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Discharge Voltage Prediction Model of Batteries in Different Degradation States Based on IWOA-ATCN

PROCESSES 2025年第1期13卷

作者： Yang, Jingwei Chen, Yitong Huang, Qiang Wu, Guilong Liu, Lin Yang, Zhimin Huang, Yu China Southern Power Grid Power Dispatching & Control Ctr Guangzhou 510525 Peoples R China

As a key technology for future decarbonization, storage batteries are widely used in areas such as electric vehicles and power systems. However, battery aging inevitably limits their broader application. To address the low accuracy in predicting discharge voltage under different aging states, this paper proposes the IWOA-ATCN method based on a TCN model with a sliding window mechanism. First, the improved whale optimization algorithm (IWOA) is employed to optimize the hyperparameters in the TCN model, including window size and sliding step, to obtain the optimal sample structure. Then, the temporal attention mechanism is introduced into the TCN model to accurately capture the temporal correlation of discharge voltage, thereby improving the prediction accuracy of long time series data. Finally, the model is tested on the NASA dataset with an RMSE of 0.0072, MAE of 0.0046, and R2 of 0.9984. The test results on the PL Sample dataset showed RMSE of 0.0081, MAE of 0.0040, and R2 of 0.9983. It is indicated that the prediction accuracy and stability of the IWOA-ATCN model are higher than other models, such as BP, RNN, and LSTM.

关键词： storage battery voltage prediction degraded state whale optimization algorithm TCN

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Research on short-term power load forecasting based on deep reinforcement learning with multiple intelligences

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2025年 12卷 1-10页

作者： Luo, Tianyun Zhu, Dunlin Liu, Jinming Yang, Sheng He, Jinglong Fu, Yuan Power Dispatching Control Center Guangxi Power Grid Guangxi Nanning530000 China

A reliable supply of power systems is critical for industry, commerce, and residential life. Improving the accuracy and reliability of short-term electricity load forecasting plays a crucial role in ensuring the satisfaction of electricity demand and the stable operation of the power system. Therefore, to realize accurate and efficient prediction of short-term power loads, a short-term power load prediction method based on multi-intelligence deep reinforcement learning is proposed to address the complex nonlinear characteristics of load data. In this paper, we analyze the multi-intelligence application architecture in power load forecasting, and analyze the function of each intelligent unit applied to short-term power load forecasting;based on clarifying the interaction relationship of each intelligent unit in short-term power load forecasting, we model short-term power load forecasting as a distributed and partially observable Markov decision-making process, which is suitable for multi-intelligence deep reinforcement learning;based on the MATD3 algorithm, a centralized training-distributed execution framework is used to train multiple intelligences within the model to achieve short-term power load forecasting. The experimental results show that in the August short-term electricity load forecasting using the design method, the obtained MAE value is 35.94 kW, MAPE value is 4.05%, and RMSE value is 32.71 kW. In the short-term power load forecasting evaluation conducted for December, the average absolute error (MAE) value obtained was 36.75 kilowatts, the average absolute percentage error (MAPE) value was 4.51%, and the root mean square error (RMSE) value was 34.82 kilowatts. These evaluation results fully demonstrate that the design method adopted has high prediction accuracy and forecast precision. This method has demonstrated good practical value and broad application prospects in practical applications due to its high-precision prediction performance and strong pre

关键词： Deep reinforcement learning

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Multiagent, multitimescale aggregated regulation method for demand response considering spatial–temporal complementarity of user-side resources

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Global Energy Interconnection 2025年第2期8卷 240-257页

作者： Pan, Tingzhe Li, Chao Yang, Chen Meng, Zijie Wang, Zongyi Zhu, Zean China Southern Power Grid Scientific Research Institute Co. Guangdong510525 China Electric Power Dispatching & Control Center of Guangdong Power Grid Guangdong510699 China China Southern Power Grid Co. Ltd. Guangdong510525 China

