With advancing transportation electrification, electric vehicles (EV) have become key parts of urban power consumption. Their charging demand shows spatiotemporal uncertainty, which brings severe challenges to power grid stability. This perspective critically reviews studies on EV-VPP integration. It focuses on three core areas: VPP dispatch, charging demand forecasting, and charging station recommendation. Current research faces three major limitations. They are fragmentation focused on individual component optimization, insufficient robustness under uncertainties, and inadequate analysis of real-world constraints. We propose the Multi-Scale & Multi-Agent Collaborative Robust Framework (MSMRF) as a viable solution for this research field. Centered on uncertainty, the framework establishes a closed-loop collaborative mechanism of "forecasting-dispatch-recommendation". It also integrates various practical constraints into robust algorithm design. This perspective deepens the understanding of EV-VPP integration. It notes that the key to large-scale integration lies in balancing multi-agent interests and improving overall system robustness. It should not limit optimization efforts to individual components. The critical analysis of existing technologies and the proposed innovative framework provide a new perspective. This perspective supports research on EV charging guidance under the VPP architecture.

With the rapid growth of electric vehicles (EVs) and renewable energy generation, Vehicle-to-Grid (V2G) technology has emerged as a promising approach for transforming EVs from passive charging loads into flexible distributed energy storage resources. By enabling bidirectional power exchange between EV batteries and the power grid, V2G can support renewable energy accommodation, peak shaving, demand response, ancillary services, and local grid balancing. This review provides a systematic synthesis of recent advances in V2G systems for renewable energy integration, with particular emphasis on coordinated scheduling, economic mechanisms, battery degradation, and user engagement. First, the technical foundations of V2G are introduced, including Vehicle-to-Everything operating modes, bidirectional charging architecture, aggregation mechanisms, grid-support services, and renewable accommodation pathways. Second, major scheduling strategies are reviewed, including price-based, load-based, renewable-forecast-driven, centralized, distributed, and hybrid approaches. Third, the economic feasibility of V2G is examined from the perspectives of revenue streams, pricing mechanisms, business models, battery aging costs, and compensation schemes. In addition, user participation barriers, such as range anxiety, battery lifetime concerns, loss of control, uncertain financial returns, and data privacy, are discussed. Key challenges related to communication standards, interoperability, cybersecurity, market access, policy design, and pilot-scale validation are also summarized. Finally, future development directions are identified, including AI-based scheduling, aggregator platforms, fleet-scale V2G, degradation-aware optimization, carbon-aware electricity markets, and user-centered participation mechanisms. This review highlights that large-scale V2G deployment requires the integrated coordination of technical scheduling, economic incentives, battery health protection, and user acceptance in renewable-rich power systems.

Electric vehicles (EVs) have become increasingly popular due to their environmental benefits and potential for cost savings [...]

Accurate credit default risk assessment is vital to the integrity of financial systems and a core challenge in FinTech applications. However, the extreme class imbalance inherent in credit-risk datasets, where default cases are rare and structurally divergent, limits the effectiveness of conventional machine learning models, which tend to favor the majority (non-default) class. To address this issue, we propose the Vector Decomposition Oversampling Algorithm (VDOA), a novel data-centric technique tailored for imbalanced financial datasets. VDOA leverages structural information from the majority class to guide the generation of synthetic minority-class examples through a vector decomposition framework. This cross-class approach enables more informative and representative training data, improving classifier performance on the minority (default) class without compromising overall model integrity. Extensive experiments using benchmark credit datasets and standard classification models demonstrate that VDOA consistently enhances minority-class detection and outperforms existing oversampling techniques. Our findings show VDOA's potential to support more accurate borrower risk profiling, reduce misclassification costs, and strengthen data-driven credit decision-making in FinTech environments.

The rapid proliferation of the Internet of Things (IoT) has made resource exchange and collaboration across diverse IoT domains commonplace, necessitating secure and privacy-preserving cross-domain authentication. However, existing schemes suffer from critical limitations: they lack time-bound access control, leading to persistent unauthorized access and heightened security risks, and most are incomplete, requiring resource-intensive redeployment of cryptographic mechanisms and increasing management overhead. To address these challenges, we propose a dynamic time-bound anonymous complete cross-domain authentication scheme that leverages consortium blockchain for decentralized trust, embeds dual temporal constraints, expiration time and permissible authentication periods, into credentials for fine-grained access control and automatic natural revocation, and employs accumulators and non-interactive zero-knowledge proofs (NIZKs) to enable anonymous authentication while ensuring strong privacy protection. Crucially, the proposed scheme achieves complete cross-domain authentication without modifying existing cryptographic mechanisms, significantly reducing overhead in computational, communication, and storage. Security and performance analyses confirm that the proposed scheme not only guarantees robust security and privacy but also outperforms existing schemes in efficiency.

