EAI Endorsed Transactions on Energy Web is an open access, peer-reviewed scholarly journal focused on cross-section topics related to IT and Energy. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a continuous frequency. INDEXING: Scopus (CiteScore: 1.7), Compendex, DOAJ, ProQuest, EBSCO, DBLP eISSN: 2032-944X
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Grid-Interactive Hyperscale Data Centers: Deep Reinforcement Learning for Joint Workload–Cooling Scheduling to Enable Demand Response and Renewable Integration
Driven by artificial intelligence and cloud computing, hyperscale data centers are becoming one of the fastest-growing electrical loads worldwide and are increasingly recognized as a new class of flexible loads capable of supporting demand response (DR) and the integration of variable renewable energy (VRE). However, their two principal control levers—IT workload scheduling and cooling system operation—have traditionally been managed in a decoupled manner, leaving both energy efficiency and demand-side flexibility under-exploited. This paper proposes a deep reinforcement learning (DRL) framework that jointly co-schedules computing and thermal resources so that a hyperscale data center can operate as a grid-interactive flexible load. We formulate the joint problem as a constrained Markov Decision Process and develop an actor-critic algorithm combining Deep Deterministic Policy Gradient with a safety shield mechanism to guarantee thermal constraint satisfaction during both training and deployment. A high-fidelity digital twin simulation environment enables safe Sim-to-Real training. Extensive experiments demonstrate that the proposed approach reduces total electricity consumption by 18-25% compared to baseline controllers, cuts thermal violations by over 90%, and maintains service level agreement compliance, while broadening the controllable power envelope of the facility to provide a technical basis for participating in DR programs and aligning data-center power profiles with renewable generation. The framework bridges IT-side and facility-side control and supports the evolution of hyperscale data centers from passive electricity consumers toward active, grid-interactive participants in renewable-penetrated power systems.
Copyright (c) 2026 Youchun Qiu https://creativecommons.org/licenses/by-nc-sa/4.0
Multi-objective Optimization of Energy Storage Capacity for Wind-Solar Microgrids Based on Digital Twins and a TCN-SLSTM-MHA Hybrid Model
To address the challenges in wind-solar microgrids caused by the intermittency and volatility of renewable energy—such as power imbalance, unreasonable energy storage capacity configuration, and the difficulty in achieving coordinated optimization of economy, reliability, and environmental sustainability under limited capacity—a multi-objective energy storage capacity optimization method is proposed based on digital twin technology and a hybrid model combining Temporal Convolutional Network (TCN), Stacked Long Short-Term Memory Network (SLSTM), and Multi-Head Attention (MHA) mechanism (TCN-SLSTM-MHA). First, a digital twin model of the wind-solar microgrid is constructed to enable real-time mapping, monitoring, and simulation analysis between the physical system and its virtual counterpart, overcoming the limitations of traditional models in adapting to dynamic operational scenarios. Second, a TCN-SLSTM model is introduced, enhanced with the MHA mechanism to dynamically assign weights across time steps, thereby improving the accuracy of source (generation) and load forecasting. Finally, a multi-objective optimization function is established that simultaneously considers the minimization of Life Cycle Cost (LCC), maximization of Power Supply Reliability (PSR), and reduction of Carbon Emission Intensity (CEI), enabling the optimal determination of storage capacity. Experimental results demonstrate that, in terms of source-load forecasting, the proposed model improves prediction accuracy by 16.9% and 16.7%, respectively, compared to the conventional TCN-LSTM model. In energy storage capacity optimization, compared to the traditional weighted sum method, the proposed approach reduces LCC by 15.2%, increases PSR by 4.3%, and decreases CEI by 12.5%, validating the effectiveness and superiority of the proposed model.
Copyright (c) 2026 Tong Xia, Jichao Ye, Yijia Lü, Xinwei Hu, Hui Huang, Yonghai Xu https://creativecommons.org/licenses/by-nc-sa/4.0
Fine-grained fault diagnosis methods for offshore wind turbine gearboxes driven by artificial intelligence—by multi-source data analysis and deep learning
INTRODUCTION: Offshore wind turbine gearboxes operate under complex conditions and are highly prone to faults. Traditional single-source diagnostic methods are sensitive to noise and load fluctuations, limiting diagnostic reliability. OBJECTIVES: This study aims to improve the diagnostic accuracy and recognition performance of gearbox fault categories. METHODS: Multi-source monitoring data were preprocessed and fused, followed by feature optimization and construction of a deep learning-based diagnosis model for fault detection and classification. RESULTS: The proposed model achieved accuracies of 0.951, 0.947, and 0.938 under different load conditions, with Area Under the Curve AUC values above 0.96, outperforming benchmark models. CONCLUSION: The proposed method improves diagnostic accuracy, robustness, and engineering applicability for offshore wind turbine gearbox fault diagnosis.
Copyright (c) 2026 Hongfeng Chen, Chao Chen, Xingdu Li, Huihui Han, Xinfa Shi, Xin Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Research on MPPT control strategy for photovoltaic power generation based on improved perturbation and observation method
INTRODUCTION: The output of photovoltaic (PV) arrays has nonlinear characteristics and is affected byambient conditions, so maximum power point tracking (MPPT) is crucial for efficient photovoltaic power generation. OBJECTIVES: This study aims to propose an MPPT control strategy based on improved perturbation and observation (P&O) method to overcome the shortcomings of traditional MPPT methods. METHODS: The method proposed integrates slope control and fuzzy control algorithms for hybrid control to achieve adaptive adjustment of control step size. In addition, the simulation test for start-up and sudden changes in operating conditions of PV were conducted to verify the practicability and progressiveness of the improved method proposed. RESULTS: The results confirm that the improved method proposed can reduce the output fluctuations of PV array and achieve shorter MPPT time and higher MPPT efficiency. CONCLUSION: The improved MPPT method has better MPPT performance, which provide effective reference for further improving the efficiency of PV power generation.