The integration of substantial renewable energy and controllable resources disrupts the supply–demand balance in distribution grids. Secure operations are dependent on the participation of user-side resources in demand response at both the day-ahead and intraday levels. Current studies typically overlook the spatial–temporal variations and coordination between these timescales, leading to significant day-ahead optimization errors, high intraday costs, and slow convergence. To address these challenges, we developed a multiagent, multitimescale aggregated regulation method for spatial–temporal coordinated demand response of user-side resources. Firstly, we established a framework considering the spatial–temporal coordinated characteristics of user-side resources with the objective to minimize the total regulation cost and weighted sum of distribution grid losses. The optimization problem was then solved for two different timescales: day-ahead and intraday. For the day-ahead timescale, we developed an improved particle swarm optimization (IPSO) algorithm that dynamically adjusts the number of particles based on intraday outcomes to optimize the regulation strategies. For the intraday timescale, we developed an improved alternating direction method of multipliers (IADMM) algorithm that distributes tasks across edge distribution stations, dynamically adjusting penalty factors by using historical day-ahead data to synchronize the regulations and enhance precision. The simulation results indicate that this method can fully achieve multitimescale spatial–temporal coordinated aggregated regulation between day-ahead and intraday, effectively reduce the total regulation cost and distribution grid losses, and enhance smart grid resilience. © 2025

关键词： Demand response

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Wind power Prediction and Dynamic Economic Dispatch Strategy Optimization Based on BST-RGOA and NDO-WOA

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第6期49卷 71-86页

作者： Hu, Yuyang Yang, Xiao Chen, Binbin Gu, Guangfeng Pan, Lianrong Electric Power Dispatching and Control Center Guangxi Power Grid Co. Ltd Nanning530023 China

In order to address the new challenges brought by large-scale wind power grid integration to the safe operation of the power system, a non-deterministic optimization whale swarm optimization algorithm is introduced to predict wind power. This method uses trend optimization function to optimize control coefficients and weight coefficients to ensure the algorithm randomness and avoid falling into local optima. On this basis, combined with the evolutionary strategy of bee species and the fast global search mechanism, a fast global optimization algorithm for bee species transition is proposed to achieve dynamic economic scheduling. The results showed that under clear weather conditions, the mean root mean square error of the prediction obtained by the non-deterministic optimization-whale optimization algorithm was only 2.68%, while the least squares support vector machine method was as high as 5.43%. Under cloudy conditions, the mean root mean square error of this algorithm was only 5.82%, which was 6.28% lower than the particle swarm optimization back propagation algorithm. Under rainy conditions, the mean root mean square error of the algorithm was only 6.75%. In addition, the average running time of the rapid global optimization algorithm for bee species transition on dynamic economic scheduling problems was only 28.9 seconds. The runtime of the artificial bee colony algorithm was as long as 198.6. Overall, the wind power prediction algorithm and dynamic economic dispatch algorithm have achieved significant advantages in prediction accuracy and solution effectiveness. This is conducive to achieving efficient optimization and scheduling of the power system, improving the stability and economy of the power system. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Wind forecasting

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Application of intelligent Internet of Things technology in the security monitoring system of power Internet of Things network

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Discover Internet of Things 2025年第1期5卷 1-15页

作者： Song, Lei Lan, Haibo Du, Jin Wang, Kai Kang, Wei Automation Division of Electric Power Dispatching and Control Center State Grid Jibei Electric Power Company Limited Beijing100054 China Power Dispatching Control Center Dispatching Control Department State Grid Jibei Electric Power Company Limited Beijing100054 China State Grid Jibei Information & Telecommunication Company Beijing100054 China Dispatch and Control Center State Grid Jibei Electric Power Company Limited Beijing100054 China State Grid Jibei Electric Power Co. Beijing100054 China

The Internet of Things (IoT) refers to the latest iteration of the Internet, enabling communication and interaction among interconnected items. It is thriving and permeating every aspect of our life, including school, home, automobiles, and healthcare. IoT serves as a valuable aid in healthcare and plays a crucial role in a wide range of applications for monitoring medical services. Various wireless body area network devices and sensors offer real-time health monitoring services. The data produced by sensor-based devices requires secrecy, reliability, and end-to-end security in order to provide secure transmission across public networks. The implementation of IoT in healthcare will pose significant risks if patient information is not securely managed during transmission across unsecured networks or while stored by administrators. Recent research introduced an inflatable and anonymity-preserving user identification mechanism for IoT-based healthcare. Nonetheless, this technique was susceptible to certain attacks. These attacks compromise both the patients' medical data and their personal information. This study presents a security framework designed for real-time health monitoring systems with the objective of guaranteeing data secrecy, reliability, and security. This paper proposes AEAD (authentication, encryption and anomaly detection) approach for secure IoT healthcare monitoring. The authentication protocol is proposed based on the hash and XoR with biometric cryptographic operation. The medical data is encrypted using XoR based encoding scheme. Next an efficient machine learning based algorithm is used to detect the anomaly medical data. Different evaluation metrics (computational cost, encryption time, decryption time, and accuracy) are used to analyze the performance of the AEAD approach. The practical tests and analysis have demonstrated the practicality and efficacy of the monitoring system. The results indicate that it is capable of efficiently analyzing la

