining wake models with high-resolution data and complex environmental factors to optimize wind farm design and enhance energy production, especially in emerging markets like India.

Leveraging AI and Machine Learning Models for Enhanced Efficiency in Renewable Energy Systems

Abstract. Artificial intelligence (AI) and machine learning (ML) have great potential in the management and optimization of renewable energy systems, through more in-depth AI-assisted analysis of store demand in the energy system, and through active learning models for energy optimization and scheduling. Among them, artificial intelligence and active learning can be combined to improve the utilization efficiency of renewable energy in a high range. In particular, recurrent neural network RNN and long- and short-term memory network LSTM in active learning can be used to predict energy and power demand, so as to achieve more effective intelligent power management and system optimization. Two of these models have their own advantages. LSTM and other active learning models can optimize the long-term dependence of traditional time series models and provide more efficient and accurate prediction model results for wind power forecasting and management and other renewable energy forecasting tasks. This study shows that compared with the traditional model, the LSTM model has a high advantage in processing long-term relationships and complex time series data, and the prediction results are more accurate and obvious. Through the dual combination of artificial intelligence and machine learning for renewable energy, this study provides theory, support and certain experimental guidance for the innovation of global energy systems, so as to make certain contributions to the development of renewable energy.

Assessing the impact of renewable energy integration on energy efficiency within the China-Pakistan economic corridor (CPEC)

This study investigates the impact of renewable energy integration on energy efficiency within the China-Pakistan Economic Corridor (CPEC) from 2000 to 2022. Efficient renewable energy utilization is crucial for the CPEC’s sustainable development. We employed multiple linear regression analysis on data from national energy statistics databases to examine the relationship between energy efficiency defined as the ratio of total renewable energy output to input in (TWh) and four renewable energy sources: hydropower, biofuel, solar PV, and geothermal. Our model controlled for potential confounding factors and met assumptions of linearity, multivariate normality, and absence of multicollinearity. Hydropower exhibited a highly significant negative correlation with energy efficiency (-0.632, p < 0.001), with a regression coefficient of -7.642 × 10⁻⁴ (p < 0.001). Similarly, biofuel showed a significant negative correlation (-0.222, p < 0.001) and a coefficient of -9.580 × 10⁻⁴ (p < 0.001). These findings suggest that increased production from these sources is associated with decreased energy efficiency, potentially due to transmission losses or inefficiencies in conversion technologies. In contrast, solar PV (-0.027, p = 0.638) and geothermal (-0.014, p = 0.806) showed no statistically significant relationship with energy efficiency, indicating that at their current scales of deployment, they do not significantly impact overall efficiency. A moderate negative correlation (r=-0.114, p = 0.047) was observed between solar PV and geothermal production, possibly reflecting resource allocation choices within the renewable energy portfolio. These results highlight the need for targeted interventions to improve the efficiency of hydropower and biofuel production and emphasize the potential for future contributions from solar PV and geothermal as their deployment and grid integration expands. The findings contribute to a more nuanced understanding of the complex interplay between renewable energy generation and overall energy efficiency within large-scale development projects.

A Current‐Adaptive Evaporator with Optimized Energy Utilization for Self‐Cleaning High‐Salinity Desalination

AbstractSolar steam generation (SSG) shows great potential in brine desalination to address the global water crisis. However, the salt accumulation has become a severe bottleneck that destroys devices and weakens efficiencies. Traditional evaporators are difficult to simultaneously realize clean salt‐rejection and efficient operation during high‐salinity desalination. In this work, a weaved cylinder evaporator with both efficient static and dynamic energy utilization is fabricated for self‐cleaning high‐salinity desalination. The energy utilization performance is optimized through a balanced water supply, omnidirectional solar absorption, infiltration content regulation, and dynamic current adaptation. Hence, an outstanding SSG rate of 1.91 kg·m−2·h−1 is achieved under 1 sun radiation. The dynamic self‐cleaning characteristics ensure competitive and durable high‐salinity desalination (≈11 kg·m−2·day−1) during weekly practical operation without salt deposition. Overall, the design overcomes the trade‐off between desired mass transfer and undesired heat loss.

