Pub Date : 2025-01-10DOI: 10.17775/CSEEJPES.2024.00900
Kai Zhao;Ying Liu;Yue Zhou;Wenlong Ming;Jianzhong Wu
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.
{"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}
Pub Date : 2025-01-10DOI: 10.17775/CSEEJPES.2023.09100
Dong Yan;Zhan Shi;Xinying Wang;Yiying Gao;Tianjiao Pu;Jiye Wang
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.
{"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}
Global warming has motivated the world's major countries to actively develop technologies and make policies to promote carbon emission reduction. Focusing on interconnected multi-regional power systems, this paper proposes a coordinated planning model for interconnected power systems considering energy storage system planning and transmission expansion. A market-based carbon emission quota trading market that helps reduce carbon emissions is built and integrated into the coordi-nated planning model, where entities can purchase extra or sell surplus carbon emission quotas. Its effects on promoting carbon emission reduction are analyzed. Considering the limitations on information exchange between interconnected regional power systems, the proposed model is decoupled and solved with the analytical target cascading algorithm. A modified two-region 48-bus system is used to verify the effectiveness of the proposed model and solving method.
{"title":"Coordinated Planning of Interconnected Multi-Regional Power Systems Considering Large-Scale Energy Storage Systems, Transmission Expansion, and Carbon Emission Quota Trading","authors":"Jia Liu;Biao Jiang;Zao Tang;Pingliang Zeng;Tong Su;Yalou Li;Qiuwei Wu","doi":"10.17775/CSEEJPES.2023.06230","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.06230","url":null,"abstract":"Global warming has motivated the world's major countries to actively develop technologies and make policies to promote carbon emission reduction. Focusing on interconnected multi-regional power systems, this paper proposes a coordinated planning model for interconnected power systems considering energy storage system planning and transmission expansion. A market-based carbon emission quota trading market that helps reduce carbon emissions is built and integrated into the coordi-nated planning model, where entities can purchase extra or sell surplus carbon emission quotas. Its effects on promoting carbon emission reduction are analyzed. Considering the limitations on information exchange between interconnected regional power systems, the proposed model is decoupled and solved with the analytical target cascading algorithm. A modified two-region 48-bus system is used to verify the effectiveness of the proposed model and solving method.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 2","pages":"490-502"},"PeriodicalIF":6.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860788","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}
Pub Date : 2025-01-10DOI: 10.17775/CSEEJPES.2024.00780
Feng Ji;Lu Gao;Chang Lin
This paper proposes to analyze the motion stability of synchronous generator-based power systems using a Lagrangian model derived in the configuration space of generalized position and speed. A Lagrangian model of synchronous generators is derived based on Lagrangian mechanics. The generalized potential energy of inductors and the generalized kinetic energy of capacitors are defined. The mechanical and electrical dynamics can be modelled in a unified manner by constructing a Lagrangian function. Taking the first benchmark model of sub-synchronous oscillation as an example, a Lagragian model is constructed, and a numerical solution of the model is obtained to validate the accuracy and effectiveness of the model. Compared with the traditional EMTP model in PSCAD, the obtained Lagrangian model is able to accurately describe the electromagnetic transient process of the system. Moreover, the Lagrangian model is analytical, which enables the analysis of the motion stability of the system using Lyapunov's motion stability theory. The Lagrangian model can not only be used for discussing the power angle stability but also for analyzing the stability of node voltages and system frequency. It provides the feasibility for studying the unified stability of power systems.