关键词： Authentication

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Security situation prediction of artificial intelligence network based on wireless sensor

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International Journal of Intelligent Information and Database Systems 2025年第2期17卷 217-235页

作者： Han, Wenxi Yunnan Power Grid Power Dispatching and Control Center Dianchi Road Xishan District Yunnan Province Kunming City China

The security situational prediction aims to assess the present state of network security, forecast its future development trends, inform individuals to implement appropriate defensive measures and secure the network environment by analysing and extracting wireless sensor network (WSN) data. This study proposed a multilayer perceptron feed forward neural network model in WSN (MPFNN-WSN) that relies on accurate forecasting and mitigating security prediction of AI networks. The study offers multilayer perceptron networks for attack detection and network security prediction systems for hacker identification and threat level. This new AI-powered wireless measurement system has revolutionised data analysis by adding new computing and analysing capabilities to existing innovations. According to experiments that compared it to state-of-the-art evolutionary methods, the strategy considerably reduced root-mean-squared errors (RMSEs) and accuracy for different test sets. Results are better with the proposed method when contrasted with the industry standard. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Mean square error

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Enhanced Microgrid Energy Optimization: Integrating Load Prioritization and Dynamic Temporal Adaptation

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2025年第3期20卷 1207-1225页

作者： Pan, Ting-Zhe Hou, Jue Wang, Zong-Yi Li, Ke Jin, Xin Hao, Wen-Huan China Southern Power Grid Co Ltd Elect Power Res Inst CSG Guangzhou 510700 Peoples R China China Southern Power Grid Co Ltd Guangdong Prov Key Lab Intelligent Measurement & A Guangzhou 510663 Peoples R China China Southern Power Grid Co Ltd Elect Power Dispatching & Control Ctr Guangdong Po Guangzhou 510600 Peoples R China

In the context of island mode operation, a microgrid may can not supply sufficient power for loads due to various factors such as weather condition. To prioritize power supply for critical loads and improve microgrid energy management efficiency simutaneously, this study proposes a method integrating load power supply priority and dynamic time intervals for MG energy optimization *** prioritize power critical loads' energy demands, a penalty-based approach is introduced to minimize unsatisfied load power demands. Each load type has a defined unit penalty cost, regulating user demand response. In addition, dynamic time intervals are adopted to reduce the number of iterations as well as improve the RAM memory and processor utilization required for solving the microgrid energy optimization management model. Simulation tests on a microgrid comprized of distributed generations, battery energy storage systems, and energy consumption units were conducted to prove the effectiveness of the proposed method. Test results prove that when appropriately configuring dynamic time intervals, solution results obtained using the proposed method closely align with that derived from fixed time interval-based methods. Finally, the energy optimization management effects of the proposed method were compared with that of two latest methods. The comparison results demonstrate that if microgrid underwent four different disconnection scenarios from the main distribution network, the proposed method saves 19.27%, 18.75%, 20.74%, 34.92%, 25.47% time to achieve energy optimization management compared with that of the first latest method, and 20.51%, 20.81%, 21.10%, 36.05%, 27.89% time than that of the second latest method.

关键词： Load prioritization Dynamic temporal adaptation Demand response Microgrid Energy optimization

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Dynamic Identification of Critical Transmission Lines in power Systems With Wind power Integration Based on Maximum Influence Theory

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IEEE ACCESS 2025年 13卷 10689-10701页

作者： Ruan, Gang Shao, Changzheng Wang, Hao Jin, Yingxu Zhu, Lingzi Chang, Dongxu Chongqing Univ Sch Elect Engn Chongqing 400044 Peoples R China Guizhou Power Grid Co Ltd Power Dispatching Control Ctr Guiyang 550000 Peoples R China Elect Power Res Inst China Southern Power Grid Guangzhou 510663 Peoples R China

