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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Reactive Power Optimization of Distribution Network Considering DG Based on Improved Ant Lion Algorithm
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.
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
Malfunction Diagnosis of Microgrid Devices Based on Optimized Graph Neural Network
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.
Copyright (c) 2026 Qiyue Huang, Qi Song, Hao Lei https://creativecommons.org/licenses/by-nc-sa/4.0
Uncertainty Pattern Recognition of Multi-Type Renewable Power Plants and the Coupling Mechanism with Multi-Level Voltage Sensitivity
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.
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
Capacity Optimization for Minimizing the Carbon Footprint of Power Systems
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.
Copyright (c) 2026 Xinzhi Wang, Bo Chen, Yunpei Cheng https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Key Technologies of AI-Based Source-Load Coordinated Regulation at the Edge of Distribution Networks
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.
Copyright (c) 2026 Jie Chen, Haodong Ren, Jiaxu Zhou, Binyu Liu, Guangru Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
A Medium and Long-term Forecasting Method for Electric Load Curves Integrating Dynamic Variable Selection and Sparse Attention Mechanism
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.
Copyright (c) 2026 Bingqian Chen, Jinlin Liao, Shiyuan Ni, Sudan Lai https://creativecommons.org/licenses/by-nc-sa/4.0
Multidimensional Uncertainty Integration based on Spatiotemporal Hybrid Copula: A Resilience-Oriented Modeling Paradigm for IES and Virtual Power Plant
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.
Copyright (c) 2026 Junxiong Ge, Lijun Liu, Zhaojun Wang, Xiao Li, Hongxia Guo, Haimin Hong https://creativecommons.org/licenses/by-nc-sa/4.0
Controllable Load Regulation Model of Distribution Network Source Load Energy Storage Based on Machine Learning and Flexible Interconnection Optimization Framework
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.
Machine learning based flexible interconnection optimization
Copyright (c) 2026 Ziqing Qiu, Qiuyue Huang, Guanpeng Fu, Yan Chen, Zhengui Yang https://creativecommons.org/licenses/by-nc-sa/4.0
Construction and Optimization of Knowledge Base for Commissioning Plans of New Power System Devices
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.
AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability
Copyright (c) 2026 Yaopu Zheng, Xiongfeng Lin, Shifeng Peng, Guangyu Deng, Yunsheng Huang, Jian Huang https://creativecommons.org/licenses/by-nc-sa/4.0
Control Method for Overvoltage During Power Supply Loss in Medium-Voltage Flexible DC Sections Based on Guided Strategy Adaptive Particle Swarm Optimization
To address the challenge of overvoltage control during power loss in medium-voltage flexible DC sections under complex and variable operating conditions, a control method based on guided-strategy adaptive particle swarm optimization (GS-APSO) is proposed to ensure voltage stability. This method analyzes the overvoltage formation mechanism during faults according to the topology of the medium-voltage flexible DC section. Building on this analysis, an adaptive nearest-level modulation (ANLM) strategy with voltage-limiting functionality is employed. The strategy adjusts the DC voltage based on the changes in the average submodule capacitor voltage, constraining the voltage near the target value. Furthermore, to enhance the overvoltage suppression performance, the guided-strategy adaptive particle swarm algorithm optimizes the voltage-limiting coefficient, deriving the coefficient that minimizes the overvoltage decay ratio, thereby improving the overvoltage loss control in the medium-voltage flexible DC section. The experimental results demonstrate that under different unbalanced power conditions, the proposed method achieves overvoltage control within 1.5 s, enabling rapid voltage recovery and stabilization. Even under significant power fluctuations, it effectively restricts the peak voltage variation range. After control, the overvoltage decay ratio remains below 0.015 on average.
