CSEE Journal of Power and Energy Systems最新文献

{"title":"A Planning Model for Optimal Sizing of Integrated Power and Gas Systems Capturing Frequency Security","authors":"Yi Wang;Goran Strbac","doi":"10.17775/CSEEJPES.2024.00240","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.00240","url":null,"abstract":"Large renewable penetration has been witnessed in power systems, resulting in reduced level of system inertia and increasing requirements for frequency response services. There have been plenty of studies developing frequency-constrained operation models for power system security. However, most existing literature only focuses on operational level rather than planning level. To fill this gap, this paper proposes a novel planning model for the optimal sizing problem of integrated power and gas systems, capturing both under and over frequency security requirements. A detailed unit commitment setup considering different ramping rates is incorporated into the planning model to accurately represent the scheduling behavior of each individual generator and accurate inertia calculation. The power importing and exporting behaviors of interconnectors are considered, which can influence the largest loss of generation and demand, accounting for under and over frequency security, respectively. Additionally, a deep learning-based clustering method featured by concurrent and integrated learning is introduced in the planning model to effectively generate representative days. Case studies have been conducted on a coupled 6-bus power and 7-node gas system as well as a 14-bus power and 14-node gas system to verify the effectiveness of the proposed planning model in accurate clustering performance and realistic investment decision making.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 2","pages":"580-594"},"PeriodicalIF":6.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838252","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Interaction and Stability Analysis of Grid-following Converter Integrated Into Weak Grid","authors":"Shuang Li;Haijiao Wang;Yuehui Huang;Guoqing He;Chun Liu;Weisheng Wang","doi":"10.17775/CSEEJPES.2024.04920","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.04920","url":null,"abstract":"The grid-connected converter with grid-following control (GCC-GFL) for renewable energy sources has a risk of instability when integrated into the weak grid. This paper aims to investigate the dynamic interactions and stability characteristics of the GCC-GFL system. From a control system perspective, the mechanism of small-signal instability in the system is revealed through dynamic interaction analysis between the GCC-GFL and the weak grid. Meanwhile, a novel stability evaluation index is proposed based on the real and imaginary parts of the equivalent loop gain in a multi-loop control system. On this basis, the dominant loop of the control system leading to system instability is identified. Furthermore, quantitative analyses are conducted to investigate the stability region of the GCC-GFL, considering the influence of AC grid strength, steady-state operating points, and converter control parameters. Finally, the correctness and effectiveness of the proposed methods are verified by the impedance analysis method, the time-domain simulations, and the experiments, respectively.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 2","pages":"552-566"},"PeriodicalIF":6.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838264","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frequency-Voltage Active Support Strategy for Hybrid Wind Farms Based on Grid-Following and Grid-Forming Hierarchical Subgroup Control","authors":"Haiyu Zhao;Qihang Zong;Hongyu Zhou;Wei Yao;Kangyi Sun;Yuqing Zhou;Jinyu Wen","doi":"10.17775/CSEEJPES.2024.02340","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.02340","url":null,"abstract":"The GFL-GFM hybrid wind farm (HWF) combines the voltage source control advantages of grid-forming (GFM) wind turbines (WTs) with the current source control advantages of grid-following (GFL) wind turbines. It becomes a new type of large-scale grid-connected wind power generation. In this paper, we propose an HWF frequency-voltage active support based on GFL and GFM hierarchical subgroup control. It aims to realize the support of active power and reactive power under the premise of ensuring system stability. The strategy consists of the determination of the control objectives of the GFM-GFL subgroups, the distributed control (DC) of the GFM-GFL subgroups, and the adaptive control and switching of each unit of the GFM and GFL groups. The GFM-group maintains the grid-connected voltage stability and the GFL-group exhausts the active support. DC at the group level and adaptive control at the unit level are included under the hierarchy of the respective objectives. Finally, a GFL-GFM HWF model is established on the MATLAB/Simulink platform, and the simulation verifies that the proposed strategy can realize the enhancement of the frequency-voltage support capability of the HWF under the premise of grid-connected stability.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 1","pages":"65-77"},"PeriodicalIF":6.