Search Results (1,657)

Battery-swapping stations (BSSs) are pivotal for supplying energy to electric heavy-duty trucks. However, their operations face challenges in accurate demand forecasting for battery-swapping and fair revenue allocation. This study proposes an optimization strategy for the economic operation of BSSs that optimizes revenue allocation and load balancing to enhance financial viability and grid stability. First, factors including geographical environment, traffic conditions, and truck characteristics are incorporated to simulate swapping behaviors, supporting the construction of an accurate demand-forecasting model. Second, an optimization problem is formulated to maximize the weighted difference between BSS revenue and squared load deviations. An economic operations strategy is proposed based on an adaptive Shapley value. It enables precise evaluation of differentiated member contributions through dynamic adjustment of bias weights in revenue allocation for a strategy that aligns with the interests of multiple stakeholders and market dynamics. Simulation results validate the superior performance of the proposed algorithm in revenue maximization, peak shaving, and valley filling. Full article

It is well known that aqueous solutions can emit electromagnetic waves in the radio frequency range. However, the physical nature of this process is not yet fully understood. In this work, the possible role of gas nanobubbles formed in the bulk liquid is considered. We develop a theoretical model based on the concept of gas bubbles stabilized by ions, or “bubstons”. The role of bicarbonate and hydronium ions in the formation and stabilization of bubstons is explained through the use of quantum chemical simulations. A new model of oscillating bubstons, which takes into account the double electric layer formed around their gas core, is proposed. Theoretical estimates of the frequencies and intensities of oscillations of such compound species are obtained. It was determined that oscillations of negatively charged bubstons can occur in the GHz frequency range, and should be accompanied by the emission of electromagnetic waves. To validate the theoretical assumptions, we used dynamic light scattering (DLS) and showed that, after subjecting aqueous solutions to vigorous shaking with a force of 4 or 8 N (kg·m/s²) and a frequency of 4–5 Hz, the volume number density of bubstons increased by about two orders of magnitude. Radiometric measurements in the frequency range of 50 MHz to 3.5 GHz revealed an increase in the intensity of radiation emitted by water samples upon the vibrational treatment. It is argued that, according to our new theoretical model, this radiation can be caused by oscillating bubstons. Full article

With the increasing penetration of distributed generation and the diversification of electrical equipment, distribution networks face issues like three-phase unbalance and harmonic currents, while the voltage stability and inertia of the grid-connected system also decrease. A certain amount of energy storage is needed in a Distribution Static Synchronous Compensator (DSTATCOM) to manage power quality and actively support voltage and inertia in the network. This paper first addresses the limitations of traditional $dq0$ compensation algorithms in effectively filtering out negative-sequence twice-frequency components. An improved $dq0$ compensation algorithm is proposed to reduce errors in detecting positive-sequence fundamental current under unbalanced three-phase conditions. Second, considering the impedance ratio characteristics of the distribution network, while reactive power voltage regulation is common, active power regulation is more effective in high-resistance distribution networks. A grid-forming model-based active and reactive power coordinated voltage regulation method is proposed. This method uses synchronous control to establish a virtual three-phase voltage internal electromotive force, forming a comprehensive compensation strategy that combines power quality improvement and active voltage support, exploring the potential of energy storage DSTATCOM applications in distribution networks. Finally, simulation and experimental results demonstrate the effectiveness of the proposed control method. Full article

The ability to predict capacity retention is critical for ensuring the long-term reliability of supercapacitors in energy storage systems. This study presents a comprehensive machine learning framework that integrates both electrical cycling data and experimentally derived material and structural features to forecast the degradation behavior of commercial supercapacitors. A total of seven supercapacitor samples were tested under various current and voltage conditions, resulting in over 70,000 charge–discharge cycles across three case studies. In addition to electrical measurements, detailed physical and material characterizations were performed, including electrode dimension analysis, Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and Thermogravimetric Analysis (TGA). Three machine learning models, Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP), were trained using both cycler-only and combined cycler + material features. Results show that incorporating material features consistently improved prediction accuracy across all models. The MLP model exhibited the highest performance, achieving an R² of 0.976 on the training set and 0.941 on unseen data. Feature importance analysis confirmed that material descriptors such as porosity, thermal stability, and electrode thickness significantly contributed to model performance. This study demonstrates that combining electrical and material data offers a more holistic and physically informed approach to supercapacitor health prediction. The framework developed here provides a practical foundation for accurate and robust lifetime forecasting of commercial energy storage devices, highlighting the critical role of material-level insights in enhancing model generalization and reliability. Full article