Copyright (c) 2026 Asleng, Jiadong Zhao, Peng Wang, Jun Guo, Ran Lu, Yang Liu https://creativecommons.org/licenses/by-nc-sa/4.0
A deep reinforcement learning-based adaptive multivariate state estimation fault diagnosis method for a photovoltaic storage grid-connected system under a high proportion of new energy integration
With the increasing penetration of photovoltaic and other renewable energy sources in power systems, the operational characteristics of photovoltaic storage grid-connected systems have become increasingly complex. The stochastic nature of renewable energy output and the alternating charging/discharging states of energy storage systems due to diurnal cycles lead to significant time-varying operational conditions, under which the performance of conventional multivariate state estimation methods for fault diagnosis markedly deteriorates. To address this, targeting high-penetration renewable energy integration scenarios, this paper proposes an adaptive state estimation-based fault diagnosis method for photovoltaic-storage grid-connected systems using deep reinforcement learning. Within the deep reinforcement learning framework, an agent is deployed to adaptively match and update modeling samples according to the real-time system state, thereby enhancing the adaptability of state estimation and the fault diagnosis accuracy. Test results under multivariate typical operational scenarios demonstrate that, compared to existing advanced fault diagnosis methods, the proposed approach achieves an average improvement of 10% in fault detection rate and reduces the missed detection rate by 3%, validating its superior adaptability and diagnostic performance in systems with complex time-varying characteristics. This research provides a new technical pathway for ensuring the safe and stable operation of photovoltaic-storage grid-connected systems under high-penetration renewable energy integration, contributing to enhanced intelligent operation and maintenance capabilities and improved fault response.
high proportion of new energy integration
photovoltaic storage grid connected system
Copyright (c) 2026 Yiguo Guo, Zhiwei Wang, Fumu Lu, Yimu Fu, Zhiqing Wang, Jinghui Meng https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Optimization of Flexibility Resources for Urban Power Grids Considering Energy Storage Devices and Industrial Load Power Regulation
Faced with the challenges of uncertainty and volatility in power output brought by high-penetration new energy integration, traditional regulation resources struggle to independently support the stable operation of urban power grids, making it urgent to establish an effective optimization mechanism for flexible resource allocation. To this end, this paper aims to investigate the synergistic planning of energy storage systems and industrially adjustable loads to enhance the resilience, security, and economic efficiency of urban power grids. To achieve this objective, a bi-level optimization model based on long- and short-term time scale coupling is constructed: the outer-level model focuses on ensuring secure grid operation under extreme scenarios such as N-1 contingencies, determining the minimum required capacity of energy storage and adjustable loads at each node through multi-contingency power flow analysis, thereby establishing a rigid safety boundary for the system. Building upon this, the inner-level model aims for optimal economic performance in typical daily operation, comprehensively considering new energy fluctuations, load characteristics, and regulation costs. It dynamically optimizes energy storage charging/discharging strategies, industrial load regulation pathways, and necessary resource expansion plans, while incorporating wind and solar curtailment variables to enhance model adaptability. Through iterative solving between the two levels, the model achieves an organic integration of safety boundaries from the planning phase with economic dispatch in the operational phase.Case study results based on a six-node system demonstrate that the proposed model can provide differentiated energy storage configurations and load retrofit schemes for various scenarios such as real-time control, peak shaving, and valley filling, effectively leveraging the differences in load characteristics among nodes. A comparative study shows that, under the premise of meeting the same flexibility requirements, the synergistic "storage-load" configuration scheme can effectively reduce both the scale of energy storage investment and the total system cost (by approximately 7.3%) compared to a single-mode approach relying solely on storage, and also helps reduce new energy curtailment losses. This research confirms the effectiveness of the constructed bi-level optimization model in coordinating safety and economic objectives and promoting complementary synergy among diverse resources. It provides a theoretically sound and practically guiding methodology and decision-making support for flexible resource planning in urban power grids under high-penetration new energy integration.
Copyright (c) 2026 Yucheng Ding, Guanglong Xie, Xiaoling Jin https://creativecommons.org/licenses/by-nc-sa/4.0
Voltage Fluctuation Control Strategy Based on Reinforcement Learning Digital Twin Model for Wind-Solar-Water-Storage Smart Microgrid Energy Storage Converter Side
With the increasing penetration of distributed renewable energy (DRE), the stable operation of smart microgrids integrating wind, solar, hydro, and storage faces significant challenges. The core issue lies in the strong intermittency and random-ness of distributed generation outputs, which can easily trigger voltage fluctuations, particularly at the grid connection points of Power Conversion Systems (PCS), seriously threatening power quality and supply reliability. This study innovatively integrates Deep Reinforcement Learning and Digital Twin (DRL+DT) technologies to establish a novel control framework, which constructs a digital twin of the microgrid. Based on the Proximal Policy Optimization (PPO) algorithm, a DRL controller is designed, with clearly defined state spaces, action spaces, and a composite reward function incorporating multi-objective constraints. This modeling approach transforms the voltage control problem into a sequential decision-making task solvable through data-driven methods. The trained policy neural network serves as an intelligent controller for performance comparison in subsequent sections. To validate the proposed approach, simulation tests verify that the proposed method suppresses voltage fluctuations more rapidly and smoothly compared to conventional PI control and single DRL approaches. The voltage deviation is reduced by 62.4%, while the State of Charge (SOC) of the energy storage system is effectively maintained within a healthy range. The experimental results not only verify the technical advantages of the combined DRL+DT framework in addressing complex energy control challenges in microgrids but also demonstrate its capability to support the development of highly resilient and self-healing smart microgrid control systems, highlighting both advanced functionality and practical utility.