{"title":"Lagrangian Modelling and Motion Stability of Synchronous Generator-based Power Systems","authors":"Feng Ji;Lu Gao;Chang Lin","doi":"10.17775/CSEEJPES.2024.00780","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.00780","url":null,"abstract":"This paper proposes to analyze the motion stability of synchronous generator-based power systems using a Lagrangian model derived in the configuration space of generalized position and speed. A Lagrangian model of synchronous generators is derived based on Lagrangian mechanics. The generalized potential energy of inductors and the generalized kinetic energy of capacitors are defined. The mechanical and electrical dynamics can be modelled in a unified manner by constructing a Lagrangian function. Taking the first benchmark model of sub-synchronous oscillation as an example, a Lagragian model is constructed, and a numerical solution of the model is obtained to validate the accuracy and effectiveness of the model. Compared with the traditional EMTP model in PSCAD, the obtained Lagrangian model is able to accurately describe the electromagnetic transient process of the system. Moreover, the Lagrangian model is analytical, which enables the analysis of the motion stability of the system using Lyapunov's motion stability theory. The Lagrangian model can not only be used for discussing the power angle stability but also for analyzing the stability of node voltages and system frequency. It provides the feasibility for studying the unified stability of power systems.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 1","pages":"13-23"},"PeriodicalIF":6.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838272","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430351","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}
Pub Date : 2025-01-10DOI: 10.17775/CSEEJPES.2024.01340
Zhengyang Hu;Bingtuan Gao;Zhao Xu;Sufan Jiang
Wind power plants (WPPs) are increasingly mandated to provide temporary frequency support to power systems during contingencies involving significant power shortages. However, the frequency support capabilities of WPPs under derated operations remain insufficiently investigated, highlighting the potential for further improvement of the frequency nadir. This paper proposes a bi-level optimized temporary frequency support (OTFS) strategy for a WPP. The implementation of the OTFS strategy is collaboratively accomplished by individual wind turbine (WT) controllers and the central WPP controller. First, to exploit the frequency support capability of WTs, the stable operational region of WTs is expanded by developing a novel dynamic power control approach in WT controllers. This approach synergizes the WTs' temporary frequency support with the secondary frequency control of synchronous generators, enabling WTs to release more kinetic energy without causing a secondary frequency drop. Second, a model predictive control strategy is developed for the WPP controller. This strategy ensures that multiple WTs operating within the expanded stable region are coordinated to minimize the magnitude of the frequency drop through efficient kinetic energy utilization. Finally, comprehensive case studies are conducted on a real-time simulation platform to validate the effectiveness of the proposed strategy.
{"title":"Optimized Temporary Frequency Support for Wind Power Plants Considering Expanded Operational Region of Wind Turbines","authors":"Zhengyang Hu;Bingtuan Gao;Zhao Xu;Sufan Jiang","doi":"10.17775/CSEEJPES.2024.01340","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.01340","url":null,"abstract":"Wind power plants (WPPs) are increasingly mandated to provide temporary frequency support to power systems during contingencies involving significant power shortages. However, the frequency support capabilities of WPPs under derated operations remain insufficiently investigated, highlighting the potential for further improvement of the frequency nadir. This paper proposes a bi-level optimized temporary frequency support (OTFS) strategy for a WPP. The implementation of the OTFS strategy is collaboratively accomplished by individual wind turbine (WT) controllers and the central WPP controller. First, to exploit the frequency support capability of WTs, the stable operational region of WTs is expanded by developing a novel dynamic power control approach in WT controllers. This approach synergizes the WTs' temporary frequency support with the secondary frequency control of synchronous generators, enabling WTs to release more kinetic energy without causing a secondary frequency drop. Second, a model predictive control strategy is developed for the WPP controller. This strategy ensures that multiple WTs operating within the expanded stable region are coordinated to minimize the magnitude of the frequency drop through efficient kinetic energy utilization. Finally, comprehensive case studies are conducted on a real-time simulation platform to validate the effectiveness of the proposed strategy.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 1","pages":"51-64"},"PeriodicalIF":6.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838224","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430449","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}
This paper presents a quantitative assessment of the transient stability of grid-forming converters, considering current limitations, inertia, and damping effects. The contributions are summarized in two main aspects: First, the analysis delves into transient stability under a general voltage sag scenario for a converter subject to current limitations. When the voltage sag exceeds a critical threshold, transient instability arises, with its severity influenced by the inertia and damping coefficients within the swing equation. Second, a comprehensive evaluation of these inertia and damping effects is conducted using a model-based phase-portrait approach. This method allows for an accurate assessment of critical clearing time (CCT) and critical clearing angle (CCA) across varying inertia and damping coefficients. Leveraging data obtained from the phase portrait, an artificial neural network (ANN) method is presented to model CCT and CCA accurately. This precise estimation of CCT enables the extension of practical operation time under faults compared to conservative assessments based on equal-area criteria (EAC), thereby fully exploiting the system's low-voltage-ride-through (LVRT) and fault-ride-through (FRT) capabilities. The theoretical transient analysis and estimation method proposed in this paper are validated through PSCAD/EMTDC simulations.
