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Power and Electrical engineering (acPEE), 5th Asia Conference on

作者： Hussain Haider Yang Jun Ghamgeen Izat Rashed Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation Wuhan University Wuhan Hubei Province China School of Electrical Engineering and Automation Wuhan University Wuhan Hubei Province China

ISBN: (数字)9798350309638

ISBN: (纸本)9798350309645

This study contributes a data-driven two-stage robust optimization framework to account for the uncertainties in renewable energy sources within microgrid systems. The framework integrates Bayesian inference with Gaussian Process regression (GPR) analysis to evaluate and model the output uncertainties of photovoltaic (PV) and Wind turbine (WT) power sources. Furthermore, the correlation between these sources is established at various uncertainty levels. The two-stage optimization approach incorporates the day-ahead dispatch stage and real-time adjusting under economic factors in the second stage. So, the framework controls day-ahead dispatch strategies to optimize microgrids in the prospect of varying PV-WT output and electricity demand. Subsequently, it involves real-time optimization under the worst-case scenario, enhancing the microgrid’s economic viability and operational efficiency. Finally, a column-and-constraint generation (C&CG) method is employed to address the robust optimization model by incorporating different levels of uncertainty. The proposed method is established on an IEEE 33-bus distribution network, ensuring its applicability and reliability.

关键词： Economics Renewable energy sources Analytical models Uncertainty Correlation Microgrids Gaussian processes

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research on Multi-source Cooperative Operation Strategy of Urban Distribution network Based on Data-driven Distributionally Robust 6

Research on Multi-source Cooperative Operation Strategy of U...

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6th International Conference on Electrical engineering and Control Technologies, CEECT 2024

作者： Shi, Yihui He, Le Xia, Huaimin Tang, Dingchao Zhu, Wei Chen, Shuang Yin, Zhengsheng Huang, Mengqi State Grid Hubei Electric Power Co. Ltd. Wuhan Power Supply Company Wuhan China Wuhan Charging and Swapping Public Facilities Management Co. Ltd. Wuhan Power Supply Company Wuhan China Wuhan University School of Electrical Engineering and Automation Wuhan University Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation Wuhan China

ISBN: (纸本)9798331528096

The interaction between electric vehicle charging stations(EVCS) and PV, energy storage, and other equipment progressively increases as the penetration rate of electric vehicles and distributed new energy increases. Simultaneously, the safe operation of urban distribution network will be significantly impacted by the volatility and randomness introduced by new energy access. Consequently, the issue of examining the multisource cooperative operation strategy of urban distribution network in light of uncertainty has emerged as a pressing requirement for urgent research and resolution. To address the uncertainty of PV prediction, the data-driven algorithm of the Wasserstein generative adversarial network is employed to generate PV output scenarios. PV typical scenarios are obtained through K-mediods clustering cuts, and the probabilistic confidence intervals of the distribution of the typical scenarios are constrained by the 1 -paradigm and ∞-paradigm numbers. Subsequently, the typical scenario data of PV is employed to develop a data-driven distributionally robust urban distribution grid multi-source cooperative operation model that takes into account electric vehicle charging stations, energy storage, and PV. The first stage of this model ascertains the power procured from the primary grid, whereas the second stage identifies the PV power relinquished, the storage charging and discharging capacity, and the equivalent load of the EVCS. Finally, the suggested model's validity is confirmed using data from an urban distribution network. © 2024 IEEE.

关键词： Charging stations

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Coordinated optimization of control parameters for suppressing transient overvoltage in wind power dc transmission systems

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Frontiers in Energy research 2024年 12卷

作者： Zhao, Jie Dou, Jinqiu Li, Ming Wu, Fangjie Shen, Xiaolin Liang, Yilin Shang, Lei Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation Wuhan University Wuhan China State Grid Economic and Technological Research Institute Co. Ltd. Beijing China