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838248","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating Transient Stability Regions of Large-Scale Power Systems Part I: Koopman Operator and Reduced-Order Model","authors":"Yuqing Lin;Tianhao Wen;Lei Chen;Q. H. Wu;Yang Liu","doi":"10.17775/CSEEJPES.2024.01170","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.01170","url":null,"abstract":"This paper presents an estimation of transient stability regions for large-scale power systems. In Part I, a Koopman operator based model reduction (KOMR) method is proposed to derive a low-order dynamical model with reasonable accuracy for transient stability analysis of large-scale power systems. Unlike traditional reduction methods based on linearized models, the proposed method does not require linearization, but captures dominant modes of the original nonlinear dynamics by employing a Koopman operator defined in an infinite-dimensional observable space. Combined with the Galerkin projection, the obtained dominant Koopman eigenvalues and modes produce a reduced-order nonlinear model. To approximate the Koopman operator with sufficient accuracy, we introduce a Polynomial-based Multi-trajectory Kernel Dynamic Mode Decomposition (PMK-DMD) algorithm, which outperforms traditional DMD in various scenarios. In the end, the proposed method is applied to the IEEE 10-machine-39-bus power system and IEEE 16-machine-68-bus power system, which demonstrates that our method is significantly superior to the modal analysis method in both qualitative and quantitative aspects.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 1","pages":"24-37"},"PeriodicalIF":6.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838254","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Scheduling of Integrated Electricity and Gas System with Numerical Stability Condition-Free Method","authors":"Suhan Zhang;Chi Yung Chung;Wei Gu;Ruizhi Yu;Shuai Lu;Pengfei Zhao","doi":"10.17775/CSEEJPES.2024.04140","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.04140","url":null,"abstract":"Though an accurate discretization approach for gas flow dynamics, the method of characteristics (MOC) is liable to instability for inappropriate step sizes. This letter addresses the numerical stability limitation of MOC, in the context of lEGS's optimal scheduling. Specifically, the proposed method enables flexible temporal step sizes without sacrificing accuracy, significantly reducing non-convergence due to numerical oscillations. The effectiveness of the proposed method is validated through case studies in different simulation settings.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 2","pages":"607-611"},"PeriodicalIF":6.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed Optimal Power Flow for Large-Scale Multi-Area Interconnected Power Systems","authors":"Qingju Luo;Jizhong Zhu;Haohao Zhu;Di Zhang","doi":"10.17775/CSEEJPES.2024.02430","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.02430","url":null,"abstract":"Distributed optimal power flow (OPF) of large-scale multi-area interconnected power systems is a challenging problem. This letter proposes a distributed OPF approach based on the modified decomposition-coordination interior point method (DCIPM). The proposed method eliminates the zero rows of the coupling matrix and partially factorizes the augmented Newton matrix on the foundation of DCIPM, which speeds up the computation. Eliminating zero rows significantly reduces the size of the coupling matrix, and the partial decomposition of the augmented Newton matrix exploits the sparsity of the coupling matrix. The proposed distributed OPF approach is more convergent and efficient than the traditional distributed optimization methods and faster than the centralized MATPOWER, as verified in different systems, the largest of which contains 70,000 buses.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 3","pages":"1423-1428"},"PeriodicalIF":6.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid AC/DC Collection and HVDC Transmission Topology for Large-scale Offshore Wind Farms","authors":"Wang Xiang;Rui Tu;Mingyu Han;Jinyu Wen","doi":"10.17775/CSEEJPES.2024.05450","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.05450","url":null,"abstract":"Conventional offshore wind integration systems use 33kV or 66kV AC cables to collect wind energy and employ high voltage direct current (HVDC) transmission technology to deliver wind power to onshore grids. This scheme suffers from high costs for collection systems and offshore platforms when the capacity of offshore wind farms increases. This paper proposes a hybrid AC/DC collection and HVDC transmission concept for the large-scale offshore wind integration system. Wind farms near the offshore converter platform are integrated using AC collection cables, while the remaining wind farms are integrated using DC collection cables. The AC and DC collection cables infeed to the offshore converter platform, which features a three-terminal hybrid AC/DC/DC hub. The system layout and operating principle of the hybrid AC/DC collection and HVDC transmission system are introduced in detail. The control strategy and parameter design of the hybrid AC/DC/DC hub are presented. An economic evaluation comparing conventional AC collection and HVDC transmission schemes is conducted. It is indicated that the proposed integration concept can reduce the operating power capacity and power loss of the offshore converter, enhancing the economic efficiency of the overall integration system. Finally, the effectiveness of the proposed integration technology is validated in a 2000MW offshore wind power integration system by PSCAD/EMTDC simulation analysis.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 3","pages":"949-959"},"PeriodicalIF":6.