Copyright (c) 2026 Changzheng Zhou, Wei An, Xiaoliang Zhu, Hua Zhong, Jun Li, Longwei Jiao https://creativecommons.org/licenses/by-nc-sa/4.0
Research and Implementation of Intelligent Fault Report Generation Technology for Earthquake Emergency Response in Regional Power Grids
INTRODUCTION: Earthquake-induced disturbances require fast, standardized fault reports to support regional power-grid emergency response. OBJECTIVES: To automate report drafting while improving terminology, structure, and traceability compared with manual writing and simple template filling. METHODS: We design a workflow that combines slot filling, metadata-driven image proxy summarization, and controlled prompt-driven paragraph generation, then assembles outputs into a standardized report template and software system. RESULTS: On public earthquake and outage datasets, the proposed method outperforms baseline template filling (BLEU-4 1.00 vs 0.37, ROUGE-L 0.90 vs 0.50, BERTScore 0.93 vs 0.85, field coverage 1.00 vs 0.75) and reduces end-to-end report production from ~180 min (manual) to 2 min 15 s. CONCLUSION: The system enables reliable, high-efficiency fault report generation for earthquake emergency response in regional power grids, with clear potential for deployment.
Copyright (c) 2026 Kan Shi, Zuoqing Li, Huali Hu, Lei Zhang, Desong Wang, Yifan Han, Xiang Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Intent Recognition Enhanced by RAG and Knowledge Graphs for Regulation Execution and O&M Support in Power Engineering Secondary Systems
INTRODUCTION: Secondary systems in power engineering involve complex regulatory documents, operational procedures, dispatching requirements, and maintenance knowledge. Natural-language queries with implicit domain semantics create difficulties for conventional intent recognition in regulation matching and operation-support scenarios. OBJECTIVES: This study aims to enhance regulatory semantic understanding and intent-recognition accuracy, thereby supporting dispatching, maintenance, regulation execution, and operation and maintenance of power engineering secondary systems. METHODS: A knowledge-enhanced intent-recognition method combining Retrieval-Augmented Generation (RAG) and Knowledge Graphs (KGs) is proposed. A seven-label regulatory corpus is constructed, and structured semantic triples together with retrieved regulatory contexts are used as composite inputs. RESULTS: Experimental results show that the proposed method outperforms traditional techniques in terms of Accuracy and Macro-F1 score, improving the recognition of regulation-related, operation-guidance, maintenance-related, and procedure-support intents. CONCLUSION: The proposed approach strengthens the semantic alignment between technical queries and regulatory knowledge, providing an effective foundation for dispatching support, maintenance guidance, regulation execution, and intelligent operation and maintenance in the power industry.
Copyright (c) 2026 Qiangchao Xu, Kairu Chen, Zhaolun Deng, Jinhua Wu, Dan Lin, Zhaolin Shi https://creativecommons.org/licenses/by-nc-sa/4.0
Power–hydrogen system configuration integrating an improved Honey Badger Algorithm and mixed-integer programming
INTRODUCTION: Wind power variability challenges system regulation as renewables expand. Hydrogen storage provides long-term balancing. OBJECTIVES: To optimize the operation and configuration of a wind-storage-hydrogen-gas turbine system. METHODS: An Improved Honey Badger Algorithm (IHBA) with chaotic mapping and nonlinear parameters was combined with Mixed-Integer Programming (MIP) in a bi-level framework. RESULTS: Using real data from Eastern Inner Mongolia, IHBA achieved a fitness of 0.424, outperforming PSO, GA and original HBA by 20.3%, 14.9% and 8.0%. The optimal system included 47.9 MW wind, 94.3 MWh battery, 23.8 MW electrolyzer, 38.9 t H₂ storage, 13.7 MW fuel cell and 7.1 MW H₂-blended turbine, at a minimal cost of 4.57×10⁸ CNY. Annual H₂ output reached 68,900 Nm³, storage level 0.2-0.8, LCOE 0.438 CNY/kWh and payback period 18.4 years. Additionally, comparisons across multiple energy sources, sensitivity and robustness analyses, and ablation experiments indicate that the system configuration is stable and the algorithm optimization performs reliably, providing a solid reference for design. CONCLUSION: The IHBA-MIP framework is efficient and cost-effective for power-hydrogen system design, aiding large-scale renewable integration.
Copyright (c) 2026 Mingyuan Lai, Dandan Li, Guoxian Luo, Jieren Tan https://creativecommons.org/licenses/by-nc-sa/4.0
Model-free predictive allocation control of large-scale pumped-storage clusters for active grid balancing via cutting-plane method
INTRODUCTION: Pumped-storage hydropower plants (PSHPs) are indispensable for maintaining grid balancing amid high-penetration renewable energy integration, yet optimizing their predictive control is severely hindered by the scale of interconnected units and the resulting decision-making complexity. OBJECTIVES: This paper aims to develop a computationally efficient and operationally scalable assignment predictive control framework tailored for modern PSHP fleets. METHODS: To alleviate the computational burden and enhance real-time responsiveness, a model-free assignment predictive control scheme incorporating a cutting-based method is proposed to effectively streamline the solution space while guaranteeing grid stabilization. RESULTS: Furthermore, the framework is extended to multi-PSHP clusters, establishing a cooperative model-free predictive architecture that maximizes collective balancing capacity across diverse geographical regions. Numerical simulations validate the proposed method's efficacy. CONCLUSION: The results indicate that the proposed cooperative framework provides a practical and highly scalable solution for active grid balancing and renewable integration in large-scale PSHP systems.