{"title":"Comprehensive Assessment of Transient Stability for Grid-Forming Converters Considering Current Limitations, Inertia and Damping Effects","authors":"Jinlei Chen;Qingyuan Gong;Yawen Zhang;Muhammad Fawad;Sheng Wang;Chuanyue Li;Jun Liang","doi":"10.17775/CSEEJPES.2024.03160","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.03160","url":null,"abstract":"This paper presents a quantitative assessment of the transient stability of grid-forming converters, considering current limitations, inertia, and damping effects. The contributions are summarized in two main aspects: First, the analysis delves into transient stability under a general voltage sag scenario for a converter subject to current limitations. When the voltage sag exceeds a critical threshold, transient instability arises, with its severity influenced by the inertia and damping coefficients within the swing equation. Second, a comprehensive evaluation of these inertia and damping effects is conducted using a model-based phase-portrait approach. This method allows for an accurate assessment of critical clearing time (CCT) and critical clearing angle (CCA) across varying inertia and damping coefficients. Leveraging data obtained from the phase portrait, an artificial neural network (ANN) method is presented to model CCT and CCA accurately. This precise estimation of CCT enables the extension of practical operation time under faults compared to conservative assessments based on equal-area criteria (EAC), thereby fully exploiting the system's low-voltage-ride-through (LVRT) and fault-ride-through (FRT) capabilities. The theoretical transient analysis and estimation method proposed in this paper are validated through PSCAD/EMTDC simulations.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 1","pages":"1-12"},"PeriodicalIF":6.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430347","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}
Pub Date : 2025-01-10DOI: 10.17775/CSEEJPES.2023.07070
Baoqin Li;Pengfei Fan;Qixin Chen;Rong Li;Kaijun Lin
Deep learning technology is identified as a valid tool for transient stability assessment (TSA). Moreover, the superior performance of the TSA model depends on generously labeled samples. However, the power grid is dynamic, and some topologies or operation conditions change substantially. The traditional method generates a significant quantity of samples for each specific topology. Nonetheless, generating these labeled samples and establishing TSA models is very time-consuming. This paper proposes a high-quality sample generation framework based on data-driven methods to build a high-quality offline samples database for TSA model training and updating. Firstly, the representative topologies provided by the system operator are clustered into four different categories by density-based spatial clustering of applications with noise (DBSCAN). Thus the corresponding samples are collected. Then, when a new topology is encountered in the online application, scenario matching is used to match the most similar topology category. After that, instance-based transfer learning is implemented from a database of the best-matched topology category. Finally, a deep convolutional generative adversarial network (DCGAN) is constructed to mitigate the class imbalance problem. That is, unstable scenarios occur far more rarely than stable scenarios. Consequently, a high-quality and balanced TSA model training and updating database is constructed. The comprehensive test results on the Central China Power Grid illustrate that the proposed framework can generate high-quality and balanced TSA samples. Furthermore, the sample generation time is dramatically shortened. In addition, the metrics of accuracy, reliability and adaptability of the TSA model are significantly enhanced.