Local government intervention and energy utilization efficiency: evidence from China’s NEDC policy

Improving energy utilization efficiency is an essential way to save energy and reduce emissions. This article collects data from 3,164 samples in China and uses the SBM-DEA method to calculate energy utilization efficiency. Then, we construct the DID model based on China’s New-Energy-Demonstration-City (NEDC) policy to test the impacts of local government intervention on energy utilization efficiency (EUE). The following conclusions can be drawn. Firstly, the NEDC policy can still significantly improve EUE. Secondly, heterogeneity analysis shows that the NEDC policy is beneficial for enhancing urban EUE, whether for traditional industrial bases or non-traditional industrial bases. The impact on non-traditional industrial bases is greater. The NEDC policy can significantly promote EUE in the eastern cities and high economic development areas. In contrast, its impact on EUE in the central and western cities or low economic development areas is insignificant. Finally, mechanism analysis shows that NEDC policy can promote energy utilization efficiency through industrial structure adjustment and green innovation.

High-Capacity Energy Storage Devices Designed for Use in Railway Applications

High-Capacity Energy Storage Devices Designed for Use in Railway Applications

Research on enhancing thermal conductivity of phase change microcapsules with nano-copper

Abstract The unique ability of phase change materials (PCMs) to store and release heat makes their integration into building materials promising for reducing energy consumption and enhancing sustainability. In this work, a novel high-thermal-conductivity microencapsulated phase change material was prepared, with nano-copper embedded in the microcapsule structure. This modification enhanced thermal conductivity while largely preserving the material’s latent heat storage capacity. Poly(ethyl acrylate) (PEA) is chosen as the capsule shell, whereas a eutectic mixture of decanoic acid (CA) and lauric acid (LA) serves as the core material. The analysis results indicate that as the shell-core mass ratio decreases, the microcapsule size increases, and both thermal conductivity and thermal diffusivity gradually decrease. Moreover, the latent heat capacity of microencapsulated phase change material (MEPCM) increases. When the shell-core mass ratio is 1:1.5, the melting latent heat and solidification latent heat are 81.85 J/g and 88.68 J/g, respectively. nano-copper doping enhances the material's thermal conductivity and thermal diffusivity by 47.5 % and 50 %, respectively, leading to a 20.3 % improvement in heat storage efficiency. After 200 cycles of testing, the material maintains good thermal reliability and chemical stability. Mortar-based composite materials containing microcapsules were prepared. The mortar composite materials containing microcapsules exhibited minimal influence from heating and cooling, with those containing nano-copper microcapsules demonstrating superior thermal response speeds. The method of doping and modifying MEPCM with nano-copper is a promising approach for effectively reducing the impact of temperature fluctuations on the internal comfort of buildings, improving energy utilization efficiency, and providing reliable solutions for temperature-sensitive applications.

Unveiling the kinetic predominance of fluoride electrosorption at S-modified defect-rich interface: Microscale disordered reconstruction induced accessible synergistic platform

