EAI Endorsed Transactions on Energy Web
type: channel
description: 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
publisher: EAI
language: en
publication name: EAI Endorsed Transactions on Energy Web
issn: 2032-944X
copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
items: Intelligent Distribution-Network Fault Handling via Cross-Modal Semantic Fusion and Knowledge-Graph Reasoning
items: Retraction Notice: Energy-Efficient Design of Seabed Substrate Detection Model Leveraging CNN-SVM Architecture and Sonar Data
items: Retraction Notice: Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm
items: Enhanced Thrust Ripple Suppression Method in PMLMUsing Indirect Adaptive Robust Control
items: Research on Accurate Matching Model of New Energy Supply and Demand Empowered by Blockchain
items: Dynamic Error Compensation Method for Binocular Ranging of Transmission Lines Integrating IMU Data
items: An Early Warning Method for Abnormal Fluttering Behavior in Transmission Lines Based on Deep Binocular Vision
items: Partial Discharge Detection and Identification in High-Voltage Cable Systems Based on Heterogeneous Sensor Fusion and Enhanced Deep Learning
items: Photovoltaic Power Adaptive Hybrid Forecasting Model Integrated with Multi-Dimensional Error Compensation
items: Time-Sensitive Service Scheduling Method for LEO Broadband Satellite-Based Power Emergency Networks
items: Lightweight Real-Time power quality disturbance recognition using Time-Frequency fusion with Cross-Attention mechanism
items: Research on Electromagnetic-Thermal-Mechanical Multiphysics Coupling Simulation Analysis of Converter Transformers
items: A Novel Hybrid Collaborative Forecasting Method for Offshore Wind Power Based on Intelligent Optimization
items: Data-driven Predictive Individual Pitch Control for Floating Offshore Wind Turbines
items: Design and Implementation of a Low-Frequency Current Comparator Bridge Based on Quantized Hall Resistance
items: Short-Term Power Load Forecasting for Industrial Parks Using CNN-BiLSTM Network with Kernel Density Estimation-based Interval Prediction Method
items: Analysis of the Impact of Active Power Recovery Rate of Wind Farms on Power System Transient Stability
items: Optimal scheduling of renewable power distribution systems combining deep learning and particle swarm optimization
items: Reinforcement Learning-enhanced Policy-aware Modeling of Smart Grid Efficiency under Carbon Constraints: Integration of SBM DEA and Dynamic Policy Response Simulation
items: Mining method of power consumption behavior pattern of different subject resource objects in virtual power plant based on graph neural network
items: Research on Active Equalization of Energy Storage Lithium Batteries under a Modular Layered Architecture for Smart Grid Applications
items: Reactive Power Optimization of Distribution Network Considering DG Based on Improved Ant Lion Algorithm
items: Malfunction Diagnosis of Microgrid Devices Based on Optimized Graph Neural Network
items: Uncertainty Pattern Recognition of Multi-Type Renewable Power Plants and the Coupling Mechanism with Multi-Level Voltage Sensitivity
items: Capacity Optimization for Minimizing the Carbon Footprint of Power Systems
items: Research on Key Technologies of AI-Based Source-Load Coordinated Regulation at the Edge of Distribution Networks
items: A Medium and Long-term Forecasting Method for Electric Load Curves Integrating Dynamic Variable Selection and Sparse Attention Mechanism
items: Multidimensional Uncertainty Integration based on Spatiotemporal Hybrid Copula: A Resilience-Oriented Modeling Paradigm for IES and Virtual Power Plant
items: Controllable Load Regulation Model of Distribution Network Source Load Energy Storage Based on Machine Learning and Flexible Interconnection Optimization Framework
items: Construction and Optimization of Knowledge Base for Commissioning Plans of New Power System Devices

Intelligent Distribution-Network Fault Handling via Cross-Modal Semantic Fusion and Knowledge-Graph Reasoning
type: item
description: 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 th…
subject: Research articles
  topic: section
subject: Power distribution network fault handling
  topic: keywords
subject: semantic communications
  topic: keywords
subject: cross-modal fusion
  topic: keywords
subject: knowledge graph reasoning
  topic: keywords
subject: deep reinforcement learning
  topic: keywords
creator: Fangzhou Liu
creator: Yang Zhao
creator: Jiqiao Chen
creator: Ting Luo
rights: Copyright (c) 2026 Fangzhou Liu, Yang Zhao, Jiqiao Chen, Ting Luo https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-23
publication date: 2026-06-23
doi: 10.4108/ew.13683

Retraction Notice: Energy-Efficient Design of Seabed Substrate Detection Model Leveraging CNN-SVM Architecture and Sonar Data
type: item
description: 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”.
subject: Retractions
  topic: section
creator: Keming Wang
creator: Chengli Wang
creator: Wenbing Jin
creator: Liuming Qi
rights: Copyright (c) 2026 Keming Wang, Chengli Wang, Wenbing Jin, Liuming Qi https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-21
publication date: 2026-05-21
doi: 10.4108/ew.13110

Retraction Notice: Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm
type: item
description: 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”.
subject: Retractions
  topic: section
creator: D. Devikanniga
creator: Arulmurugan Ramu
creator: Anandakumar Haldorai
rights: Copyright (c) 2026 D. Devikanniga, Arulmurugan Ramu, Anandakumar Haldorai https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-21
publication date: 2026-05-21
doi: 10.4108/ew.13109

Enhanced Thrust Ripple Suppression Method in PMLMUsing Indirect Adaptive Robust Control
type: item
description: 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.
subject: Digital Twin Technologies for Smart Energy Systems
  topic: section
subject: permanent magnet linear motor (PMLM)
  topic: keywords
subject: indirect adaptive robust control (IARC)
  topic: keywords
subject: recursive least squares (RLS)
  topic: keywords
subject: thrust ripple suppression
  topic: keywords
subject: motion control
  topic: keywords
creator: Yuanfeng He
creator: Weizhen Wang
creator: Rong Li
creator: Wenyuan Yan
creator: Jiutong Yang
creator: Chi Zhang
rights: Copyright (c) 2026 Yuanfeng He, Weizhen Wang, Rong Li, Wenyuan Yan, Jiutong Yang, Chi Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-22
publication date: 2026-06-22
doi: 10.4108/ew.12952

Research on Accurate Matching Model of New Energy Supply and Demand Empowered by Blockchain
type: item
description: 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.
subject: Research articles
  topic: section
subject: blockchain
  topic: keywords
subject: green Power
  topic: keywords
subject: supply-demand matching
  topic: keywords
subject: accurate model
  topic: keywords
subject: simulation verification
  topic: keywords
creator: Chuang Li
creator: Junshi Zhang
creator: Lei Zhou
creator: Lili Li
creator: Jiangtao Li
creator: Guomin Li
rights: Copyright (c) 2026 Chuang Li, Junshi Zhang, Lei Zhou, Lili Li, Jiangtao Li, Guomin Li https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-04
publication date: 2026-05-04
doi: 10.4108/ew.12895