The advancement of smart grid, facilitated by the extensive integration of information communication, automated control, and artificial intelligence (AI) technologies, signifies a significant transformation of the power system towards holistic perception, intelligent management, and secure operation. This article focuses on the security and ethical compliance of smart grid, intending to offer guiding insights for this new technological domain. This study initially delineates the potential applications, technical attributes, and design of smart grid, followed by a thorough examination of the security threats and ethical dilemmas arising from technological advancements. This study examines the pivotal role of AI in smart grid and its intricate interplay with security and ethical concerns. It performs a comprehensive analysis of the possible technical deficiencies and ethical challenges of AI systems in smart grid and assesses the extensive repercussions that these difficulties may entail. This study presents a security ethics evaluation methodology for smart grid, which thoroughly examines the ethical implications of AI technology in power grid applications and identifies existing obstacles and threats. This paper conducts a thorough policy analysis to evaluate the present security and ethical conditions of smart grid, with the objective of offering substantive theoretical support to enhance their security and ethical advancement, thereby fostering their healthy and sustainable development.

Accurate electric energy metering (EEM) of fast-charging stations (FCSs) is the cornerstone for ensuring fair electric energy transactions in the electric vehicle (EV) industry. Hence, monitoring EEM errors of FCSs is of significant practical importance. However, traditional field verification methods are constrained by high costs and low efficiency,s while existing data-driven approaches struggle to achieve highly reliable and accurate estimation of EEM errors. In response, a novel estimation method, i.e., metering performance comparison (MPC) method, is proposed. In the MPC method, by utilizing the measurement data from EVs' battery management system (BMS) as a medium, comparison chains of EEM errors of multiple FCSs are established. Combining with big data analytics, high-accuracy estimation of EEM errors is achieved. According to on-site charging data, the simulation results indicate that for FCSs with an accuracy grade of 2%, the discriminative accuracy of the MPC method exceeds 95%. In the future, with the continuous increase of FCSs, the MPC method is expected to enhance the foundation for a fair electricity trading market at a low cost. However, the MPC method is anticipated to accelerate the digital transformation of EEM errors verification for FCSs.

Large Language Models (LLMs) are rapidly transitioning from research concepts to transformative artificial intelligence components within the power and energy domain. Their ability to fuse diverse data, spanning SCADA logs, real-time sensor readings, and regulatory documentation enables unprecedented capabilities in forecasting, operator decision support, anomaly detection, and wide-area situational awareness for future intelligent grids. However, the integration of LLMs into safety-critical and highly regulated power systems introduces a convergence of novel and severe security risks. Beyond exhibiting model-intrinsic vulnerabilities like hallucination, prompt injection, and data poisoning, these models are susceptible to system-level threats that could compromise grid stability, distort energy market operations, or facilitate the leakage of sensitive operational data. Moreover, integrating LLM workloads into cloud or hybrid architectures necessitates strict compliance with critical standards and emerging governance frameworks like the EU AI Act. While existing surveys address AI security in power systems, general LLM security, and AI in smart grids separately, this paper bridges these threads by providing a unified treatment of LLM-specific risks, power-system deployment constraints, and emerging governance frameworks—a combination not covered in prior surveys. We provide a systematic taxonomy of risks across five dimensions: cybersecurity, privacy, robustness, explainability, and governance. We synthesize technological advances, clarify the complex interplay between LLM failure modes and grid security, and propose a forward-looking research agenda to guide future investigation. This work aims to be an indispensable resource for researchers, utility operators, and policymakers in designing resilient, trustworthy, and compliant AI-enabled energy infrastructures.