Excessive hazardous fluoride (F−) have necessitated the advancement of fluoride removal electrode materials to mitigate their environmental impact. Herein, a sulfur-modified Bi-containing mesoporous synergistic platform encapsulated in a carbon shell (BiMCS-P) that provides an open nanostructured framework and high-density defects is reported. The BiMCS-P electrode removes fluoride by electrical double layer (EDL) capacitance and additional reversible redox reactions. Due to the diminished reliance of sluggish diffusion and the reduction of activation energy barrier after appropriate sulfidation, the fluoride can be further captured, as verified with high removal capacity (60.23 mg g−1 at an initial pH of 7) when processed in a 100 mg L−1F− solution, efficient energy utilization (72.8 %), and good applicability (about 80 % capacity retention at weakly acid solution). The BiMCS-P electrode can reduce the fluoride concentration from 5 mg L−1 and 2.5 mg L−1 to below the existing regulatory standards (1.5 mg L−1). The intrinsic defects optimize the electron distribution in an unsaturated coordination environment. The topological defects created during the disordered crystallographic conversion from Bi to Bi2S3 reduce the energy barrier of faradaic conversion and improves the redox activity. Moreover, the unique mesoporous framework achieves more defluoridation sites on the electrode’s surface and enables rapid ion storage behavior dominated by capacitive-controlled process at the solid–liquid interface. This study demonstrates a new strategy to improve the electrosorption performance, offering a promising advancement of defluoridation, particularly for materials that exhibit purely capacitive or purely Faradaic electrochemical characteristics.

Boosted photoinduced charge separation in FeNi2S4@Cd0.9Zn0.1S Step-scheme heterojunction for photothermal assisted photocatalytic water splitting into hydrogen

Rational design of photocatalysts with photothermal effect to maximize light utilization is pivotal in achieving superior photocatalytic efficiency. In this work, by coating Cd0.9Zn0.1S (CZS) nanorods on hollow FeNi2S4 (FNS) microspheres with photothermal effect, a novel FeNi2S4@Cd0.9Zn0.1S (FNS@CZS) Step-scheme (S-scheme) heterojunction photocatalyst was constructed for efficient photothermal-assisted hydrogen (H2) evolution via simultaneously employing light and thermal energy. The hollow structure of FNS microsphere not only provides abundant reactive sites but also serves as a supportive substrate for CZS nanorods, effectively inhibiting their agglomeration. And the unique hollow structure of FNS allows for multiple reflections and refractions of visible light, enhancing the local temperature of the photocatalyst. This effectively minimizes thermal losses within the composite system, thereby enhancing the efficiency of light energy utilization. Furthermore, the formation of the S-scheme heterojunction facilitates the efficient separation of photogenerated charge carriers and enhances the activity of redox reactions, thereby boosting the overall photocatalytic activity. Experimental results reveal that the H2 production rate of the FNS@CZS heterojunction reaches 12.9 mmol·g−1·h−1, which is 25.1 times higher than that of pristine CZS. By controlling the experimental temperature, the impact of the photothermal effect on the photocatalytic H2 production rate has been clarified. According to relevant evidence from infrared thermography and DFT calculations, comprehensive analyses and discussions were conducted on the photothermal effect and S-scheme charge transfer mechanisms. This study offers guidance on designing photothermal-enhanced photocatalysts to achieve satisfactory H2 production activity.

OntoRLE: an ontological-based compression algorithm for improving energy efficiency and memory utilization in 5G WSNs

OntoRLE: an ontological-based compression algorithm for improving energy efficiency and memory utilization in 5G WSNs

Thienothiophene-Benzopyran Derivative and AQ4N-Assembled Liposomes for Near-Infrared II Fluorescence Imaging-Guided Phototherapy, Chemotherapy, and Immune Activation.

Phototherapy, including photodynamic therapy (PDT) and photothermal therapy (PTT), has attracted wide attention in tumor treatment. However, the hypoxic tumor microenvironment and the heat shock proteins produced by tumor cells significantly reduce their efficacy. Developing effective phototherapy agents that have high reactive oxygen species generation efficiency and photothermal conversion efficiency (PCE) while simultaneously utilizing the hypoxic tumor microenvironment is of great importance. Here, a thienothiophene-benzopyran derivative, BTPIC4F-C10 is designed and synthesized, with near-infrared (NIR) absorption and fluorescence. Then the lipid nanoparticles (LipBFCA NPs) which encapsulated BTPIC4F-C10 in a phospholipid bilayer together with hypoxia-activated prodrug banoxanthrone (AQ4N) are constructed for NIR-II fluorescence imaging-guided synergistic PDT/PTT/chemotherapy and immune activation. Under 808nm laser irradiation, LipBFCA NPs is a high singlet oxygen quantum yield of 20.2% and PCE of 78.8%. With ultra-high photon energy utilization efficiency of 99%, LipBFCA NPs is an excellent phototherapy effect. The hypoxic environment caused by phototherapy can further activate AQ4N to transform into chemically toxic AQ4 radicals to kill tumor cells. Moreover, phototherapy can induce immunogenic cell death, release tumor-associated antigens, and activate immune responses. This work provides a new way for the clinical application of fluorescence imaging in guiding tumor diagnosis and treatment.