Copyright (c) 2026 Erbao Yan https://creativecommons.org/licenses/by-nc-sa/4.0
Prompt-TSF: A Power Load Forecasting Model Based on Prompt Learning and Two-Stage Fine-Tuning
Accurate power load forecasting is vital for grid stability. Traditional models struggle with complex nonlinearities, while adapting Large Language Models (LLMs) to time-series data faces modal alignment challenges. This paper proposes Prompt-TSF, a framework employing prompt learning and two-stage fine-tuning for multivariate forecasting. We utilize a high-frequency dataset of load and weather variables from Quanzhou, China. A custom tool transforms numerical data into natural language prompts, bridging the modal gap and enriching context. Furthermore, a unique two-stage fine-tuning strategy trains the LLM first on domain knowledge and formatting, followed by specific predictive regression. Experiments demonstrate that Prompt-TSF significantly outperforms traditional forecasting methods.
Copyright (c) 2026 Hongxiang Cai, Zhiyong Wang, Yangping Tang, Jin Ao, Qian Li https://creativecommons.org/licenses/by-nc-sa/4.0
Capacity optimization of hybrid energy storage system considering battery loss
INTRODUCTION: Wind power instability in microgrids impairs system economy, and rational hybrid energy storage system (HESS) capacity allocation is critical for mitigation. OBJECTIVES: To address this, this study aims to optimize HESS configuration by minimizing battery loss and life cycle cost while ensuring stable operation. METHODS: The approach involves: constructing a battery equivalent running time model via the rain-flow counting method, establishing a dual-objective optimization model, decomposing HESS suppression power into K intrinsic mode function(IMF) components using MISOA-VMD, and deriving optimal energy storage configurations and critical mode point with the NSGA-II algorithm. RESULTS: Validation through comparative analysis of different schemes' minimum costs confirms the proposed scheme's superiority. CONCLUSION: This study provides a reliable HESS configuration method to enhance the economy of wind power-integrated microgrids.
Copyright (c) 2026 Hong Na, Jiacheng Wu, Guoping Lei, Jing An, Xue He https://creativecommons.org/licenses/by-nc-sa/4.0
Topology optimization and collaborative development planning of electro-hydrogen coupling based on multi-objective solution algorithms
INTRODUCTION: Renewable energy microgrids need planning methods to manage volatility and balance performance. OBJECTIVES: Develop a collaborative planning method for electro-hydrogen coupling systems, optimizing economy, environment and reliability. METHODS: Build a unified planning model for siting, capacity and topology. Solve using improved NSGA-II with adaptive operators and constraint handling. RESULTS: Based on 8760-hour data, the system increased energy self-sufficiency from 52% to >95%. With 28% extra investment, carbon emissions fell 54% and reliability rose 55%. Lower electrolyzer cost further cut emissions; carbon price at 300 CNY/ton improved scheme competitiveness. CONCLUSION: Electro-hydrogen coupling enhances microgrid performance. Multi-objective optimization finds the best trade-off. Falling costs and carbon policies will promote system application, aiding low-carbon transformation.
Copyright (c) 2026 Xiaowei Li, Guoxian Luo, Dandan Li, Peng Yan https://creativecommons.org/licenses/by-nc-sa/4.0
Research on distributed energy resources operation and control information model based on IEC 61850
INTRODUCTION: High-ratio distributed energy resources (DERs) impact distribution network operation and require effective scheduling. IEC 61850 7-420 defines a DER model, but massive small/medium DERs in low-voltage networks hinder station-area aggregated management. OBJECTIVES: Enable aggregated management, dynamic scheduling of small/medium DERs and ensure DER data security. METHODS: 1. Analyze station-area structure and information needs via cluster control 2.Establish a general information model of DER equipment. 3.Build a new communication security control logical node 4.Realize the source-side dynamic conversion of inverter data RESULTS: Key results: 1) IEC 61850-based DER scheduling model; 2) DER data security logical node; 3) Inverter data protocol conversion. CONCLUSION: The proposed model, node and method resolve core DER management issues, providing a feasible distribution network scheduling solution.