Copyright (c) 2026 Kaiqiang Li, Liande Liu, Dongdong Zhang, Yang Dong, Ben Ao, Leixin Li, Biao Wang, Zhe Wang, Wei Zhang, Jingxin Yan, Zhong Du, Zhiqiang Pang https://creativecommons.org/licenses/by-nc-sa/4.0
A Minute-Level High-Frequency Data Acquisition and Lightweight Edge Diagnosis Algorithm for Low-Voltage Distributed Photovoltaic Systems Based on a Temporal Graph Convolutional Network
The performance disparities and operational characteristics among diverse devices within low-voltage distributed photovoltaic (PV) systems lead to variations in fault manifestations. The limited sampling frequency inherent in conventional approaches fails to capture critical fault features and temporal information in a timely manner. Moreover, the presence of periodic fluctuations in PV fault data contributes to persistently high false alarm rates. This paper proposes a high-frequency, minute-level data acquisition framework and a lightweight edge diagnosis algorithm for low-voltage distributed PV systems based on a temporal graph convolutional network. The proposed approach employs intelligent PV edge terminals to enable high-frequency data acquisition from distributed PV generation units. A graphical representation of the low-voltage distributed PV plant is constructed, from which dynamic temporal features of PV generation data are extracted using temporal convolutional layers, while topological correlations among PV devices are captured through graph convolutional layers. This enables the spatiotemporal joint modeling of fault characteristics. To accommodate the computational constraints of edge devices, a customized adaptation of the MobileNet-V3 architecture is introduced. By integrating attention mechanisms and implementing layer pruning, the model is tailored for enhanced performance in photovoltaic fault classification, thereby achieving lightweight edge diagnosis. Experimental results demonstrate that the proposed algorithm accurately diagnoses output voltage fluctuation faults in PV inverters and effectively identifies abnormal phase voltage fluctuation faults. It achieves high precision, recall, and F1 scores across various fault types. The model exhibits rapid training convergence with a low loss function value, satisfying the requirements for lightweight edge diagnosis in low-voltage distributed PV systems.
Copyright (c) 2026 Xuekai Hu, Shiwei Xue, Xingzhi Chang, Kun Jiang, Da Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Proxy-Cost-Aware Explainable Graph Neural Network for Multi-Domain O&M Decision Support in Digital Twin-Enabled Smart-City IoT Systems
INTRODUCTION: Digital twin-enabled smart-city IoT systems may interact with interconnected transportation, parking, environmental, communication, and edge-computing infrastructures. Failures or congestion in one domain may propagate across service chains and increase operational burden, yet existing spatiotemporal graph models rarely represent cost semantics, risk propagation, and maintenance actions jointly. OBJECTIVES: This study develops a cost-aware explainable graph neural network for multi-domain operation and maintenance decision support, with emphasis on proxy-cost prediction, risk identification, rule-based action ranking, and interpretable attribution. METHODS: A multi-layer digital twin graph integrates sensing, communication, computing, service, maintenance, and proxy-cost nodes. A state–risk–cost three-channel propagation mechanism separately models operational states, abnormal-risk diffusion, and cost-impact transmission. Rule-based graph interventions, including maintenance, task migration, link switching, and sampling adjustment, are used to estimate action-induced proxy-cost changes. CityPulse is used as a heterogeneous multi-domain proxy scenario, while Caltrans PeMS provides a homogeneous traffic-sensor reference. Performance is evaluated using MAE, F1, NDCG@5, and Fidelity, where NDCG@5 measures consistency with predefined action-ranking rules. RESULTS: On CityPulse, the proposed method achieved lower MAE and higher NDCG@5 and Fidelity than the strongest corresponding baselines, although its F1 was slightly lower than STAEformer. On PeMS, it achieved the highest Fidelity but remained below the best traffic-specific models in MAE, F1, and NDCG@5. Ablation results showed the respective contributions of cost nodes, explicit three-channel propagation, rule-based intervention evaluation, and explanation constraints. CONCLUSION: The method is more effective for heterogeneous multi-domain O&M scenarios than for homogeneous traffic forecasting. Because both datasets provide proxy rather than real financial, energy-system, and intervention records, the results support proxy-based methodological validation but do not constitute direct physical-energy-system validation or evidence of realized cost savings and causal intervention effectiveness.
Digital Twin Technologies for Smart Energy Systems
Copyright (c) 2026 Fujing Qin https://creativecommons.org/licenses/by-nc-sa/4.0
Fault diagnosis method of fire-proof oil system in thermal power plant based on real-time monitoring
INTRODUCTION: Fire-proof oil systems in thermal power plants are prone to pump wear, leakage, oil degradation, and valve jamming, threatening operational safety. Real-time fault diagnosis is required. OBJECTIVES: To develop a real-time and accurate fault diagnosis method for fire-proof oil systems. METHODS: A multi-sensor monitoring framework integrating pressure, flow, temperature, and oil-quality signals was established. A lightweight deep learning model based on multi-source feature fusion was constructed, with a confidence-driven decision mechanism to reduce the influence of low-quality samples. Experiments were conducted on an open hydraulic-system dataset under multiple operating conditions. RESULTS: The proposed method achieved accuracies of 0.96–0.93 and AUC values of 0.98–0.97. Average diagnostic time was 3.17–3.58 ms, with alarm delays of 49.36–54.82 ms. The model outperformed MBDNN and TDANet in accuracy, efficiency, and robustness under noise. CONCLUSION: The proposed framework enables reliable real-time fault diagnosis of fire-proof oil systems and supports intelligent operation and maintenance in thermal power plants.