Dynamic Error Compensation Method for Binocular Ranging of Transmission Lines Integrating IMU Data
type: item
description: This paper proposes a dynamic error compensation method that integrates inertial measurement unit (IMU) data. The initial distance is obtained based on the binocular vision triangulation ranging principle. By analyzing the dynamic error sources such as camera movement, cable vibration and environmental interference, the IMU multi-sensor is integrated to collect the system's motion, attitude and environmental parameters in real time. The IMU data is integrated and processed to calculate the compensation amount of multi-source errors. With the help of the PID controller, the proportional-integral-derivative operation and linear combination of the total compensation amount are carried out to achieve effective compensation of dynamic errors. Experiments show that this method can significantly improve the ranging stability under different working conditions, and the coefficient of variation is always lower than 0.2.
subject: Research articles
  topic: section
subject: IMU Data
  topic: keywords
subject: Transmission Lines
  topic: keywords
subject: Binocular Ranging
  topic: keywords
subject: Sources of Dynamic Error
  topic: keywords
subject: Error Compensation
  topic: keywords
subject: PID Controller
  topic: keywords
creator: Zhimeng Zhang
creator: Jie Liu
creator: Cuiying Sun
creator: Xiaoyu Yi
creator: Yixin Wang
rights: Copyright (c) 2026 Zhimeng Zhang, Jie Liu, Cuiying Sun, Xiaoyu Yi, Yixin Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-04
publication date: 2026-05-04
doi: 10.4108/ew.12830

An Early Warning Method for Abnormal Fluttering Behavior in Transmission Lines Based on Deep Binocular Vision
type: item
description: Under adverse weather conditions, ice accumulation and strong winds cause low-frequency and high-amplitude swaying of transmission lines, increasing the risk of wire fatigue, hardware damage, and tower collapse, posing a threat to the safety of the power grid. The existing monitoring methods suffer from inaccurate 3D spatial data acquisition, leading to errors in edge detection, difficulty in suppressing background interference, insufficient feature extraction, and reduced accuracy in detecting abnormal oscillations. Therefore, this article proposes a warning method based on deep binocular vision, which captures three-dimensional data with a stereo camera and converts it into two-dimensional images. Mathematical morphological operators are used to determine the edge of the transmission line, solving the problem of inaccurate edge detection. The morphological edge results are input into the ResNet LSTM network to suppress background noise and extract features. Finally, compare the detection results with the actual values to achieve multi-level alarms. The experiment shows that this method is efficient and accurate, with a detection efficiency of 0.958-0.968. Improved the reliability of early warning.
subject: Research articles
  topic: section
subject: Deep Binocular Vision
  topic: keywords
subject: Power Transmission Lines
  topic: keywords
subject: Morphological Edges
  topic: keywords
subject: Background Suppression
  topic: keywords
subject: Flutter Anomaly Behavior
  topic: keywords
subject: Early Warning Method
  topic: keywords
creator: Zhimeng Zhang
creator: Jie Liu
creator: Cuiying Sun
creator: Xiaoyu Yi
creator: Yixin Wang
rights: Copyright (c) 2026 Zhimeng Zhang, Jie Liu, Cuiying Sun, Xiaoyu Yi, Yixin Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-04
publication date: 2026-05-04
doi: 10.4108/ew.12827

Partial Discharge Detection and Identification in High-Voltage Cable Systems Based on Heterogeneous Sensor Fusion and Enhanced Deep Learning
type: item
description: High-voltage cable systems are critical components of power transmission networks, where partial discharge (PD) can cause insulation degradation and severe operational failures. Accurate detection and reliable identification of PD are therefore essential, yet conventional methods are often vulnerable to electromagnetic interference and exhibit limited recognition performance. This paper proposes a PD detection and identification framework for high-voltage cable systems based on heterogeneous sensor fusion and enhanced deep learning. A high-speed optical electric-field sensor is employed to localize potential insulation defects, while an acoustic pressure wave sensor is used to confirm PD occurrence and intensity. An improved adaptive-threshold discrete wavelet transform is applied for signal denoising, and an optimized Gramian Angular Field transformation converts one-dimensional signals into two-dimensional feature representations. A residual convolutional neural network incorporating an efficient channel attention mechanism is then developed for PD pattern identification. Experiments involving corona, void, and surface discharges demonstrate that the proposed system achieves a 100% PD detection rate and a recognition accuracy of 96.0% under laboratory conditions. Field tests on 220 kV tunnel-laid cables further verify that both detection and identification accuracies reach 100%, with superior robustness and environmental adaptability compared with conventional approaches.
subject: Research articles
  topic: section
subject: Partial discharge
  topic: keywords
subject: High-voltage cable systems
  topic: keywords
subject: Heterogeneous sensor fusion
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Pattern identification
  topic: keywords
subject: Condition monitoring
  topic: keywords
creator: Ling Gao
creator: Lixing Zhang
creator: Qi Hu
creator: Lining Jia
creator: Xue Zhao
creator: Penglong Liu
creator: Bozhi Liu
creator: Shunjin Shi
rights: Copyright (c) 2026 Ling Gao, Lixing Zhang, Qi Hu, Lining Jia, Xue Zhao, Penglong Liu, Bozhi Liu, Shunjin Shi https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-04
publication date: 2026-05-04
doi: 10.4108/ew.12824

Photovoltaic Power Adaptive Hybrid Forecasting Model Integrated with Multi-Dimensional Error Compensation
type: item
description: INTRODUCTION: With the deepening of China's "Dual Carbon" goals, the scale of photovoltaic (PV) installations continues to expand, placing stringent requirements on the accuracy, scenario adaptability, and robustness of day-ahead power prediction for PV plants in grid frequency regulation, peak shaving, and electricity market trading. However, existing single prediction models have obvious limitations: linear models struggle to capture nonlinear power disturbances under cloudy days with sudden irradiance changes and extreme weather; nonlinear models are prone to overfitting during stable, high-irradiance sunny periods, leading to redundant accuracy; and most methods lack sufficient robustness against meteorological fluctuations and data noise, resulting in large prediction errors that seriously affect the economy and security of grid operation. OBJECTIVES: Aiming at the problems in PV plant prediction where a single model finds it difficult to balance linear patterns and nonlinear disturbances, and prediction accuracy is greatly affected by meteorological fluctuations and data noise, this paper proposes a hybrid prediction model based on Linear Regression and Gradient Boosting Tree, along with a multi-dimensional error compensation mechanism. METHODS: 1. Otptimize feature engineering design, selecting time features, historical power lag terms, and meteorological interaction features as inputs, unifying their scales via Z-score standardization to simplify model complexity and construct a high-quality training set. 2. Design a dynamic weight allocation strategy based on irradiance intensity grading and intraday time periods, integrating the precise fitting advantage of Linear Regression during high-irradiance periods with the nonlinear fluctuation capture capability of Gradient Boosting Tree to establish a hybrid prediction model. 3. Use hourly operational data from a specific PV plant from May 2024 to March 2025 as a sample, performing validation combining an error c…
subject: Research articles
  topic: section
subject: Power prediction
  topic: keywords
subject: Gradient Boosting Tree
  topic: keywords
subject: Error compensation
  topic: keywords
subject: Historical deviation pattern
  topic: keywords
creator: Shuye Liu
creator: Bingkuan Gong
creator: Cuiyu Cui
creator: Kun Zang
rights: Copyright (c) 2026 Shuye Liu, Bingkuan Gong, Cuiyu Cui, Kun Zang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-28
publication date: 2026-04-28
doi: 10.4108/ew.12813

