Pub Date : 2026-10-01Epub Date: 2026-10-02DOI: 10.1109/TSTE.2026.3739559
{"title":"2026 Index IEEE Transactions on Sustainable Energy","authors":"","doi":"10.1109/TSTE.2026.3739559","DOIUrl":"https://doi.org/10.1109/TSTE.2026.3739559","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 4","pages":"4419-4501"},"PeriodicalIF":10.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11719433","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149130770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-07-01Epub Date: 2026-01-12DOI: 10.1109/TSTE.2026.3652790
Daan P. K. Truijen;Babak Mehdizadeh Gavgani;Thomas Neve;Kurt Stockman;Jeroen D. M. De Kooning
This paper presents a nonlinear model predictive control (MPC) architecture for low-head contra-rotating reversible pump-turbines (CR RPTs) in turbine mode. The developed MPC is compared with a baseline feedback controller in a frequency containment reserve (FCR) application. The MPC is designed to optimize the power response and efficiency online, with the former prioritized. A nonuniform time discretization and increasing weighting factors over the prediction horizon are implemented to reduce computational load and emphasize long-term optimality, respectively. Full reserve power step simulations on a 10 MW system demonstrate that the MPC reduces the rise and settling time by an average of 32% and 23%, respectively. The improved performance is achieved with minimal efficiency compromise, with the deficit becoming less than 0.1% within 8.5 s after reaching the setpoint. In FCR scenarios, the root mean square power tracking error is reduced by an average of 27% across various frequency datasets with a negligible reduction in efficiency. The MPC is experimentally validated on a reduced-scale hardware-in-the-loop setup, confirming the improved performance in a physical environment. The results show that the developed MPC enhances the dynamic power response of CR RPTs for grid support applications in low-head pumped hydropower storage systems.
{"title":"Online Optimization of Power Response and Efficiency of Contra-Rotating Turbines Using Nonlinear Model Predictive Control","authors":"Daan P. K. Truijen;Babak Mehdizadeh Gavgani;Thomas Neve;Kurt Stockman;Jeroen D. M. De Kooning","doi":"10.1109/TSTE.2026.3652790","DOIUrl":"https://doi.org/10.1109/TSTE.2026.3652790","url":null,"abstract":"This paper presents a nonlinear model predictive control (MPC) architecture for low-head contra-rotating reversible pump-turbines (CR RPTs) in turbine mode. The developed MPC is compared with a baseline feedback controller in a frequency containment reserve (FCR) application. The MPC is designed to optimize the power response and efficiency online, with the former prioritized. A nonuniform time discretization and increasing weighting factors over the prediction horizon are implemented to reduce computational load and emphasize long-term optimality, respectively. Full reserve power step simulations on a 10 MW system demonstrate that the MPC reduces the rise and settling time by an average of 32% and 23%, respectively. The improved performance is achieved with minimal efficiency compromise, with the deficit becoming less than 0.1% within 8.5 s after reaching the setpoint. In FCR scenarios, the root mean square power tracking error is reduced by an average of 27% across various frequency datasets with a negligible reduction in efficiency. The MPC is experimentally validated on a reduced-scale hardware-in-the-loop setup, confirming the improved performance in a physical environment. The results show that the developed MPC enhances the dynamic power response of CR RPTs for grid support applications in low-head pumped hydropower storage systems.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 3","pages":"2530-2542"},"PeriodicalIF":10.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148515612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-07-01Epub Date: 2025-12-17DOI: 10.1109/TSTE.2025.3645462
Lai Wei;Juan Wei;Sheng Huang;Canbing Li;Lin Zhu;Kerui Chen;Shuaifeng Wang;Luobin Wang
The heterogeneous wind farms (WFs) will face serious output frequency fluctuations, huge terminal voltage deviations and insufficient voltage support capacity during continuous fault ride-through (CFR) periods. To solve above issues, this article proposes a hierarchical optimal CFR control (HOCFR) scheme to improve the CFR capability for heterogeneous WFs. Considering the difference in CFR response between GFL and GFM WTs, a hierarchical CFR controller is designed to achieve optimal CRT performance by enhancing the CFR capability of GFL and GFM WTs. In the first-layer controller, the output power reference is optimized to suppresses the terminal voltage fluctuation of WTs, which is used as input to the second-layer controller. In the second-layer controller, the virtual impedance, virtual inertia, voltage loop integral parameters, and weak magnetic current (WMC) are regulated to suppress output frequency fluctuations and improve the voltage support capacity by fully utilizing the kinetic energy storage capability for GFM WTs. For GFL WTs, active power, reactive power (Var), and WMC are regulated optimized to minimizing the voltage deviation while fully utilizing the kinetic energy storage capability. The simulations in MATLAB prove the superiority of the proposed HOCFR method than other existing control schemes.