With the integration of large-scale renewable energy sources, the risk of power system operation has increased significantly. To effectively prevent large-scale blackout incidents, it is essential to identify the critical transmission lines within the system accurately. However, most of the existing identification methods are based on complex network theory and use static graphs to model the propagation of cascading faults, which cannot consider the impact of wind power output fluctuations. To address this issue, this paper introduces and improves the maximum influence model of the community network to identify the critical lines. By developing the time-sequence cascading faults graph, the proposed method can comprehensively consider the impact of wind power output fluctuations on the propagation of cascading faults. Based on the influence maximization model in time-sequence cascading faults graph (IMTG), the line fault influence calculation algorithm (LFIC) and improved critical line identification algorithm (ILIT) are proposed to identify the critical transmission lines. In order to verify the effectiveness of the proposed method, case studies are carried out on the IEEE 39-bus system and the IEEE 118-bus system. The results show that the critical lines identified by the proposed method can effectively cope with the fluctuation of wind power output in the future, and can provide timely feedback for power system operators.

关键词： Wind power generation power systems Load flow Fault diagnosis power transmission lines Fluctuations Indexes Transmission line matrix methods Wind farms power system stability Wind power integration critical transmission line identification maximal influence theory time-sequence cascading faults graph (TCFG)

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Age of Information-Optimized Differential Protection Strategy for Smart grids

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IEEE Access 2025年 13卷 78905-78914页

作者： Fu, Jiajia Kang, Zhongmiao Zeng, Ying Wu, Zanhong The Electric Power Dispatching Control Center of Guangdong Power Grid Co. Ltd Guangzhou510320 China

Differential protection is a fundamental mechanism in power systems for detecting and isolating faults. However, traditional protection schemes face significant challenges in modern smart grids due to communication delays and the inability to dynamically adapt to real-time information. These limitations often result in reduced fault detection accuracy and delayed system response, threatening the reliability and stability of the grid. To address these shortcomings, we propose an Age of Information (AoI)-optimized differential protection strategy tailored for smart grids. By modeling the differential protection problem as a remote Markov Decision Process (MDP), we incorporate AoI as a critical factor to capture the freshness of information in decision-making under stochastic delays. Our analysis reveals that treating AoI as auxiliary side information, rather than a standalone optimization goal, significantly enhances the timeliness of information and indirectly improves fault detection accuracy. By leveraging AoI, we show that it is possible to enhance the timeliness and accuracy of fault detection, even under adverse network conditions, by ensuring that the system relies on the freshest information available. In particular, our results demonstrate a 6.5% improvement in information freshness compared to traditional methods, leading to enhanced system performance. This study provides new insights into leveraging information freshness to overcome the inherent limitations of traditional differential protection schemes, thereby improving the efficiency and resilience of modern power systems. © 2013 IEEE.

关键词： Markov processes

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control, Modeling and Stability Analysis of DRU-based grid-forming Converter

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Chinese Journal of Electrical Engineering 2025年第1期11卷 83-92页

作者： Wencong Wu Minzhi Wang Liang Tu Haiqing Cai Guanglei Yan Xiaohui Qu Haohan Gu Wei Chen State Key Laboratory of HVDC China Southern Power GridGuangzhou 510663China National Energy Power Grid Technology R&D Center Guangzhou 510663China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power Systems China Southern Power GridGuangzhou 510663China CSG Key Laboratory for Power System Simulation China Southern Power GridGuangzhou 510663China School of Electrical Engineering Southeast UniversityNanjing 210096China

Diode rectifier unit(DRU)-based high-voltage direct current(HVDC)transmissionsystems areeffectivein achieving the stableandeconomical operation of offshore *** theuncontrollable characteristicsof DRUs,a grid-forming(GFM)strategy forwind-turbine converters isnecessary to support offshore AC voltageand ***,the active power-synchronization control in traditional GFM converters is unsuitable for DRU-based GFM ***,the stability issue for DRU-based HVDC systems involving DRU-based GFM and grid-following(GFL)converters has not yet been *** solve these issues,this study begins with the characteristics of a DRU-based HVDC system and presents a control scheme for DRU-based GFM converters for power ***,the dq-frame impedance model of the DRU-based GFM converteris proposed for the stability analysis of the entire HVDC ***,a simulation platform is built to verify the model accuracy and system stability.

关键词： Diode rectifier unit offshore wind-powergeneration grid-forming converter impedance model stabilityanalysis

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