Time-Sensitive Service Scheduling Method for LEO Broadband Satellite-Based Power Emergency Networks
type: item
description: INTRODUCTION: The frequent occurrence of extreme natural disasters poses severe threats to power grids, necessitating robust emergency communication. Low Earth Orbit (LEO) broadband satellite networks offer a promising solution but suffer from high dynamics and bit error rates, leading to jitter and disorder in time-sensitive services. OBJECTIVES: To address these challenges, this paper proposes a time-sensitive service scheduling method for LEO satellites in power emergency networks. The primary goal is to facilitate a transition from "traffic-driven" to "service-driven" paradigms, ensuring the prioritized and orderly delivery of critical control instructions. METHODS: An improved QUIC scheduling framework is introduced, incorporating the Acknowledgment Fast Return and Distinguish Loss (AFR-DL) mechanism to optimize congestion control. Furthermore, the Multiplex Data Arrive In Order (MDAIO) algorithm is proposed, which combines throughput prediction with dynamic error correction and service priority strategies to manage multipath transmission. RESULTS: Simulation results demonstrate that the AFR-DL algorithm improves throughput compared to OLIA, while the MDAIO algorithm significantly outperforms traditional schedulers like Lowest-RTT and DPSAF. The proposed method effectively reduces data disorder and transmission delay for high-priority services. CONCLUSION: The proposed method significantly enhances throughput and reliability, effectively meeting the high real-time requirements of power emergency communication. This work provides a viable solution for guaranteeing the stability of time-sensitive services in harsh LEO satellite environments.
subject: Research articles
  topic: section
subject: LEO broadband satellite
  topic: keywords
subject: power emergency communication
  topic: keywords
subject: time-sensitive service scheduling
  topic: keywords
creator: Xing Yang
creator: Linhui Yang
rights: Copyright (c) 2026 Xing Yang, Linhui Yang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-28
publication date: 2026-04-28
doi: 10.4108/ew.12735

Lightweight Real-Time power quality disturbance recognition using Time-Frequency fusion with Cross-Attention mechanism
type: item
description: INTRODUCTION: Accurate power quality disturbances (PQDs) classification is critical for maintaining grid stability and reliability in modern power systems. However, existing deep learning methods predominantly rely on single-domain feature extraction, limiting their discriminative capability for complex composite disturbances under noisy conditions. This study addresses these limitations by proposing a dual-pathway architecture that synergistically integrates time-domain and frequency-domain representations through cross-attention fusion. OBJECTIVES: This work aims to develop a robust PQDs classification framework capable of accurately identifying 24 disturbance classes, including complex composite types, while maintaining high noise immunity and computational efficiency for real-time monitoring applications. METHODS: A dual-pathway deep learning architecture is proposed, comprising parallel CNN-BiLSTM branches for time-domain temporal modeling and FFT-based frequency-domain spectral analysis. A cross-attention mechanism dynamically fuses complementary features from both pathways. The model is trained and evaluated on a comprehensive dataset containing 24 PQDs classes under multiple noise levels. RESULTS: The proposed model achieves 99.73% accuracy on the validation set and maintains 98.94% accuracy under 30dB noise conditions. Ablation studies confirm the dual-pathway structure improves accuracy by 6.51 percentage points over single-branch variants, while the cross-attention mechanism contributes an additional 2.08 percentage points. The model converges within 43 epochs with inference latency of 251μs per sample, satisfying real-time requirements. CONCLUSION: The proposed dual-pathway cross-attention architecture demonstrates superior performance in PQDs classification, effectively balancing accuracy, noise robustness, and computational efficiency. This approach provides a viable solution for intelligent power quality monitoring in practical smart grid applications…
subject: Research articles
  topic: section
subject: Power quality disturbances
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Dual-Pathway Architecture
  topic: keywords
subject: Cross-Attention Mechanism
  topic: keywords
subject: Time-Frequency Fusion
  topic: keywords
subject: Smart Grid Monitoring
  topic: keywords
creator: Fei Yu
creator: Haoyang Xue
creator: Jiaming Qi
creator: Xiaoqian Yue
creator: Xinrui Pei
rights: Copyright (c) 2026 Fei Yu, Haoyang Xue, Jiaming Qi, Xiaoqian Yue, Xinrui Pei https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-28
publication date: 2026-04-28
doi: 10.4108/ew.12734

Research on Electromagnetic-Thermal-Mechanical Multiphysics Coupling Simulation Analysis of Converter Transformers
type: item
description: Addressing the limitations of conventional single-physics analysis methods in accurately revealing the internal electro-thermal-mechanical interaction mechanisms, this study established a fully coupled electro-magnetic-thermal-mechanical model for converter transformer windings. This model comprehensively accounts for the effects of harmonic currents and DC bias, employing bidirectional coupling calculations to elucidate the complete propagation process from electromagnetic losses to temperature distribution and structural stresses. Simulation results indicate that harmonic losses constitute the primary factor driving localised hotspot temperature rise. The resulting non-uniform thermal expansion, coupled with electromagnetic forces, generates significant stress concentrations in regions such as the winding ends.
subject: Research articles
  topic: section
subject: Converter transformer
  topic: keywords
subject: Multiphysics coupling
  topic: keywords
subject: Harmonic currents
  topic: keywords
subject: DC bias
  topic: keywords
subject: Hotspot temperature rise
  topic: keywords
creator: Zhichuang Li
creator: Tan Li
creator: Yongfan Guo
creator: Shenghui Wang
rights: Copyright (c) 2026 Zhichuang Li, Tan Li, Yongfan Guo, Shenghui Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-22
publication date: 2026-04-22
doi: 10.4108/ew.12727