A new dynamic control strategy for a solar-driven absorption thermal energy storage system: Modeling and performance evaluation

Solar heating is a vital technology to promote the decarbonization of building energy supply systems. However, the mismatch between the intermittency of solar energy supply and the fluctuating heating demands poses significant challenges to its application. Current heat storage systems often exhibit low energy density, significant heat losses, and limited temperature regulation capabilities, which restrict their effectiveness in practical applications. This paper proposed a new real-time control strategy for a solar-driven absorption thermal energy storage system, integrated with an absorption heat pump, which can resolve the mutual constraints between solar energy utilization efficiency and the heating temperature, and then improve the system flexibility. The dynamic control strategy is implemented to manage concentration and temperature by regulating the inlet and outlet flow rates of three tanks, namely the absorbate tank, the weak solution tank, and the strong solution tank. A thermodynamic model is developed based on Aspen Plus and Matlab. Results show that within a 24-hour cycle, the system, driven by intermittent solar energy, can effectively meet the varying user-side heating demands through flow rate regulation. An optimized tank design, based on flow variation analysis, reduces the total volume by approximately 35 %. Additionally, the system can recover low-grade heat from the environment through the absorption heat pump, achieving an energy storage efficiency of 1.07 and an energy storage density of 56.13 kWh/m3. The proposed system and its dynamic control method are well-suited to intermittent solar energy, increasing the system flexibility, and thus expanding renewable energy utilization potential, especially those involving multi-energy complementary systems.

Full-operating-condition performance improvement strategy and optimal design of a two-stage ejector with auxiliary secondary flow for MED-TVC systems

Periodic fluctuation of steam-source pressure occurs in the thermal vapor compression multi-effect distillation (MED-TVC) systems. Because ejectors are sensitive to the boundary conditions, as a key component for recovering excess steam in systems, ejectors experience a rapid decline in performance when the steam-source pressure fluctuates and does not satisfy the design condition. This decline negatively impacts the overall system performance and reduces the energy utilization efficiency. Therefore, a two-stage ejector with auxiliary secondary flow (ASF-TSE) and a full-operating-condition performance improvement strategy were developed in this study to enhance the ejector performance under full working conditions and broaden the efficient operation range of ejectors. The flow field characteristics and performance of the ASF-TSE, single ejector (SE), and two-stage ejector (TSE) in the low and design-pressure ranges were compared and investigated. The results showed that within the design-pressure range, the maximum and average increase ratios of the ASF-TSE for entrainment performance compared with the TSE are 17.53 % and 13.43 %. Moreover, the entrainment capacities of the three ejectors were compared and analyzed in the full operating range. Simulation results show that compared with the SE and TSE, the entrainment performance of the ASF-TSE is improved by 87.5 % and 8.0 %, respectively.

Experimental study of adhesion tension of wet coal dust and coal slurry

ABSTRACT The coal dusting process produces corrosive and adhesive coal slurry, which corrodes the surface of equipment and reduces its service life. To investigate the effect of velocity and water contents on the adhesion tension of wet coal dust and coal slurry, this paper experimentally measures the adhesion tension of wet coal dust and coal slurry in contact with metal surfaces. The results show that the trends of the adhesion pull and displacement relationship between wet coal dust and coal slurry are basically the same; the adhesion pull obtained by fast lifting is larger than that of slow lifting under the same water content, and the adhesion pull of coal slurry is larger than that of coal dust; the exponential decay function and the peak normal distribution function of adhesion pull are used to fit the change process of the adhesion pull and validate it. Through the validation experiments, we verify the two functions accuracy and reliability, which can provide expressions with high credibility for the prediction of the results of adhesion pull under the conditions of this experiment. Meanwhile, it also provides assistance for the selection and preparation of coal dust and coal slurry, which can help to improve the energy utilization and economic efficiency in the process of coal processing, conveying, and storage.