Copyright (c) 2026 Zhigang Shan, Jianhui Lan, Bing Yang, Yongbin Li, Huihui Han, Xinfa Shi https://creativecommons.org/licenses/by-nc-sa/4.0
A Transfer Learning-Based Method for Cross-Scenario Operation Step Generation and Generalization Optimization in Distribution Networks
In response to the issue of limited generalization in automatic generation of operation steps across different stations and operating conditions in distribution networks, this paper proposes a temporal-topological joint modeling method driven by transfer learning. The method takes device state sequences and network topology as inputs, utilizing a Transformer to extract temporal features and Graph Convolutional Networks (GCN) to model topological constraints. During the transfer phase, a “freeze the lower layers – progressively unfreeze” fine-tuning strategy is applied, combined with distribution alignment and domain adversarial learning to achieve cross-scenario adaptation. Evaluations on the Caltech 28-Bus Digital Twin dataset show that, compared to No Transfer and Fine-tuning, the proposed method reduces the Mean Squared Error (MSE) from 0.15 to 0.06, the Mean Absolute Error (MAE) from 0.39 to 0.245, and the Generalization Performance Index (GPI) to approximately 0.47 in the target domain. When only 10% of the target domain samples are used, the Relative Normalized Squared Error (RNSE) is approximately 0.55. For the sake of generation quality, BLEU is 0.83, and the Semantic Consistency Score (SCS) is 0.92. These findings show that the suggested framework can reliably produce operation steps that are semantically coherent and logically consistent across various topologies and operating conditions, offering a workable technological route for the automation of operation step generation in distribution network operation scheduling.
Copyright (c) 2026 Fangzhou Hao, Shibo Li, Jiangwei Wu, Shuhua Chen https://creativecommons.org/licenses/by-nc-sa/4.0
GNN-Based Method for Data Flow Path Completion in Electric Power Information Networks
The reliability of power information networks is paramount for smart grid security, yet incomplete data transmission paths due to link failures or cyberattacks critically impair topology awareness and operational decision-making. To address the limitations of existing methods in handling the dynamic and structurally complex nature of these networks, this paper proposes a Topology-Enhanced Dynamic Perception Graph Neural Network (TEDP-GNN) for accurate path completion. The core methodology revolves around three integrated innovations. First, we introduce a topological deep learning framework that models high-order node interactions and network connectivity patterns beyond pairwise relationships. This is achieved by employing simplicial complexes to capture multi-node dependencies inherent in power data routing structures. Second, a novel dynamic perception mechanism is designed to continuously monitor link state changes. This mechanism utilizes a temporal gating unit that ingests real-time network alert data, enabling the model to adaptively reweight message-passing pathways in response to topological disturbances such as failures or attacks. Finally, the task is formulated as a topology-constrained optimization problem. Within this framework, we deploy a hybrid attention module that simultaneously computes both node-level and path-level attentions, alongside a multi-dimensional feature aggregation strategy that synthesizes information from topological embeddings, dynamic states, and historical transmission patterns. Preliminary validation on simulated power communication topologies demonstrates that TEDP-GNN significantly outperforms baseline models in predicting missing links, showing marked improvements in precision and recall. The model provides a robust, topology-aware solution for maintaining data path integrity, thereby enhancing the situational awareness and resilience of power information infrastructures.
Copyright (c) 2026 Xing Liu, Zhengqi Wang, Wangyong Guo, Liang Guo, Shuang Chen, Yizhou Chen https://creativecommons.org/licenses/by-nc-sa/4.0
AI and Abductive Inference-Based Techniques for Power-System Abnormal Alarm Identification and On-Site Grid Operational Behavior Analysis
During the operation of power-system industrial control and monitoring platforms (e.g., SCADA/EMS and substation automation), strong coupling among components and multi-source heterogeneous data often lead to alarm flooding and complicate root cause identification. To address this, this paper proposes an intelligent abnormal-alarm identification and on-site operation behavior analysis method, combining artificial intelligence with an abductive inference framework. Under fixed parameters, the method first aggregates raw alarm streams by events to enhance structure and interpretability. Then, a diagonal-covariance Gaussian Hidden Markov Model (HMM) is trained with normal data, and a path-deviation metric ranks root cause candidates. Multi-source evidence chains—integrating temporal, network, and semantic features—further improve inference interpretability for grid operation and maintenance. Using annotated operation logs, four quantitative metrics (MTTA, MTTR, action rate, consistency) assess the link between model outputs and actual handling behaviors. Experiments on five test sets show the method achieves a 78% alarm compression rate and a 0.43 average silhouette coefficient. Top-1 and Top-3 root cause localization hit rates are 71.8% and 88.5%, with path score fluctuations under 0.05 nats. The average MTTA and MTTR are 186s and 792s, with an 84% action rate and 72% consistency. These results confirm the method’s effectiveness in mitigating alarm flooding, improving root cause localization, and supporting on-site decision-making in power-grid operational scenarios.
Copyright (c) 2026 Kan Shi, Jiao Yang, Yongmei Chen, Shengyan Ye, Xuanli Xia, Jingrui Shang, Mian Li, Jianwei Yang, Tianguo Yang, Shuangwu Li https://creativecommons.org/licenses/by-nc-sa/4.0
A Convolutional Neural Network Based Approach for Cyber Intrusion Detection in Smart Grids
Smart grids, increasingly reliant on information and communication technologies (ICT), are vulnerable to complex cyberattacks, thereby mandating the deployment of intelligent and adaptable intrusion detection systems (IDS). However, the efficacy of existing IDS techniques is frequently constrained by their limited capacity to extract distinguishing features from the high-dimensional, heterogeneous data characteristic of grid operations. In order to overcome this, we suggest a novel intrusion detection model that uses a convolutional neural network (CNN) to automatically extract hierarchical features from network traffic. The suggested CNN model outperforms conventional signature-based and SVM-based techniques with an accuracy of 98.8%, precision of 98.6%, and recall of 99.3% using the KDD-CUP99 dataset. Validation on a semi-realistic dataset from the IEEE 14-bus system, which uses IEC 61850 communication protocols, shows that it is 97.3% accurate. This means that it works well when physical and cyber layers are combined. Feature importance analysis shows that cyber-layer features, such as the continuity of GOOSE sequence numbers, are very important for detection. This research introduces a feature-learning-based intrusion detection system (IDS) framework. It works well and shows potential for practical use in improving the cybersecurity of smart grids.