A Novel Hybrid Collaborative Forecasting Method for Offshore Wind Power Based on Intelligent Optimization
type: item
description: INTRODUCTION: With the rapid growth of offshore wind power integration and increasing wind power penetration, accurate power forecasting has become essential for maintaining grid stability and supporting economic dispatch. OBJECTIVES: This paper aims to develop a high-accuracy offshore wind power forecasting framework that can effectively handle noisy, non-stationary data and reduce the impact of outliers on prediction performance. METHODS: A two-stage data cleaning procedure is first constructed by combining density-based spatial clustering of applications with noise (DBSCAN) and polynomial regression to accurately identify and correct anomalous power data. The cleaned series is then decomposed using a sequential scheme that applies complete ensemble empirical mode decomposition followed by particle-swarm-optimized variational mode decomposition, producing multiple intrinsic mode components. Each component is fed into a hybrid temporal convolutional-gated recurrent unit (TCN-GRU) network, whose hyperparameters are globally tuned using an intelligent optimization algorithm, and the component-wise forecasts are aggregated to obtain the final power prediction. RESULTS: Simulation studies based on measured data from an offshore wind farm show that the proposed method significantly reduces forecasting errors compared with conventional forecasting models and single-stage decomposition approaches. CONCLUSION: The results demonstrate that the proposed adaptive optimization-based composite collaborative framework effectively improves both the accuracy and robustness of offshore wind power forecasting.
subject: Research articles
  topic: section
subject: Wind Power Forecasting
  topic: keywords
subject: Data Cleaning
  topic: keywords
subject: Modal Decomposition
  topic: keywords
subject: Particle Swarm Optimization
  topic: keywords
subject: Convolutional Neural Networks
  topic: keywords
creator: Ruanming Huang
creator: Chen Qian
creator: Tianli Song
creator: Yumeng Jiang
creator: Hongyu Wen
creator: Bing Wang
rights: Copyright (c) 2026 Ruanming Huang, Chen Qian, Tianli Song, Yumeng Jiang, Hongyu Wen, Bing Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-28
publication date: 2026-04-28
doi: 10.4108/ew.12728

Data-driven Predictive Individual Pitch Control for Floating Offshore Wind Turbines
type: item
description: A method of data-driven predictive individual pitch control is proposed for floating offshore wind turbine. It can reduce platform motion and asymmetric aerodynamic loads effectively. The method is designed, which includes model predictive controller with feedforward compensation, to generate the voltage control signal component required by the pitch mechanism. Firstly, the models of floating offshore wind turbines and wind turbine loads are presented. Then, a model predictive controller utilizes a data-driven methodology grounded in subspace identification for the nonlinear wind turbines. The feedforward compensation with observing past system responses and control experience reduces the external turbulence and enhances the accuracy of the control signal. The simulations indicate that the proposed strategy performs better in terms of reducing fatigue loads and improving the system's stability and reliability.
subject: Research articles
  topic: section
subject: Individual pitch control
  topic: keywords
subject: Data-driven predictive approach
  topic: keywords
subject: Floating offshore wind turbine
  topic: keywords
subject: Nonlinear characteristics
  topic: keywords
creator: Xiaosuo Luo
creator: Sergey Gorbachev
rights: Copyright (c) 2026 Xiaosuo Luo, Sergey Gorbachev https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-22
publication date: 2026-04-22
doi: 10.4108/ew.12726

Design and Implementation of a Low-Frequency Current Comparator Bridge Based on Quantized Hall Resistance
type: item
description: Quantumized Hall resistance (QHR) provides a traceable absolute reference for resistance measurement. However, the traditional transfer system based on super-conducting current comparator (CCC) relies on cryogenic liquid helium environment, which is complicated to operate and expensive. In this paper, a wide-range QHR transfer bridge scheme based on room temperature low-frequency current comparator (LFCC) is proposed and implemented. The system works under low-frequency AC excitation (<10 Hz), uses high-precision active/slave current sources to drive precision proportional windings, and achieves flux balance through detection windings and resonant amplifier feedback loops, thereby achieving high-precision resistance value transfer. The LFCC core magnetic circuit model, detection sensitivity model and closed-loop control model are established, and the proportional transfer relationship and error source are derived in detail. An experimental device including multi-layer magnetic shielding proportional windings, low-noise current sources, high-Q resonant detection amplifiers and digital control systems is designed and constructed. Experimental results show that under room temperature conditions, the LFCC bridge can achieve a proportional transfer uncertainty (k=2) better than 10⁻⁸ in the 1kΩ range. This solution gets rid of the dependence on liquid helium and has a relatively simple structure. It provides an effective technical approach for wide-range and high-precision resistance calibration in new power systems and has significant engineering application value.
subject: Technical Article
  topic: section
subject: Quantized Hall Resistance
  topic: keywords
subject: Low-frequency Current Comparator
  topic: keywords
subject: Resistance Bridge
  topic: keywords
subject: Wide Range Transfer
  topic: keywords
subject: Uncertainty
  topic: keywords
creator: Jingfen Bai
creator: Zongrong Li
creator: Jing Meng
creator: Xiaomeng Duan
creator: Ailing Geng
rights: Copyright (c) 2026 Jingfen Bai, Zongrong Li, Jing Meng, Xiaomeng Duan, Ailing Geng https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-28
publication date: 2026-04-28
doi: 10.4108/ew.12710

Short-Term Power Load Forecasting for Industrial Parks Using CNN-BiLSTM Network with Kernel Density Estimation-based Interval Prediction Method
type: item
description: Predicting electrical power loads in industrial parks is challenging due to the uncertainty and variance in model construction. This paper proposes an interval forecasting approach based on a residual-form CNN-BiLSTM, which effectively captures spatial patterns and bidirectional temporal dependencies in time series data. To handle uncertainty, a kernel density estimation (KDE)-based method is employed to generate probabilistic prediction intervals. Experiments using 18 days of real industrial data validate the model’s performance. Results show superior robustness and accuracy compared with LSTM, BiLSTM, and GRU. The proposed model achieves perfect prediction interval coverage probability (PICP = 1.0), narrow normalized interval width (PINAW = 0.0828), and minimal CWC = 0.0828, while baseline models exhibit low coverage or excessively wide intervals. These findings confirm that the method provides both reliable and sharp uncertainty quantification, making it suitable for practical energy forecasting applications.
subject: Research articles
  topic: section
subject: load forecasting
  topic: keywords
subject: neural network
  topic: keywords
subject: kernel density function
  topic: keywords
subject: probabilistic prediction interval
  topic: keywords
creator: Rui Hua
creator: Tao Han
creator: Yue Ran
creator: Qiongxiao Wu
rights: Copyright (c) 2026 Rui Hua, Tao Han, Yue Ran, Qiongxiao Wu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-28
publication date: 2026-04-28
doi: 10.4108/ew.12681