{"title":"Hierarchical Optimal Continuous Fault Ride-through Control Scheme for Heterogeneous Wind Farm With Grid-Forming and Grid-Following Wind Turbines","authors":"Lai Wei;Juan Wei;Sheng Huang;Canbing Li;Lin Zhu;Kerui Chen;Shuaifeng Wang;Luobin Wang","doi":"10.1109/TSTE.2025.3645462","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3645462","url":null,"abstract":"The heterogeneous wind farms (WFs) will face serious output frequency fluctuations, huge terminal voltage deviations and insufficient voltage support capacity during continuous fault ride-through (CFR) periods. To solve above issues, this article proposes a hierarchical optimal CFR control (HOCFR) scheme to improve the CFR capability for heterogeneous WFs. Considering the difference in CFR response between GFL and GFM WTs, a hierarchical CFR controller is designed to achieve optimal CRT performance by enhancing the CFR capability of GFL and GFM WTs. In the first-layer controller, the output power reference is optimized to suppresses the terminal voltage fluctuation of WTs, which is used as input to the second-layer controller. In the second-layer controller, the virtual impedance, virtual inertia, voltage loop integral parameters, and weak magnetic current (WMC) are regulated to suppress output frequency fluctuations and improve the voltage support capacity by fully utilizing the kinetic energy storage capability for GFM WTs. For GFL WTs, active power, reactive power (Var), and WMC are regulated optimized to minimizing the voltage deviation while fully utilizing the kinetic energy storage capability. The simulations in MATLAB prove the superiority of the proposed HOCFR method than other existing control schemes.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 3","pages":"2233-2248"},"PeriodicalIF":10.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148517185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-07-01Epub Date: 2026-02-09DOI: 10.1109/TSTE.2026.3662805
Aizuo Chen;Yue Zhu;Hang Zhao
Various control strategies have been proposed to enhance the transient stability of grid-following (GFL) inverters. However, most existing approaches are tailored to specific operating conditions and often lack theoretical guarantees, limiting their adaptability. Furthermore, these methods tend to complicate the controller design and parameter tuning. To address these limitations, this paper introduces a novel current control synchronization (CCS)-based control strategy for GFL inverters. The stability of the CCS-based control scheme is rigorously verified using the qualitative theory of differential equations and phase portrait analysis, establishing almost global asymptotic stability. Moreover, the controller parameters can be tuned with exceptional simplicity, greatly streamlining the design process. The performance of the CCS-based approach is validated through electromagnetic transient (EMT) simulations and controller hardware-in-the-loop (CHIL) experiments conducted on the StarSim platform. The results show that the proposed method matches phase-locked loop-based schemes in power tracking and frequency synchronization, while significantly outperforming them in transient stability. These findings further underscore the enhanced adaptability and practical feasibility of the proposed method.
