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}
The rapid growth of renewable energy sources, such as wind and solar power, together with global carbon neutrality targets, is driving the transformation of the energy system. To mitigate the intermittency inherent in renewable energy, the integration of energy storage systems has become imperative. In China, the expansion of electric vehicles (EVs) has positioned them as mobile energy storage units, with the stock of new energy vehicles (NEVs) reaching 31.4 million by 2024. While retired EV batteries retain 70% to 80% of their original capacity, they are suitable for second-life applications, such as grid peak shaving and distributed storage, offering both environmental and economic benefits. However, safety concerns persist, requiring accurate predictions of state of health (SOH) for safe operation and optimal utilization of these batteries. To address this challenge, this paper proposes an improved Transformer model, where discrete wavelet transform (DWT) is first employed to deal with the inherent noise during charge/discharge cycles. The serial Convolutional Neural Network (CNN) structure is utilized to mine local health factors and position information based on residual connections encoded into the Transformer network. The trend fusion module is added to improve the network integration capability. Evaluations using both public center for advanced life cycle engineering (CALCE) and experimental lifetime battery datasets B_X demonstrate the superiority and effectiveness of the DWT-CNN-Transformer model. It showcases faster convergence speed and higher optimization accuracy compared with other baseline approaches, significantly bolstering the precision and robustness of SOH predictions.
{"title":"SOH Prediction of Li-ion Batteries for Second-Life Applications in Renewable Energy Systems","authors":"Qingsong Wang;Annuo Yu;Hao Ding;Ming Cheng;Giuseppe Buja","doi":"10.17775/CSEEJPES.2024.02890","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2024.02890","url":null,"abstract":"The rapid growth of renewable energy sources, such as wind and solar power, together with global carbon neutrality targets, is driving the transformation of the energy system. To mitigate the intermittency inherent in renewable energy, the integration of energy storage systems has become imperative. In China, the expansion of electric vehicles (EVs) has positioned them as mobile energy storage units, with the stock of new energy vehicles (NEVs) reaching 31.4 million by 2024. While retired EV batteries retain 70% to 80% of their original capacity, they are suitable for second-life applications, such as grid peak shaving and distributed storage, offering both environmental and economic benefits. However, safety concerns persist, requiring accurate predictions of state of health (SOH) for safe operation and optimal utilization of these batteries. To address this challenge, this paper proposes an improved Transformer model, where discrete wavelet transform (DWT) is first employed to deal with the inherent noise during charge/discharge cycles. The serial Convolutional Neural Network (CNN) structure is utilized to mine local health factors and position information based on residual connections encoded into the Transformer network. The trend fusion module is added to improve the network integration capability. Evaluations using both public center for advanced life cycle engineering (CALCE) and experimental lifetime battery datasets B_X demonstrate the superiority and effectiveness of the DWT-CNN-Transformer model. It showcases faster convergence speed and higher optimization accuracy compared with other baseline approaches, significantly bolstering the precision and robustness of SOH predictions.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 6","pages":"3032-3042"},"PeriodicalIF":5.9,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10748584","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729371","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 : 2024-09-19DOI: 10.17775/CSEEJPES.2023.07130
Bin Deng;Xiaosheng Xu;Mengshi Li;Tianyao Ji;Q. H. Wu
Although integrated energy systems (IES) are currently modest in size, their scheduling faces strong challenges, stemming from both wind generation disturbances and the system's complexity, including intrinsic heterogeneity and pronounced non-linearity. For this reason, a two-stage algorithm called the Multi-Objective Group Search Optimizer with Pre-Exploration (MOGSOPE) is proposed to efficiently achieve the optimal solution under wind generation disturbances. The optimizer has an embedded trainable surrogate model, Deep Neural Networks (DNNs), to explore the common features of the multi-scenario search space in advance, guiding the population toward a more efficient search in each scenario. Furthermore, a multi-scenario Multi-Attribute Decision Making (MADM) approach is proposed to make the final decision from all alternatives in different wind scenarios. It reflects not only the decision-maker's (DM) interests in other indicators of IES but also their risk preference for wind generation disturbances. A case study conducted in Barry Island shows the superior convergence and diversity of MOGSOPE in comparison to other optimization algorithms. With respect to numerical performance metrics HV, IGD, and SI, the proposed optimizer exhibits improvements of 3.1036%, 4.8740%, and 4.2443% over MOGSO, and 4.2435%, 6.2479%, and 52.9230% over NSGAII, respectively. What's more, the effectiveness of the multi-scenario MADM in making final decisions under uncertainty is demonstrated, particularly in optimal scheduling of IES under wind generation disturbances.