Analysis of the Impact of Active Power Recovery Rate of Wind Farms on Power System Transient Stability
type: item
description: INTRODUCTION: The post‑fault active‑power recovery of DFIG wind farms strongly influences transient‑stability assessment, yet conventional equivalent models fail to capture variations in recovery rate during LVRT. This paper proposes an equivalent modeling method that embeds optimized recovery characteristics. First, the post‑fault recovery behavior is analyzed. A single‑machine equivalent with an optimized recovery rate and a multi‑machine equivalent representing piecewise recovery are then developed. Differences from a detailed wind‑farm model are quantified using error indices and curve similarity, and impacts on simulation credibility are assessed. Results show the proposed models better reproduce recovery dynamics and improve transient‑stability accuracy. OBJECTIVES: characterize post‑fault active‑power recovery in wind farms, develop accurate equivalents for the restoration stage, and reveal how integration models affect power‑system transient stability. METHODS: Single‑machine and multi‑machine wind‑farm equivalencing with optimized active‑power recovery (and turbine time constants), plus the transient energy‑function method. RESULTS: Compared with conventional methods reported in the literature, the proposed wind farm equivalencing methods reduce the relative error of model equivalencing from 18.84% to 9.49% and 3.70%, respectively. In addition, the relative error in transient stability analysis is reduced from 22.68% to 9.19% and 2.35%, respectively. CONCLUSION AND SIGNIFICANCE: To establish a high-accuracy equivalent model of the wind farm, thereby providing a reliable modeling basis for transient stability analysis of power systems with wind power integration.
subject: AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability
  topic: section
subject: Power systems
  topic: keywords
subject: active power recovery rate
  topic: keywords
subject: ransient stability
  topic: keywords
subject: wind farm equivalent model
  topic: keywords
creator: Hongxuan Zhang
creator: Peng Zhou
creator: Jianxin Zhang
creator: Qing Gao
creator: Tuo Jiang
creator: Huanhuan Yang
creator: Yanzhe Chen
rights: Copyright (c) 2026 Hongxuan Zhang, Peng Zhou, Jianxin Zhang, Qing Gao, Tuo Jiang, Huanhuan Yang, Yanzhe Chen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-18
publication date: 2026-05-18
doi: 10.4108/ew.12533

Optimal scheduling of renewable power distribution systems combining deep learning and particle swarm optimization
type: item
description: Introduction: To address the optimal scheduling problem of renewable power distribution systems, this paper proposes an integrated framework combining deep learning-based load forecasting with particle swarm optimization. Objectives: The first objective is accurate multivariate load forecasting (cooling, heating, and electric loads). The second objective is minimizing network loss by optimally scheduling electric vehicle (EV) charging locations and times. Methods: For load forecasting, a hybrid RCNN-SVR model is constructed. The convolutional neural network (CNN) acts as a feature extractor to implicitly capture representative patterns from input data, while support vector regression (SVR) produces the final load predictions. Missing and outlier data are pre-processed. For optimal scheduling, a dual-layer particle swarm optimization (PSO) algorithm is developed. The inner layer enforces system constraints, and the outer layer minimizes network loss. EV charging load is simulated using the Monte Carlo method, and two cases (variable vs. fixed charging addresses) are optimized. Results: Experimental results demonstrate that the proposed RCNN-SVR model achieves high prediction accuracy, with mean absolute percentage error as low as 2.41% for winter electric loads. The dual-layer PSO reduces peak system load from 11.2×10³ kW to 10.5×10³ kW and decreases network loss by 2.13%, effectively smoothing grid fluctuations. Conclusion: The RCNN-SVR model significantly improves multivariate load prediction accuracy compared to separate forecasting methods. The dual-layer PSO successfully converts disorderly EV charging into orderly scheduling, reducing peak load and network loss. Together, they provide a practical solution for renewable distribution system scheduling.
subject: Research articles
  topic: section
subject: Renewable distribution system
  topic: keywords
subject: Optimal dispatch
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Convolutional neural network (CNN)
  topic: keywords
subject: Support vector regression (SVR)
  topic: keywords
subject: Load forecasting
  topic: keywords
creator: Qiuyong Yang
creator: Bin Chen
creator: Yanlu Huang
creator: Xudong Hu
rights: Copyright (c) 2026 Qiuyong Yang, Bin Chen, Yanlu Huang, Xudong Hu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-01
publication date: 2026-06-01
doi: 10.4108/ew.12491

Reinforcement Learning-enhanced Policy-aware Modeling of Smart Grid Efficiency under Carbon Constraints: Integration of SBM DEA and Dynamic Policy Response Simulation
type: item
description: Artificial intelligence-based sensing, forecasting, and decision optimization are being rapidly integrated into smart grid operations. Although reinforcement learning has created new opportunities for dispatch optimization and low-carbon transition under carbon constraints, most existing studies focus primarily on short-term economic or operational objectives and rarely incorporate system-level carbon reduction efficiency benchmarks into the learning process. To address this gap, this study proposes an integrated SBM-DEA and reinforcement learning framework for policy-aware smart-grid dispatch, in which carbon reduction efficiency scores are transformed from static evaluation results into dynamic learning signals for dispatch optimization. Using panel data from 30 provinces in China over the period 2011–2022, this study develops an indicator system covering capital input, labor input, electricity service output, and electricity-related carbon dioxide emissions. An SBM-DEA model with undesirable outputs is employed to measure the carbon reduction efficiency of smart grids. The estimated efficiency scores are then embedded into both the state representation and reward function of a reinforcement learning framework, where the agent learns dispatch policies that balance economic performance, carbon constraints, and efficiency improvement. A dynamic policy response simulation environment is further constructed, incorporating a hybrid energy storage system comprising battery storage and pumped hydro storage. The results show that the carbon reduction efficiency of smart grids in China exhibits stage-specific fluctuations, with annual average values ranging from 0.505 to 0.568 and pronounced interprovincial disparities. In the simulation experiments, the reinforcement learning agent trained with efficiency-based penalties achieves 7.3% lower operational costs and 8.5% higher average efficiency compared to an economic-only agent. The trained policies also exhibit clear poli…
subject: AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability
  topic: section
subject: artificial intelligence
  topic: keywords
subject: reinforcement learning
  topic: keywords
subject: smart grid
  topic: keywords
subject: carbon reduction efficiency
  topic: keywords
subject: SBM-DEA
  topic: keywords
subject: dynamic policy response
  topic: keywords
creator: Gemei Shi
rights: Copyright (c) 2026 Gemei Shi https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-04
publication date: 2026-06-04
doi: 10.4108/ew.12473