To support the national goals of carbon peaking and carbon neutrality, this study proposes a household carbon emission prediction model based on Gene Expression Programming (GEP) for low-carbon retrofitting of aging residential areas in arid-cold regions. Focusing on 15 typical aging communities in Kundulun District, Baotou City, a 17-dimensional dataset encompassing building characteristics, demographic structure, and energy consumption patterns was collected through field surveys. Key influencing factors (e.g., electricity usage and heating energy consumption) were selected using Pearson correlation analysis and the Random Forest (RF) algorithm. Subsequently, a hybrid prediction model was constructed, with its parameters optimized by minimizing the root mean square error (RMSE) as the fitness function. Experimental results demonstrated that the model achieved an R² value of 0.81, reducing RMSE by 77.1% compared to conventional GEP models and by 60.4% compared to BP neural networks, while significantly improving stability. By combining data dimensionality reduction with adaptive evolutionary algorithms, this model overcomes the limitations of traditional methods in capturing complex nonlinear relationships. It provides a reliable tool for precision-based low-carbon retrofits in aging residential areas of arid-cold regions and offers a methodological advance for research on building carbon emission prediction driven by urban renewal. Full article

The widespread adoption of electric vehicles introduces significant challenges to power grid stability due to uncoordinated large-scale charging and discharging behaviors. By addressing these challenges, mobile energy storage systems emerge as a flexible resource. To maximize the synergistic potential of jointly scheduling electric vehicles and mobile energy storage systems, this study develops a collaborative scheduling model incorporating the prediction of geographically and chronologically varying distributions of electric vehicles. Non-dominated sorting genetic algorithm-III is then applied to solve this model. Validation through case studies, conducted on the IEEE-69 bus system and an actual urban road network in southern China, demonstrates the model’s efficacy. Case studies reveal that compared to the initial disordered state, the optimized strategy yields a 122.6% increase in profits of the electric vehicle charging station operator, a 44.7% reduction in costs to the electric vehicle user, and a 62.5% decrease in voltage deviation. Furthermore, non-dominated sorting genetic algorithm-III exhibits superior comprehensive performance in multi-objective optimization when benchmarked against two alternative algorithms. Full article

This paper presents the results of the mathematical modeling and experimental studies of charging a traction lithium-ion battery of a passenger electric car using an integrated charger based on a traction voltage inverter. An original three-stage charging algorithm (3PT/PN) has been developed and implemented, which provides a sequential decrease in the charging current when the specified voltage and temperature levels of the battery module are reached. As part of this study, a comprehensive mathematical model has been created that takes into account the features of the power circuit, control algorithms, thermal effects and characteristics of the storage battery. The model has been successfully verified based on the experimental data obtained when charging the battery module in real conditions. The maximum error of voltage modeling has been 0.71%; that of current has not exceeded 1%. The experiments show the achievement of a realized capacity of 8.9 Ah and an integral efficiency of 85.5%, while the temperature regime remains within safe limits. The proposed approach provides a high charge rate, stability of the thermal state of the battery and a long service life. The results can be used to optimize the charging infrastructure of electric vehicles and to develop intelligent battery module management systems. Full article

In the context of the “double carbon” goals and energy transformation, the integration of energy and transportation has emerged as a crucial trend in their coordinated development. Wind power prediction serves as the cornerstone technology for ensuring efficient operations within this integrated framework. This paper introduces a wind power prediction methodology based on an NGBoost–GRU fusion model and devises an innovative dynamic charging optimization strategy for electric vehicles (EVs) through deep collaboration. By integrating the dynamic feature extraction capabilities of GRU for time series data with the strengths of NGBoost in modeling nonlinear relationships and quantifying uncertainties, the proposed approach achieves enhanced performance. Specifically, the dual GRU fusion strategy effectively mitigates error accumulation and leverages spatial clustering to boost data homogeneity. These advancements collectively lead to a significant improvement in the prediction accuracy and reliability of wind power generation. Experiments on the dataset of a wind farm in Gansu Province demonstrate that the model achieves excellent performance, with an RMSE of 36.09 kW and an MAE of 29.96 kW at the 12 h prediction horizon. Based on this predictive capability, a “wind-power-charging collaborative optimization framework” is developed. This framework not only significantly enhances the local consumption rate of wind power but also effectively cuts users’ charging costs by approximately 18.7%, achieving a peak-shaving effect on grid load. As a result, it substantially improves the economic efficiency and stability of system operation. Overall, this study offers novel insights and robust support for optimizing the operation of integrated energy systems. Full article