{"title":"Almost Globally Stable GFL Inverter Control Scheme Based on Current Control Synchronization","authors":"Aizuo Chen;Yue Zhu;Hang Zhao","doi":"10.1109/TSTE.2026.3662805","DOIUrl":"https://doi.org/10.1109/TSTE.2026.3662805","url":null,"abstract":"Various control strategies have been proposed to enhance the transient stability of grid-following (GFL) inverters. However, most existing approaches are tailored to specific operating conditions and often lack theoretical guarantees, limiting their adaptability. Furthermore, these methods tend to complicate the controller design and parameter tuning. To address these limitations, this paper introduces a novel current control synchronization (CCS)-based control strategy for GFL inverters. The stability of the CCS-based control scheme is rigorously verified using the qualitative theory of differential equations and phase portrait analysis, establishing almost global asymptotic stability. Moreover, the controller parameters can be tuned with exceptional simplicity, greatly streamlining the design process. The performance of the CCS-based approach is validated through electromagnetic transient (EMT) simulations and controller hardware-in-the-loop (CHIL) experiments conducted on the StarSim platform. The results show that the proposed method matches phase-locked loop-based schemes in power tracking and frequency synchronization, while significantly outperforming them in transient stability. These findings further underscore the enhanced adaptability and practical feasibility of the proposed method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 3","pages":"2926-2938"},"PeriodicalIF":10.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148519318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In integrated community energy systems (ICESs), the efficiency parameters of energy conversion devices are influenced not only by current operating conditions (e.g., device load rate, temperature, and atmospheric pressure) but also by the dynamic characteristics of historical operating conditions. Neglecting the nonlinear dynamic relationship between the efficiency parameters and time series operating conditions can notably reduce the prediction accuracy for the efficiency parameters, thereby disrupting the energy supply-demand balance and reducing the economic performance of the ICES. To address this issue, a model predictive control (MPC) method is proposed for an ICES based on a gated recurrent unit (GRU)-driven dynamic energy hub (G-DEH). First, a G-DEH model is developed to capture the time series correlations in the operating conditions of the ICES, thereby accurately modeling the nonlinear and time-varying relationships between the efficiency parameters and these conditions. On this basis, an optimal scheduling model for the ICES, focused on enhancing the economic performance, is developed via the G-DEH model. The model is subjected to iterative rolling calibration through the MPC strategy, dynamically adjusting the degree of matching between the operating conditions and efficiency parameters, thereby enhancing the adaptability of the scheduling model to complex and dynamic operating conditions. The simulation results from a case study demonstrate that the proposed method significantly outperforms traditional methods in terms of the prediction accuracy for the efficiency parameters and the economic performance of the ICES. This confirms the significant value of the method in reducing operating costs and enhancing dynamic adaptability.
{"title":"Model Predictive Control Method for an Integrated Community Energy System Based on a GRU-Driven Dynamic Energy Hub","authors":"Yanze Xu;Yunfei Mu;Hongjie Jia;Jiarui Zhang;Xiaolong Jin;Zeqing Wu","doi":"10.1109/TSTE.2026.3651984","DOIUrl":"https://doi.org/10.1109/TSTE.2026.3651984","url":null,"abstract":"In integrated community energy systems (ICESs), the efficiency parameters of energy conversion devices are influenced not only by current operating conditions (e.g., device load rate, temperature, and atmospheric pressure) but also by the dynamic characteristics of historical operating conditions. Neglecting the nonlinear dynamic relationship between the efficiency parameters and time series operating conditions can notably reduce the prediction accuracy for the efficiency parameters, thereby disrupting the energy supply-demand balance and reducing the economic performance of the ICES. To address this issue, a model predictive control (MPC) method is proposed for an ICES based on a gated recurrent unit (GRU)-driven dynamic energy hub (G-DEH). First, a G-DEH model is developed to capture the time series correlations in the operating conditions of the ICES, thereby accurately modeling the nonlinear and time-varying relationships between the efficiency parameters and these conditions. On this basis, an optimal scheduling model for the ICES, focused on enhancing the economic performance, is developed via the G-DEH model. The model is subjected to iterative rolling calibration through the MPC strategy, dynamically adjusting the degree of matching between the operating conditions and efficiency parameters, thereby enhancing the adaptability of the scheduling model to complex and dynamic operating conditions. The simulation results from a case study demonstrate that the proposed method significantly outperforms traditional methods in terms of the prediction accuracy for the efficiency parameters and the economic performance of the ICES. This confirms the significant value of the method in reducing operating costs and enhancing dynamic adaptability.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 3","pages":"2460-2471"},"PeriodicalIF":10.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148519393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-07-01Epub Date: 2025-12-17DOI: 10.1109/TSTE.2025.3645280
Haotian Ma;Wanxing Sheng;Siyi Wang;Qing Duan
Most distributed photovoltaics (PVs) are installed behind-the-meter (BTM) and measured together with user loads, making the specific PV generation and native load invisible. Existing methods typically learn patterns from detailed data collected at fully observable users, modeling PV generation and native load separately, and then developing net load decomposition algorithms. However, these algorithms require substantial amounts of data, and their accuracy decreases when the total number of users is insufficient or when the observation rate is low. To this end, a novel Bayesian Transfer Learning (BTL) based PV-load decomposition strategy for the BTM system is proposed in this paper. First, feature extraction and clustering are performed on the net load nighttime sequence data based on their characteristics, while a data augmentation algorithm is used to expand the user dataset. Second, a two-layer Bayesian network model is designed, where the outer layer learns common features from all user data, and a parameter transfer strategy is employed to transfer the posterior distribution to the inner layer for learning the individual characteristics of data within each cluster. Finally, the trained BTL model is used to separate the PV generation and native load from the unobservable user net load. The effectiveness of the proposed method is validated across datasets from various user cases.