On the contact and vibration characteristics of TBM cutter with abnormal damage under hard rock conditions

Tunnel Boring Machines (TBM) play a pivotal role in the construction of modern tunnels, with their performance directly determining the progress, economic benefits, and safety levels of the construction. As the core component interfacing with the rock, the condition of the TBM cutter significantly impacts the machine's boring performance. Faced with increasingly complex geological conditions, the cutters frequently suffer from abnormal damage, including partial wear, polygon wear, and chipping. Such damages are typically accompanied by reduced rock-breaking ability and increased vibration intensity, leading to severe performance degradation. To propose effective monitoring and maintenance strategies to prevent serious consequences of abnormal damage, such as cutterhead damage or personnel injuries, it is necessary to understand the changes in the operational characteristics of the cutter in such conditions and to accurately assess the specific impacts of abnormal damage on cutter performance. This study uses bench tests and discrete element-dynamics coupled simulations to explore the differences in load-bearing, cutter-rock contact, vibration, and noise characteristics between the normal wear cutter and those with abnormal damages. The results show that abnormal damages affect the cutter's load-bearing and cutter-rock contact properties, thereby increasing the proportion of cutting resistance and reducing energy utilization and rock-breaking efficiency. Additionally, partial wear and polygon wear not only significantly increase the noise/vibration levels of the cutter but also cause the mode of noise and vibration to shift from random to periodic, thus increasing the likelihood of forced vibrations in the system. By combining simulation and experimental results, this study elucidates the mechanisms by which different types of abnormal damage affect cutter performance by altering the motion form and cutter-rock contact mode. The operational characteristics of cutters with abnormal damages, as revealed in this study, in terms of load, vibration, and noise, provide theoretical support for the state monitoring and maintenance strategies of TBM cutters.

A typha orientalis-inspired 3D Janus solar evaporator with controllable wettability for highly efficient and stable solar desalination

Solar-powered interfacial evaporators, though promising for seawater desalination and wastewater treatment, but suffer from the problems of monolithic structure and complex preparation. It is of great importance to develop a multi-layered solar evaporator that possesses stable and high-efficiency photothermal performance. Our research employs simple liquid-phase polymerization and chemical deposition methods to achieve this. The objective of this study is to develop a highly efficient solar evaporator, designated as SA/MWCNTs@PPy/MWCNTs-NH2@PU (SMPMPU), by integrating stearic acid (SA), multi-walled carbon nanotubes (MWCNTs), polypyrrole (PPy), and aminated multi-walled carbon nanotubes (MWCNTs-NH2) within a polyurethane (PU) matrix. Inspired by typha orientalis, this evaporator boasts a highly antibacterial, self-floating Janus bilayer structure. This double-layered Janus solar evaporator utilizes the capillary adsorption effect to regulate the flow of water. It slows down heat transfer and significantly improves energy utilization efficiency. The resulting SMPMPU solar evaporator demonstrates a solar steam conversion efficiency with an evaporation rate of 2.40 kg·m−2·h−1, which is attributed to the material's efficient absorption of light energy. Its evaporation efficiency under one sun illumination reaches 92.76 %, which can be credited to the Janus interface facilitating the absorption and evaporation of water molecules. The Janus bilayer structured solar evaporator developed in this study holds great promise and is anticipated to find widespread applications in seawater desalination and other related fields.