Vibration energy offers promising potential for renewable energy harvesting, especially in conditions where conventional sources such as solar power may be limited or intermittent. This study proposes a rain energy harvester (REH) that converts the kinetic energy of raindrops into electrical energy using nonlinear thin plates, integrated with piezoelectric elements. Two plate configurations—fully hinged (H-H-H-H) and clamped–hinged–free–hinged (C-H-F-H)—are investigated. Theoretical modeling and simulation results are compared with experimental data, with special attention paid to the role of slapping forces in improving prediction accuracy. A power management system is also introduced to stabilize and regulate the harvested voltage. Results confirm the feasibility of rain-induced energy harvesting, showing potential for application in rain-prone areas and integration with existing infrastructure such as solar panels, tents, or canopies. Full article

The no-insulation (NI) technique improves the stability and defect-tolerance of high-temperature superconducting (HTS) coils by enabling current redistribution, thereby reducing the risk of quenching. NI–HTS coils are widely applied in DC systems such as high-field magnets and superconducting field coils for electric machines. However, the presence of turn-to-turn contact resistance makes current distribution uneven, rendering traditional simulation methods unsuitable. To address this, a finite element method (FEM) based on the T–A formulation is proposed. This model solves coupled equations for the magnetic vector potential (A) and current vector potential (T), incorporating turn-to-turn contact resistance and anisotropic conductivity. The thin-strip approximation simplifies second-generation HTS materials as one-dimensional conductors, and a homogenization technique further reduces computational time by averaging the properties between turns, although it may limit the resolution of localized inter-turn effects. To verify the model’s accuracy, simulation results are compared against the H formulation, distributed circuit network (DCN) model, and experimental data. The proposed T–A model accurately reproduces key transient characteristics, including magnetic field evolution and radial current distribution, in both circular and racetrack NI coils. These results confirm the model’s potential as an efficient and reliable tool for transient electromagnetic analysis of NI–HTS coils. Full article

With the increasing load and renewable energy capacity in interconnected power grids, the system voltage stability faces significant challenges. Tie-line transmission power is a critical factor influencing the voltage stability margin. To address this, this paper proposes a fully data-driven global sensitivity calculation method for the tie-line power-voltage stability margin, aiming to quantify the impact of tie-line power on the voltage stability margin. The method first constructs an online estimation model of the voltage stability margin based on system measurement data under ambient excitation. To adapt to changes in system operating conditions, an online updating strategy for the parameters of the margin estimation model is further proposed, drawing on incremental learning principles. Subsequently, considering the source–load uncertainty of the system, a global sensitivity calculation method based on analysis of variance (ANOVA) is proposed, utilizing online acquired voltage stability margin and tie-line power data, to accurately quantify the impact of tie-lines on the voltage stability margin. The accuracy of the proposed method is verified through the Nordic test system and the China Electric Power Research Institute (CEPRI) standard test case; the results show that the error of the proposed method is less than 0.3%, and the computation time is within 1 s. Full article

The natural gas pipeline network transmission system involved in the coordinated operation of pipeline networks and gas-fired power generation facilities is complex. It consists of multiple components, such as gas sources, users, valves, compressor stations, and pipelines. The addition of natural gas-fired power generation facilities overlaps with the high and low peak periods of civil gas, imposing dual peak-shaving pressures on pipeline networks and requiring more stringent operational control strategies for maintaining system stability. To address the aforementioned issues and improve the overall operating revenues of the system, we proposed the coordinated optimization model of gas-fired power generation facilities, pipeline networks, gas storage, and compressor stations. The optimization algorithm is written using the penalty function method of the Interior Point OPTimizer (IPOPT) solver. Meanwhile, the basic parameters of the system’s pipeline networks, users, gas storage, natural gas-fired power generation facilities, compressors, and electricity prices were input into the solver. The research results reveal that the algorithm ensures solution accuracy while accounting for computational efficiency and practical applicability. The algorithm can be used to effectively calculate the ideal coordinated operation solution, significantly improve the operating revenues of the system, and achieve safe, stable, coordinated, and efficient operation of the system. Full article

