• Energy Research
• 2021-2025close
• CN close
• AE close
• Applied Sciences close

The growth of energy consumption has led to the depletion of fossil energy and the increasing greenhouse effect. In this case, low carbonization has become an important trend in the world’s energy development, in which clean energy occupies an important position. The uncertainties brought by the large-scale integration of wind power, photovoltaic and other renewable energy sources into the grid pose a serious challenge to system dispatch. The participation of demand response (DR) resources can flexibly cooperate with renewable energy, optimizing system dispatch and promoting renewable energy consumption. Thus, we propose a flexible DR scheduling strategy based on multiple response modes in this paper. We first present a DR resource operation model based on multivariate response modes. Then, the uncertainties are considered and dealt with by scenario generation and reduction technology. Finally, a day-head dispatch strategy considering flexible DR operation and wind power uncertainties is established. The simulation results show that the proposed strategy promotes wind power consumption and reduces system operation costs.

Production system flexibility has become an important factor to meet the needs of diversified markets in an ever-changing and turbulent market environment. Thus, this paper addresses the problems of flexibility evaluation for decision making by evaluation method driven by digital twin, which models, predicts, and evaluates the flexibility of a given production system design scheme (PSDS). Based on the definition and characteristic analysis of flexibility in the production system design phase, and combined with data modelling (static function model (SFM) and dynamic prediction model (DPM)) and data analysis (AHP-based and simulation-based analysis), the proposed framework evaluates system flexibility from the static and dynamic levels, helps enterprises quickly obtain the system’s comprehensive flexibility evaluation index, realizing the rapid screening of the production system design scheme (PSDS). The successful application of the evaluation framework in case enterprise has fully proved the feasibility and effectiveness of the method.

As a key component in overhead cables, insulators play an important role. However, in the process of insulator inspection, due to background interference, small fault area, limitations of manual detection, and other factors, detection is difficult, has low accuracy, and is prone to missed detection and false detection. To detect insulator defects more accurately, the insulator defect detection algorithm based on You Only Look Once version 5 (YOLOv5) is proposed. A backbone network was built with lightweight modules to reduce network computing overhead. The small-scale network detection layer was increased to improve the network for small target detection accuracy. A receptive field module was designed to replace the original spatial pyramid pooling (SPP) module so that the network can obtain feature information and improve network performance. Finally, experiments were carried out on the insulator image dataset. The experimental results show that the average accuracy of the algorithm is 97.4%, which is 7% higher than that of the original YOLOv5 network, and the detection speed is increased by 10 fps, which improves the accuracy and speed of insulator detection.

In this article, we comprehensively reviewed Argentina’s Vaca Muerta formation, which encompasses a geological overview, advances in extraction technologies, the potential environmental impact, and economic analysis. Detailed geological analysis is discussed, emphasizing the stratigraphy, lithology, and depositional environments of the formation, which is crucial for understanding the distribution and quality of hydrocarbon resources. The latest advancements in hydraulic fracturing and horizontal drilling are explored, which have significantly improved efficiency and increased recoverable resources. The environmental implications of these extraction methods are critically examined. This includes a discussion of the necessity of sustainable practices in hydrocarbon extraction, highlighting the balance between resource development and environmental stewardship. The economic viability of the Vaca Muerta formation is analyzed, with a focus on cost-effectiveness, market trends, and investment patterns. This section assesses the formation’s potential as a profitable venture and its impact on the global energy market. Finally, the review anticipates future technological and policy developments. The strategic importance of the Vaca Muerta formation in the global energy sector is underscored, and its potential role in shaping future hydrocarbon exploration and production strategies is examined. In short, this essay not only presents data and findings, but also contextualizes them within the broader scope of energy production, environmental sustainability, and economic viability. This comprehensive approach provides a multi-faceted understanding of the Vaca Muerta formation’s significance in the global energy landscape.