Mining method of power consumption behavior pattern of different subject resource objects in virtual power plant based on graph neural network
type: item
description: INTRODUCTION: With the advancement of global energy internet and smart grid, massive power big data with complex temporal features is generated by widely deployed smart meters. Accurate identification of power consumption behavior patterns is essential for dispatching a virtual power plant (VPP). OBJECTIVES: This paper proposes a fine-grained mining framework that combines temporal feature extraction and a graph neural network to support VPP dispatch requirements. METHODS: Linear interpolation and same-type day mean filling are used for data cleaning and normalization. A user relationship graph is constructed via physical connection and Pearson correlation-based behavioral similarity. A GCN-based unsupervised graph autoencoder is adopted for spatio-temporal feature embedding, and an improved DPC-FCM clustering algorithm is proposed to optimize it. RESULTS: The method effectively solves the inherent defects of traditional clustering: initial center sensitivity and noise interference. CONCLUSION: This framework realizes accurate fine-grained mining of power consumption patterns, providing reliable support for VPP dispatch decision-making.
subject: AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability
  topic: section
subject: Consumption Behavior Pattern Mining
  topic: keywords
subject: Graph Neural Network (GNN)
  topic: keywords
subject: Density Peak Clustering (DPC)
  topic: keywords
subject: Fuzzy C-Means (FCM)
  topic: keywords
subject: Clustering Algorithm
  topic: keywords
creator: Duan Zhiguo
creator: Jie Zhao
creator: Xi Chen
creator: Jiakun An
creator: Wenyi Fan
rights: Copyright (c) 2026 Duan Zhiguo, Zhao Jie, Chen Xi, An Jiakun, Wenyi Fan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-16
publication date: 2026-06-16
doi: 10.4108/ew.12319

Research on Active Equalization of Energy Storage Lithium Batteries under a Modular Layered Architecture for Smart Grid Applications
type: item
description: INTRODUCTION: In smart grid applications, energy storage systems (ESS) are critical for balancing power supply and demand, but they often suffer from performance degradation due to State of Charge (SOC) inconsistencies in series-configured lithium battery packs. These disparities can compromise grid stability and battery lifespan. OBJECTIVES: This study proposes an active equalization method based on a novel modular layered architecture for ESS in smart grids. The core innovation lies in the synergistic combination of a hierarchical bidirectional Buck-Boost topology and a multivariable fusion fuzzy logic control strategy, aiming to enhance battery consistency, efficiency, and reliability for grid support. METHODS: A hierarchical BUCK-BOOST-based circuit is designed to enable bidirectional energy transfer, incorporating a multivariable fuzzy controller for real-time regulation of balancing currents. This approach facilitates cooperative equalization within and between battery groups, optimizing energy flow. RESULTS: Simulations based on an eight-cell model in Matlab/Simulink demonstrate that the proposed hierarchical topology reduces equalization time by 11.53% compared to the conventional single-layer topology. Furthermore, with the proposed multivariable fusion fuzzy logic control algorithm, the equalization time is further reduced by 26%, significantly improving both the equalization speed and adaptability to dynamic grid conditions. CONCLUSION: The proposed strategy effectively mitigates battery inconsistencies, enhancing the overall performance and safety of energy storage systems in practical applications. It provides a reliable technical approach for battery management in smart grids.
subject: AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability
  topic: section
subject: Energy storage lithium battery, State of charge (SOC), Active equalization, Hierarchical equalization circuit, Fuzzy control
  topic: keywords
creator: Qingdong Luo
creator: Xiyuan Wan
creator: Qiangwei Liu
creator: Jingjing Lou
creator: Zhikang Jin
creator: Chaoqun Jin
creator: Pengfei Zhen
rights: Copyright (c) 2026 Qingdong Luo, Xiyuan Wan, Qiangwei Liu, Jingjing Lou, Zhikang Jin, Chaoqun Jin, Pengfei Zhen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-07
publication date: 2026-05-07
doi: 10.4108/ew.11711

Reactive Power Optimization of Distribution Network Considering DG Based on Improved Ant Lion Algorithm
type: item
description: Large-scale and high-proportion access of distributed generation to distribution network breaks the original power flow structure of distribution network, which affects the power quality and safe operation of distribution network system. Voltage stability can be maintained through reactive power optimization, and the system operation economy can be improved. In this paper, the influence of distributed generation access on the stability of distribution network is analyzed. On this basis, the mathematical model of reactive power optimization is established with the comprehensive consideration of active power loss and node voltage deviation of distribution network with distributed generation access, and the improved ant lion algorithm with dynamic weight coefficient is used to solve the model. Finally, the simulation analysis is carried out in IEEE33-bus system, and the improved ant lion algorithm and standard ant lion algorithm are used to solve the reactive power optimization of distribution network. The comparison of the optimization results of the two algorithms proves the feasibility and superiority of the improved ant lion algorithm in solving the reactive power optimization problem.
subject: Research articles
  topic: section
subject: Distributed generation
  topic: keywords
subject: Ant lion algorithm
  topic: keywords
subject: Reactive power optimization
  topic: keywords
creator: Yuan Hu
creator: Jing Yang
creator: Yuehua Cui
creator: Xingjun Tian
creator: Pan He
creator: Peng Wu
creator: Fusheng Zhang
creator: Jianpeng Bian
rights: Copyright (c) 2026 Yuan Hu, Jing Yang, Yuehua Cui, Xingjun Tian, Pan He, Peng Wu, Fusheng Zhang, Jianpeng Bian https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-14
publication date: 2026-04-14
doi: 10.4108/ew.12188

Malfunction Diagnosis of Microgrid Devices Based on Optimized Graph Neural Network
type: item
description: Malfunction diagnosis based on deep learning ensures the stable operation of microgrid. However, the microgrid system with new energy access appears to be increasingly complex and changeable, and its frequently changing topology brings challenges to the training of traditional neural network models. A method for malfunction diagnosis of microgrid devic-es based on optimized Graph Neural Network is proposed in the paper. Firstly, data reconstruction is completed through the mapping of nodes, edges and graphs in order to standardize changeable data. Secondly, abnormal data is processed to improve the quality of database. In that way, device misoperation caused by extreme data loss can be avoided, thus im-proving the training efficiency. Then, aiming at fully capturing the characteristics of time and space dimensions of data at different latitudes, the graph-convolution network under Multi-head Attention mechanism is adopted in the process. Finally, a microgrid simulation model is built to prove that the proposed scheme has the advantages of good robustness, strong adaptability, high reliability and accurate diagnosis.
subject: Research articles
  topic: section
subject: Graph neural network
  topic: keywords
subject: Malfunction diagnosis
  topic: keywords
subject: Data repairing
  topic: keywords
subject: Multi-head attention
  topic: keywords
creator: Qiyue Huang
creator: Qi Song
creator: Hao Lei
rights: Copyright (c) 2026 Qiyue Huang, Qi Song, Hao Lei https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-14
publication date: 2026-04-14
doi: 10.4108/ew.12180