With the increasing proportion of new energy in power systems, the instability issues of novel power systems become more prominent. This paper addresses the transient overvoltage problem in wind power dc transmission systems by proposing a method for transient overvoltage suppression based on doubly-fed wind turbines, high-voltage dc transmission systems, and SVC control parameter optimization. The electromagnetic transient model of the wind power dc transmission system is combined with intelligent optimization algorithms through joint invocation to achieve coordinated optimization of control parameters. Case studies validate the effectiveness of the proposed model and method. The results indicate that the improved genetic algorithm, SEGA, proposed in this paper, effectively suppresses overvoltage at the grid connection point of wind turbines. Furthermore, by simultaneously applying coordinated control strategies based on DFIG and SVC and optimizing parameters of HVdc, DFIG, and SVC, transient overvoltage phenomena can be effectively eliminated. Copyright © 2024 Zhao, Dou, Li, Wu, Shen, Liang and Shang.

关键词： Electromagnetic transients

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Optimal Reactive Power Allocation in Power Systems Based on Improved Binary Table Particle Swarm Optimization

Optimal Reactive Power Allocation in Power Systems Based on ...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Xiaodong Yu Jie Dang Mo Chen Jiale Wang Fei Tang Ying Wang Peng Yuan Central China Branch State Grid Corporation of China Wuhan China Wuhan University Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network / School of Electrical Engineering and Automation Wuhan China Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network/Wuhan University School of Electrical Engineering and Automation Wuhan China

In the construction of modern power systems, the transient voltage characteristics of backbone networks are becoming more and more complicated, and transient reactive power compensation devices play an important role in supporting the transient voltage. Therefore, this paper proposes an index system based on an improved binary table for evaluating the risk of transient voltage instability and the configuration effect of transient reactive devices; at the same time, it establishes a differentiated transient reactive compensation device configuration model based on this index system, and takes the configuration expense and the transient voltage support effect as the two optimization objectives, and weighs the configuration expense of reactive devices and the transient voltage dips suppression effect, and carries out differentiated reactive device configuration and differentiated reactive device configuration according to the differentiated transient voltage indicators of different nodes to be compensated. according to the differentiated transient voltage indexes of different nodes to be compensated, the differentiated reactive power compensation device configuration is carried out by weighing the reactive power device configuration expenditure and the transient voltage drop suppression effect. Finally, the IEEE39 node system is used as a backbone network simulation example, and the improved particle swarm algorithm is applied to solve the model. The method proposed in this paper effectively improves the transient voltage stability margin of the system, which verifies the validity of the method.

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Vulnerability analysis of power grid structure based on complex network theory

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Frontiers in Energy research 2024年 12卷

作者： Yang, Zhichun Min, Huaidong Zhao, Jie Dong, Xuzhu Yang, Fan Wang, Chenhao Zhang, Nan State Grid Hubei Electric Power Company Electric Power Research Institute Hubei Wuhan China Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation Wuhan University Wuhan China State Grid Hubei Electric Power Co. Ltd. Wuhan Power Supply Company Hubei Wuhan China

Introduction: The key nodes of an intelligent distribution network significantly impact the reliability and stability of the distribution network’s operation. The failure of these key nodes can severely affect the safe operation of the distribution network. Therefore, vulnerability analysis of key nodes is particularly important. Methods: This article proposes a comprehensive weighting method for evaluating indicators,combining the analytic hierarchy process (AHP) and entropy weighting method, while considering the structure and operational status of the power system grid. Key node structural evaluation indicators, such as node degree, node shrinkage centrality, and electric mediator, are established considering "significance" and "destructiveness." State indicators are established based on the degree of impact of current, voltage, and load changes on the grid, as well as the uniformity of their distribution, including the improved current distribution entropy, voltage terre entropy, and three-phase state indicators of lost load. Subsequently, based on the AHP and entropy weighting method, a comprehensive weighting method is proposed to assign subjective and objective weights to the comprehensive evaluation indicators, obtaining the comprehensive weights of the indicators. Finally, the gray correlation degree is introduced to improve the ideal solution of the multi-objective decision making method, obtaining the criticality of the grid nodes and then identifying the critical nodes. Results and Discussion: The example analysis presented in this article shows that the identified critical nodes of the power grid have a high degree of overlap with the identification results of different methods, can better identify edge nodes, and validate the effectiveness of the proposed evaluation indicators and key node identification methods. Copyright © 2025 Yang, Min, Zhao, Dong, Yang, Wang and Zhang.

