The output fluctuations and uncertainty of distributed renewable energy, combined with load fluctuations, make overloads of power lines occur more frequently in regional power grids. Power line overloading can be divided into forward heavy overload and reverse heavy overload. Flexible resources within regional power grids have great adjustment potential and can participate in line overloading regulation. However, how to effectively utilize flexible resources to eliminate the line overloading risk and how to improve solving efficiency when there are a large number of flexible resources need to be studied in depth. To solve the above problems, a coordinated rolling optimization method for multiple flexible resources is proposed, which takes flexible resource pre-selection into account. First, the impact of different types and locations of flexible resources on the overload of the specific line, including whether they can contribute, which type of overload they can contribute, and how much they can contribute, is analyzed. In particular, this paper mainly focuses on the relevant analyses in the loop network scenario. Based on this, this paper proposes a resource preselection method considering the adjustment characteristics of flexible resources to quickly select the most economical set of flexible resources that meets the overload demand. Finally, this paper proposes a coordinated rolling optimization method for multiple flexible resources, mitigating the impact of prediction errors on power grid operations through rolling optimization. The simulation results on a loop network system and the comparison with a rolling optimization method without preselection, validate that the proposed method can effectively resolve the power line overloading problem and enhance computational efficiency.

Compared with traditional resources, load-side resources are diverse, and their regulatory capacity is uncertain, making it difficult for them to participate in energy and peak regulation markets simultaneously. To address the challenge that the uncertainty of load-side resources cannot be accurately described by a known probability distribution, a day-ahead joint bidding strategy of electricity energy and peak regulation markets based on the distributional robust chance constraint (DRCC) and risk expectation is proposed. Firstly, a data-driven approach is employed to characterize the uncertainty in the adjustable capacity of load-side resources. An ambiguity set based on the Wasserstein distance is constructed, which does not require prior assumptions about the specific probability distribution of the underlying random variables. Then, the bidding strategy of load-side resource integrators is proposed to minimize the risk expectation. Finally, the effectiveness of the proposed model is assessed by case studies. The proposed method overcomes the problem that the robust model is too conservative, and its computational adaptability is better than that of the stochastic model, achieving a good balance between robustness and economy.

Nowadays, the integrated energy system (IES) faces significant security challenges from both natural occurrences and intentional disruptions, even minor disruptions can impact equipment operation and the system’s energy supply. To mitigate such disruptions in IES, a protective decision optimization model (PDOM) has been proposed based on complex network theory, which combines three protective measures: backup protection, physical protection, and new lines connection to minimize the weighted betweenness loss expectation. The result of the calculation, i.e., an optimized pre-disaster protection scheme, aims to determine which nodes and lines need to be protected (and different-type protection) within the specified budget. Furthermore, EvoRein-mixed integer linear programming (EvoRein-MILP) algorithm is presented to optimize the proposed model’s scheme. Simulation results demonstrate the effectiveness and feasibility of the obtained scheme, as verified by three validation indicators (system fragmentation, energy transmission efficiency, and load losing rate). Finally, compared to the genetic algorithm (GA)-MILP algorithm and adaptive GA-MILP algorithm, the EvoRein-MILP algorithm exhibits superior optimization and operation time performance under identical conditions.

Abstract：Accurate forecasting of wind power ramp events (WPREs) is essential for improving the flexibility and operational reliability of power system dispatch. However, the variation and complexity of ramp duration significantly affect the accuracy of ramp detection and prediction. In particular, bumps and time-varying noise can cause inaccurate identification of the start and end points of the duration, and variations in the duration length can lead to inaccurate predictions. Accordingly, this paper proposes a novel WPREs forecasting method combining a dual-constrained extended locking method for duration-preserving ramp detection and a Dilated Causal Convolutional Informer (DCAInformer) model for duration-adaptive learning. First, an improved dynamic adaptive L1 trend filtering approach is employed to suppress time-varying noise and outliers while retaining the main ramp trend changes. Then, a dual-constraint extended locking detection method is proposed to identify ramp events while avoiding the mis-segmentation of continuous ramps caused by bumps. Furthermore, DCAInformer model is constructed to better capture ramp features under different durations by introducing the dilated causal convolution into encoder of Informer, which can adapt the receptive field. Case studies using data from three wind farms in China demonstrate that the proposed method outperforms benchmark models in both ramp detection and forecasting accuracy.