The electric vehicle (EV) business model integrates advanced battery technology, dynamic power train architectures, and intelligent energy management systems with ecosystem strategies and digital services. It incorporates environmental sustainability through lifecycle analysis and renewable energy integration. China, with 9.49 million EV sales in 2023 (33% market share), faces infrastructure gaps constraining further growth. China is strategically mitigating CO₂ emissions while fostering economic expansion, notwithstanding constraints such as suboptimal battery technology advancements, elevated production expenditure, and enduring ecological impacts. This Political, Economic, Social, Technological, Legal, Environmental (PESTLE) assessment, operationalized through a survey of 800 stakeholders and Statistical Package for the Social Sciences IBM SPSS SPSS (Version 28) quantitative analysis (factor loading = 0.73 for Technology; eigenvalue = 4.12), identifies infrastructure gaps as the dominant barrier (72% of stakeholders). Political factors (β = 0.82) emerged as the strongest adoption predictor, outweighing economic subsidies in significance. The adoption of EVs in China presents a significant prospect for reducing CO₂ emissions and advancing technology. However, economic barriers, market dynamics, inadequate infrastructure, regulatory uncertainty, and social acceptance issues are addressed in the assessment. The study recommends prioritizing infrastructure investment (e.g., 500 K fast-charging stations by 2027) and policy stability to overcome adoption barriers. This study provides three key advances: (1) quantification of PESTLE factor weights via factor analysis, revealing technological (infrastructure) and political factors as dominant; (2) identification of infrastructure gaps, not subsidies, as the primary adoption barrier; and (3) demonstration of infrastructure’s persistence post-subsidy cuts. These insights redefine EV adoption priorities in China. Full article

Electricity theft remains a persistent challenge, particularly in developing economies where infrastructure limitations and socioeconomic disparities contribute to illegal connections. This study analyzes the determinants influencing electricity theft in Kinshasa, the Democratic Republic of Congo, using a logistic regression model applied to 385 observations, which includes random bootstrapping sampling for enhanced stability and power analysis validation to confirm the adequacy of the sample size. The model achieved an AUC of 0.86, demonstrating strong discriminatory power, while the Hosmer–Lemeshow test (p = 0.471) confirmed its robust fit. Our findings indicate that electricity supply quality, financial stress, tampering awareness, and billing transparency are key predictors of theft likelihood. Households experiencing unreliable service and economic hardship showed higher theft probability, while those receiving regular invoices and alternative legal energy solutions exhibited lower risk. Lasso regression was implemented to refine predictor selection, ensuring model efficiency. Based on these insights, a multifaceted policy approach—including grid modernization, prepaid billing systems, awareness campaigns, and regulatory enforcement—is recommended to mitigate electricity theft and promote sustainable energy access in urban environments. Full article

Although the Power Internet of Things (PIoT) significantly improves operational efficiency by enabling real-time monitoring, intelligent control, and predictive maintenance across the grid, its inherently open and deeply interconnected cyber-physical architecture concurrently introduces increasingly complex and severe security threats. Existing IoT security solutions are not fully adapted to the specific requirements of power systems, such as safety-critical reliability, protocol heterogeneity, physical/electrical context awareness, and the incorporation of domain-specific operational knowledge unique to the power sector. These limitations often lead to high false positives (flagging normal operations as malicious) and false negatives (failing to detect actual intrusions), ultimately compromising system stability and security response. To address these challenges, we propose a domain knowledge-driven threat source detection and localization method for the PIoT. The proposed method combines multi-source features—including electrical-layer measurements, network-layer metrics, and behavioral-layer logs—into a unified representation through a multi-level PIoT feature engineering framework. Building on advances in multimodal data integration and feature fusion, our framework employs a hybrid neural architecture combining the TabTransformer to model structured physical and network-layer features with BiLSTM to capture temporal dependencies in behavioral log sequences. This design enables comprehensive threat detection while supporting interpretable and fine-grained source localization. Experiments on a real-world Power Internet of Things (PIoT) dataset demonstrate that the proposed method achieves high detection accuracy and enables the actionable attribution of attack stages aligned with the MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) framework. The proposed approach offers a scalable and domain-adaptable foundation for security analytics in cyber-physical power systems. Full article