Copyright (c) 2026 Hechong Chen, Fan Yang, Zhichun Yang, Chao Wang, Yu Chen https://creativecommons.org/licenses/by-nc-sa/4.0
Research on MPPT control strategy for photovoltaic power generation based on improved incremental conductance method
INTRODUCTION: Maximum power point tracking (MPPT) is an important strategy for optimizing the power output of photovoltaic arrays. OBJECTIVES: The primary objective of this study is to develop an enhanced MPPT control method to address the inherent limitations of long tracking time and low tracking accuracy in the traditional conductance incremental method for MPPT. METHODS: An improved incremental conductance method is proposed, which introduces the fuzzy control algorithm to the incremental conductance method to dynamically adjusts the perturbation step size. RESULTS: The simulation results show that the improved method proposed shortens the MPPT time and improves the MPPT accuracy under start-up and sudden change operating condition comparing with other incremental conductance method. CONCLUSION: This study provides an effective and reference-worthy control method for further improving the efficiency of photovoltaic power generation.
Copyright (c) 2026 Changzheng Zhou, Wei An, Xiaoliang Zhu, Jun Li, Longwei Jiao https://creativecommons.org/licenses/by-nc-sa/4.0
Identification and operational optimization of virtual energy storage characteristics for customer-side resources
INTRODUCTION: This paper addresses the challenges of integrating diverse customer-side resources into virtual energy storage (VES) systems to enhance power system flexibility and renewable energy utilization. OBJECTIVES: The study aims to develop an integrated framework for the identification and optimal scheduling of VES systems, aggregating multi-type resources such as thermostatically controlled loads, electric vehicles, and distributed energy storage systems. METHODS: Quantitative models were established to characterize the operational features of each resource. A data-driven clustering algorithm was employed to aggregate heterogeneous resources into unified VES units. A bi-level optimization model was formulated to minimize the aggregator's operational cost while incorporating comfort penalty functions to maintain user satisfaction. RESULTS: Simulations across four scenarios (sunny, cloudy, high load, high EV penetration) demonstrated significant improvements: the peak-to-valley difference was reduced by 21.6%–28.4%, photovoltaic utilization exceeded 98%, and electricity purchase costs decreased by 4.8%–7.3%. CONCLUSION: The proposed framework provides an effective and scalable approach for VES scheduling, significantly enhancing renewable energy integration and operational flexibility in modern distributed power systems.
Copyright (c) 2026 Shiwang Yang, Jian Huang, Junshi Chen, Penghao Sun, Ziyi Zhan, Xinyi Zhao https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Anti-Islanding Protection Communication Based on IEC 61850 Standard
INTRODUCTION: As a high proportion of distributed power sources are widely connected to the distribution network, they change the topology of the traditional distribution network and the characteristics of electrical quantities, among others. OBJECTIVES: In order to prevent unplanned islanding from occurring in the distribution network, which affects the power supply quality and personal safety of the distribution network, the distribution network needs to be equipped with anti-islanding protection. METHODS: In this paper, the typical unplanned islanding scenarios of active distribution networks and the information interaction requirements for anti-islanding protection are analyzed. Based on the analysis of anti-islanding protection information interaction requirements, a SCADA fault detection information model is established based on IEC 61850 standard. A line topology description method based on IEC 61850 SCL is proposed to solve the problem of reduced reliability of anti-islanding protection detection due to line topology changes, inability to accurately determine the boundary of the island, and susceptibility to omission or misjudgement. Meanwhile, a subset of information models for anti-islanding protection has also been refined in conjunction with the functional requirements of typical active distribution network scenarios. RESULTS: Finally, the effectiveness of this paper's method is verified by examples, and the related results are effectively used in the smart power distribution management system CONCLUSION: Aiming at the aggregated management challenges of massive medium and small-capacity DERs in low-voltage distribution network station areas, this paper establishes an IEC 61850-compliant universal DER information model, constructs a communication security control logical node, and realizes source-side dynamic conversion of inverter data from Modbus to IEC 61850 protocol.
