In the context of the reform of transmission and distribution tariff mechanism, the drawbacks of the existing two-part tariff system which cannot reasonably reflect the real cost of electricity consumption by power users have gradually emerged. The two-part tariff mechanism is responsible for allocating the space for electricity generation, transmission, distribution and sale tariffs, and regulating the resources of the power system. Therefore, it is urgent to improve the existing two-part tariff mechanism. This paper, focusing on the two-part tariff mechanism, first, introduces the basic theory and billing ratio of the two-part tariff, and studies the method of apportioning transmission and distribution costs based on different load rates and voltage levels. Then, it summarizes the electricity tariff mechanisms such as load rate packages and time-of-use tariffs and the basic tariff mechanisms such as tariff, load adjustments, and improved billing ratios respectively for the collection methods of two-part tariffs. Afterwards, it analyzes the implementation mode of two-part tariff mechanism theory by combining the practical experience of two-part system in the United States, France, Japan, and other foreign countries. Finally, it proposes the future development direction and suggestions of China’s two-part tariff mechanism.

In the context of the development of new power systems, a three-dimensional spatio-temporal adaptation assessment model based on grid satisfaction, spatio-temporal resources, and effectiveness improvement is proposed to address the problems of distribution network planning and construction, the adaptability of key distribution network technologies to the space and time in which they are applied, and the difficulty of quantifying application defects. Subjective assignment in hierarchical analysis is improved using continuous interval ordered weighted average operator, and the problem of bias of the single assignment method is solved by the introduction of a subjective-objective combination assignment method constructed by the conflicting correlation among criteria method. The degree of affiliation of each indicator is determined using the fuzzy integrated evaluation method and the evaluation level is then obtained. The case studies show that the proposed method can quantify the degree of fit between the key technologies of the distribution network and the space-time, identify the adaptability of the key technologies to different space-times and the degree of satisfaction of the application of the technologies in different space-times, and reveal the weaknesses of the key technologies of the distribution network, which can help improve the efficiency and effectiveness of the investment in the distribution network and better serve the high-quality development of the economy and society.

In order to solve the problems existing in the traditional frequency analysis method for the frequency analysis of grids with a high proportion of new energy, such as the large amount of calculation, the difficulty of modeling, and the prominent contradiction between the calculation speed and the calculation accuracy, this paper proposes a new frequency characteristic prediction method for the new power system based on convolutional neural network (CNN). First, the main frequency indexes of the power system with a high proportion of new energy under power disturbances are predicted using one-dimensional CNN, including the initial frequency change rate, frequency extremum, and frequency steady-state value. The prediction accuracy is improved by setting reasonable input characteristics and optimizing the parameters of the neural network. Then, the impact of disturbance location and disturbance type is further considered, and the power system characteristic data set containing disturbance information is established by the method of data dimensionality reduction. The input characteristics are constructed by using the principle of three primary channels for reference, and the extended two-dimensional CNN is used to predict the frequency security index, which improves the adaptability of CNN in the frequency analysis of grids with a high proportion of new energy. Finally, the method is verified by an example in the improved BPA 10-machine 39-node model, and the results are compared with the prediction results of the recurrent neural network, which proves that the proposed method has a high accuracy and adaptability.

With the gradual marketization of energy trading, different economic entities such as the integrated energy service provider (IESP) and user aggregators (UAs) will form within the regional integrated energy system (RIES). The coordination of all entities to participate in joint planning for a globally optimal planning scheme while protecting privacy has posed a new challenge for RIES planning. First, the structure of the coupled electrical-gas-thermal RIES is clarified in this paper, and a mathematical model is constructed from IESP, UA, and electrical-gas-thermal network. Then, a RIES joint planning model considering IESP and multiple UAs is proposed with the objective of economical optimization. Afterwards, in order to adapt to the non-convexity of the sub-problem of the joint planning problem, the enhanced Benders algorithm is proposed to realize the distributed solution of the model, which protects the privacy of all entities to the greatest extent. Finally, the advantages of the joint planning scheme in terms of economy and energy efficiency are analyzed by comparing four sets of cases, and the good convergence of the proposed distributed algorithm is verified.

With the increase in the proportion of renewable energy connected to the grid, a large number of N-1 security constraints should be considered in the security-constrained economic dispatch (SCED) to ensure the reliable operation of the power system, which causes a great computational burden. However, only a small number of constraints in the N-1 security constraints are contributing factors. Therefore, the elimination of redundant constraints can significantly improve the solution efficiency of the SCED model. First, a new SCED model considering wind power and photovoltaic is established, and a low-rank approximation (LRA) surrogate model is constructed based on the historical operation information of the SCED model. Then, the active constraints are identified based on the estimation results of the LRA surrogate model and the active constraint set is constructed. Next, an iterative solution process of the SCED model based on LRA surrogate model is proposed. Finally, the simulations are conducted on an IEEE 39-bus system. The simulation results show that the error between the solution results of the LRA surrogate and the SCED models is less than 10%, the active constraint identification accuracy is raised, and the average iterative solution time of the proposed solution process is reduced by more than 50%, which indicates the improvement of the solution efficiency of the SCED model.