Copyright (c) 2026 Ying Lan, Chenye Zhu, Fan Wu, Qibin Hu https://creativecommons.org/licenses/by-nc-sa/4.0
MC-YOLO: A Lightweight Insulator Defect Detection Model Based on an Improved YOLOv8
Due to the fast growth of China’s electric power industry, the total length of high-voltage transmission lines has been continuously increasing. As a key component of high-voltage transmission systems, insulators play a critical role, and achieving efficient and accurate defect detection for insulators is of great significance. To address the practical challenges of limited size, limited computational resources, restricted energy supply, and complex environmental conditions on Unmanned Aerial Vehicle (UAV) platforms, this paper proposes a lightweight insulator defect detection model, MC-YOLO, based on an improved YOLOv8 architecture. Specifically, the original backbone network is replaced with the lighter MobileNetV3 module, reducing the model parameters and GFLOPs to 21.3% and 20% of those of the original model, respectively. In addition, a Convolutional Block Attention Module (CBAM) is integrated into the network neck structure to effectively improve the extraction of key features, resulting in a 1.2 percentage points improvement in detection accuracy. Finally, the loss function is changed to Wise-IoU (WIoU) v3, which increases the localization capacity of the model and further increases the accuracy by 1.4 percentage points. Experimental results demonstrate that the proposed MC-YOLO model achieves a lightweight design while maintaining high detection performance, providing a viable technical solution for edge deployment in real-world engineering applications.
Copyright (c) 2026 Junlian Wang, Zhixiong Li, Jinquan Yang, Hongbing Ren, Wenchao Pan, Yang Feng, Lei Xiong, Chune Li, Yigong Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Big Data-Driven Short-Term Load Forecasting in Smart Grids: A Spatio-Temporal Dynamic Graph Transformer with Uncertainty-Aware Attention
Short-term load forecasting for modern smart grids must jointly model long-range temporal patterns, correlations across many metering points, and the uncertainty required for operational decision-making. We propose ST-DGT-UA, a spatio-temporal dynamic graph Transformer that (i) embeds heterogeneous covariates with convolutional projections and Time2Vec, (ii) learns a time-varying adjacency matrix from node representations, (iii) couples spatial graph attention with temporal ProbSparse attention for efficient long-sequence modeling, and (iv) outputs multiple conditional quantiles trained with multi-quantile Pinball loss. Experiments on GEFCom2014 and the UCI Electricity Load Diagrams datasets show that ST-DGT-UA achieves MAPE/RMSE of 1.92/145.6 on GEFCom2014 and 2.65/42.3 on UCI, and improves probabilistic quality with Pinball Loss 0.024 and CRPS 0.043. These results indicate that learning dynamic, data-driven spatial dependencies is beneficial for multi-node load forecasting where correlations evolve over time.
Copyright (c) 2026 Jing Zhou, Jiyue Long, Xiaohong Long https://creativecommons.org/licenses/by-nc-sa/4.0
Computational methods for optimizing operational strategies of energy storage systems based on generative artificial intelligence
INTRODUCTION: The energy storage systems (ESSs) require the coordination of renewable generation, load demand, electricity prices and safety constraints but operational strategy optimization is much dependent on manual modeling and is hard to change based on users preferences. THE OBJECTIVES ARE: The study will come up with an automated and constraint-based optimization structure of ESS scheduling. It is proposed that a constraint-improved generative artificial intelligence (AI)-based model to optimize energy storage operation strategies, called CGAI-ESO, be developed. METHODOLOGY: The model is based on natural language processing to understand the requests of energy management system (EMS) developers or end-users, retrieval-augmented generation (RAG) to fetch domain and optimization data, and a generative AI agent to develop objective functions, constraints, and executable optimization codes. The combination of an optimization solver with a digital twin feedback module checks the validity of economic performance, safety compliance, battery degradation and constraint satisfaction. The results of experiments with public energy data and microgrid simulations demonstrate that CGAI-ESO minimizes operational cost by 18.72 percent, maximizes peak shaving rate to 28.5 percent, enhances the use of renewable energy to 96.0 percent and minimizes the level of constraint violation to 0.02 percent. It is also better than large language model (LLM)-only and LLM-RAG baselines in terms of model generation, constraint completeness and code executability. CONCLUSION: CGAI-ESO provides a feasible route for intelligent and reliable ESS operation strategy optimization.
AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability
Copyright (c) 2026 Jiaxin Huang, Xielin Shen, Dongdong Chen https://creativecommons.org/licenses/by-nc-sa/4.0
Intelligent Distribution-Network Fault Handling via Cross-Modal Semantic Fusion and Knowledge-Graph Reasoning
Power distribution network fault handling imposes stringent requirements on both response timeliness and decision reliability. The high penetration of distributed renewable generation, the widespread deployment of power-electronic devices, and increasing operating-condition fluctuations further intensify the complexity and uncertainty of fault mechanisms. To address the challenges of cross-validating multi-source heterogeneous information, structurally invoking fault-handling knowledge, and balancing latency and accuracy under resource constraints, this paper develops a cross-modal semantic fusion and knowledge-graph reasoning–based fault-handling system, termed CM-SKG. The system takes synchronous phasor measurement unit (PMU) electrical measurements and UAV dual-spectrum inspection images as its primary information sources, and establishes a closed-loop architecture that integrates semantic sensing, transmission, and reasoning. Specifically, task-oriented semantic representations and controllable compression are performed at the edge device; at the edge server, a learnable semantic alignment matrix and a bidirectional interactive attention mechanism are employed for cross-modal fusion, effectively mitigating granularity discrepancies across heterogeneous data and producing consistent fault semantic evidence; at the control center, a fault-handling knowledge graph is introduced for reasoning, mapping fused semantics to executable handling actions, while a tunable reasoning-depth mechanism enables smooth switching between fast response and deeper inference. Furthermore, we propose a semantic communication efficiency (SCE) metric that jointly accounts for cross-modal fusion quality, reasoning reliability, and end-to-end latency, and use it to drive the coordinated optimization of compression ratio, reasoning depth, and computational resources. Online policy learning is realized via a distributional soft actor–critic (DSAC) algorithm. Simulation results demonstrate that CM-SKG significantly improves fault-handling accuracy and decision stability while satisfying real-time constraints.
Power distribution network fault handling
Copyright (c) 2026 Fangzhou Liu, Yang Zhao, Jiqiao Chen, Ting Luo https://creativecommons.org/licenses/by-nc-sa/4.0
Research on the Impact of Redundancy Configuration and Opportunistic Maintenance on the Reliability of Ultra-High Voltage Flexible Direct Current Transmission Systems
INTRODUCTION: Ultra-High Voltage Flexible Direct Current (UHVDC-F) transmission systems have become the preferred choice for long-distance power transmission due to their superior control flexibility and power quality, with their reliability directly determining regional grid stability. OBJECTIVES: To ensure stable operation, it is essential to evaluate system reliability, quantify the enhancement effects of redundant components, and identify critical system vulnerabilities. METHODS: This paper proposes an evaluation framework considering diverse redundancy schemes. For component-level redundancy, a state transition model incorporating spare resource constraints is developed. For sub-module level redundancy, an opportunistic maintenance strategy is introduced. The framework utilizes sequential Monte Carlo simulation to accurately capture the stochastic evolution and dynamic transitions of the system. RESULTS: Results indicate that converter transformer redundancy most significantly boosts overall reliability. Furthermore, opportunistic maintenance for converter valves effectively mitigates degraded operation risks and extends the Mean Time Between Failures. CONCLUSION: This research establishes a high-fidelity digital twin foundation for complex UHVDC-F operational states. Beyond enhancing assessment accuracy, these quantitative matrices can be seamlessly integrated into AI-driven asset management and cost optimization engines, transforming risk evaluation into active O&M economic scheduling to fortify large-scale grid resilience.
Ultra-High Voltage Flexible Direct Current transmission systems
Copyright (c) 2026 Kaixin Wu, Shilong Gao, Jinhua Dai; Yong Yang; Wanxin Feng, Shaogang Du, Yu Yu https://creativecommons.org/licenses/by-nc-sa/4.0
CNN-Attention-Based Renewable Energy Generation and Load Forecasting Method for Stable Operation of Smart Grids
High renewable penetration increases source-load uncertainty and challenges the stable operation of smart grids. This paper proposes a prediction-driven framework that integrates CNN-Attention-based joint renewable generation and load forecasting with rolling optimization of a battery–supercapacitor hybrid energy storage system. The main innovation lies in coupling source-load forecasting with hybrid energy storage dispatch, so that predicted renewable generation and load trajectories can directly support rolling scheduling decisions. Multi-source temporal inputs, including wind power, photovoltaic power, load demand, meteorological variables, electricity price, and periodic time encodings, are used to characterize source-load coupling. One-dimensional convolution extracts local fluctuation features, while temporal attention assigns adaptive weights to informative historical periods. In the dispatch stage, the battery mainly undertakes low-frequency energy balancing, whereas the supercapacitor suppresses high-frequency power fluctuations. The optimization objective jointly considers grid purchase cost, storage degradation cost, renewable curtailment penalty, load shedding penalty, and grid power fluctuation penalty. Experiments were conducted on one-year operational data from a regional wind–photovoltaic–storage microgrid, with a 15-min sampling interval and 35,040 time steps. Compared with LSTM, GRU, CNN, CNN-LSTM, and Transformer baselines, the proposed model achieved the lowest forecasting errors, with MAE/RMSE/MAPE of 6.692 kW/8.322 kW/3.288% for renewable generation and 3.747 kW/4.490 kW/0.406% for load forecasting. At the dispatch level, the proposed strategy reduced the total operating cost index to 360, decreased grid power fluctuation to 3.4%, improved renewable accommodation to 90.6%, and lowered curtailment to 2.8%.
