The Digital Twin (DT) technology provides a promising solution to bridge the gap between steady-state optimization and real-time control for the integrated scheduling and control problem of hybrid AC/DC microgrids (HMGs). However, introducing DT-based deduction poses new challenges in ensuring both scheduling timeliness and control stability across multiple timescales. To address these issues, this paper introduces the Holomorphic Embedding Method (HEM) into HMGs and proposes a Unified HEM (UHEM) that achieves unified modeling of AC/DC subsystems within a consistent analytical framework. The proposed approach enables dual-timescale DT real-time deduction, coordinating upper-level steady-state optimal power flow (OPF) with lower-level transient domain simulation (TDS). By flexibly utilizing the embedded complex variable, the method ensures smooth transition between steady-state and transient responses, enhancing both computational efficiency and control continuity. Comparative results demonstrate that the proposed UHEM substantially improves real-time performance compared to conventional intra-day scheduling and other recent advanced methods, at a minimal cost to dynamic response accuracy. The algorithm has been implemented and validated on a verified DT-equipped HMG platform, confirming its effectiveness and potential for real-world deployment.

Hybrid AC/DC microgrids (MGs) are compatible with legacy AC grids and enable the integration of renewable distributed energy resources (DERs). However, emergencies such as unscheduled islanding expose a mismatch between day-ahead scheduling and sub-second dynamics. A digital-twin (DT) enabled dual-timescale framework is proposed that unifies upper-level optimal power flow (OPF) scheduling and lower-level model predictive control (MPC) via scenario-driven deduction in a bilevel formulation. Validated on an oil-and-gas well-station MG, the strategy issues consistent pre- and post-event process commands and preserves steady-state optimality. Relative to a second-order cone programming (SOCP) based OPF baseline, average AC frequency deviation and DC bus voltage deviation during islanding are reduced by 74.1% and 49.6%, respectively. Against two state-of-the-art methods, the tracking consistency between dispatched commands and measured transients improves by 23.8% and 31%. Computation for transient sequences completes within 6-10 s across scenarios, supporting real-time deduction on commodity hardware.

The uncertainty and variability of advancing wildfires present significant challenges to the resilience of power systems. This paper proposes a hierarchical dispatch strategy of multi-type virtual power plants (VPPs) for enhancing resilience of power systems under wildfires, which encompass geographically distributed VPPs (GDVPPs) based on Internet data centers (IDCs) and geographically concentrated VPPs (GCVPPs) that aggregate flexible loads (FLs). The proposed strategy enhances resistance to wildfire-induced uncertainties by facilitating coordinated operations between these two types of VPPs. At the upper level, an improved maximum flow model is introduced to quantify the dynamic changes in the workload transfer capability of IDC (WTCI) under wildfire conditions, and stochastic model predictive control (SMPC) is employed to perform rolling optimization of generator outputs, IDC workload transfers, and load shedding, thereby minimizing the total regulation costs. Based on the load shedding instructions from the upper level, the lower level integrates GCVPPs to provide load curtailment services, effectively offsetting the load shedding power. Subsequently, the lower level feeds back the load rebound (LR) resulting from these load curtailment services to the upper-level strategy, serving as a basis for its rolling optimization. The SMPC integrates an event-driven deductive model to address the fine-grained modeling of the operational state, effectively overcoming challenges posed by discrepancies in simulation time steps arising from power system cascading failures, variations in IDC adjustment capacity, and LR effects. Finally, a modified 39-bus power system, integrated with an 8-bus IDC network, is used as a case study to validate the effectiveness of the proposed strategy.