虽然综合能源系统（IES）目前的规模不大，但由于风力发电的干扰和系统的复杂性，包括内在的异质性和明显的非线性，它们的调度面临着巨大的挑战。为此，提出了一种两阶段的多目标群搜索优化器预探索算法（multiobjective Group Search Optimizer with Pre-Exploration， MOGSOPE），以有效地实现风力发电扰动下的最优解。优化器具有嵌入式可训练代理模型深度神经网络（Deep Neural Networks, dnn），可以提前探索多场景搜索空间的共同特征，引导人群在每个场景中进行更有效的搜索。在此基础上，提出了一种多场景多属性决策（MADM）方法，对不同风场下的所有备选方案进行最终决策。它不仅反映了决策者对IES其他指标的兴趣，也反映了决策者对风力发电扰动的风险偏好。在巴里岛进行的实例研究表明，与其他优化算法相比，MOGSOPE具有更好的收敛性和多样性。在数值性能指标HV、IGD和SI方面，该优化器比MOGSO分别提高了3.1036%、4.8740%和4.2443%，比NSGAII分别提高了4.2435%、6.2479%和52.9230%。此外，还验证了多场景MADM在不确定条件下做出最终决策的有效性，特别是在风力发电干扰下IES最优调度的有效性。
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Pub Date : 2024-09-19DOI: 10.17775/CSEEJPES.2023.08300
Zhongjie Guo;Jiayu Bai;Wei Wei;Shengwei Mei;Weihao Hu
Multistage robust unit commitment (MRUC) is an important decision-making problem in power system operations. The affine policy facilitates problem-solving, but it compromises flexibility. This letter proposes a partially affine policy for MRU C problem with fast-ramping units; this policy imposes affine relations to coupling variables only and leaves the remaining variables to be optimized in the real-time dispatch. As a result, the real-time flexibility of fast-ramping units is retained. By adopting this approach, MRU C with a partially affine policy becomes a special two-stage adaptive robust optimization problem. Numerical tests verify that the proposed partially affine policy significantly reduces the conservativeness compared with affine policy, improving the dispatch economy and flexibility.
{"title":"Partially Affine Policy for Multistage Robust Unit Commitment with Fast-Ramping Units","authors":"Zhongjie Guo;Jiayu Bai;Wei Wei;Shengwei Mei;Weihao Hu","doi":"10.17775/CSEEJPES.2023.08300","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.08300","url":null,"abstract":"Multistage robust unit commitment (MRUC) is an important decision-making problem in power system operations. The affine policy facilitates problem-solving, but it compromises flexibility. This letter proposes a partially affine policy for MRU C problem with fast-ramping units; this policy imposes affine relations to coupling variables only and leaves the remaining variables to be optimized in the real-time dispatch. As a result, the real-time flexibility of fast-ramping units is retained. By adopting this approach, MRU C with a partially affine policy becomes a special two-stage adaptive robust optimization problem. Numerical tests verify that the proposed partially affine policy significantly reduces the conservativeness compared with affine policy, improving the dispatch economy and flexibility.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 1","pages":"477-480"},"PeriodicalIF":6.9,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684519","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403937","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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