The rapid growing electric vehicle (EV) charging load in highway service areas bringing pressure to the power grid. To solve this problem, this paper proposes a joint optimization and scheduling method that combines energy of photovoltaic (PV) power, wind power, and battery energy storage systems (BESS). A multi-objective model is developed with three goals: minimizing voltage fluctuations, maximizing renewable energy utilization, and restoring the state of charge (SOC) of the BESS. A Memetic Genetic Algorithm (GA) is used for optimization, incorporating simulated binary crossover, polynomial mutation, and local search. The simulation results demonstrate that the Memetic GA algorithm outperforms the Classic GA in terms of voltage control, scheduling stability, and renewable energy utilization optimization. Compared to the classic GA algorithm, the Memetic GA algorithm reduces voltage variance by 74

Shared Inter Data Center Wide Area Networks (inter-DC WANs) increasingly carry both delay-sensitive ISPfacing traffic and bulk inter-DC transfers. Existing traffic engineering (TE) often optimizes latency or utilization in isolation, while availability guarantees are typically coarse-grained and weakly tied to SLA outcomes. We present Tri-classification Traffic Engineering (TTE), an availability-aware TE framework for shared inter-DC WANs that differentiates three traffic classes with heterogeneous latency and bandwidth-availability targets, and jointly optimizes egress selection and multi-path routing under an SLA-driven objective. TTE formulates the control problem as a constrained optimization that couples maximum link utilization, end-to-end latency, and probabilistic bandwidthavailability requirements. To enable fast adaptation to demand fluctuations, TTE learns a deep reinforcement learning agent to recommend class-aware egress decisions, while a lightweight optimizer computes feasible routing splits and availability allocations. Trace-driven experiments on WAN topologies demonstrate that TTE improves SLA satisfaction and latency-utilization trade-offs with low decision latency.

In this paper, an expert system for distribution capacity regulating transformer health index based on intuitionistic fuzzy set theory is designed to address the current problems in distribution capacity regulating transformer health assessment, such as the difficulty of systematic extraction and utilization of expert experience, the lack of intelligent reasoning mechanism in the diagnostic process, and the inability of knowledge to be self-updated. The system takes the comprehensive seminar hall as the core knowledge source, constructs the index-empirical knowledge base structure, and introduces the ternary function of support degree, nonsupport degree and hesitation degree to express the uncertainty of expert evaluation. In the reasoning process, the intuitionistic fuzzy distance method is used to match the input data with the knowledge rules, and the optimal empirical output is selected through the multilevel discriminative strategy. At the same time, the system introduces the inference feedback mechanism to realize the dynamic confidence adjustment and versioning management of knowledge rules. In this paper, the hardware and software architecture design of the expert system, data platform synergy mechanism, knowledge extraction and reasoning mechanism design are given in detail, and the effectiveness and interpretability of the system in actual fault diagnosis scenarios are verified through examples. The results show that the system can realize the structured management and effective use of expert experience, and improve the accuracy and automation level of distribution capacity regulating transformer health state assessment. The study provides theoretical and engineering references for constructing an intelligent power diagnosis system with adaptive capability.

When implementing the digital monitoring platform for a provincial photovoltaic substation digital monitoring platform, fault detection is a key link. A reasonable and scientific fault detection model can enhance the platform’s sensitivity and provide timely warnings for faults. However, the current fault monitoring models for photovoltaic power plants have the problem of overly relying on a large number of fault samples. In view of this, this study introduces the Apriori algorithm that does not rely on sample data and the density-based clustering algorithm, and combines the two. The improved Apriori algorithm and the density-based clustering algorithm were used to mine and cluster the sample data. Based on this, a fault detection model for photovoltaic power plants was constructed. This research aims to establish a more accurate fault detection model, improve monitoring systems, repair faults in a timely manner, and reduce economic losses for owners and power plants. The research algorithm’s accuracy is 93.55%, and its recall rate is 96.67%, both higher than those of the unmodified model. The research model’s average F1 value is 97.53%, which is significantly higher than that of support vector machine and decision tree models. The performance superiority of fault detection models has been verified, demonstrating the research model’s applicability in fault detection of photovoltaic power plants. It further improves the monitoring system of the digital monitoring platform for provincial photovoltaic stations and also provides theoretical and data support for fault detection in power stations.