To enhance the fault handling capacity of low-voltage DC (LVDC) distribution networks and mitigate the detrimental effects of faults on converter apparatus, this research introduces an innovative modular fault suppression and handling strategy. The proposed approach entails the integration of multi-port fault-limiting modules (FLM) with DC circuit breakers (DCCBs) and DC transformers, thereby enhancing the system's current and voltage regulation. This integration allows for the use of economical mechanical DCCBs while improving the fault-ride-through (FRT) capabilities of the conversion equipment. Under normal operating conditions, the FLM is bypassed to reduce system disturbances; in the event of a fault, it is engaged to curtail fault currents and preserve voltage stability. The validity of the proposed method is verified through simulations in PSCAD/EMTDC. Simulation results demonstrate that the proposed strategy effectively reduces the peak fault current and limits the transient voltage compared to conventional schemes. Furthermore, the system successfully achieves a fault ride-through by maintaining the fault current within a safe range of the rated current, ensuring robust protection and rapid recovery.

ABSTRACT Cable joints are among the most failure‐prone components in distribution cable systems, making their condition monitoring essential for reliable operation and maintenance in distribution networks. Existing discharge‐based monitoring methods typically provide binary defect identification, leaving degradation‐stage information in discharge observations underexploited and limiting their support for fine‐grained risk‐informed maintenance under system‐level impacts. To address this gap, this paper proposes a stage‐aware degradation modelling paradigm for cable joints based on ordered degradation discharge event sequences. A forward‐labelling strategy is introduced to formulate degradation evolution as a supervised learning problem, and a conditional degradation‐to‐failure propensity model (CDPM) is developed to map ordered degradation discharge event sequences to a probability‐related failure‐tendency score for the next degradation event. The failure‐tendency score is further integrated with post‐fault consequence severity to quantify system‐level risk and support maintenance prioritisation in distribution networks. The proposed CDPM and risk quantification framework are evaluated using full‐scale cable‐joint degradation experiments and an improved 62‐bus distribution network.

With the widespread application of Distributed Generation (DG) in modern distribution networks, the operational and fault characteristics of distribution networks have become increasingly complex. Traditional fault diagnosis methods are difficult to meet real-time diagnostic needs due to high computational complexity. This paper proposes a fault characteristic linearization calculation model based on Taylor expansion, combined with an adaptive piecewise linearization method, to address the uncertainty challenges brought by DG output power fluctuations. Firstly, a linear relationship between DG output power and system fault characteristics is established using Taylor expansion around the operating point, simplifying complex calculations to reduce model complexity. Secondly, the adaptive piecewise linearization method dynamically adjusts the operating point and optimizes the partitioning to reduce linearization errors, ensuring high accuracy even when there are significant fluctuations in DG output. Simulation results show that the proposed method can effectively track nonlinear fault characteristic changes, significantly reducing computational errors and providing reliable theoretical support for real-time fault diagnosis in distribution networks.

With the rapid development of renewable energy, AC-DC hybrid microgrids have become a crucial research focus in modern power systems due to their ability to efficiently integrate distributed energy sources and meet diverse load demands. However, the operation optimization of hybrid microgrids faces several challenges, including the coupling of continuous dynamics and discrete states, frequent switching of equipment modes, and voltage stability issues within the system. Traditional optimization methods often struggle to handle these complex characteristics simultaneously, leading to discrepancies between optimization results and actual operation. To address these challenges, this paper proposes an operation optimization strategy for hybrid DC microgrids based on the Hybrid Logic Dynamic (MLD) model. By unifying the system's continuous dynamics and discrete logic, and integrating the interaction mechanism of digital-physical hybrid simulations, the proposed strategy enables the pre-correction of microgrid operation schemes, thus enhancing the system's economic performance and stability.