Integrated energy systems (IESs) inherently involve multiple energy carriers and operational objectives, making the exploration of the Pareto frontier a central task in their optimal operation. However, existing multi-objective optimization methods for IESs usually require the double-layer iterative computation to obtain Pareto optimal solutions, where the upper layer determines the weights of the objectives and the lower layer performs optimization computations for the determined weights, resulting in a considerable computational burden. To address this challenge, this paper proposes a novel balance-supervised reinforcement learning framework that accelerates the Pareto optimization of IESs by integrating dynamic weights and agent learning. First, a novel balance supervisor agent is proposed to directly endow weights with learnable uncertainty via the Bayesian-based balance algorithm. Then, the balance-supervised framework can transform the double-layer computations into single-layer computations and enable the objectives to adapt to a theoretically infinite range of weight combinations, significantly reducing the computational burden and expanding the exploration of Pareto frontier. In addition, the Bayesian uncertainty layer is proposed to enhance the model’s adaptability to uncertainty. Case study shows that, compared to the state of the art methods, the proposed framework can effectively achieve optimal performance of the IESs in multi-objective scenarios.

Modern power grids are increasingly exposed to diverse cyber-physical attacks that originate in the cyber layer and disrupt physical operation. A critical defense challenge is to rapidly determine whether an attack has occurred and identify its type. To address this challenge, this paper aims to develop a generalized detection scheme capable of identifying multiple types of attacks, particularly unknown ones. It is first observed that, under normal operation, cyber-layer data and physical-layer states are consistent across layers and evolve smoothly over time, while attacks disrupt these consistencies. Building on this insight, a self-supervised Cross-Layer Cross-Temporal Contrastive Learning (CL-CTCL) framework is proposed, which employs a Graph Attention Network-Gated Recurrent Unit (GAT-GRU) encoder to capture spatial dependencies and temporal dynamics. Two complementary contrastive objectives are introduced: Variance-Invariance-Covariance Regularization (VICReg) for cross-layer alignment and Information Noise-Contrastive Estimation with Momentum Contrast (InfoNCE-MoCo) for temporal consistency. Residual deviations from these consistencies provide reliable attack fingerprints, enabling detection and classification of both known and unknown attacks. Experiments on an IEEE 118-bus cyber-physical testbed show that the proposed CL-CTCL achieves high accuracy on known attacks and generalizes well to unknown ones, outperforming baseline methods across standard metrics.

For day-ahead security constrained unit commitment (SCUC), pre-screening transmission constraints and retaining only the active ones in the optimization process can significantly improve computational efficiency. Most existing methods assume that similar system total load or net load directly leads to similar line flows and similar active/inactive statuses. However, even when total net loads are similar, the spatial distributions of the net loads may differ, which results in different power flows and the corresponding transmission constraints statuses. To alleviate this issue, this paper proposes a hybrid data-driven method for screening active transmission constraints by matching similar hourly hybrid estimated power flow (HEPF). The HEPF integrates a deterministic component derived from the product of forecasted nodal net loads and power transfer distribution factors (PTDFs) with a data-driven surrogate model designed to comprehensively quantify the impact of net loads spatial distributions and UC schedules on power flows. Furthermore, an asymmetric Euclidean distance K-nearest neighbors (AED-KNN) algorithm shortens the similarity distance between the Historical HEPF and Current HEPF when the magnitude of Historical HEPF minus Current HEPF is positive, which effectively reduces false negatives (FNs). Numerical results on the 118-bus system show that the proposed method achieves average recall above 99% and average time saving of 47.59% compared with the original SCUC. Additional results on a 500-bus realistic synthetic system show that the proposed method still maintains high screening performance and average time saving of over 60%, further verifying its scalability.

To accommodate the diversity and time-varying characteristics of grid regulation demands and flexible resource, efficient aggregation mechanisms are required to support grid regulation services. Dynamic aggregation adaptively adjusts resource composition according to grid demands, overcoming the limitations of the traditional fixed resource composition in virtual power plants (VPPs). However, the computational complexity of the large-scale resource composition is a new bottleneck. This paper introduces a dynamic aggregation optimization framework based on submodularity, which includes resource selection and coordination. A lazy submodular optimization method is proposed in the resource selection process to enhance the efficiency of flexible resource aggregation. Using the diminishing-return property of submodular functions, the proposed method caches previous aggregation gains as upper bounds, significantly reducing redundant evaluations. The method greatly improves computational efficiency while maintaining optimal aggregation quality. Simulations highlight the efficacy of the proposed method in dynamic aggregation.