关键词： AHP comprehensive evaluation indexes comprehensive weights improved TOPSIS method key nodes

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PJ-ADMM Based Parallel Energy Sharing Mechanism Between Prosumer Aggregator and Charging Station

PJ-ADMM Based Parallel Energy Sharing Mechanism Between Pros...

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Electrical engineering, Big Data and Algorithms (EEBDA), IEEE International Conference on

作者： Hongli Wang Jun Yang Fengran Liao Legang Jia Nianjiang Du Tianhui Li Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation Wuhan University Wuhan China State Grid Yantai Power Supply Company Yantai China

Flexible resources such as renewable energy and electric vehicles are expanding. In order to efficiently utilize the energy from various types of prosumers, an energy sharing market mechanism between prosumer aggregator and charging stations is proposed. Based on the PJ-ADMM algorithm, the proposed mechanism is capable of parallel iterative computation to realize market clearing. We validate the effectiveness of the proposed mechanism through a case study. After participating in energy sharing, the social welfare is improved and the resources can be optimally allocated.

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Bat Algorithm-Based Shunt active Power Filter for Harmonics Control in PV Grid-Connected Systems Under Nonlinear Load

Bat Algorithm-Based Shunt Active Power Filter for Harmonics ...

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Power and Electrical engineering (acPEE), 5th Asia Conference on

作者： Hashim Ali I. Gony Ghamgeen Izat Rashed Ansumana Badjan O. M. Ahmed Bahageel Hualiang Hualiang Husam I. Shaheen Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation Wuhan University Wuhan Hubei Province China School of Electrical Engineering and Automation Wuhan University Wuhan Hubei Province China Changsha University Changsha Huhan China

ISBN: (数字)9798350309638

ISBN: (纸本)9798350309645

The increasing prevalence of Nonlinear Loads (NLL) in modern electric grids, particularly in photovoltaic grid-connected systems (PV-GCS), poses challenges with regard to power quality standards. These loads introduce harmonics. In order to guarantee reliable operation, Adequate and effective approaches are needed. emphasizing the importance of addressing these challenges, to ensure the reliability and stability of PV-GCS. This study introduces a novel Bat Algorithm (BA) for mitigating harmonics. BA appears as a powerful technique for reducing distortion in source currents. The BA is carefully employed to achieve accurate suppression of the harmonic components. Furthermore, the algorithm’s dynamic and adaptable nature helps to provide fast response while maintaining high performance and increasing overall system reliability. This unique technique exceeds other approaches such as the Golden Eagle (GE) algorithm by displaying a significant decrease in total harmonic distortion (THD) and dc bus voltage (V dc ) regulation. The MATLAB/Simulink platform is extensively used for evaluation. The demonstrated results provide critical understanding for the performance of PV-GCS and achieve optimal reliability under NLL.

关键词： Total harmonic distortion Heuristic algorithms Stability criteria Harmonic analysis active filters Power system reliability Reliability

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Predictive Modelling of Base Station Energy Consumption:Insights and Application of STL-LSTM networks

Predictive Modelling of Base Station Energy Consumption:Insi...