Uncertainty Pattern Recognition of Multi-Type Renewable Power Plants and the Coupling Mechanism with Multi-Level Voltage Sensitivity
type: item
description: With high penetrations of renewable power plants, increased output volatility and uncertainty exacerbate local voltage-stability risks. Targeting heterogeneous renewable facilities, this paper proposes an integrated analysis framework that couples deep feature disentanglement, joint forecasting, and multi-level voltage sensitivity. First, using event logs as the driver, we combine variational mode decomposition (VMD) with a residual convolutional autoencoder (Res-CAE) to extract multi-band disturbance modes from power time series. An adversarial mechanism is then employed to disentangle meteorological disturbances from device-driven behaviors, yielding canonical uncertainty labels dominated by high-/mid-frequency components. Next, a spatio-temporal attention–driven multi-task sequence model is designed to jointly forecast active/reactive power and nodal voltage, providing stable inputs for subsequent sensitivity assessment. Finally, dynamic voltage sensitivity is estimated via sliding-window regression, and the disturbance–response chain and its propagation scope are quantified across local, dynamic, and system-level tiers. Event-driven simulations on the SoCal 28-Bus benchmark indicate that, under high- and mid-frequency dominated scenarios, dynamic voltage sensitivity increases by approximately 58.3% relative to low-volatility regimes, revealing a nonlinear transition by which renewable uncertainty is amplified through multi-frequency modes toward critical nodes. The proposed paradigm offers a transferable pathway for modeling and predicting complex source–network coupling in renewable-dominated power systems and provides data support for proactive voltage-control strategy design.
subject: Research articles
  topic: section
subject: renewable power plant
  topic: keywords
subject: uncertainty modeling
  topic: keywords
subject: joint forecasting
  topic: keywords
subject: spatio-temporal attention
  topic: keywords
subject: voltage sensitivity
  topic: keywords
subject: feature disentanglement
  topic: keywords
creator: Xiaoyu Luo
creator: Li Xiong
creator: Lili Lv
creator: Zhi Zhang
creator: Yulong Li
creator: Mingzhao Meng
creator: Lei Zhuo
creator: Xin Zhou
rights: Copyright (c) 2026 Xiaoyu Luo, Li Xiong, Lili Lv, Zhi Zhang, Yulong Li, Mingzhao Meng, Lei Zhuo, Xin Zhou https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-14
publication date: 2026-04-14
doi: 10.4108/ew.12177

Capacity Optimization for Minimizing the Carbon Footprint of Power Systems
type: item
description: The carbon footprint of electricity supply is strongly influenced by power system capacity configuration and operational flexibility under uncertainty. This paper proposes a carbon-footprint-oriented capacity optimization framework for integrated power systems comprising thermal power, wind power, photovoltaic generation, and energy storage. A dispatch model is developed to represent coordinated system operation under uncertain load and renewable generation, which are modeled using information gap decision theory and Monte Carlo simulation. The carbon footprint of electricity supply is evaluated on a life-cycle basis as carbon emissions per unit of delivered electricity, together with electricity cost and power fluctuation rate as performance indicators. A multi-objective capacity optimization problem is solved using the non-dominated sorting genetic algorithm II (NSGA-II), and a practical optimal solution is selected from the Pareto front using the technique for order preference by similarity to an ideal solution (TOPSIS). Case studies show that the optimized capacity configuration reduces the carbon footprint of electricity supply by approximately 28%, while electricity cost and power fluctuation rate decrease by about 7% and 27%, respectively. Meanwhile, renewable energy utilization increases from 71.4% to 86.8%, and the renewable energy share rises from 42.0% to 55.6%, demonstrating that coordinated deployment of renewable energy and energy storage is essential for achieving carbon-efficient electricity supply under uncertainty.
subject: Research articles
  topic: section
subject: Power system
  topic: keywords
subject: Carbon footprint
  topic: keywords
subject: Capacity optimization
  topic: keywords
subject: Renewable energy
  topic: keywords
subject: Energy storage
  topic: keywords
creator: Xinzhi Wang
creator: Bo Chen
creator: Yunpei Cheng
rights: Copyright (c) 2026 Xinzhi Wang, Bo Chen, Yunpei Cheng https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-14
publication date: 2026-04-14
doi: 10.4108/ew.12163

Research on Key Technologies of AI-Based Source-Load Coordinated Regulation at the Edge of Distribution Networks
type: item
description: INTRODUCTION: The integration of a high proportion of renewable energy into distribution networks introduces significant challenges, including increased source-load volatility, uncertainty, and operational difficulties. OBJECTIVES: This study aims to address these challenges by proposing an artificial intelligence-based framework for source-load coordination at the grid edge. METHODS: The proposed framework integrates deep reinforcement learning, federated learning, and edge computing technologies. It employs multi-timescale optimization and distributed cooperative control methods to enhance the distribution network's capability to accommodate renewable energy. RESULTS: Experimental results on the IEEE 33-node system demonstrate that the proposed framework can reduce the wind and solar curtailment rate by 65.5% and decrease operational costs by 12.7% compared to traditional methods, while simultaneously satisfying all distribution network security constraints. CONCLUSION: This research provides a theoretical foundation and a technical pathway for building a new data-driven intelligent control system for distribution networks.
subject: Research articles
  topic: section
subject: distribution network
  topic: keywords
subject: source-load coordination
  topic: keywords
subject: deep reinforcement learning
  topic: keywords
subject: federated learning
  topic: keywords
subject: edge computing
  topic: keywords
subject: multi-timescale optimization
  topic: keywords
subject: renewable energy integration
  topic: keywords
creator: Jie Chen
creator: Haodong Ren
creator: Jiaxu Zhou
creator: Binyu Liu
creator: Guangru Zhang
rights: Copyright (c) 2026 Jie Chen, Haodong Ren, Jiaxu Zhou, Binyu Liu, Guangru Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-15
publication date: 2026-04-15
doi: 10.4108/ew.12162

A Medium and Long-term Forecasting Method for Electric Load Curves Integrating Dynamic Variable Selection and Sparse Attention Mechanism
type: item
description: Accurate forecasting of electric load curves is critical for stable power system operation, efficient dispatch, and scientific planning, with medium- and long-term forecasting providing key support for grid expansion, energy allocation, and electricity market decisions. Existing methods struggle with insufficient use of load-related multi-variable data and inefficient extraction of long-term temporal features from load measurements, limiting forecasting accuracy and efficiency. To address these issues, this paper proposes a medium- and long-term electric load curve forecasting model integrating dynamic variable selection and a sparse attention mechanism, with a focus on load measurement correlation. The model takes static variables and temporal variables related to load changes as inputs. Static variables include industry type and location, and temporal variables cover historical load and meteorological data. It uses a gated feedforward network to adaptively weight variables based on their correlation with actual load measurements and filters redundant information. Meanwhile, it adopts a dual-layer encoding structure with sparse attention to prioritize key features from long load measurement sequences, reducing computational complexity while enhancing capture of long-term dependencies between historical load measurements and future loads. Experiments on three datasets show the model outperforms baselines including Informer, Autoformer and TFT. The proposed model reduces average relative error by 14.3%–55.1% and improves computational efficiency, providing reliable technical support for power system medium- and long-term planning by closely linking load measurements to forecasting performance.
subject: Research articles
  topic: section
subject: electric load curve forecasting
  topic: keywords
subject: medium and long-term prediction
  topic: keywords
subject: dynamic variable selection
  topic: keywords
subject: sparse attention mechanism
  topic: keywords
subject: power system planning
  topic: keywords
creator: Bingqian Chen
creator: Jinlin Liao
creator: Shiyuan Ni
creator: Sudan Lai
rights: Copyright (c) 2026 Bingqian Chen, Jinlin Liao, Shiyuan Ni, Sudan Lai https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-15
publication date: 2026-04-15
doi: 10.4108/ew.12161