{"title":"High-Quality Sample Generation for Power System Transient Stability Assessment Based on Data-Driven Methods","authors":"Baoqin Li;Pengfei Fan;Qixin Chen;Rong Li;Kaijun Lin","doi":"10.17775/CSEEJPES.2023.07070","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.07070","url":null,"abstract":"Deep learning technology is identified as a valid tool for transient stability assessment (TSA). Moreover, the superior performance of the TSA model depends on generously labeled samples. However, the power grid is dynamic, and some topologies or operation conditions change substantially. The traditional method generates a significant quantity of samples for each specific topology. Nonetheless, generating these labeled samples and establishing TSA models is very time-consuming. This paper proposes a high-quality sample generation framework based on data-driven methods to build a high-quality offline samples database for TSA model training and updating. Firstly, the representative topologies provided by the system operator are clustered into four different categories by density-based spatial clustering of applications with noise (DBSCAN). Thus the corresponding samples are collected. Then, when a new topology is encountered in the online application, scenario matching is used to match the most similar topology category. After that, instance-based transfer learning is implemented from a database of the best-matched topology category. Finally, a deep convolutional generative adversarial network (DCGAN) is constructed to mitigate the class imbalance problem. That is, unstable scenarios occur far more rarely than stable scenarios. Consequently, a high-quality and balanced TSA model training and updating database is constructed. The comprehensive test results on the Central China Power Grid illustrate that the proposed framework can generate high-quality and balanced TSA samples. Furthermore, the sample generation time is dramatically shortened. In addition, the metrics of accuracy, reliability and adaptability of the TSA model are significantly enhanced.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 4","pages":"1681-1692"},"PeriodicalIF":5.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831951","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}
Optimal Power Flow (OPF) plays a crucial role in optimization and operation of the bipolar DC distribution network (Bi-DCDN). However, existing OPF models encounter difficulties in the power optimization of Bi-DCDNs due to the optimal power expressed as a product form, i.e., the product of voltage and current. Hence, this brief formulates the OPF problem of Bi-DCDNs using the branch flow model (BFM). The BFM employs power, instead of current, to account for the unique structure of Bi-DCDNs. Convex relaxation and linear approximation are sequentially applied to reformulate the BFM-based OPF, presenting it as a second-order cone programming (SOCP) problem. Further, the effectiveness of the proposed OPF model is verified in case studies. The numerical results demonstrate that the BFM-based OPF is a feasible and promising approach for Bi-DCDNs.
{"title":"Optimal Power Flow Based on Branch Flow Model for Bipolar DC Distribution Networks","authors":"Yiyao Zhou;Qianggang Wang;Xiaolong Xu;Tao Huang;Jianquan Liao;Yuan Chi;Xuefei Zhang;Niancheng Zhou","doi":"10.17775/CSEEJPES.2023.08530","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.08530","url":null,"abstract":"Optimal Power Flow (OPF) plays a crucial role in optimization and operation of the bipolar DC distribution network (Bi-DCDN). However, existing OPF models encounter difficulties in the power optimization of Bi-DCDNs due to the optimal power expressed as a product form, i.e., the product of voltage and current. Hence, this brief formulates the OPF problem of Bi-DCDNs using the branch flow model (BFM). The BFM employs power, instead of current, to account for the unique structure of Bi-DCDNs. Convex relaxation and linear approximation are sequentially applied to reformulate the BFM-based OPF, presenting it as a second-order cone programming (SOCP) problem. Further, the effectiveness of the proposed OPF model is verified in case studies. The numerical results demonstrate that the BFM-based OPF is a feasible and promising approach for Bi-DCDNs.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 2","pages":"944-948"},"PeriodicalIF":6.9,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10748589","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856205","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}
Pub Date : 2024-11-11DOI: 10.17775/CSEEJPES.2023.10060
Lin Yu;Shiyun Xu;Huadong Sun;Bing Zhao;Guanglu Wu;Xiaoxin Zhou
Inverter-based resources (IBRs), such as wind and photovoltaic generation, are characterized by low capacity and extensive distribution, which can exacerbate the weak properties of power systems. Precise identification of weak system status is essential for ensuring the security and economic efficiency of IBR integration. This paper proposes the index of the multiple renewable short-circuit ratio (MRSCR) and its critical value calculated by the voltage (CMRSCR) to provide a comprehensive assessment of power system strength in the presence of high IBR penetration, enhancing the accuracy and reliability of system strength evaluation. First, we introduce a single-infeed equivalent model of the power system integrating multiple IBRs. We examine the factors associated with system properties that are crucial in the strength assessment process. Subsequently, the MRSCR is derived from this analysis. The MRSCR describes the connection between system strength and voltage variation caused by power fluctuations. This implies that voltage variation caused by IBR power fluctuations is more pronounced under weak grid conditions. Following this, the CMRSCR is proposed to precisely evaluate the stability boundary. The disparity between MRSCR and CMRSCR is utilized to evaluate the stability margin of the power system. Unlike a fixed value, the CMRSCR exhibits higher sensitivity as the system approaches a critical state. These indexes have been implemented in the PSD power tools and power system analysis software package, facilitating engineering calculation and analysis of bulk power systems in China. Finally, simulation results validate the effectiveness of the proposed indexes and the research findings.