Electric vehicles have become a trend as a replacement to gasoline-powered vehicles, and been promoted by worldwide policy makers as a solution to combat environmental problems and stimulate economy, whereas the lack of extreme fast charging infrastructure has become one main obstacle to broad adoption of electric vehicles. To promote the commercial success of electric vehicles, effective placement of electric vehicle (EV) charging stations is pivotal. While numerous studies address EV charging station placement, the integration of transportation network traffic, specifically equilibrium traffic assignment, where flows stabilize as drivers seek routes to minimize travel time, has been relatively limited. This research investigates equilibrium traffic assignment with the inclusion of extreme fast charging (XFC) stations and introduces an algorithmic solution. We assess diverse charging station placement strategies, including node-based and network-based approaches, weighing their respective advantages and drawbacks. Extensive experiments on real transportation networks of varying scales validate our algorithm and evaluate different charging station placement strategies. Many interesting findings are drawn from the study. For instance, increasing the number of XFC charging stations may not always result in reduced traffic time; the added value of extra stations beyond a certain threshold can be quite limited. The findings offer valuable insights for strategically deploying EV charging infrastructure, thus promoting electric vehicle adoption.

Traditional methods for urban power grid design have struggled to meet the demands of multi-energy integration and high resilience scenarios due to issues such as delayed updates of terminology and semantic ambiguity. Current techniques for constructing domain-specific lexicons face challenges like the insufficient coverage of specialized vocabulary and imprecise synonym mining, which restrict the semantic parsing capabilities of intelligent design systems. To address these challenges, this study proposes a framework for constructing a domain-specific lexicon for urban power grid design based on Large Language Models (LLMs). The aim is to enhance the accuracy and practicality of the lexicon through multi-level term extraction and synonym expansion. Initially, a structured corpus covering national and industry standards in the field of power was constructed. An improved Term Frequency–Inverse Document Frequency (TF-IDF) algorithm, combined with mutual information and adjacency entropy filtering mechanisms, was utilized to extract high-quality seed vocabulary from 3426 candidate terms. Leveraging LLMs, multi-level prompt templates were designed to guide synonym mining, incorporating a self-correction mechanism for semantic verification to mitigate errors caused by model hallucinations. This approach successfully built a domain-specific lexicon comprising 3426 core seed words and 10,745 synonyms. The average cosine similarity of synonym pairs reached 0.86, and expert validation confirmed an accuracy rate of 89.3%; text classification experiments showed that integrating the domain-specific dictionary improved the classifier’s F1-score by 9.2%, demonstrating the effectiveness of the method. This research innovatively constructs a high-precision terminology dictionary in the field of power design for the first time through embedding domain-driven constraints and validation workflows, solving the problems of insufficient coverage and imprecise expansion of traditional methods, and supporting the development of semantically intelligent systems for smart urban power grid design, with significant practical application value.

In a sustainable energy system, managing the charging demand of electric vehicles (EVs) becomes increasingly critical. Uncontrolled charging behaviors of large-scale EV fleets will exacerbate loads imbalanced in a multi-microgrid (MMG). At the same time, the time cost of users will increase significantly. To improve users’ charging experience and ensure stable operation of the MMG, we propose a new joint scheduling strategy that considers both time cost of users and spatial load balancing among MMGs. The time cost encompasses many factors, such as traveling time, queue waiting time, and charging time. Meanwhile, spatial load balancing seeks to mitigate the impact of large-scale EV charging on MMG loads, promoting a more equitable distribution of power resources across the MMG system. Compared to the Shortest Distance Matching Strategy (SDMS) and the Time Minimum Matching Strategy (TMMS) methods, our approach improves the average peak-to-valley ratio by 9.5% and 10.2%, respectively. Similarly, compared to the Load Balancing Matching Strategy (LBMS) and the Improved Load Balancing Matching Strategy (ILBMS) methods, our approach reduces the average time cost by 31.8% and 25% while maintaining satisfactory spatial load balancing. These results demonstrate that the proposed method achieves good results in handling electric vehicle scheduling problems.