To improve the accuracy of short-term wind power forecasting, this paper proposed a two-stage short-term wind power forecasting method based on wind speed prediction and a wind speed-power conversion model. In the first stage, the Support Vector Machine (SVM) model and the Jaya optimization algorithm were used to predict wind speed, yielding accurate wind speed forecasts. In the second stage, the predicted wind speed, along with environmental factors and system state parameters, was used to construct a dynamic wind speed-to-power conversion model using the Gradient Boosting Decision Tree (GBDT). Compared with conventional forecasting methods and models based on power curves, this approach fully considered the impact of environmental and system factors, resulting in higher reliability and accuracy. Experimental results demonstrated that the performance of the two-stage forecasting model surpassed that of the baseline models, enhancing the effectiveness of wind power forecasting and increasing the credibility of the prediction results.

Due to the massive integration of distributed photovoltaic systems, seasonal production activities, holidays, and temperature changes, the seasonal overload and low voltage problems of transformers in mountainous area distribution networks are particularly prominent, seriously affecting the stable and reliable supply of electricity. Considering that the problem only occurs seasonally, traditional methods such as increasing capacity and adding distribution transformers have problems such as low equipment utilization and high investment. Therefore, how to ensure normal power supply and stable operation of distribution transformers during seasonal peak load periods with limited investment is an urgent problem to be solved. This article first analyzes the characteristics of overload and low voltage problems in the distribution transformers of a regional power grid. Then, a method of installing fans on distribution transformers was proposed to ensure their safe and stable operation under short-term overload conditions. Secondly, in response to the low voltage problem caused by a long power supply radius, low-voltage AC/DC hybrid technology has been adopted to solve the problems of high cost and limited improvement effect on long-distance line voltage through traditional mitigation strategies. Finally, the feasibility and economy of the mitigation strategies proposed in this article were verified through a regional power grid project.

Carbon emission accounting is the core means to grasp the carbon emission level of the park and identify the main carbon emission sources. The boundaries of carbon emission accounting in existing parks are vague, and the accounting methods have not yet formed a clear framework. At the same time, research on carbon emission models mostly stays at theoretical analysis, making it difficult to achieve quantitative goals, and there is no industry recognized unified carbon emission calculation model. This article focuses on tracking the carbon footprint of low-carbon parks, clarifying the geographical and operational emission sources, as well as the scope of carbon emission gas accounting. Multiple accounting methods are compared, and a planning stage based on emission factor method and dynamic simulation of operational stage are proposed to establish an overall dynamic calculation model with carbon reduction accounting, providing quantitative support for energy conservation and carbon reduction in parks.

Conducting real-time online security analysis for power grids and determining the system’s security margin are pivotal research areas in the field of power systems. The thermal security region plays a crucial role in evaluating how random fluctuations in power growth direction affect the thermal stability of power systems. By employing the DC power flow model, the high-dimensional boundary surface of the steady-state security region can be approximated as a hyperplane. However, as the scale of the system expands, the inaccuracies in the analytical model increasingly distort the security region boundary. To tackle this challenge, this paper presents a hybrid approach that integrates physical model-driven and data-driven methods to modify the steady-state security region boundary. This approach not only retains essential information from the physical model but also harnesses the power of data analysis to uncover hidden linear error patterns. Extensive testing on various IEEE standard test systems demonstrates the model’s exceptional accuracy.

This study aims to analyze the correlation between the charging load at electric vehicle charging stations and pricing policies to clarify the impact of price factors on user charging behavior. Employing Random Forest Regression and Ridge Regression models, the study models and analyzes the relationship between charging load and time-of-use electricity prices, as well as service fees. Descriptive statistical analysis was conducted to thoroughly examine the characteristics of the data, followed by the application of T-tests and the 3s principle to process outliers, ensuring the accuracy of the analysis. The model outcomes revealed the differential effects of time-of-use electricity prices on charging load across various periods and the sensitivity of charging behavior to service fee adjustments. The findings indicate that during peak periods, the suppressive effect of electricity prices on charging demand is limited, while adjustments to service fees can effectively guide user charging behavior. This research provides data support and strategic recommendations for pricing strategies and load management at charging stations, offering practical guidance for optimizing the operation of charging stations.

This study proposes a coordinated optimization method for intelligent building clusters, leveraging the mobile energy storage characteristics of electric vehicles (EVs) for enhanced energy management. A two-stage scheduling model is developed, integrating centralized and distributed algorithms to optimize EV charging and discharging across multiple buildings. The approach explores demand-side flexibility, improves renewable energy utilization, and ensures building autonomy. Case studies demonstrate that the proposed strategy promotes energy complementation, reduces dependence on the main grid, and boosts the local consumption rate of renewable energy. The mobile energy storage model effectively addresses multi-trip scenarios, providing a comprehensive solution for spatiotemporal energy optimization. The DSG algorithm ensures efficient energy sharing and cost-effective power exchange between buildings. Future research should focus on further improving renewable energy integration by considering diverse user energy preferences and the unique characteristics of flexible resources within buildings.

