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EAI Endorsed Transactions on Energy Web
Scopus
ISSN 2032-944X · European Alliance for Innovation (EAI) · 731 articles in the index
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Journal Article Full text
Research on Wind Power Prediction Based on PSO-GSA-CS Synergistic Optimization of BP Neural Network
Accurate wind power prediction is essential for enhancing the dispatchability of wind power and the stability of power grid operation. However, traditional single-group intelligent algorithms for optimizing BP neural network modeling often suffer from large result fluctuations and poor stability…
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Research on short-term photovoltaic power prediction based on LMD-IPSO-LSVM
With the rapid development of human society, resource shortages and environmental degradation have become increasingly pressing issues. To address these challenges, solar energy has garnered significant attention due to its high efficiency, safety, and pollution-free nature. This paper proposes a…
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AC/DC Superimposed Breakdown Characteristics and Electric Field Simulation Analysis of Oil-Paper Insulation in Converter Transformers
This study investigates the electric field distribution and breakdown characteristics of oil-paper insulation in a 500 kV converter transformer under AC/DC superimposed voltages. The electric field distribution within the winding end insulation structure at the valve side was analyzed under various…
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Dynamic Spatial-Temporal Load Forecasting for Multi-Regional Power Systems via Graph Attention Networks
Accurate multi-region load forecasting (MRLF) can effectively ensure the stable operation of the power system and is essential for the economic dispatch of the power system. However, with the gradual deepening of the dynamic spatial dependence among multi-region loads and the unmeasured nature of…
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Long-term Wind Power Forecasting Based on Bayesian Optimization-Based CNN-GRU Model
The safe and stable operation of the power grid, the optimization of power system dispatching, and the improvement of the economic benefits of wind farms all rely on long-term wind power prediction. Long-term wind power prediction requires handling complex time series and spatial dependencies…
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Deep Reinforcement Learning-Based Coordinated Voltage Control of HST and DG
With the increasing penetration of distributed generation in active distribution networks, voltage fluctuation and voltage violation problems have become more prominent, especially in weak grids. Conventional voltage control methods based on empirical parameter tuning or single-device regulation…
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Physics-Informed Deep Reinforcement Learning with Adaptive Action Masking for Secure Second-Level Emergency Scheduling in Energy Networks
With the integration of a high proportion of volatile renewable energy into modern power grids, systems frequently face severe supply-demand imbalances and escalated situational risks under extreme events. Addressing the high-dimensional risks and stringent response timing requirements in real-time…
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A Blockchain-Driven Stackelberg Game and MILP Algorithmic Framework for Dynamic Pricing in Multi-Agent Microgrid Networks
INTRODUCTION: Decentralized coordination and data transparency in multi-agent networks face significant computational and security bottlenecks when performing joint energy and ancillary service trading, particularly due to nonlinear optimization challenges in complex multi-domain environments…
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An Integrated MOPSO and Fuzzy Decision-Making Algorithmic Framework for Multi-Objective Configuration in High-Proportion Renewable Networks
The optimal sizing and configuration of energy storage in networks with high renewable penetration represent a highly complex, multi-constraint, and non-linear optimization problem. Existing planning methods often struggle with premature convergence and lack systematic multi-criteria evaluation for…
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ICT-Supported Scenario-Aware Adaptive Setting Method for Line Current Protection in Distribution Networks with High Proportion of Distributed Generation
INTRODUCTION: With the large-scale integration of distributed generators (DGs), distribution networks are gradually evolving into multi-source power supply systems. Although high-penetration DG integration improves system operational flexibility and power supply reliability, it changes fault…
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A Novel Health State Assessment Method of Low-voltage Distribution Network Based on CV-G2-ER Rule
INTRODUCTION: As the distribution network segment closest to end users, the health status of low-voltage distribution networks directly affects the quality of people's production and daily life. Aiming to address the key limitations existing in the health status evaluation of low-voltage…
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A hybrid LPMS-Transformer-ARFIMA framework for wind power forecasting with SPSO-based global hyperparameter optimization
The severe stochastic nature, multi-scale variations, and non-stationary characteristics typical of wind power temporal data creates substantial difficulties for reliable forecasting. To tackle these specific obstacles, this paper develops an advanced hybrid framework for wind power forecasting…
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Life Cycle Cost Modeling and Financial Benefit Assessment of Energy Storage Systems Incorporating Digital Twins
INTRODUCTION: Energy storage systems are increasingly evaluated under dynamic operating conditions in which battery degradation, efficiency decay, maintenance events, replacement timing, outage losses, and residual value jointly affect investment returns. However, conventional life-cycle cost and…
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Mechanism–Data Dual-Driven Outage Identification Framework for Distribution Networks with High Penetration of Inverter-Based Distributed Generation
INTRODUCTION: High penetration of inverter-based distributed generators (IBDGs) changes power-flow patterns and fault transients in distribution networks, reducing the effectiveness of conventional outage identification. Under the adopted low-voltage ride-through control model, IBDG fault currents…
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Research on Target Recognition and 3D Reconstruction Technology for Distribution Network Patrol Based on BeiDou and Edge Computing
INTRODUCTION: Power transmission and distribution lines operate in complex natural and electromagnetic environments, making them susceptible to risks such as tree encroachment, ice accumulation, insulator damage, and foreign object interference. Traditional inspection methods, including manual…
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Real-Time Electricity Price Forecasting with Dynamic Graph Convolution and Adaptive Temporal Fusion
INTRODUCTION: Real-time electricity price forecasting is critical for real-time dispatch, automatic generation control, spot trading and risk management in electricity markets. OBJECTIVES: Since large-scale wind and photovoltaic renewable energy grid integration brings strong random fluctuation of…
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AI-Enabled Lightweight Video Stream Processing for Electric Shock Prevention Monitoring in Smart Grid Edge Scenarios
INTRODUCTION: Real-time early warning of electric shock risks is urgently needed in edge scenarios of smart grids. Existing visual methods are still limited by scene adaptability, end-to-end lightweight design, and insufficient field verification. Objective: The objective of the study is to propose…
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Intelligent Question-Answering System and Inference Mechanism for Grid Dispatching with Energy Storage Access Scenarios Based on Semantic Models
INTRODUCTION: In the power grid energy storage dispatch system, after the large-scale integration of new energy storage systems, the demand for inquiries regarding the operation and maintenance of storage equipment as well as the coordinated power regulation has significantly increased. The…
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Comparative study on the accuracy and robustness of piezoelectric valve flow prediction models based on deep learning
To enhance the dependability and autonomy of the μg/s piezoelectric flow control device in long-term on-orbit operations, the on-orbit adaptability and robustness of various deep learning prediction models are compared and analyzed. Multidimensional time series data such as excitation voltage…
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Short-Term Power Load Forecasting Based on EMGCSO-Elman
INTRODUCTION: In short-term electrical load forecasting, Elman neural architectures often suffer from convergence instability and limited prediction accuracy due to random parameter initialization. To address this issue, an Elite Multi-mode Guided Chicken Swarm Optimization (EMGCSO) methodology is…