Copyright (c) 2026 Wei Luo https://creativecommons.org/licenses/by-nc-sa/4.0
Heterogeneous Node-Oriented Consistency Optimization Method for Instrument Transformer Monitoring Data in Energy-Storage-Integrated Smart Grids
INTRODUCTION:Smart grid digital twins depend on reliable instrument transformer monitoring data for real-time state estimation and downstream operational analysis. However, heterogeneous current transformers, voltage transformers, capacitive voltage transformers, merging units, and edge terminals often generate asynchronous, biased, and locally inconsistent measurements, which may degrade digital twin reconstruction accuracy and affect downstream grid analytics. OBJECTIVES: We explicitly distinguish this heterogeneity problem from conventional bad data, random noise, missing values, and topology errors, as it arises from cross-device calibration mismatch and timevarying sensing semantics rather than isolated measurement corruption. METHODS:To address this problem, this paper proposes a Heterogeneous Topology-Constrained Consistency Optimization (HTCCO) method. HTCCO performs delay-aware temporal alignment, heterogeneity-aware node representation, and time-varying bias correction, followed by trust-weighted aggregation and graph Laplacian-based consistency regularization to enforce topological coherence before state estimation. In this framework, each heterogeneous measurement stream is first mapped into a unified latent space via a learnable compatibility transformation, enabling cross-device alignment prior to aggregation, while trust weights are learned as independent reliability scores rather than purely relative competition. Unlike conventional preprocessing or state estimation methods, HTCCO focuses on front-end measurement correction and consistency enforcement, rather than directly estimating system states. RESULTS:Experiments on IEEE 14-bus, IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems show that HTCCO consistently reduces MAE, RMSE, MAPE, topology consistency residual, and state estimation error compared with WLS-SE, PI-GNN, GAEN, and TAGNN-SE. We further report relative improvements over the strongest baseline across all metrics to explicitly quantify the performance gain. Robustness and ablation results further verify the effectiveness of each module. Finally, we clarify that HTCCO is not directly evaluated in closed-loop storage scheduling tasks; instead, its contribution is a measurement-level consistency layer that improves the reliability of downstream grid optimization and control modules. CONCLUSION: These results demonstrate that HTCCO provides a reliable front-end data correction and consistency optimization layer for smart grid digital twins, improving the reliability of voltage, current, and power measurements used in downstream monitoring and decision-making tasks.
Copyright (c) 2026 Liang Zhang, Shenglei Du, Xu Yan, Yuren Gao, Jun Liu https://creativecommons.org/licenses/by-nc-sa/4.0
Design of intelligent inspection system for 500 kV substation based on multi-source data fusion
INTRODUCTION: The safe and stable operation of 500 kV substations is essential for ensuring power grid reliability. Traditional manual inspection methods suffer from low efficiency, limited coverage, and poor real-time performance, making intelligent inspection technologies increasingly important. OBJECTIVES: This study aims to develop an intelligent inspection system for 500 kV substations based on multi-source data fusion to improve equipment status monitoring, fault diagnosis, and operational reliability. METHODS: The proposed system integrates infrared thermal images, visible-light images, sound signals, and vibration data for comprehensive equipment perception. A multi-modal deep learning network with an attention mechanism (AMM-Net) is designed for adaptive feature extraction. In addition, pixel-level, feature-level, and decision-level fusion strategies are combined with a trust-based distributed Kalman filtering algorithm (Trust-DKF) to improve robustness and anti-interference capability. RESULTS: Experimental results show that the system achieves 94.7% accuracy and 93.1% F1-score in equipment status recognition. Under noise and occlusion interference, performance decreases by only 10.7%. The edge-device inference time is optimized to 28.9 ms with low energy consumption of 0.12 J per operation. CONCLUSION: The proposed system significantly improves the efficiency, accuracy, and reliability of intelligent substation inspection.
Copyright (c) 2026 Yaoshan Zhang, Zhiqiang Xiao, Zhihua Lin, Xiuquan Hu, Yue Zhou https://creativecommons.org/licenses/by-nc-sa/4.0
Analysis of optimal scheduling and energy-saving measures for shipyard microgrid based on IMOPSO algorithm
INTRODUCTION: This research study presents the development of a microgrid system specifically designed for a shipyard environment based on the energy consumption characteristics of major shipyard equipment. OBJECTIVES: The algorithm simultaneously optimizes three different objectives as follows: (1) economic cost; (2) carbon emission; and (3) power fluctuation (stability). METHODS: To accomplish the objective of creating an optimal microgrid configuration, the IMOPSO was used to solve the microgrid model. RESULTS: Results indicate that there are considerable trade-offs between economic cost versus carbon emission and between economic cost versus power fluctuation, which require optimally balanced approaches in order to meet all the different objectives. Among various energy-saving measures, the adoption of the dehumidification system with heat recovery wheel proves especially effective in reducing power consumption, economic cost and carbon emission. CONCLUSION: Pareto front analysis reveals that the weak emission-fluctuation conflict creates an opportunity to effectively balance the critical cost-emission trade-off for obtaining superior solutions. Equipment energy-saving measures, particularly the dehumidification with heat recovery and hybrid welding, significantly improve the microgrid's economic and environmental performance.
Copyright (c) 2026 Weipeng Li, Jun Du, Jiao Wang, Tong Fu, Sitong Zhu https://creativecommons.org/licenses/by-nc-sa/4.0
Retraction Notice: Energy-Efficient Design of Seabed Substrate Detection Model Leveraging CNN-SVM Architecture and Sonar Data
We, the publisher, have retracted the following article: Wang et al. (2024). Energy-Efficient Design of Seabed Substrate Detection Model Leveraging CNN-SVM Architecture and Sonar Data. https://doi.org/10.4108/ew.6097 The article has been retracted due to misconduct during the peer review process. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
Copyright (c) 2026 Keming Wang, Chengli Wang, Wenbing Jin, Liuming Qi https://creativecommons.org/licenses/by-nc-sa/4.0
Retraction Notice: Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm
We, the Publisher, have retracted the following article: Devikanniga et al (2020). Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm. https://doi.org/10.4108/eai.13-7-2018.164177 The article has been retracted due to misconduct during the peer review process. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
Copyright (c) 2026 D. Devikanniga, Arulmurugan Ramu, Anandakumar Haldorai https://creativecommons.org/licenses/by-nc-sa/4.0
An MT-Transformer Framework for Coordinated Wind–Solar–Load Forecasting with Net-Load-Based Coal-Power Regulation Demand Identification
Data-driven forecasting has become increasingly important for describing the temporal interactions among heterogeneous variables in modern power systems. To capture the nonlinear coupling, heterogeneous fluctuations, and multi-scale temporal variations between renewable generation and load demand, this study develops an MT-Transformer framework for coordinated wind–solar–load forecasting. Meteorological variables, historical renewable output, historical load, and temporal labels are integrated as model inputs. A shared Transformer encoder is used to learn common temporal representations, and task-specific forecasting heads are designed to generate synchronized predictions for wind power, PV power, and load. The experimental results show that MT-Transformer achieves an MAE of 0.0848, an RMSE of 0.1254, and an R² of 0.9302. Compared with the Persistence Model, the MAE and RMSE decrease by 36.05% and 30.49%, respectively. The predicted outputs are further converted into a net-load sequence, from which fluctuation indicators are derived. The peak–valley difference reaches 0.462 p.u., and the maximum ramp rate reaches 0.087 p.u./h, indicating evident peak-shaving pressure and short-term regulation demand. These findings confirm that the proposed framework improves coordinated forecasting performance and provides quantitative evidence for coal-power peak regulation, reserve capacity allocation, and ancillary service demand identification.