The integration of high proportions of distributed photovoltaic (PV) systems has led to voltage violations in distribution power grid. Using the remaining capacity of PV inverters for reactive power regulation is an efficient voltage control method. Existing studies typically address PV reactive power capabilities statically, failing to fully account for dynamic variations caused by solar radiation fluctuations. This paper proposes a data-driven probabilistic assessment and hierarchical reactive power optimization method. A spatiotemporal probabilistic prediction model is constructed to perform multi-quantile predictions of PV active power and node loads, and dynamically constructs reactive power support boundaries to assess PV reactive voltage support. A hierarchical optimization strategy is developed to coordinate the adjustment of various reactive power resources in the distribution power grid. Case study results using an improved IEEE 33-bus system show that the proposed method effectively mitigates voltage fluctuations and improves the efficiency of reactive power regulation.

Rapid, early, and accurate detection of cyber intrusions is increasingly vital for ensuring the security and operational resilience of modern power systems, as cyber-attacks grow in both frequency and sophistication. However, existing approaches primarily focus on either cyber or physical data independently, ignoring critical interactions across these two layers. To bridge this gap, this paper proposes a novel Cross-Layer Graph Attention-Transformer Network (CL-GTN), which simultaneously incorporates information from both the cyber and physical layers, and effectively integrates them through a learnable physics-informed point-to-bus projection mechanism. Specifically, the projection matrix constructed from power system sensitivity factors explicitly captures the fine-grained mapping from cyber data points in communication packets to affected physical grid nodes, providing a robust cross-layer fusion basis. Graph attention networks (GAT) are employed separately at the cyber and physical layers to extract relevant local and topological features, followed by a Transformer module that dynamically aggregates cross-layer temporal dependencies for accurate attack identification. Extensive experiments on multiple benchmark systems, including the IEEE 118-bus and large-scale ACTIVSg2000 systems, confirm that CL-GTN achieves high detection accuracy (over 0.98) and strong early-warning capability, successfully detecting up to 90% of attacks before execution with an average lead of 2.4 time steps. Comprehensive ablation and robustness studies further validate the model’s stability and component effectiveness. In addition, interpretability analyses show that the proposed projection-matrix mechanism accurately identifies critical cyber–physical coupling points, thereby enhancing situational awareness and defense capability.

Renewable power forecasting (RPF) is a classic time series forecasting (TSF) problem. During the modeling process, long-term dependencies in the time series can pose significant challenges, potentially reducing the accuracy of deep learning-based predictions. Nonetheless, the power data used for prediction often presents fine-grained patterns, such as second- or minute-level resolution, which further complicates the ability of DL models to accurately capture long-term dependencies. To address this problem, we use a uniform temporal sampling strategy to convert fine-grained data into several coarse-grained groups (CGGs), encompassing a reference CGG alongside its neighboring CGGs, each standardized with consistent resolution. Subsequently, we propose a novel CGG-based model (CGGNet) for multi-step RPF. In the proposed CGGNet, we design two modules: cross-group modeling within CGGs (CGG-cross) module and granular-based attention module. The CGG-cross module is used to build connections between the reference CGG and its neighboring CGGs; the granular-based attention module uses a novel CGG-dependent attention to obtain weighted representations of the reference CGG based on its neighboring CGGs. The proposed CGGNet is used to perform RPF on three datasets, including two wind power datasets and one solar power dataset. On the three datasets, the proposed model presents average improvements by 29.24% for root mean square error (RMSE), 32.03% for mean absolute error (MAE), 16.01% for coefficient of determination (R2). The results demonstrate that the proposed model can ensure a remarkable prediction response.

Learning an efficient feature representation from wind power data is crucial for improving the accuracy of wind power forecasting (WPF). Compared to supervised learning-based models, self-supervised representation learning models, such as contrastive learning (CL), demonstrate advantages in capturing feature representations from time series data, especially for data with strong fluctuations. Consequently, we propose a CL-based model to extract more accurate feature representations from wind power data. Unlike traditional CL-based models that rely on constructing both similar and non-similar pairs of data, the proposed model only requires similar data due to the sparsity of time series data. In addition, since the similarity in time series data is primarily reflected in the trend, especially for wind power data with significant fluctuations, the proposed model incorporates dynamic temporal granularity data (TGD) to generate similar data for a given wind power data, which can strengthen trend-consistent characteristics. Subsequently, the proposed model designs two distinct yet weighting-sharing networks, referred to as the online network and the target network, to model the two different augmented views with similar trend generated from the TGD, enabling the bootstrapping of point-wise feature representations from wind power data. To demonstrate the superiority of the proposed model, we compare it with state-of-the-art (SoTA) models on two wind power datasets, showing average enhancements of 7.34% for mean absolute error (MAE), 4.75% for root mean square error (RMSE), and 3.92% for R-squared (R2), respectively. The results show that the proposed framework can generate improved performance with accuracy and robustness.