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838251","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal Data Graph Modeling and Exploration of Application Scenarios in “Power Grid One Graph”","authors":"Peng Li;Zhen Dai;Yachen Tang;Guangyi Liu;Jiaxuan Hou;Qinyu Feng;Quanchen Lin","doi":"10.17775/CSEEJPES.2024.00960","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.00960","url":null,"abstract":"By modeling the spatiotemporal data of the power grid, it is possible to better understand its operational status, identify potential issues and risks, and take timely measures to adjust and optimize the system. Compared to the bus-branch model, the node-breaker model provides higher granularity in describing grid components and can dynamically reflect changes in equipment status, thus improving the efficiency of grid dispatching and operation. This paper proposes a spatiotemporal data modeling method based on a graph database. It elaborates on constructing graph nodes, graph ontology models, and graph entity models from grid dispatch data, describing the construction of the spatiotemporal node-breaker graph model and the transformation to the bus-branch model. Subsequently, by integrating spatiotemporal data attributes into the pre-built static grid graph model, a spatiotemporal evolving graph of the power grid is constructed. Furthermore, the concept of the “Power Grid One Graph” and its requirements in modern power systems are elucidated. Leveraging the constructed spatiotemporal node-breaker graph model and graph computing technology, the paper explores the feasibility of grid situational awareness. Finally, typical applications in an operational provincial grid are showcased, and potential scenarios of the proposed spatiotemporal graph model are discussed.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 2","pages":"538-551"},"PeriodicalIF":6.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838247","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Twin-Supported Battery State Estimation Based on TCN-LSTM Neural Networks and Transfer Learning","authors":"Kai Zhao;Ying Liu;Yue Zhou;Wenlong Ming;Jianzhong Wu","doi":"10.17775/CSEEJPES.2024.00900","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.00900","url":null,"abstract":"Estimating battery states such as State of Charge (SOC) and State of Health (SOH) is an essential component in developing energy storage technologies, which require accurate estimation of complex and nonlinear systems. A significant challenge is extracting pertinent spatial and temporal features from original battery data, which is crucial for efficient battery management systems. The emergence of digital twin (DT) technology offers a novel opportunity for performance monitoring and management of lithium-ion batteries, enhancing collaborative capacity among different battery state estimation techniques and enabling optimal operation of battery storage units. In this study, we propose a DT-supported battery state estimation method, in collaboration with the temporal convolutional network (TCN) and the long short-term memory (LSTM), to address the challenge of feature extraction. Firstly, we introduce a 4-layer hierarchical DT to overcome computational and data storage limitations in conventional battery management systems. Secondly, we present an online algorithm, TCN-LSTM for battery state estimation. Compared to conventional methods, TCN-LSTM outperforms other cyclic networks in various sequence modelling tasks and exhibits reduced reliance on the initial state conditions of the battery. Our methodology employs transfer learning to dynamically adjust the neural network parameters based on fresh data, ensuring real-time updating and enhancing the DT's accuracy. Focusing on SOC, SOH and Remaining Useful Life (RUL) estimation, our model demonstrates exceptional results. When testing with 90 cycle data, the average root mean square error (RMSE) values for SOC, SOH, and RUL are 1.1 %, 0.8%, and 0.9 % respectively, significantly outperforming traditional CNN's 2.2%, 2.0% and 3.6% and others. These results un-equivocally demonstrate the contribution of the DT model to battery management, highlighting the outstanding robustness of our proposed method, showcasing consistent performance across various conditions and superior adaptability compared to other models.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 2","pages":"567-579"},"PeriodicalIF":6.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838241","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient and Stable Learning for Distribution Network Operation: A Model-Based Reinforcement Learning Approach","authors":"Dong Yan;Zhan Shi;Xinying Wang;Yiying Gao;Tianjiao Pu;Jiye Wang","doi":"10.17775/CSEEJPES.2023.09100","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.09100","url":null,"abstract":"This paper discusses the application of deep reinforcement learning (DRL) to the economic operation of power distribution networks, a complex system involving numerous flexible resources. Despite the improved control flexibility, traditional prediction-plus-optimization models struggle to adapt to rapidly shifting demands. Modern artificial intelligence (AI) methods, particularly DRL methods, promise faster decision-making but face challenges, including inefficient training and real-world application. This study introduces a reward evaluation system to assess the effectiveness of various strategies and proposes an enhanced algorithm based on the Model-based DRL approach. Incorporating a state transition model, the proposed algorithm augments data and enhances dynamic deduction, improving training efficiency. The effectiveness is demonstrated in various operational scenarios, showing notable enhancements in rationality and transfer generalization.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 3","pages":"1080-1092"},"PeriodicalIF":6.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838271","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}