{"title":"Multiple Renewable Short-Circuit Ratio for Assessing Weak System Strength with Inverter-Based Resources","authors":"Lin Yu;Shiyun Xu;Huadong Sun;Bing Zhao;Guanglu Wu;Xiaoxin Zhou","doi":"10.17775/CSEEJPES.2023.10060","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.10060","url":null,"abstract":"Inverter-based resources (IBRs), such as wind and photovoltaic generation, are characterized by low capacity and extensive distribution, which can exacerbate the weak properties of power systems. Precise identification of weak system status is essential for ensuring the security and economic efficiency of IBR integration. This paper proposes the index of the multiple renewable short-circuit ratio (MRSCR) and its critical value calculated by the voltage (CMRSCR) to provide a comprehensive assessment of power system strength in the presence of high IBR penetration, enhancing the accuracy and reliability of system strength evaluation. First, we introduce a single-infeed equivalent model of the power system integrating multiple IBRs. We examine the factors associated with system properties that are crucial in the strength assessment process. Subsequently, the MRSCR is derived from this analysis. The MRSCR describes the connection between system strength and voltage variation caused by power fluctuations. This implies that voltage variation caused by IBR power fluctuations is more pronounced under weak grid conditions. Following this, the CMRSCR is proposed to precisely evaluate the stability boundary. The disparity between MRSCR and CMRSCR is utilized to evaluate the stability margin of the power system. Unlike a fixed value, the CMRSCR exhibits higher sensitivity as the system approaches a critical state. These indexes have been implemented in the PSD power tools and power system analysis software package, facilitating engineering calculation and analysis of bulk power systems in China. Finally, simulation results validate the effectiveness of the proposed indexes and the research findings.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 6","pages":"2271-2282"},"PeriodicalIF":6.9,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10748596","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859061","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}
Pub Date : 2024-11-11DOI: 10.17775/CSEEJPES.2023.01310
Tianyao Ji;Shiyu Wang;Luliang Zhang;Q. H. Wu
When disturbed, the interaction between power grid and wind farm may cause serious sub/super-synchronous oscillation (SSO), affecting the security and stability of the system. It is therefore important to detect the time-varying amplitude and frequency of SSO to provide information for its control. The matching synchroextracting wavelet transform (MSEWT) is a new method proposed in this paper to serve this purpose. Based on the original synchrosqueezing wavelet transform, MSEWT uses a synchronous extraction operator to calculate the time-frequency coefficients and a chirp-rate estimation to modify the instantaneous frequency estimation. Thus, MSEWT can improve the concentration degree and reconstruction accuracy of the signal's time-frequency representation without iterative calculation, and can achieve superior noise robustness. After the time-frequency analysis and modal decomposition of the SSO by MSEWT, the amplitudes and frequencies of each oscillation component can be obtained by Hilbert transform (HT). The simulation studies demonstrate that the proposed scheme can accurately identify the modal parameters of SSO even in the case of noise interference, providing a reliable reference for stable operation of power system time-frequency.
{"title":"Sub/Super-Synchronous Oscillation Detection Based on Matching Synchroextracting Wavelet Transform","authors":"Tianyao Ji;Shiyu Wang;Luliang Zhang;Q. H. Wu","doi":"10.17775/CSEEJPES.2023.01310","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.01310","url":null,"abstract":"When disturbed, the interaction between power grid and wind farm may cause serious sub/super-synchronous oscillation (SSO), affecting the security and stability of the system. It is therefore important to detect the time-varying amplitude and frequency of SSO to provide information for its control. The matching synchroextracting wavelet transform (MSEWT) is a new method proposed in this paper to serve this purpose. Based on the original synchrosqueezing wavelet transform, MSEWT uses a synchronous extraction operator to calculate the time-frequency coefficients and a chirp-rate estimation to modify the instantaneous frequency estimation. Thus, MSEWT can improve the concentration degree and reconstruction accuracy of the signal's time-frequency representation without iterative calculation, and can achieve superior noise robustness. After the time-frequency analysis and modal decomposition of the SSO by MSEWT, the amplitudes and frequencies of each oscillation component can be obtained by Hilbert transform (HT). The simulation studies demonstrate that the proposed scheme can accurately identify the modal parameters of SSO even in the case of noise interference, providing a reliable reference for stable operation of power system time-frequency.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 2","pages":"649-660"},"PeriodicalIF":6.9,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10748577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860827","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}
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