Optimization scheduling of microgrid cluster based on improved moth-flame algorithm

With the rapid development of renewable energy, microgrid cluster systems are gradually being applied. To promote the development of microgrid cluster scheduling technology, maximize economic benefits while reducing the operating cost required for microgrid scheduling, an optimized scheduling scheme is proposed by constructing a function to minimize the operating cost of microgrids. Then, chaos mutation and Gaussian mutation are applied to improve the moth-flame algorithm that easily falling into local optima. A microgrid cluster optimization scheduling model on the basis of the improved moth-flame algorithm is constructed. The experimental results showed that the operating cost in islanding mode was 4286.21 yuan after 160 iterations. After optimizing the scheduling, the operating cost was 3912.3 yuan, with a decrease of 8.7%. The improved moth-flame algorithm had a stable average loss value of 20% and an operating efficiency of 97.19% after 10–50 iterations, which was significantly higher than other intelligent algorithms. This indicates that the improved moth-flame algorithm has high reliability and effectiveness in microgrid cluster optimization scheduling. Therefore, the proposed model effectively optimizes the scheduling scheme of microgrid cluster, providing new solutions for the efficient utilization of smart grids and renewable energy in the future.

Research on Microgrid Optimal Scheduling Based on an Improved Honey Badger Algorithm

As global energy demands continue to grow and environmental protection pressures increase, microgrids have garnered widespread attention due to their ability to effectively integrate distributed energy sources, improve energy utilization efficiency, and enhance grid stability. Due to the complexity of internal structure, variety of energy sources, and uncertainty of load demand, the optimal scheduling problem of microgrids becomes extremely complicated. Traditional optimization methods often perform poorly in complex and dynamic microgrid environments, and it is assumed that the complexity is low or that more simplification is needed, which leads to poor convergence and local optimality when dealing with uncertainty and nonlinear problems, making intelligent optimization algorithms a crucial solution to this problem. To address the shortcomings of the traditional honey badger algorithm, such as the slow convergence speed and a tendency to fall into local optima in complex microgrid optimal scheduling problems, this paper proposes a multi-strategy improved honey badger algorithm. During the population initialization phase, a combined opposition-based learning strategy is introduced to enhance the algorithm’s exploration and exploitation capabilities. Additionally, the introduction of variable spiral factors and a linearly decreasing strategy for parameters improves the overall efficiency of the algorithm and reduces the risk of local optima. To further enhance population diversity, a hunger search strategy is employed, providing stronger adaptability and global search capabilities in varying environments. The improved honey badger algorithm is then applied to solve the multi-objective optimal scheduling problem in grid-connected microgrid modes. The simulation results indicate that the improved honey badger algorithm effectively enhances the economic and environmental benefits of microgrid operations, improving system operational stability.

Location optimization of EV charging stations: A custom K-means cluster algorithm approach

The strategic deployment of EV (EV) charging stations is crucial for promoting sustainable transportation and facilitating the widespread adoption of EVs. However, the lack of readily accessible charging station continues to be a significant barrier to the mainstream adoption of EVs. This study presents a comprehensive approach to optimizing the location of charging stations using a custom K-means clustering algorithm. The algorithm incorporates various factors including charging demand, energy consumption, population density, and existing stations, to identify optimal locations for the charging stations. The proposed methodology aims to minimize the distance between charging stations and areas with high EV demand while considering energy efficiency and avoiding redundancy near low-weighted point locations. The algorithm iteratively assigns data points to clusters and updates the centroids, converging to an optimal solution that balances coverage, accessibility, and sustainability. Folium map in Python and Python code was used for case study analysis. The innovation of the finding shows the effectiveness of the custom K-means clustering algorithm in optimizing charging station placement, by introducing a penalty term to avoid repositioning charging stations close to a low-weighted (charging demand) point charging station, also ensuring convenient access for EV owners, and promoting efficient energy utilization. The study emphasizes on strategic planning and data-driven approaches in developing a comprehensive and sustainable EV charging station network to address the challenge of limited charging station and support the growth of electric mobility.