{"title":"A Bayesian Transfer Learning-Based PV-Load Decomposition Method for Behind-The-Meter Systems Utilizing Small User Sample Datasets","authors":"Haotian Ma;Wanxing Sheng;Siyi Wang;Qing Duan","doi":"10.1109/TSTE.2025.3645280","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3645280","url":null,"abstract":"Most distributed photovoltaics (PVs) are installed behind-the-meter (BTM) and measured together with user loads, making the specific PV generation and native load invisible. Existing methods typically learn patterns from detailed data collected at fully observable users, modeling PV generation and native load separately, and then developing net load decomposition algorithms. However, these algorithms require substantial amounts of data, and their accuracy decreases when the total number of users is insufficient or when the observation rate is low. To this end, a novel Bayesian Transfer Learning (BTL) based PV-load decomposition strategy for the BTM system is proposed in this paper. First, feature extraction and clustering are performed on the net load nighttime sequence data based on their characteristics, while a data augmentation algorithm is used to expand the user dataset. Second, a two-layer Bayesian network model is designed, where the outer layer learns common features from all user data, and a parameter transfer strategy is employed to transfer the posterior distribution to the inner layer for learning the individual characteristics of data within each cluster. Finally, the trained BTL model is used to separate the PV generation and native load from the unobservable user net load. The effectiveness of the proposed method is validated across datasets from various user cases.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 3","pages":"2206-2218"},"PeriodicalIF":10.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148519415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-07-01Epub Date: 2026-02-17DOI: 10.1109/TSTE.2026.3665816
Wen Gao;Kaishun Xiahou;Yang Liu;Zhigang Li;Chuanyue Li;Jun Liang;Q. H. Wu
To address the challenges presented by the complex nonlinear dynamics and limited disturbance resilience of offshore wind farms (OWFs) integrated via voltage source converter based multi-terminal high voltage direct current (VSC-MTDC) system under large disturbances, an improved reachability analysis method is proposed to capture dynamic behaviors of the system. The analysis framework encompasses: 1) high-dimensional nonlinear differential and hybrid models of VSC-MTDC integrated OWFs system are constructed based on Kron reduction method to enhance computational efficiency and mitigate the limitations imposed by algebraic constraints; 2) an adaptive time-step optimization algorithm is implemented to further improve computational efficiency while enhancing the capability to capture both transient and steady-state dynamics of hybrid system; 3) the improved reachability algorithm is designed to quantitatively analyze the operational states of VSC-MTDC integrated OWFs system under uncertainties and varying operating conditions. Case studies on VSC-MTDC integrated OWFs system are conducted to demonstrate the effectiveness of the proposed method in revealing complex nonlinear characteristics and safety verification under large disturbances.