Copyright (c) 2026 Yubo Yuan, Juan Li, Yunlong Jiang, Sudi Xu, Haiou Cao https://creativecommons.org/licenses/by-nc-sa/4.0
Refined 3D Gaussian Splatting Method for Distribution Network Modeling Towards AR-Oriented Operation and Maintenance
Aiming at the problems of missing slender structure modeling, complex background interference, and algorithm efficiency bottlenecks in the 3D reconstruction of distribution networks, this paper proposes an improved 3D Gaussian splatting-based algorithm for 3D reconstruction of distribution network scenes. First, through a semantics-guided density control strategy combined with adversarial background suppression, power equipment and vegetation artifacts are effectively separated. Second, a direction-sensitive covariance optimization method is designed to enhance the geometric continuity and detail fidelity of ultra-slender structures such as power lines and utility poles. Finally, a lightweight progressive splatting framework is constructed to achieve real-time rendering at ≥35 fps under 1080p resolution. Experiments show that the improved algorithm significantly increases the completeness rates of power lines, utility poles, and insulators to 90%, 95%, and 80%, respectively, reduces background artifacts to 2% in area, and shortens the reconstruction time to 0.4 hours. This study provides a high-precision modeling tool for digital twins of distribution networks and supports real-time applications of augmented reality (AR) technology in inspection navigation, fault localization, and remote collaboration.
Copyright (c) 2026 Conghan Liu, Lijuan Yan, Linjun Lu, Huazhang Tan https://creativecommons.org/licenses/by-nc-sa/4.0
Research on flexible load configuration of hydrogen production and storage and operation optimization of distribution network based on bi-level optimization
INTRODUCTION: High penetration of wind and photovoltaic generation causes voltage deviation, line congestion, and renewable curtailment in distribution networks. Hydrogen production and storage offer flexible regulation capabilities that can effectively improve system operational stability. OBJECTIVES: This study aims to construct a bi-level optimization framework for capacity allocation and day-ahead scheduling in a wind–PV–hydrogen coupled system. The goal is to enhance voltage quality, reduce thermal loading, and increase renewable energy utilization. METHODS: The upper level minimizes investment cost, network loss, and voltage deviation through a particle swarm optimization algorithm. The lower level solves a mixed-integer dynamic scheduling model in YALMIP considering grid trading cost, curtailment penalty, and peak-valley regulation benefits. RESULTS: Simulation results show that the minimum node voltage rises from 0.913 p.u to 0.952 p.u, while the maximum line loading decreases from 0.903 to 0.714 after introducing hydrogen flexibility. The system load curve becomes smoother and renewable consumption significantly improves. CONCLUSION: The proposed bi-level approach effectively enhances system flexibility and reduces operational constraints in distribution networks. The method offers practical support for coordinated planning of high-penetration renewable energy systems.
Copyright (c) 2026 Xuejie Wang, Changhao Song, Peng Ye, Tianyu Li, Zhiwei Xie, Shuo Yang, Jianming Qi, Yingxue Gao, Liang Dong https://creativecommons.org/licenses/by-nc-sa/4.0
Research on power generation prediction of photovoltaic power station based on improved neural network
The large-scale construction of photovoltaic power plants makes more and more green power connected and utilized, which also brings great impact on the carrying capacity of the power grid. Forecasting the power generation of photovoltaic power plants can better promote the stable regulation of the power grid and promote the consumption of new energy. In this paper, the influence of main environmental factors on photovoltaic power generation is analyzed by Pearson similarity, and a photovoltaic prediction model is established. The model is solved by combining adaptive genetic algorithm and improved Drosophila algorithm to optimize neural network. Finally, the historical sample data of photovoltaic power plants are used for simulation. By comparing the photovoltaic power generation prediction effects of single BP algorithm, GA-BP combined algorithm and GA-FOA-BP intelligent algorithm, it is proved that the method of combining improved Drosophila algorithm to optimize neural network and genetic algorithm to solve the prediction model can reduce the instability of photovoltaic output prediction to some extent and has good application value.
Copyright (c) 2026 Yuan Hu, Qin Xie, Zhao Liu, Mengjin Hu, Hongwei Li, Bo Yuan, Yaping Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Research on the Cause Diagnosis Method of Line Loss Anomaly Based on the Analysis of Electricity Fluctuation in the Substation Area
This paper presents a three-stage line loss anomaly diagnosis method based on power consumption fluctuation analysis in transformer districts. The proposed approach integrates causal forest modeling, SHAP value attribution, and TCN time series convolution networks to identify anomalies. By applying threshold-based analysis of power supply rate changes and consumption rate variations, it differentiates between grid-side anomalies, user-side anomalies, and composite anomalies. The causal forest model quantifies individual user processing effects, while the TCN network calculates load curve similarity to precisely detect hidden anomalies (such as minor power surges accompanied by load pattern deviations) that traditional methods might miss. SHAP values are used to visualize key feature contributions, enhancing diagnostic interpretability and field verification efficiency. Using real-world data from typical transformer districts, this study demonstrates the effectiveness and practicality of the proposed method, discusses its limitations, and outlines future research directions.