Copyright (c) 2026 Meng Huang, Lei Wang, Teng Luo, Chao Ju, Wenchao Guo, Chenghao Ning https://creativecommons.org/licenses/by-nc-sa/4.0
Dynamic Path Selection in SDN Based on Reinforcement Learning and Link Utilization
INTRODUCTION: The development of modern power systems imposes stringent requirements on communication networks, including highly dynamic loads, low latency, and high reliability. Although recent Software-Defined Networking routing, link-utilization-aware scheduling, and reinforcement learning-based methods improve path optimization, challenges remain in bottleneck-link perception, congestion feedback, and stable decision-making under dynamic traffic conditions. OBJECTIVES: To counteract problems like delayed response times and inadequate congestion identification in conventional path selection algorithms owing to dynamic link modifications, this research paper presents a path selection model that combines bottleneck link usage and reinforcement learning. METHODS: Under the Software-Defined Networking control architecture, the proposed model incorporates link utilization, Graph Convolutional Network structures, and Gated Recurrent Units, while introducing a deep reinforcement learning algorithm to optimize routing strategies. RESULTS: Experimental results demonstrate that under a 60 Mbit/s load, the proposed method achieves a throughput of 57 Mbit/s, maintains the minimum transmission delay within 0.043 s, and yields a link utilization rate of 89%. In experiments carried out to evaluate dynamic decision-making, the approach for choosing paths adopted by the model averages convergence at the 15th round, resulting in an error rate of 4.2%, minimal load balancing at 0.23, and median latency time in real-time decisions of only 31 ms, which is better than other models. CONCLUSION: These results demonstrate that the model achieves superior state awareness and adaptive routing performance in multi-source heterogeneous networks. The model also exhibits good performance in terms of congestion control and path optimization and hence can be used effectively for intelligent routing in next-generation power communication networks.
Copyright (c) 2026 Ying Zeng, Xingnan Li, Yuben Bao, Guangyu Hu, Jikai Hong, Fan Zha https://creativecommons.org/licenses/by-nc-sa/4.0
Enhanced Thrust Ripple Suppression Method in PMLMUsing Indirect Adaptive Robust Control
INTRODUCTION: The indirect adaptive robust control (IARC) can effectively improve servo performance under external disturbances, including thrust ripple. In the conventional IARC method, the recursive least squares (RLS) treats the low-pass-filtered total control signal as the measured output. Such filtering introduces parameter estimation errors caused by phase lag in the measured signal, whereas direct filter removal results in algebraic loop issues. OBJECTIVES: To address these limitations, a novel method using the indirect adaptive robust control with a measured-output-reconfigured RLS (IARC-MORRLS) is proposed in this paper. METHODS: Firstly, the proposed method utilizes the feedback control signal directly as the measured output of RLS, thereby improving the accuracy of parameter estimation by mitigating phase-lag-induced errors. Then, a variable-gain mechanism is developed to guarantee the complete convergence of parameter estimates. Finally, a slope-judgment mechanism is incorporated into projection mapping to suppress oscillations in initial parameter estimation. RESULTS: Simulations and experimental validations were used on a permanent magnet linear motor (PMLM) driven motion system. The validation experiment proves that IARC-MORRLS method proposed in this paper exhibits better performance in thrust ripple suppression over the conventional IARC method. CONCLUSION: It will help PMLM system reach better performance under external disturbances.
Copyright (c) 2026 Yuanfeng He, Weizhen Wang, Rong Li, Wenyuan Yan, Jiutong Yang, Chi Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Accurate Matching Model of New Energy Supply and Demand Empowered by Blockchain
Under the guidance of the "dual carbon" goals, green electricity has become the core carrier of the new energy transition. However, issues such as mismatched supply and demand of green electricity, information asymmetry, and lack of trust have seriously restricted the efficient development of green electricity marketization. Blockchain technology, with its core features of distributed ledger, immutability, and smart contracts, provides technical support for addressing the pain points in green electricity supply - demand matching. This paper constructs an accurate matching model for green electricity supply and demand based on blockchain, clarifies the model's technical architecture, participating entities, and operation process, designs a full - process collaboration mechanism, and verifies the model's effectiveness through simulation experiments. The results show that this model can increase the accuracy of green electricity supply - demand matching to over 92%, which is 15 - 20 percentage points higher than the traditional model; the transaction cycle is shortened to less than 7 days, with an efficiency improvement of over 60%; at the same time, it reduces the trust verification cost by more than 30%, significantly strengthens the trust relationship between supply and demand parties, optimizes the allocation of green electricity resources, and provides a technical path and practical reference for the market - oriented collaborative development of green electricity.
Copyright (c) 2026 Chuang Li, Junshi Zhang, Lei Zhou, Lili Li, Jiangtao Li, Guomin Li https://creativecommons.org/licenses/by-nc-sa/4.0