Access of large-scale distributed power supply to the distribution network brings dimensionality disaster to the optimal dispatching of the distribution network. Therefore, it is necessary to cluster the distributed power supply to reduce the difficulty of regulation and control, and a reasonable division of distributed power supply cluster is very important. However, the incomplete real-time measurement data of the distribution network has caused difficulty and low time efficiency in real-time cluster division of the distribution network. Therefore, this paper proposes a distributed power cluster division strategy based on the smart local moving (SLM) algorithm and the radial basis function (RBF) neural network. First, the range of active power and reactive power regulation and the sensitivity of active power and reactive power to voltage are selected as the indexes of cluster division. By constructing a similarity matrix, the SLM algorithm is used to form the historical strategy library of cluster division of distributed power sources. Then, a voltage fitting model is established offline, which can observe the relationship between the power and voltage of buses in real time. Meanwhile, a voltage-division result model is established offline, and the real-time division result is obtained through the voltage online, which solves the problem that cluster division cannot be performed when the power flow model is missing, and improves the real-time performance of cluster division. Finally, the rationality and superiority of the algorithm are verified by simulation on MATLAB platform.

Renewable power plant based on the grid-forming converter has a similar performance with the traditional thermal power plant on the function of active support for the frequency and voltage in the power system. An aggregation model is proposed for the frequency and voltage stability analysis of new power system, the overall operation characteristics of the plant are analyzed, and a method for identifying and selecting the parameters of the aggregation model is proposed. The proposed aggregation model can accurately reflect the dynamic process of the interaction between the renewable power plant and the grid, and ensure a quick simulation rate. In comparison with the electromagnetic transient model for grid-forming renewable power plant, the effectiveness of the proposed aggregation model is verified in MATLAB/Simulink. The accuracy and rapidity of the proposed aggregation model is verified in the frequency and voltage stability simulation analysis of power system based on the case study in the IEEE 39 bus system.

The output of wind power and photovoltaic has the characteristics of randomness, volatility, and intermittency. Direct grid connection will lead to a lower power generation income of the power station, a greater volatility of grid connection of electric energy, and more wind and photovoltaic power discards, resulting in lower carbon emission reductions. The addition of pumped storage power plants effectively reduces the above impacts. Therefore, this paper studies the application scenario of wind photovoltaic and pumped storage combined power generation, establishes a multi-objective optimization model that comprehensively considers the three objectives of maximizing the economic benefits of the combined system, minimizing the system power fluctuation, and maximizing carbon emission reduction, and converts the multi-objective problem into a single objective problem for solution by normalization. In this paper, the gray wolf algorithm, which can realize the adaptive adjustment of local search and global search, is used to simulate and optimize the grid connected power of wind power, photovoltaic, and pumped storage. The optimization results show that the established model can effectively improve the economic benefits of the system and greatly reduce the fluctuation of power grid connection. In addition, the efficient use of new energy also greatly improves the carbon emission reduction capacity of the joint system, which proves that the model has high feasibility.

During the long-term operation of marine sediment microbial fuel cell (MSMFC) on the ocean floor, the sediment porosity affects the horizontal diffusion coefficient of organic matter near the anode,which finally influences the anodic electrochemical performance and the power output of cells. In this paper, the sediment with different sediment porosities is established in lab by artificially adjusting sediment porosity to investigate its influence on the performance of MSMFC, so as to creatively build the mathematical relationship between the energy production of MSMFC and the horizontal diffusion coefficient. With the increase of sediment porosity, the anode kinetic activity decreases and then increases, the highest kinetic activity is 3.85 times higher than the lowest kinetic activity. When the sediment porosity is 45.2%, the maximum power output reaches 206.8 mW/m². Horizontal diffusion coefficient of organic increases with an increase in sediment porosity, and it has a linear relationship with the energy production of MSMFC. When the sediment porosity is 45.2%, the horizontal diffusion coefficient is 0.48 m²/s, and the energy production of MSMFC reaches 804.04 J. These results render a technological base for the site choice of MSMFC deploying in different marine sediments, anode design and battery operation for long term.

Micro direct methanol fuel cell (μDMFC) has the advantages of high energy density, portable use, fast replenishment, and eco-friendliness. However, the practical service life of μDMFC is often limited due to the deterioration of membrane electrode assembly in electrochemical reaction. Therefore, it is necessary to evaluate the health status and remaining useful life (RUL) of the cell to provide decision-making support for fuel cell characteristic modification and control strategy. Considering the pros and cons of data-driven and model-based methods, an RUL prediction method for μDMFC based on equivalent circuit model (ECM) is proposed. Among the degradation indicators of μDMFC, the cell output voltage can be monitored in real time to obtain the degradation trend. However, this indicator cannot provide accurate prediction results alone under dynamic operating conditions. Deeper-level information, such as the internal impedance, can be obtained by investigating the electrochemical impedance spectroscopy (EIS), but such in-depth information is difficult to be monitored in real time and can only be measured offline at low frequencies. Moreover, fuel cells are usually under dynamic operating conditions in practical applications, so their degradation and service life are affected by the operating conditions. Traditional output voltage regression-based prediction methods cannot cope with dynamic changes in operation. Therefore, the prediction model can be built through scheduled offline measurement of internal degradation indicators. The experimental results show that, compared with the traditional data-driven method, the prediction method based on the internal EIS characterization can better adapt to the variable operating conditions and has a superior performance in RUL predictions.