Multidimensional Uncertainty Integration based on Spatiotemporal Hybrid Copula: A Resilience-Oriented Modeling Paradigm for IES and Virtual Power Plant
type: item
description: Integrated Energy Systems (IES) and Virtual Power Plants (VPPs) are pivotal for achieving the "dual-carbon" goals, yet they face significant challenges from multidimensional spatiotemporal uncertainties. Traditional uncertainty modeling methods, relying on static correlation assumptions, are inadequate for dynamically capturing the complex fluctuations in source-load-market parameters, especially the aggregation uncertainties inherent in VPPs. This paper proposes a novel resilience-oriented modeling paradigm centered on a spatiotemporal hybrid Copula (ST-HC) framework. The primary innovation lies in its dynamic correlation capture mechanism, which integrates time-decay and spatial-distance factors to accurately characterize nonlinear, time-varying dependencies among cross-regional uncertainties. Furthermore,a comprehensive five-dimensional evaluation system is established, incorporating metrics for storage flexibility, degradation, market volatility, risk preference, and system efficiency—including a novel data security risk entropy indicator. Validated through case studies, the proposed approach demonstrates superior performance over conventional static models, enhancingplanning accuracy and reducing total costs by approximately 9.98%, while effectively quantifying the economic-low-carbon trade-offs under different risk strategies for IES-VPP synergy.
subject: Research articles
  topic: section
subject: IES
  topic: keywords
subject: Spationtemporal hybrid copula
  topic: keywords
subject: Dynamic correlations
  topic: keywords
subject: Multi-dimensional uncertainties
  topic: keywords
creator: Junxiong Ge
creator: Lijun Liu
creator: Zhaojun Wang
creator: Xiao Li
creator: Hongxia Guo
creator: Haimin Hong
rights: Copyright (c) 2026 Junxiong Ge, Lijun Liu, Zhaojun Wang, Xiao Li, Hongxia Guo, Haimin Hong https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-15
publication date: 2026-04-15
doi: 10.4108/ew.12159

Controllable Load Regulation Model of Distribution Network Source Load Energy Storage Based on Machine Learning and Flexible Interconnection Optimization Framework
type: item
description: With the integration of high-penetration renewable energy sources, distribution networks are confronted with increasing challenges such as source-load uncertainty and delayed regulation response. Conventional approaches like PID control struggle to accommodate millisecond-level fluctuations, while existing Model Predictive Control (MPC) approaches suffer from high computational complexity and difficulties in online updates. Relating to these problems, this study proposes a Machine Learning based Flexible Interconnection Optimization (ML-FIO) framework, which achieves breakthroughs through three technological innovations: First, a predictive-control closed-loop coupling integrating Long Short-Term Memory (LSTM) and Hierarchical Deep Q-Network (HDQN) reduces the dynamic response time of Nondominated Sorting Genetic Algorithm II (NSGA-II) to 1.89 seconds (a 60.2% improvement compared to PID control). Second, a flexible interconnection strategy based on dynamic impedance matching reduces the line loss rate to 3.01% during midday peak hours (a 47.6% reduction compared to droop control). Finally, a multi-timescale weight allocation mechanism maintains the root mean square (RMS) voltage deviation at critical nodes within 0.015 pu. Simulation results indicate that the proposed framework maintains robustness in over 80% of mutation scenarios and exhibits convergence in non-convex problems, providing an engineering-ready solution for Source-Load-Storage Coordination (SLSC) in modern power systems and establishing a rigorous mathematical foundation for the application of intelligent algorithms in power systems.
subject: Research articles
  topic: section
subject: Power distribution network
  topic: keywords
subject: State of charge
  topic: keywords
subject: Source-load-storage coordination
  topic: keywords
subject: Machine learning based flexible interconnection optimization
  topic: keywords
subject: Temporal convolutional network
  topic: keywords
creator: Ziqing Qiu
creator: Qiuyue Huang
creator: Guanpeng Fu
creator: Yan Chen
creator: Zhengui Yang
rights: Copyright (c) 2026 Ziqing Qiu, Qiuyue Huang, Guanpeng Fu, Yan Chen, Zhengui Yang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-15
publication date: 2026-04-15
doi: 10.4108/ew.12158

Construction and Optimization of Knowledge Base for Commissioning Plans of New Power System Devices
type: item
description: To address the challenge of efficiently commissioning new equipment amid grid expansion, this study proposes an intelligent start-up plan generation method based on knowledge graphs. The approach first employs a bidirectional Long Short-Term Memory-Conditional Random Field model to extract entities such as equipment and operations from historical plans. This extraction process enables the construction of a structured knowledge base. Subsequently, it enhances plan standardization and reliability through multi-source data fusion and credibility analysis optimization logic. The results show that the named entity recognition model used in the study achieves an F1 score of 0.99, with an entity recognition accuracy of 92.5% for knowledge related to new power system devices commissioning plans. Testing of the proposed knowledge base reveals a knowledge coverage rate of 93.8%, which is 10.4% higher than that of traditional methods. The efficiency of plan generation significantly improves, with a rule compliance rate reaching 97%. The study provides a feasible pathway for transforming grid startup procedures from reliance on manual experience to data-driven intelligence.
subject: AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability
  topic: section
subject: Commissioning plans
  topic: keywords
subject: Power system devices
  topic: keywords
subject: Knowledge graph
  topic: keywords
subject: Named entity recognition
  topic: keywords
subject: Data fusion
  topic: keywords
subject: Reliability analysis
  topic: keywords
creator: Yaopu Zheng
creator: Xiongfeng Lin
creator: Shifeng Peng
creator: Guangyu Deng
creator: Yunsheng Huang
creator: Jian Huang
rights: Copyright (c) 2026 Yaopu Zheng, Xiongfeng Lin, Shifeng Peng, Guangyu Deng, Yunsheng Huang, Jian Huang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-11
publication date: 2026-03-11
doi: 10.4108/ew.11269