Although the vision transformer has been used in gait recognition, its application in multi-view gait recognition remains limited. Different views significantly affect the accuracy with which the characteristics of gait contour are extracted and identified. To address this issue, this paper proposes a Siamese mobile vision transformer (SMViT). This model not only focuses on the local characteristics of the human gait space, but also considers the characteristics of long-distance attention associations, which can extract multi-dimensional step status characteristics. In addition, it describes how different perspectives affect the gait characteristics and generates reliable features of perspective–relationship factors. The average recognition rate of SMViT for the CASIA B dataset reached 96.4%. The experimental results show that SMViT can attain a state-of-the-art performance when compared to advanced step-recognition models, such as GaitGAN, Multi_view GAN and Posegait.

In the process of high-concentration photovoltaic (HCPV) power generation, multijunction cells work in the conditions of high radiation and high current. Non-uniformity of focusing, the mismatch between the focusing spectrum caused by the dispersion effect and the spectrum of multijunction solar cell design and the increase in cell temperature are the key factors affecting the photoelectric performance of the multijunction solar cell. The coupling effect of three factors on the performance of multijunction solar cell intensifies its negative impact. Based on the previous research, the light intensity and spectral characteristics under Fresnel lens focusing are calculated through the optical model, and the optical–electrical–thermal coupling model under non-uniform illumination is established. The results show that obvious changes exist in the concentration spectrum distribution, energy and non-uniformity along different optical axis positions. These changes lead to serious current mismatch and transverse current in the multijunction solar cell placed near the focal plane which decreases the output power. The lost energy makes the cell temperature highest near the focal plane. In the condition of passive heat dissipation with 500 times geometric concentration ratio, the output power of the solar cell near the focal plane decreases by 35% and the temperature increases by 15%. Therefore, optimizing the placement position of the multijunction cell in the optical axis direction can alleviate the negative effects of optical–electrical–thermal coupling caused by focusing non-uniformity, spectral mismatch and rising cell temperature, and improve the output performance of the cell. This conclusion is verified by the experimental result.

Artificial filled joints made of sand–clay mixtures with different clay weight fractions and saturations have different wave attenuation capacities. In this paper, the high amplitude impact test of sand–clay mixtures was carried out by using split Hopkinson pressure bar (SHPB) equipment. The results showed that with the increase of clay weight fraction, the particle crushing decreased continuously, while the wave attenuation coefficient decreased first and then increased. When the weight fraction of clay was 50%, the wave attenuation coefficient reached the minimum among the tested working conditions, and the ratio of transmitted energy to incident energy reached the maximum. With the increase of saturation, the particle crushing decreased first and then increased, while the wave attenuation coefficient increased first and then decreased. When the saturation was 25%, the wave attenuation coefficient reached the maximum, and the proportion of transmitted energy to incident energy reached the minimum. Because of the lubrication of water reduced the friction between particle, the specimen more prone to deformation and particle crushing reduced. As the saturation increased, this effect gradually decreased. In the case of the wave absorbing layer of protective works, special attention should be paid to the adverse effects caused by groundwater.

This paper presents a two-step approach for optimizing the configuration of a mobile photovoltaic-diesel-storage microgrid system. Initially, we developed a planning configuration model to ensure a balance between the mobility of components and a sustainable power supply. Then, we introduced a method that merges optimization and decision-making. The first phase identifies Pareto optimal solutions (POSs) with a favorable distribution by using a multi-objective evolutionary algorithm with classification-based preselection (CPS-MOEA). In the second phase, we utilize the fuzzy C-means algorithm (FCM) and the grey relational projection (GRP) method for comprehensive decision-making. This aims to select the most suitable and compromise solution from the POSs, closely aligning with the decision-maker’s preferences. Beyond addressing the optimal planning and configuration issue, the experimental results show that the method surpasses other widely used multi-objective optimization algorithms, including the Preference Inspired Co-evolution Algorithm (PICEA-g), the Multi-Objective Particle Swarm Optimization Algorithm (MOPSO), and the third stage of Generalized Differential Evolution (GDE3).