An increasing number of distributed photovoltaic systems utilize convolutional neural network (CNN)-based models for power prediction, yet face computational bottlenecks when deploying these models on resource-constrained photovoltaic edge computing terminals (PECT). To address this challenge, this paper proposes a lightweight edge stream processing framework integrated with a dynamic task scheduling mechanism, comprising three core components: a data receiving module (DRM) implements real-time task preprocessing with validity screening, a data computing module (DCM) splits and processes sub-tasks of CNNs in parallel, and realizes distributed node collaboration. and a data summarizing module (DSM) for data aggregation. The scheduling mechanism combines a modified least laxity first (MLLF) algorithm with dynamic priority adjustment and a self-monitoring allocation (SMA) algorithm enabling local resource-aware load balancing. Deployed on the iPACS-5612C1 IoT terminal, experiments show that the proposed framework achieves a 97% average CPU utilization (85% in baseline methods), a 25% reduction in computing time, and a 90% task completion rate, with the best real efficiency. The framework achieves a real efficiency improvement of 40% over cloud batch processing while maintaining prediction accuracy above 90% under dynamic conditions. Experiments also demonstrate that this framework has the potential to be deployed on larger photovoltaic clusters. These results demonstrate the effectiveness and scalability of the edge stream processing framework.

With the accelerating intelligent and digital transformation of the power industry, speech recognition technology has emerged as a critical tool for enhancing the efficiency of on-site operations, such as substation inspection, distribution network fault repair, and switching operations. However, the highly specialized terminology, prevalence of homophones, and stringent safety constraints in the power sector pose significant challenges to general-purpose speech recognition systems. This paper proposes a safety-aware and scenario-adaptive error correction method for power terminology speech recognition. The approach integrates a multi-layer fusion architecture—comprising dictionary mapping, pinyin similarity calculation, contextual semantic analysis, and knowledge graph verification—alongside a safety-criticality awareness mechanism and scenario-based weight migration strategies.

With the widespread integration of Controllable Flexible Resources (CFRs), distribution networks are progressively evolving toward multi-source global coordinated control, a paradigm that relies heavily on secondary communication networks. However, when communication becomes unstable, certain CFRs may become isolated as "information islands," rendering them unable to follow the control center's strategies for a period of time and thus posing threats to the operational security of the distribution network. To address this issue, this paper proposes an Edge-deployed Deduction Framework (EDF) within the Cloud-Edge-Terminal collaborative architecture (C-E-T), which consists of an Edge Digital Twin (EDT) and a Deduction Model for CFRs (DM4CFRs). By enabling local deduction based on limited but structured information retained at the edge, the EDF allows CFRs within communication-isolated areas to approximate coordinated operation even under prolonged communication degradation. A case study on a modified IEEE 33-node distribution network with multiple energy storage units demonstrates that the proposed framework maintains stable CFR operation during communication outages while preserving network security and economic performance.

To enhance fault handling capacity for low-voltage DC distribution networks and mitigate the detrimental effects of faults on converter apparatus, this research introduces an innovative fault suppression and handling strategy. The proposed strategy entails the integration of multi-port fault limiting modules (FLM) with DC circuit breakers (DCCBs) and conversion systems, such as DC transformers, thereby enhancing the system’s current and voltage regulation. This integration facilitates the use of more economical DCCBs while improving the fault-ride-through capabilities of the conversion equipment. Under normal operating conditions, the FLM is circumvented to reduce system disturbances. In the event of a fault, the FLM is engaged to curtail fault currents and preserve voltage stability. The validity of the proposed method is confirmed by constructing equivalent modules within PSCAD/EMTDC and conducting comprehensive simulations across a variety of fault scenarios.

To enhance the reliability of the protection system and accurately identify fault distances in DC networks, a fault location method is proposed, which leverages the coordination of existing DC circuit breakers (DCCBs) and current limiters. The proposed method effectively achieves single-end signal injection and identification by repurposing existing DCCB components and incorporating a specialized signal injection branch. Concurrently, an efficient cooperative control strategy for the components is introduced to enhance the integration of the fault location method. By analyzing the inductor current decay within the ranging circuit, the relationship between fault distance and fault resistance is established, leading to the development of a fault location algorithm. A DC distribution network model is built by PSCAD/EMTDC, and the location calculation method is simulated in MATLAB. The simulation results validate the efficacy of the proposed fault location method in accurately determining both the distance and resistance of faults. Moreover, this method has demonstrated its robustness across diverse cable types, noise levels, sampling frequencies, and resistance conditions. The proposed fault location method offers distinct advantages over existing techniques in terms of both accuracy and lower complexity of its construction.© 2017 Elsevier Inc. All rights reserved.