To reduce the current stress of dual active full-bridge converters in the buck-boost mode, a current stress optimization control strategy based on new extended-phase-shift modulation is proposed. First, the phase shift between the output high-level voltages at both ends of the transformer is defined as the external phase shift angle. Then, the working characteristics of the converter are compared and analyzed when the internal phase shift angle is on different sides of the transformer in the voltage buck-boost mode. According to the relationship between the phase shift angles, working modes are divided into three types, and the current stress expression and power model are obtained. Moreover, the improved working mode is obtained by comparative analysis, and the optimal phase shift combination of current stress is obtained by using Lagrange multipler method algorithm. Finally, an experimental platform is established to verify the converter in the buck-boost mode. The experimental results show that the current stress optimization control strategy significantly reduces the current stress of the converter in the buck-boost mode.

As a new erasure code for distributed storage systems, locally repairable codes (LRCs) can effectively realize the reliable and efficient storage of massive data. The construction of locally repairable codes with (r,t) locality has become a research hotspot recently. Therefore, the construction methods of locally repairable codes based on triangular association schemes are proposed, which can construct optimal binary locally repairable codes with arbitrary (r,t) locality. Performance analyses show that the LRCs constructed with availability t=2 reach the optimal code rate bound, the LRCs constructed with arbitrary locality r>2 and availability t>2 reach the optimal minimum distance bound. The LRC constructed in this paper performs better in terms of code rate and more flexible parameter selection than those constructed based on near-regular graphs and direct product codes, etc.

Image restoration under adverse weather conditions is of great significance for the subsequent advanced computer vision tasks. However, most existing image restoration algorithms only remove single weather degradation, and few studies has been conducted on all-in-one weather-degraded image restoration. The denoising diffusion probability model is combined with Vision Transformer to propose a Transformer-based diffusion model for all-in-one weather-degraded image restoration. First, the weather-degraded image is utilized as the condition to guide the reverse sampling of diffusion model and generate corresponding clean background image. Then, the subspace transposed Transformer for noise estimation (NE-STT) is proposed, which utilizes the degraded image and the noisy state to estimate noise distribution, including the subspace transposed self-attention (STSA) mechanism and a dual grouped gated feed-forward network (DGGFFN). The STSA adopts subspace transformation coefficient to effectively capture global long-range dependencies while significantly reducing computational burden. The DGGFFN employs the dual grouped gated mechanism to enhance the nonlinear characterization ability of feed-forward network. The experimental results show that in comparison with the recently developed algorithms, such as All-in-One and TransWeather, the method proposed obtains a performance gain of 3.68 and 3.08 dB in average peak signal-to-noise ratio while 2.93% and 3.13% in average structural similarity on 5 weather-degraded datasets.

Accurate matching between in-vehicle sensor data and high-definition (HD) maps is crucial to improve the performance of perception and localization of intelligent vehicles. A novel algorithm of HD map matching based on the developed semantic likelihood model (SLM) is proposed to achieve intelligent vehicle localization and object detection simultaneously. First, semantic pavement objects are extracted from front-view images by using U-Net, and SLM is constructed with kernel density estimation (KDE). Under a particle filter framework, the likelihood between the sensor data and HD map is calculated by projecting each sample point from HD map with pose transformation onto SLM to update the weight of each particle. Simultaneously, accurate detection of pavement markings is accomplished by projecting all elements onto the HD map with the computed localization results. In the experiment, data collected on campus and on urban roads are used to validate the proposed algorithm. The experimental results show that the localization errors in both scenarios are about 14 cm, and the mean intersection over union (MIoU) of road marking detection is above 80. The results demonstrate that the proposed algorithm can significantly improve both localization and detection performance by effectively utilizing the prior information of HD maps, compared with the state of the art (SOTA) methods, such as deep learning-based detection methods.

Medical picture archiving and communication system (PACS) is a typical application scenario with massive small files, which faces two challenges in data storage, i.e., efficient metadata management and effective performance reduction caused by fragmentization. By analyzing various key components of the full IO (input/output) path in the medical PACS imaging system, this paper optimizes the design to achieve a significant improvement in the retrieval performance of the PACS from four dimensions, PACS software retrieval algorithm, storage protocol gateway high-concurrency design, small file aggregation, and data storage service concurrency model. The results of test show that the retrieval performance after optimizion can reach 300 images per second, which is more than three times that of traditional storage, and resolves effectively the performance problem of PACS image data retrieval.