When estimating baseline load in demand response (DR), the accuracy of the results can be greatly improved if private data of resource owners are available. However, this may bring the risk of privacy leakage. Therefore, mainstream approaches advocate against using private data for baseline load estimation. To this end, this paper proposes a blockchain-based baseline load estimation and supervision framework, making it possible to use private data for baseline load estimation. Then, three principles for private data collection are established by analyzing the problems caused by collecting excessive or insufficient data, that is, first, the data collected should help calculate the baseline load effectively. Second, the data collected should ensure that the cost of data falsification by resource owners is significantly higher than the rewards they would gain in DR. Third, the rewards should far exceed the costs resource owners pay for data collection, transmission, and storage. Furthermore, this paper converts the problem of finding an optimal data collection scheme into an optimization model. A particle swarm optimization (PSO) algorithm with nonlinear inertia weight is then used to solve this problem, determining the optimal types and frequencies of data collection. Finally, this paper analyzes a case study where a shopping mall participates in DR, and points out the optimal collection types and frequencies of private data for such a scenario. The proposed model also shows effectiveness and robustness through sensitivity analysis and robustness test.

With the rapid economic development across various regions of China and the explosive growth of renewable energy generation, reliance solely on the generation side has increasingly proven insufficient to meet peak shaving demands. This necessitates the exploration of peak shaving strategies that involve both the generation and load sides. To address this, the paper first summarizes the sequential clearing bidding model for generation and load sides and innovatively proposes a joint clearing bidding model for generation and load sides. Subsequently, the corresponding bidding process is derived, and a day-ahead economic dispatch model is established, aiming to minimize system costs. Finally, the paper conducts performance simulations and evaluations of the two clearing modes using the IEEE118-node system. Based on the simulation results, optimization recommendations are proposed, tailored to the unique characteristics of China's electricity market.

The collaborative analysis of both cyber-layer and physical-layer data is crucial for improving detection accuracy and timeliness of cyber-attack. Cyber-layer features provide early indicators of attacks, while physical-layer features reflect the actual impact on the power system. To leverage this synergy, a cross-attention mechanism is introduced to generate cross-layer features to capture these cross-layer interactions. Furthermore, based on the traditional Mixture of Experts (MoE), a novel framework MoE-Transformer Dual Layers Detection (MoE-TransDLD) is proposed, which dynamically fuses multi-layer features to model cyber-physical dependencies. Specially, MoE-TransDLD assigns a dedicated expert to each layer, including a cyber-layer expert, a physical-layer expert, and a cross-layer expert, to more accurately model multi-layer data relationships in power systems. Notably, both the expert network and the gating network share a common Transformer architecture to extract global features, while maintaining corresponding independent feed-forward network (FFN), where each expert focuses on its respective domain and the gating network achieves adaptive and dynamic selection in decision making. The synthetic Texas 2000-bus model system is used as an experimental model and its physical-layer data and cyberlayer data are collected. The experimental results show that the MoE-TransDLD significantly outperforms the existing methods and achieves superior classification metrics and faster attack detection time.

As a core component of smart grids, the increasing proliferation of microgrids in regional power systems plays a pivotal role in addressing challenges arising from the rapid growth of renewable energy penetration and escalating power system complexity. However, microgrids exhibit significant uncertainties in responding to dispatch commands due to internal distributed power output fluctuations and inadequate adaptability of control algorithms. In contrast, decentralized autonomous control enables independent decision-making for microgrids and flexible resources, demonstrating unique advantages in addressing communication constraints and mitigating response uncertainties. To tackle these challenges, a three-dimensional evaluation index system is established to optimize the partitioning of autonomous entities within regional grids. Then, a decentralized control framework based on a leader-follower mode and an event-triggered mechanism is innovatively designed to address microgrid response uncertainties. The control centre monitors deviations between microgrid power responses and regulation commands, dynamically initiating leader node elections and topology adjustments when thresholds are exceeded, thereby resolving response uncertainties and enhancing grid regulation economy. Case studies demonstrate that the proposed method achieves rapid convergence to global optimal solutions under communication constraints and response uncertainty scenarios, significantly improving system robustness and economy. This research provides a novel technical pathway for decentralized autonomous operation of regional grids, offering both theoretical foundations and practical guidance for future distributed control in smart grids.