{"title":"Improved Reachability Analysis for VSC-MTDC Integrated Offshore Wind Farms Based on Adaptive Parameter Optimization","authors":"Wen Gao;Kaishun Xiahou;Yang Liu;Zhigang Li;Chuanyue Li;Jun Liang;Q. H. Wu","doi":"10.1109/TSTE.2026.3665816","DOIUrl":"https://doi.org/10.1109/TSTE.2026.3665816","url":null,"abstract":"To address the challenges presented by the complex nonlinear dynamics and limited disturbance resilience of offshore wind farms (OWFs) integrated via voltage source converter based multi-terminal high voltage direct current (VSC-MTDC) system under large disturbances, an improved reachability analysis method is proposed to capture dynamic behaviors of the system. The analysis framework encompasses: 1) high-dimensional nonlinear differential and hybrid models of VSC-MTDC integrated OWFs system are constructed based on Kron reduction method to enhance computational efficiency and mitigate the limitations imposed by algebraic constraints; 2) an adaptive time-step optimization algorithm is implemented to further improve computational efficiency while enhancing the capability to capture both transient and steady-state dynamics of hybrid system; 3) the improved reachability algorithm is designed to quantitatively analyze the operational states of VSC-MTDC integrated OWFs system under uncertainties and varying operating conditions. Case studies on VSC-MTDC integrated OWFs system are conducted to demonstrate the effectiveness of the proposed method in revealing complex nonlinear characteristics and safety verification under large disturbances.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 3","pages":"3124-3137"},"PeriodicalIF":10.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The coordinated operation of the full-size converter-based variable-speed pumped storage unit (FSC-VSPSU) and renewable energy station can help smooth the power fluctuations and improve the renewable energy utilization. However, this combined system may encounter instability problem such as medium-frequency oscillation in practical application. The impedance-based analysis method is commonly used for addressing such instability problems. Nevertheless, existing research lacks an impedance model of FSC-VSPSU and does not fully conduct the stability analysis of the system. Hence, this paper develops the impedance model for FSC-VSPSU. By integrating it with the impedance model of renewable energy units, an equivalent impedance model of the entire system is derived. The system stability is then analyzed using the impedance-based analysis method. To address the instability issues, a suppression method based on a single feedback parameter k is proposed for FSC-VSPSU. This method reshapes the equivalent impedance with minimal complexity, eliminating the need for filters that typically degrade dynamic response performance in existing solutions. Simulation and experimental results are presented to validate the effectiveness of the established impedance model and the proposed control strategy.
{"title":"Medium-Frequency Oscillation Analysis and Single-Parameter Feedback Suppression for Full-Size Converter-Based Variable-Speed Pumped Storage and Renewable Energy Combined System","authors":"Qi Zhang;Junliang Liu;Xiaoming Zou;Chengmao Du;Xiong Du;Bing Chen","doi":"10.1109/TSTE.2026.3665057","DOIUrl":"https://doi.org/10.1109/TSTE.2026.3665057","url":null,"abstract":"The coordinated operation of the full-size converter-based variable-speed pumped storage unit (FSC-VSPSU) and renewable energy station can help smooth the power fluctuations and improve the renewable energy utilization. However, this combined system may encounter instability problem such as medium-frequency oscillation in practical application. The impedance-based analysis method is commonly used for addressing such instability problems. Nevertheless, existing research lacks an impedance model of FSC-VSPSU and does not fully conduct the stability analysis of the system. Hence, this paper develops the impedance model for FSC-VSPSU. By integrating it with the impedance model of renewable energy units, an equivalent impedance model of the entire system is derived. The system stability is then analyzed using the impedance-based analysis method. To address the instability issues, a suppression method based on a single feedback parameter <italic>k</i> is proposed for FSC-VSPSU. This method reshapes the equivalent impedance with minimal complexity, eliminating the need for filters that typically degrade dynamic response performance in existing solutions. Simulation and experimental results are presented to validate the effectiveness of the established impedance model and the proposed control strategy.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 3","pages":"3048-3063"},"PeriodicalIF":10.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The oscillation risk assessment and oscillation source identification pose significant challenges for heterogeneous multi-converter systems such as hybrid grid-forming and grid-following renewable energy station (GFM/GFL-RES). Furthermore, the theoretical impact of GFM converter capacity and placement on the voltage support strength and resonant modes of each bus remains unexplored. To fill this gap, this paper proposes a stability assessment and weakness location method for hybrid GFM/GFL multi-converter systems. Firstly, a power flow method incorporating the reactive power support capability of the GFM converter and the multiple renewable short-circuit ratio is used to evaluate the voltage support strength at each bus. Then, a s-domain modal analysis method incorporating frequency-coupling is proposed. This method accurately extracts system resonant modes by combining modal impedance curves and a center-difference Newton-Raphson iterative algorithm. Finally, the steady-state stability region is established by integrating steady-state voltage stability and small-signal stability. The method in this paper is validated through a 16-converter 34-bus GFM/GFL-RES, and the influence of GFM capacity and placement on the steady-state stability region of the system is quantified. The accuracy of the proposed stability analysis method is verified by time-domain simulation.