A new method for modifying vermiculite with Na+ and K+ was proposed to improve its thermal expansion performance. High-temperature expansion experiment was used to measure the expansion rate of vermiculite before and after modification in various dimensions, and the microstructure and phase composition of various modified vermiculite were analyzed by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The result of expansion experiment indicates that the expansion ratios of modified vermiculite increase with the increase of vermiculite particle size and experiment temperature with the temperature range of 100 ℃–900 ℃ when the particle size of vermiculite increases from 0.2 mm to 0.8 mm, and the modification effect of adding KCl is better than that of adding NaHCO3. The result of SEM shows that the interlayer spacing of Na-vermiculite is smaller than that of K-vermiculite after high-temperature expansion. The result of XRD shows that there are gold mica and mixed layer minerals of vermiculite and phlogopite in vermiculite, and inorganic salts do not damage the original layered structure and strength of vermiculite after modification.

This study presents a concise and interpretable carbon footprint tracking method tailored for low-carbon parks and campuses. By integrating K-means++ clustering, regression analysis, and Shapley value interpretation, the framework enables real-time scenario classification, emission anomaly detection, and source attribution. Multi-dimensional features—including energy system configurations, environmental conditions, and user behaviors—are used to cluster parks into typical emission scenarios, each with a defined regression score threshold. When a new scenario exceeds this threshold, Shapley analysis identifies the key contributing factors. A case study of a smart office park confirms the model's effectiveness in pinpointing carbon-intensive behaviors, such as reduced solar radiation and aggressive air-conditioning usage, offering practical guidance for targeted energy optimization.

The consumption of renewable energy (RE) faces significant challenges, including supply-demand imbalances and grid access constraints. With the rapid expansion of electric vehicles (EVs), managing EV charging to align with RE availability presents a novel solution that enhances RE utilization and generates additional revenue for electric vehicle aggregators (EVAs). This study introduces a framework for EV charging management focused on optimizing RE consumption. Firstly, the Pearson correlation coefficient with a sliding time window (STW)is employed to match the RE output curves with the electric vehicle charging station (EVCS) load curves, identifying optimal time slots for different types of EVCSs to engage in RE consumption under EVAs. Secondly, a multi- objective optimization model is developed, incorporating price-demand elasticity to adjust charging fees hourly during consumption periods, thereby maximizing both RE utilization and EVA's revenue. The results show that the Pearson correlation coefficient is more effective in smoothing the RE curve, resulting in a reduction of the variance of the RE curve by about 3 %-8 %. Compared with the existing time-of-use (TOU) tariff mechanism, the proposed hourly charging management increases RE consumption by about 15 % and EVA's revenue by around 16 %. Moreover, in comparison to EVAs that only consume hydropower, the integration of RE from water, wind, and solar sources can extend the consumption periods, thereby further enhancing the consumption efficiency and economic benefits.

With the increasing integration of renewable energy, accurate classification of Power Quality Disturbances (PQDs) is essential. This paper proposes a hybrid model combining multimodal feature extraction and optimized feature selection. The model extracts spatiotemporal features in parallel via LResNet and LSTM, fuses them into Multimodal Spatiotemporal Features (MSTF), and applies Genetic Algorithm (GA) and Principal Component Analysis (PCA) for feature optimization. A Self-Organizing Map (SOM) is used for feature modeling, and a Softmax classifier performs final classification. Experiments on synthetic and real datasets show over $99.5 \%$ accuracy with a lightweight model structure suitable for practical deployment.