Detecting faulty feeder for single-phase-to-ground (SPG) faults in distribution networks is challenging due to weak fault currents and complex fault transients. The existing detection methods either possess insufficient and incomplete feature extraction capabilities or lack comprehensive credibility evaluation, thus leading to low reliability of detection results. This paper introduces a highly reliable fault detection method based on image recognition using fully convolutional generative adversarial network (FCGAN), which enhances both feature extraction and credibility evaluation capabilities. Firstly, both sampled data of zero-sequence voltage (ZSV) and zero-sequence currents (ZSC) are utilized to generate the ZSV-ZSCs images. Secondly, a FCGAN with overall evaluation ability is established to recognize the ZSV-ZSCs images across the whole waveform scale and segment the entire faulty-feeder ZSC. Thirdly, the segmented faulty-feeder ZSC is evaluated for waveform similarity and continuity, and the comprehensive evaluation metrics are calculated. Finally, a faulty-feeder detection criterion with high reliability is constructed. The performance of the proposed method has been verified using extensive simulation data and recorded data collected from various distribution systems. Experimental results demonstrate excellent detection performance in PSCAD simulation, achieving over 99.9% detection accuracy even under strong noise with 1 dB, and 100% detection accuracy in recorded data tests.

In this paper, we investigate the user scheduling and power resource allocation problem for multi-beam NGSO satellite systems with uneven user demand. To effectively manage the spatial-temporal dynamics of user demands with limited power resource, we formulate an optimization problem with the objective of minimizing the system cost, which is the weighted sum of unmet system capacity and energy consumption. This problem is mixed-integer nonlinear programming and cannot be directly solved. To this end, we decompose it into two subproblems and introduce auxiliary variables to reformulate it into a tractable form. Specifically, user scheduling is initially established through a load-balancing scheme and then updated for different time slots using graph theory. Moreover, the successive convex approximation (SCA) technique is employed to achieve a near-optimal power control solution iteratively. Numerical results demonstrate that the proposed method significantly outperforms existing benchmarks, effectively reducing unmet demand and energy consumption.

With the increase of natural gas consumption year by year, the shortage of urban natural gas reserves leads to the increasingly serious gas supply–demand imbalance. It is particularly important to establish a correct and reasonable gas daily load forecasting model to ensure the realization of forecasting function and the accuracy and reliability of calculation results. Most of the current prediction models are combined with the characteristics of gas data and prediction models, and the influencing factors are often considered less. In order to solve this problem, the basic concept of multiple weather parameter (MWP) was introduced, and the influence of factors such as the average temperature, solar radiation, cumulative temperature, wind power, and temperature change of the building foundation on the daily load of urban gas were analyzed. A multiple weather parameter–daily load prediction (MWP-DLP) model based on System Thermal Days (STD) was established, and the genetic algorithm was used to solve the model. The daily gas load in a city was predicted, and the results were analyzed. The results show that the trend between the predicted value of gas daily load obtained by the MWP-DLP model and the actual value was basically consistent. The maximum relative error was 8.2%, and the mean absolute percentage error (MAPE) was 2.68%. The feasibility of the MWP- DLP prediction model was verified, which has practical significance for gas companies to reasonably formulate and decide peak shaving schemes to reserve natural gas.

With the increasing integration of distributed energy resources (DERs) in power systems, driven by the rising demand for renewable energy, challenges such as intermittent energy outputs, complex bidirectional power flows, and dynamic grid topology have emerged. These challenges require efficient parameter identification methods to ensure grid stability, voltage control and fault detection. Traditional approaches are often computationally intensive and struggle to adapt to the complexities of grids. This study proposes a parameter identification method by integrating Graph Convolutional Networks and Long Short-Term Memory networks. The method uses GCNs to capture the spatial topology of the grid and LSTMs to model the temporal dynamics of grid parameters. experiments were conducted using real-world data, where the model demonstrated performance in parameter estimation tasks under varying grid conditions. Our Key findings include: the proposed GCN-LSTM model outperformed traditional methods in terms of accuracy and robustness, achieving RMSE of 0.2196; the model captured both spatial and temporal characteristics, ensuring reliability under grid conditions; and its adaptability to different operating scenarios, including high renewable penetration, underscores its practical applicability. This research offers contribution by combining spatial and temporal modeling to address the pressing challenges of parameter identification in power systems.

When a single phase-to-ground (SPG) fault occurs in active distribution networks, over-voltage in healthy phases can potentially initiate a secondary SPG fault in other feeders. Existing detection techniques can only work under SPG faults while fail to identify the faulty feeders under this type of successive single phase-to-ground (SSPG) faults. To eliminate the blind zone of the existing methods, a novel hybrid data-physics driven detection method based on complete waveform recognition and segmentation evaluation is proposed in this paper. Firstly, the fault characteristics of zero-sequence currents (ZSC) under SSPG faults are analyzed. Secondly, a data-driven waveform recognition approach is introduced, utilizing the proposed deep feature fusion network to precisely extract complete fault characteristics from weak ZSCs. Thirdly, a comprehensive identification criterion is developed based on segmentation evaluation of the recognition results, with the physical mechanism of SSPG faults embedded into the process. In this framework, faulty feeders are identified through the effective integration of SSPG fault occurrence detection, fault component extraction, and feeder differentiation, thus significantly enhancing the detection reliability. Large amounts of simulation data and recorded data validate the satisfactory detection performance of the proposed method under various fault scenarios, illustrating its promising application prospects.