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Power and Electrical engineering (acPEE), 5th Asia Conference on

作者： Ansumana Badjan Ghamgeen Izat Rashed Hashim Ali.I. Gony Ahmed O. M. Bahageel Fang Hualiang Husam I. Shaheen Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation Wuhan University Wuhan Hubei Province China School of Electrical Engineering and Automation Wuhan University Wuhan Hubei Province China Changsha University Changsha Hunan China

ISBN: (数字)9798350309638

ISBN: (纸本)9798350309645

The increasing demand for wireless communication services has led to a significant growth in the number of base stations, resulting in a substantial increase in energy consumption. Understanding and predicting base station energy consumption is important for optimizing energy usage and developing sustainable communication networks. This study introduces a predictive modeling approach for base station energy consumption by combining Seasonal and Trend decomposition using Loess (STL) and Long Short-Term Memory (LSTM) networks. The methodology involves decomposing the historical data into seasonal, trend, and residual components using STL. LSTM is then utilized to model the relationships between these components, capturing complex temporal dependencies and enabling accurate prediction of future energy consumption based on traffic and network load patterns. Experimental results demonstrate the effectiveness of the proposed STL-LSTM approach, outperforming traditional forecasting models and providing valuable insights into base station energy consumption dynamics.

关键词： Wireless communication Energy consumption Base stations Predictive models Market research Data models Reliability

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A Study on Search and Correction of Error Dominant Regions in Simulation Based on Measured Trajectories 3

A Study on Search and Correction of Error Dominant Regions i...

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3rd International Conference on Power Systems and Electrical technology, PSET 2024

作者： Wang, Lei Pan, Ruxiang Lv, Rui Li, Junxian Yue, Xiaoyu Liao, Siyang Laboratory of Hydro-Wind-Solar Multi-Energy Control Coordination China Yangtze Power Co. Ltd. Wuhan China Wuhan University Laboratory of Hydro-Wind-Solar Multi-Energy Control Coordination Hubei Engineering and Technology Research Center. for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation Wuhan China Co. Ltd. Beijing China

ISBN: (纸本)9798350353204

With the proposal of the carbon peak and carbon neutrality goal, a significant influx of new energy sources with random and fluctuating characteristics is being integrated into the power grid. Consequently, the adaptability of the traditional four-parameter model, as well as new energy models, to the actual power grid is becoming increasingly prominent. Conversely, simulations often rely solely on the accuracy of models and algorithms and are based on predefined conditions. To address the aforementioned challenges, this paper utilizes PMU-measured data to identify error-dominant regions. Initially, it employs a hybrid dynamic simulation method to partition the power grid simulation system into blocks. Concurrently, it establishes a simulation credibility evaluation system and employs comprehensive simulation credibility to identify error-dominant regions. Subsequently, it iteratively decouples sub-regions to systematically identify error elements. Subsequently, the perturbation method was employed to analyze the trajectory sensitivity of each parameter of the element. Parameters with low sensitivity were assigned typical values, while those with high trajectory sensitivity were chosen for identification. The genetic algorithm was then utilized for online parameter verification and optimization. Finally, the proposed method was validated using RTDS real-time simulation software. Experimental results demonstrate that the calibrated model simulation results exhibit better alignment with measured data, highlighting the accuracy and reliability of the proposed approach. © 2024 IEEE.

关键词： Perturbation techniques

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Emergency Control Strategy of Power System Considering Operation Scenario Variations

Emergency Control Strategy of Power System Considering Opera...

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Power engineering (ICPE), International Conference on

作者： Yangzhou Pei Jun Yang Binyu Ma Xiaotao Peng Kezheng Jiang Dan Liu Kan Cao Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation Wuhan University Wuhan China State Grid Hubei Electric Power Research Institute Wuhan China

Power system emergency control is the core of the second and third defense lines. The deep reinforcement learning (DRL) based control scheme breaks through the limitations of traditional control strategy simulation calculation methods based physical models, and shows great advantages in dealing with high-dimensional nonlinear system control problems. However, there is a lack of research on dealing with the renewable energy grid-connected power system control problem with operation scenarios variations, especially the frequent topological changes. Based on the DRL method, the under-voltage load shedding (UVLS) emergency control scheme considering the operation scenarios variations is proposed. And the graph convolutional network (GCN) is adopted to learn the representation of power grid topological states and solve the problem of topology feature extraction. Moreover, renewable energy grid-connected scenarios with medium proportion, high proportion and extremely high proportion permeability are set up. Case studies on the modified IEEE 39-bus system are employed to demonstrate the performance and effectiveness of the proposed method.

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