The global transition toward renewable energy and the electrification of transportation is imposing unprecedented power quality (PQ) challenges on modern distribution networks, rendering traditional governance models inadequate. To bridge the existing research gap of the lack of a holistic analytical framework, this review first establishes a systematic diagnostic methodology by introducing the “Triadic Governance Objectives–Scenario Matrix (TGO-SM),” which maps core objectives—harmonic suppression, voltage regulation, and three-phase balancing—against the distinct demands of high-penetration photovoltaic (PV), electric vehicle (EV) charging, and energy storage scenarios. Building upon this problem identification framework, the paper then provides a comprehensive review of advanced mitigation technologies, analyzing the performance and application of key ‘unit operations’ such as static synchronous compensators (STATCOMs), solid-state transformers (SSTs), grid-forming (GFM) inverters, and unified power quality conditioners (UPQCs). Subsequently, the review deconstructs the multi-timescale control conflicts inherent in these systems and proposes the forward-looking paradigm of “Distributed Dynamic Collaborative Governance (DDCG).” This future architecture envisions a fully autonomous grid, integrating edge intelligence, digital twins, and blockchain to shift from reactive compensation to predictive governance. Through this structured approach, the research provides a coherent strategy and a crucial theoretical roadmap for navigating the complexities of modern distribution grids and advancing toward a resilient and autonomous future.

The large-scale integration of renewable energy into power systems has introduced new challenges to maintaining system safety and stability, requiring advanced inverters and rapidly controllable resources to support the dynamic security of the grid. Grid-forming battery energy storage system (GFM-BESS) can emulate the external characteristics of synchronous generators to support the grid, making it a crucial component for providing support in future low-inertia power system. There are rare reports on the evaluation of the grid-forming (GFM) capabilities of GFM-BESS. To address this, this paper proposes an evaluation framework for evaluating the GFM capabilities of GFM-BESS. Firstly, the GFM control technology and the typical operating conditions are described. Then, the corresponding evaluation indicators based on frequency support, voltage support, transition between isolated and grid-connected modes, and black start service scenarios are proposed, which constitute a complete evaluation framework. Finally, the evaluation framework is validated through simulation tests and result analysis in Matlab/Simulink/PLECS. The results demonstrate that the proposed evaluation framework effectively reflects the GFM performance of GFM-BESS.

Abstract The double-layer optimization control method for distribution networks based on complementary electricity and demand response designed in this article has two significant advantages. Firstly, it takes into account the complex electricity production and transportation situation in mountainous areas and comprehensively optimizes and combines scheduling for distributed energy transportation and access to the distribution network. Secondly, considering the network with the player side, a double-layer optimization management is carried out to achieve a win-win situation for distributed energy between the distribution network and the microgrid. The present invention aims to search for multiple optimal solutions in multi-objective optimization and combined scheduling of distributed energy. In double-layer optimization management, the upper function is the maximization of the distribution network operator, and the lower objective function is to minimize the operating costs expected by each user.

To achieve space-based positioning and imaging detection of space debris under complex lighting and large relative motion conditions, in response to the high relative speed, high-resolution imaging, precision ranging, and positioning requirements faced by space-based optical payloads, this paper proposes a new system with active and passive fusion detection. It utilizes the advantages of multiple bands (visible, infrared, and laser), combines wavefront sensing, the control of composite axis, and super-resolution imaging technologies, and has the characteristics of high reliable target acquisition, high-precision tracking, ranging, and high-resolution imaging in complex scenes. The simulation analysis results show that the capture distance for targets with a radiance of 10W/sr is better than 100 kilometers, and for the targets at a relative motion speed of 6km/s, sub meter level imaging and ranging can be achieved. It provides reference for the space-based optical payload.

Accurate prediction of building carbon emissions is the foundation of building energy conservation and emission reduction. This study proposes four factors that have a significant impact on carbon emissions during the operation phase of office buildings: personnel characteristics, building characteristics, electrical equipment characteristics, and temperature characteristics. A predictive model for carbon emissions in office buildings is constructed using a recurrent neural network. The model is trained and tested using time-series data of electricity carbon emissions from three office buildings, and the experimental results validate the prediction error within a range of 4.6%. This indicates that the hidden patterns and fluctuation trends of electricity carbon emissions during the operation phase of these three office buildings have been successfully captured by the predictive model, and accurate prediction of building carbon emissions has been achieved within the error range, providing a method reference for carbon emission prediction of similar buildings.

In the movement area of an airport, the wireless communication system is built to satisfy the communication requirements of mobile stations (MSs), which include aircraft, vehicles, staff, passengers, and sensors. However, the actual airport wireless communication system is a heterogeneous wireless network with multiple individual wireless communication systems. In this paper, we design a multiprotocol base station (MPBS) to realize the access and interconnection of the MSs with different protocols. In addition, a direct forwarding scheme of MPBS is proposed to improve the performance of transmissions from one MS to another. Third, a priority order transmission scheme, which can reduce the end-to-end delay of important data, is proposed for direct forwarding MPBS. Simulation results show that the proposed direct forwarding MPBS scheme can improve the transmission performance of interactive data and reduce the load of the core network.