With the rapid development of energy internet and the increasing penetration of renewable energy, AC-DC hybrid distribution networks have emerged as a critical infrastructure in modern power systems. However, voltage sags—a prevalent power quality issue—can induce abnormal shutdowns of sensitive equipment, leading to production interruptions and equipment damage, thereby severely threatening power supply reliability and system stability. To address this challenge, this paper proposes a collaborative control method integrating rapid fault identification and dynamic voltage support functions. First, an intelligent analysis model is constructed through multi-source feature extraction to achieve millisecond-level precise identification of voltage sag event types and fault sources. This model dynamically optimizes thresholds to balance sensitivity and anti-interference capabilities. Second, Combined Active and Reactive Power (PQ) Control with Voltage and Frequency (VF) Control, a storage-based dynamic voltage restorer (DVR) compensation system is designed, incorporating a PQ-VF flexible switching control strategy. The effectiveness of this approach is validated through MATLAB/Simulink simulations under multiple fault scenarios, demonstrating its capability to mitigate voltage deviations and enhance system resilience. This work addresses the critical need for adaptive voltage regulation in hybrid AC-DC grids, offering a novel solution to mitigate the impact of voltage sags on critical loads and ensure sustainable energy integration.

Power system is an important field to achieve the goal of carbon neutrality in the future.With the aggravation of source and load uncertainties,the low-carbon economic dispatching method based on the deterministic model cannot accurately describe the impact of uncertain factors on carbon emissions.In view of the above problems,a low-carbon robust optimization model considering uncertainty is constructed at the level of power system,in which,the uncertainties of source and load are modeled using the probability model,and the significance level of the target meeting the expectation is described by chance constraint.The robust dispatching scheme under the risk aversion strategy can be obtained by maximizing the confidence level of the uncertainty.Then,an event-driven low-carbon response model is constructed at the level of power users.According to the calculation results of the level of power system,the excess carbon emission event is defined by setting the carbon emission threshold,and the low-carbon power consumption behavior of users is guided in the form of price.Finally,through the case analysis,the results show that the proposed model can effectively quantify the uncertainty level of the low-carbon economic dispatching results,give full play to the carbon reduction ability of the user side,and realize the coordination of the carbon reduction goals of both sides of the source and load.

This paper presents a comprehensive framework for Virtual Power Plant (VPP) participation in spot and ancillary service markets, integrating deep learning-based forecasting with rolling optimization strategies. The proposed approach combines Transformer-GRU hybrid architecture for renewable energy forecasting and LSTM networks for electricity price prediction, achieving forecast accuracies exceeding 96%. A three-stage optimization framework is developed: day-ahead forecast declaration, intra-day rolling optimization, and intra-day dispatch order optimization. The rolling optimization strategy maximizes VPP profitability by coordinating various distributed energy resources, including renewable generation, energy storage systems, and flexible loads. Case studies demonstrate that the proposed approach improves VPP profitability by 16.25% compared to non-optimized operations, while maintaining rapid response capabilities to grid dispatch orders within 0.1 seconds. The results validate the effectiveness of the proposed framework in enhancing both economic performance and operational efficiency of VPPs in modern electricity markets.

The uncertainty and volatility caused by the current high proportion of distributed generation access have brought new challenges to the economy and security of distribution network. Therefore, a Two-layer optimization method of web of cells network is proposed, which considers the uncertainty of source and load. Firstly, the flexibility of various source-load in the distribution network are considered, and the flexibility evaluation index is established from the flexibility supply and demand balance and branch channel adequacy. Secondly, a Two-layer optimization model of distribution network considering economy and flexibility is established. The upper model takes the grid division strategy of distribution network as the decision-making subject, and the lower model takes the flexibility of supply and demand side to adjust the output of resources as the decision-making subject. Finally, the simulation results verify the rationality and effectiveness of the proposed optimization method.