In the context of global energy conservation and emission reduction, electric vehicles (EVs) are essential for low-carbon transport. However, their rapid growth challenges power grids with load imbalances across networks and increases user charging costs. To address the issues of load balancing across large-scale distribution networks and the charging costs for users, this paper proposes an optimization strategy for EV charging behavior based on deep reinforcement learning (DRL). The strategy aims to minimize user charging costs while achieving load balancing across distribution networks. Specifically, the strategy divides the charging process into two stages: charging station selection and in-station charging scheduling. In the first stage, a Load Balancing Matching Strategy (LBMS) is employed to assist users in selecting a charging station. In the second stage, we use the DRL algorithm. In the DRL algorithm, we design a novel reward function that enables charging stations to meet user charging demands while minimizing user charging costs and reducing the load gap among distribution networks. Case study results demonstrate the effectiveness of the proposed strategy in a multi-distribution network environment. Moreover, even when faced with varying levels of EV user participation, the strategy continues to demonstrate strong performance.

The resilience of power systems against extreme events has evolved into a progressively prominent challenge. Considering that the reactive loads can also impact the system flow and voltage, ultimately affecting the restoration of active loads, this paper proposes a collaborative optimization method that integrates network reconfiguration with reactive power control of controllable distributed generations (CDGs) and energy storage systems (ESSs). Where, based on the multi-stage fault handling process of the distribution system when facing extreme events, the allocation of remote-controlled switches (RCSs) and pre-charging strategies for ESSs are optimized before the extreme event, while dynamic coordination of topology reconfiguration and reactive power compensation based on CDGs and ESSs is implemented after the extreme event. The proposed model is formulated as a MILP problem and the results on the IEEE 33-bus system validates the effectiveness of the proposed method in reducing load shedding and mitigating voltage violations.

Cloud-fog automation architecture has propelled the advancement of the Industrial Internet of Things (IIoT), significantly enhancing production efficiency and intelligence through extensive data collection and connectivity. Simultaneously, industrial cyber-physical system leverages this data to achieve intelligent control and optimization of production processes. As industrial production becomes increasingly specialized and complex, independent operations within a single domain are no longer sufficient to meet demands, making cross-domain collaborative production inevitable. Consequently, ensuring the security of cross-domain communication and data sharing has become a critical issue for IIoT under the cloud-fog automation architecture. Existing solutions encounter substantial management and computational burdens in cross-domain communication and data sharing, and they are vulnerable to privacy leakage risks. To address these challenges and enhance industrial production efficiency, this paper uses consortium blockchain to co-design a cross-domain authentication and data sharing scheme. The scheme ensures secure and private cross-domain communications with minimal computational, communication, and storage overhead. And, the proposed time-specific plaintext checkable encryption protocol can secure data during cross-domain sharing. Security and performance analyses show that the proposed scheme effectively reduces computational and communication resource demands while maintaining communication and data security.

With the development of distribution networks, the widespread use of communication devices has exposed these networks to the risk of cross-domain attacks. Based on the coupling relationship between power and communication networks, a method for the optimal allocation of distribution network resources considering this coupling is proposed. In the resource allocation stage, given the limited availability of resources, optimal allocation is carried out for resources such as distributed generations and remote-controlled switches; additionally, the resilience of the distribution network is enhanced through the reinforcement of both the distribution lines and communication links. In the prevention stage, in advance of extreme events, preventive islanding is formed through switch operations. In the degradation stage, the distribution network identifies faulted and non-faulted areas based on the fault propagation model, while the communication network assesses the fault status of communication nodes based on the virtual flow model. In the recovery stage, coordinated control of remote-controlled switches and distributed generations with normal communication is implemented for network reconfiguration to minimize load losses. Finally, the effectiveness of the proposed method is verified through the IEEE 33-node system.