{"title":"Voltage Deviation and Wideband Oscillation Constrained Stability and Weakness Analysis of Hybrid GFM/GFL Multi-Converter Systems","authors":"Xuyang Li;Zili Wang;Yandong Chen;Zhiwei Xie;Cong Luo;Boning Sun;Huijie Yu","doi":"10.1109/TSTE.2026.3665524","DOIUrl":"https://doi.org/10.1109/TSTE.2026.3665524","url":null,"abstract":"The oscillation risk assessment and oscillation source identification pose significant challenges for heterogeneous multi-converter systems such as hybrid grid-forming and grid-following renewable energy station (GFM/GFL-RES). Furthermore, the theoretical impact of GFM converter capacity and placement on the voltage support strength and resonant modes of each bus remains unexplored. To fill this gap, this paper proposes a stability assessment and weakness location method for hybrid GFM/GFL multi-converter systems. Firstly, a power flow method incorporating the reactive power support capability of the GFM converter and the multiple renewable short-circuit ratio is used to evaluate the voltage support strength at each bus. Then, a <italic>s</i>-domain modal analysis method incorporating frequency-coupling is proposed. This method accurately extracts system resonant modes by combining modal impedance curves and a center-difference Newton-Raphson iterative algorithm. Finally, the steady-state stability region is established by integrating steady-state voltage stability and small-signal stability. The method in this paper is validated through a 16-converter 34-bus GFM/GFL-RES, and the influence of GFM capacity and placement on the steady-state stability region of the system is quantified. The accuracy of the proposed stability analysis method is verified by time-domain simulation.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 3","pages":"3094-3107"},"PeriodicalIF":10.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-07-01Epub Date: 2026-03-12DOI: 10.1109/TSTE.2026.3673563
Zhiqiang He;Can Wan;Kaiming Zhang;Ping Ju
With massive integration of photovoltaic (PV) generation in power systems, multivariate probabilistic forecasting (MPF) is essential for analysis and decision-making of power systems, which provides comprehensive information about uncertainty of future PV generation. Traditional MPF methods separate probabilistic forecasting and dependency structure estimation, limiting their ability to generate multivariate distributions in an analytical form. This paper innovatively proposes a hybrid score-driven MPF method to directly generate semi-parametric joint predictive density of correlated PV generation using deep mixture density network. Firstly, an integrated framework of MPF based on multivariate Gaussian mixture models is established to unify the training of probabilistic forecasting and the optimization of conditional correlation modeling. Then, a novel hybrid score-based network loss function with the closed-form expression is meticulously devised for the robust training of MPF. In addition, graph structure learning is incorporated to adaptively capture spatial dependency among correlated PV generation. To address overwhelming parameters of joint distributions, a parameter-reduced structure of covariance matrices using incomplete Cholesky decomposition is formulated, which enhances the tractability of MPF in high-dimensional settings. Comprehensive case studies on three realistic PV power datasets are conducted to validate the superior performance of the proposed method for day-ahead multivariate probabilistic forecasting.
{"title":"Hybrid Score-Driven Multivariate Probabilistic Forecasting of Correlated PV Generation Using Deep Mixture Density Network","authors":"Zhiqiang He;Can Wan;Kaiming Zhang;Ping Ju","doi":"10.1109/TSTE.2026.3673563","DOIUrl":"https://doi.org/10.1109/TSTE.2026.3673563","url":null,"abstract":"With massive integration of photovoltaic (PV) generation in power systems, multivariate probabilistic forecasting (MPF) is essential for analysis and decision-making of power systems, which provides comprehensive information about uncertainty of future PV generation. Traditional MPF methods separate probabilistic forecasting and dependency structure estimation, limiting their ability to generate multivariate distributions in an analytical form. This paper innovatively proposes a hybrid score-driven MPF method to directly generate semi-parametric joint predictive density of correlated PV generation using deep mixture density network. Firstly, an integrated framework of MPF based on multivariate Gaussian mixture models is established to unify the training of probabilistic forecasting and the optimization of conditional correlation modeling. Then, a novel hybrid score-based network loss function with the closed-form expression is meticulously devised for the robust training of MPF. In addition, graph structure learning is incorporated to adaptively capture spatial dependency among correlated PV generation. To address overwhelming parameters of joint distributions, a parameter-reduced structure of covariance matrices using incomplete Cholesky decomposition is formulated, which enhances the tractability of MPF in high-dimensional settings. Comprehensive case studies on three realistic PV power datasets are conducted to validate the superior performance of the proposed method for day-ahead multivariate probabilistic forecasting.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 3","pages":"3187-3200"},"PeriodicalIF":10.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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