Load forecasting is fundamental to the operation and planning of power systems. Its accuracy is crucial for ensuring system security and reliability, as well as for reducing generation costs and improving economic efficiency. Recent studies demonstrate that large language models (LLMs) exhibit powerful capabilities in pattern recognition and reasoning for complex token sequences. The critical challenge lies in effectively aligning temporal patterns in time-series data with linguistic structures in natural language to leverage these capabilities. This study introduces a time-series forecasting approach for electrical load prediction that builds upon a pre-trained GPT-2 model, with its self-attention and feed-forward layers kept frozen during the process. Fine-tuning is applied exclusively to the input embedding layer and output projection layer. Experimental results demonstrate that the proposed method achieves performance comparable to or superior against existing approaches across multiple electrical load forecasting tasks.

The increasing integration of renewable energy sources (RES) into power systems exacerbates power imbalance and deteriorates frequency quality, which poses significant challenges to the system’s limited regulation capacity of synchronous generators. Procuring additional frequency regulation services from new regulation resources (NRRs), e.g., RES, energy storage, and demand response, offers a potential solution. Unlike synchronous generators, which have near-symmetry in upward/downward capability and cost, NRRs display significant asymmetries that hinder their participation. To address the issue, this paper reveals the asymmetric characteristics of NRRs through a novel cost quantification model. This model captures NRRs’ capacities and operational costs under varying conditions, highlighting the distinct disparities between upward and downward regulation. Building upon this characterization, a novel regulation capacity scheduling model is developed to jointly minimize scheduled capacity costs and expected regulation costs. This model bridges the scheduling and real-time stages by establishing a relationship between scheduled capacity and expected mileage using system-level and resource-level mileage ratios. Moreover, an adaptive regulation power allocation strategy for the automatic generation control (AGC) systems is introduced, incorporating real-time regulation costs and a Gaussian mixture model (GMM)-based uncertainty reformulation with scenario-adaptive truncation, enabling effective real-time control under uncertainty. The proposed framework, which integrates these elements for systematic asymmetric resource coordination, is validated through simulations on a modified IEEE 118-bus system. The results show significant reductions in operational costs and improved control performance. This study enhances the economic efficiency and operational reliability of power systems with high-RES penetration through systematic coordination of asymmetric resource characteristics.

Renewable energy automatic generation control (AGC) has the characteristics of rapid adjustment and flexibility, which play a critical role in frequency regulation. Abnormal outputs in renewable energy AGC may trigger frequency fluctuations and threaten grid security. To address the above problems in renewable energy, AGC, a combined model-based and data-driven method for determining the single-step allowable action threshold, is proposed. Firstly, an AGC model with multiple frequency-regulating units is built, and the threshold can be obtained through simulation considering system status parameters. Secondly, as the model-based method struggles to satisfy the requirement of rapidity, a data-driven model based on CNN-LSTM is employed to determine the threshold in real-time. The training data is provided by a model-based method. Considering the limited coverage and interpretability of neural networks, a statistical error-prevention method is proposed to avoid deviations. Then, an adaptive piecewise constant approximation algorithm is employezd to reduce threshold update frequency and the burden for dispatchers. Finally, an adaptive threshold adjustment method for extreme scenarios is proposed, ensuring the frequency regulation of renewable energy AGC under extreme scenarios. Through experiments, the reliability and validity of the proposed method in threshold determination and error prevention are validated.

Electricity load, heat load and cold load in integrated energy systems are related to each other. Inspired by Graph Neural Networks (GNN), which can capture the topological structure of graph data, this paper proposes a novel multi-energy forecasting framework with multi-level task-sharing matrices (MTMs), which connects multiple tasks to capture the coupling characteristics between multiple loads. The proposed framework overwhelms most traditional multi-task approaches because traditional models usually have shared parameters in only one stage, which reserves representation space for temporal features of individual loads but limits their ability to capture multi-task correlations across multiple stages. Specifically, the proposed framework can capture coupling features across various stages of the entire model by MTMs, significantly enhancing the connections among multiple tasks while preserving adequate representation space for temporal features. The combination of graph attention and graph convolution can further expand the representational space of coupling features, so that multi-head attention can focus on capturing the temporal characteristics of individual loads. In addition, a gradient-based multi-task balance method is proposed to adapt to the above framework, which can balance different tasks by normalizing the weights by MTMs. Case studies demonstrate that the proposed framework has superior forecasting performance for multi-energy loads.