Copyright (c) 2026 Muxin Zhang, Zhijiang Ma, Rundan Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
A Novel Power Load Identification Strategy Based on CEEMD-PCA-AE-Shapelet Dictionary Learning Algorithm
The development of the Energy Internet under the China 'dual-carbon' strategy has bring new challenges in load identification and anomaly detection, particularly in industrial applications. Traditional methods often struggle with the sparse, noisy nature of industrial load data, limiting their generalization ability and accuracy. This paper proposes a novel load identification method based on the CEEMD-PCA-AE-Shapelet dictionary learning algorithm, which addresses these challenges by combining an improved unsupervised anomaly detection model (CEEMD-PCA-AE) with the Shapelet dictionary learning algorithm. Firstly, CEEMD-PCA-AE enhances the model’s ability to handle non-linear and noisy data, significantly improving the generalization and accuracy of industrial load anomaly detection. Secondly, the Shapelet dictionary learning algorithm reduces computational complexity and improves model performance by incorporating dictionary learning into time-series classification. The proposed method outperforms traditional models, as demonstrated by numerical experiments, offering enhanced load identification accuracy, and improved detection of anomalies. This method has significant potential for optimizing the efficiency of energy management in industrial settings and can be applied globally, as shown by its effectiveness on real-world data from Liaoning Province, China.
Copyright (c) 2026 Junxiong Ge, Mingmin Yuan, Zhu Tang, Quancheng Pan, Congsu Jin, Haimin Hong https://creativecommons.org/licenses/by-nc-sa/4.0
A novel dung beetle optimization algorithm of distribution network reconfiguration for power loss reduction and reliability improvement
In the evolution of energy systems, investigations into distribution networks have concentrated on enhancing reliability and optimizing performance. Distribution networks with distributed energy resources are expected to significantly enhance power accommodation capacity, but load flow distribution and network optimization remain key challenges. This study aims to develop an improved dung beetle optimization (IDBO) algorithm to minimize active power losses and node voltage deviations in distribution networks, while validating its superiority over existing algorithms across different network configurations. The IDBO algorithm is enhanced through three key strategies: (i) A variable spiral search strategy is employed to improve search efficiency and global exploration. (ii) A levy flight strategy is introduced to prevent algorithm premature convergence. (iii) A t-distribution adjustment strategy based on iteration count is adopted to strengthen local search capability. To verify whether the IDBO algorithm can achieve the optimal load flow distribution that meets the reliability requirements of distribution network operation, relevant validations have been conducted. Experimental results demonstrate that IDBO achieves faster convergence and superior performance on classical test functions. In practical applications to IEEE 33-bus and 69-bus distribution systems, it significantly reduces power losses and improves voltage profiles compared to other algorithms. The proposed IDBO algorithm provides an effective solution for distribution network reconfiguration, demonstrating enhanced optimization capability, improved convergence characteristics, and reliable performance across various operating conditions.
Copyright (c) 2026 Yanmin Wu, Ruiliang Guo, Lu Wang, Xiangqian Zhu, Zhenjie Wan https://creativecommons.org/licenses/by-nc-sa/4.0
Design of a Live-Line Inspection Robot System for Insulators in UHVDC Transmission Lines
To address the pain points of low-zero insulators in UHVDC transmission lines, which are prone to insulation flashover, and the low accuracy, reliance on manual labor, and weak anti-interference capabilities of traditional inspection methods, this study developed an integrated live-line inspection robot that combines mechanical climbing, precise inspection, intelligent control, and electromagnetic shielding. The robot system comprises a mechanical system (tracked movement mechanism, guiding and clamping mechanism), detection system (2500V high-voltage detection circuit, MEGA128 microcontroller control), control system (L298N motor drive, frequency-hopping spread spectrum wireless data transmission), and electromagnetic shielding system (multi-layer shielding shell, opto-isolation, multi-point common ground). An innovative "equipotential bypass" detection principle is proposed to eliminate leakage current interference, and an improved PSO-SVR fusion algorithm is introduced to optimize the detection data. Experimental verification showed that in laboratory tests, the robot exhibited excellent electromagnetic interference resistance (shielding effectiveness SE≥ 60 dB) and a resistance measurement error ≤±2.5%. In actual live-line testing of 500 kV/1000 kV lines, the detection error was ≤±3%, the detection cycle for a single string (28/32 pieces) was only 92~108s, the detection rate of degraded insulators was 100%, and it eliminated the need for power outages and manual tower climbing. This robot meets the "live-line, accurate, efficient, and interference-resistant" detection requirements of UHV lines, providing reliable technical support for the intelligent operation and maintenance of power grids and has broad engineering application prospects.
Copyright (c) 2026 Yunfeng Yan, Hao Sun, Fei Hao, Peng Chen, Tianlong Zhang, Zehua Yang https://creativecommons.org/licenses/by-nc-sa/4.0
Voltage Compensation-Type Current Limiter Based on Hybrid Distribution Transformer for Power Quality Enhancement
This paper presents a voltage compensation-type current limiter based on a hybrid distribution transformer (HDT) structure, which integrates a three-winding transformer, AC/DC converter, DC/AC converter, current-limiting unit, bypass switches, and a series transformer. The HDT operates under four distinct modes: steady-state operation, voltage recovery, active current limiting, and passive current limiting. The proposed system effectively compensates voltage sags and limits fault currents by dynamically switching between these modes. Simulation results demonstrate that the method can quickly restore load-side voltage during upstream voltage sags and efficiently limit fault currents during downstream faults. The proposed strategy ensures continuous and reliable operation of sensitive loads under fault conditions.
Copyright (c) 2026 Ruiayo Wang, Jianhua Wang, Han Yan, Weiming Chen, Yuanliang Fan https://creativecommons.org/licenses/by-nc-sa/4.0
Multi-State Reliability Modeling and Evaluation of Islanded Microgrids with Semi-Active Hybrid Energy Storage Systems
Accurate reliability assessment of islanded microgrids (iMGs) increasingly depends on intelligent system modeling and data-driven integration between physical assets and digital control frameworks. To address the limited adaptability of conventional reliability models, this paper proposes an ICT-integrated framework for reliability evaluation of iMGs equipped with semi-active lithium-ion battery-supercapacitor hybrid energy storage systems (HESS). The framework employs a 32-state Markov-based model to represent component-level degradation and failure dynamics, enabling state-aware reliability analytics through automatic state aggregation and transition probability learning. These states are further abstracted into representative operating modes that can be seamlessly interfaced with energy management systems for online evaluation. A minimum load curtailment strategy is embedded within a time-series Monte Carlo simulation environment to quantify the interactive impact between multi-state HESS behavior and overall system reliability. Standard indices-outage frequency, duration, and availability-are computed to characterize resilience under varying storage conditions. Comparative results verify that the proposed model substantially improves the reliability of iMGs by enabling degraded yet continuous operation during partial failures. The study provides a scalable and digitally implementable reliability framework for HESS-enabled microgrids, bridging the gap between detailed component modeling and real-time operational analytics.
Copyright (c) 2026 Wencong Wu, Haiqing Cai, Wei Chen https://creativecommons.org/licenses/by-nc-sa/4.0
Assessing Lightweight YOLO Models for Vision-Based Fault Diagnosis in Wind Turbine and Cold Chain Inspection Systems
The reliability of wind energy systems depends on the timely detection of surface and structural defects in turbine blades. This paper compares six YOLO (You Only Look Once) architectures (v8n–v13n) for automatic fault detection in wind turbine inspection images. All models were trained under identical experimental settings and evaluated by precision, recall, mAP@0.50, mAP@[0.50:0.95], and inference latency. Results show that YOLOv12n achieved the highest performance (mAP@0.50 = 0.867, computed as the mean over three seeds using the Top-3 class protocol), while YOLOv10n delivered the lowest inference time of 0.7 ms. These findings support the suitability of lightweight YOLO variants for real-time fault inspection and predictive maintenance in wind energy systems and the same framework can be extended to visual quality inspection tasks in intelligent cold chain logistics systems.
Copyright (c) 2026 Siming Lin, Yongqiang Xiao, Yifang Gao, Rui Ni, Tianxiang Huang https://creativecommons.org/licenses/by-nc-sa/4.0