Pub Date : 2025-03-21DOI: 10.1109/TSTE.2025.3553209
{"title":"Share Your Preprint Research with the World!","authors":"","doi":"10.1109/TSTE.2025.3553209","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3553209","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1487-1487"},"PeriodicalIF":8.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10936646","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667744","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 : 2025-03-21DOI: 10.1109/TSTE.2025.3547400
{"title":"IEEE Transactions on Sustainable Energy Publication Information","authors":"","doi":"10.1109/TSTE.2025.3547400","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3547400","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"C2-C2"},"PeriodicalIF":8.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937137","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675954","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 : 2025-03-12DOI: 10.1109/TSTE.2025.3548931
Zechuan Lin;Xuanrui Huang;Yifei Han;Xi Xiao;John V. Ringwood
Centralized control of wave energy converter (WEC) arrays for grid-scale generation can achieve higher energy production than decentralized (independent) control, due to its capability of fully exploiting mutual radiation effects. However, the state-of-the-art centralized model predictive control (CMPC) is significantly more computationally challenging than decentralized MPC (DMPC), since the number of control moves to be optimized grows in proportion to the number of WECs. In this paper, a fast CMPC controller is proposed, whose idea is to optimize only the first few control moves while rolling out future system trajectories using a fixed controller. A linear, two-degree-of-freedom (2-DoF) controller with a sea-state-dependent control coefficient tuning strategy is further proposed to serve as the rollout controller. It is shown that the proposed rollout-based CMPC (R-CMPC) can maintain almost the same energy production as conventional CMPC under a wide range of sea states, while significantly reducing the optimization dimension (in the studied case, by a factor of 6), enabling ultra-fast online computation (about 40 times faster than conventional CMPC).
{"title":"Fast Centralized Model Predictive Control for Wave Energy Converter Arrays Based on Rollout","authors":"Zechuan Lin;Xuanrui Huang;Yifei Han;Xi Xiao;John V. Ringwood","doi":"10.1109/TSTE.2025.3548931","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3548931","url":null,"abstract":"Centralized control of wave energy converter (WEC) arrays for grid-scale generation can achieve higher energy production than decentralized (independent) control, due to its capability of fully exploiting mutual radiation effects. However, the state-of-the-art centralized model predictive control (CMPC) is significantly more computationally challenging than decentralized MPC (DMPC), since the number of control moves to be optimized grows in proportion to the number of WECs. In this paper, a fast CMPC controller is proposed, whose idea is to optimize only the first few control moves while rolling out future system trajectories using a fixed controller. A linear, two-degree-of-freedom (2-DoF) controller with a sea-state-dependent control coefficient tuning strategy is further proposed to serve as the rollout controller. It is shown that the proposed rollout-based CMPC (R-CMPC) can maintain almost the same energy production as conventional CMPC under a wide range of sea states, while significantly reducing the optimization dimension (in the studied case, by a factor of 6), enabling ultra-fast online computation (about 40 times faster than conventional CMPC).","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2224-2235"},"PeriodicalIF":8.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330320","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 : 2025-03-12DOI: 10.1109/TSTE.2025.3546294
Yunda Xu;Ruifeng Yan;Tapan Kumar Saha
As renewable energy integration increases, ensuring stability of Inverter-Based Resources (IBRs) in weak grids is crucial, as grid-following (GFL) converters often become unstable under such conditions. Integrating virtual synchronous generator (VSG) batteries has shown potential to improve GFL stability, but determining the optimal size of the VSG required for stability remains an open question. Existing research typically relies on small-signal or impedance models for stability analysis, which are only valid at a single operating point and do not consider the full range of operating conditions, including various dispatch scenarios and grid strengths. This paper addresses this gap by proposing a novel methodology to visualize the system's stable operating region, offering insights into stability boundaries across various real power and grid impedance variations. Additionally, it introduces an optimal VSG battery sizing strategy that accounts for these variations, ensuring stability while minimizing VSG capacity. The strategy's effectiveness is validated through comprehensive PSCAD simulations, demonstrating its reliability across a wide range of real power and grid impedance operating points.
{"title":"Optimal VSG BESS Sizing for Improving Grid-Following Converter Stability Under Various Dispatch Scenarios and Grid Strengths","authors":"Yunda Xu;Ruifeng Yan;Tapan Kumar Saha","doi":"10.1109/TSTE.2025.3546294","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3546294","url":null,"abstract":"As renewable energy integration increases, ensuring stability of Inverter-Based Resources (IBRs) in weak grids is crucial, as grid-following (GFL) converters often become unstable under such conditions. Integrating virtual synchronous generator (VSG) batteries has shown potential to improve GFL stability, but determining the optimal size of the VSG required for stability remains an open question. Existing research typically relies on small-signal or impedance models for stability analysis, which are only valid at a single operating point and do not consider the full range of operating conditions, including various dispatch scenarios and grid strengths. This paper addresses this gap by proposing a novel methodology to visualize the system's stable operating region, offering insights into stability boundaries across various real power and grid impedance variations. Additionally, it introduces an optimal VSG battery sizing strategy that accounts for these variations, ensuring stability while minimizing VSG capacity. The strategy's effectiveness is validated through comprehensive PSCAD simulations, demonstrating its reliability across a wide range of real power and grid impedance operating points.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2210-2223"},"PeriodicalIF":8.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331633","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 : 2025-03-12DOI: 10.1109/TSTE.2025.3550563
Ahmed Shaban Omar;Ramadan El-Shatshat
This paper proposes a hybrid mixed-integer quadratic programming-constrained deep reinforcement learning (MIQP-CDRL) framework for energy management of multi-energy communities. The framework employs a hierarchical two-layer structure: the MIQP layer handles day-ahead scheduling, minimizing operational costs while ensuring system constraint satisfaction, while the CDRL agent makes real-time adjustments. The goal of this framework is to combine the strengths of CDRL in addressing sequential decision-making problems in stochastic systems with the advantages of a mathematical programming model to guide the agent's exploration during the training and reduce the dependency on opaque policies during real-time operation. The system dynamics are modeled as a constrained Markov decision process (CMDP), which is solved by a model-free CDRL agent built upon the constrained policy optimization (CPO) algorithm. Practical test results demonstrate the effectiveness of this framework in improving the optimality and feasibility of the real-time solutions compared to existing stand-alone DRL approaches.
{"title":"Energy Management of Multi-Energy Communities: A Hierarchical MIQP-Constrained Deep Reinforcement Learning Approach","authors":"Ahmed Shaban Omar;Ramadan El-Shatshat","doi":"10.1109/TSTE.2025.3550563","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3550563","url":null,"abstract":"This paper proposes a hybrid mixed-integer quadratic programming-constrained deep reinforcement learning (MIQP-CDRL) framework for energy management of multi-energy communities. The framework employs a hierarchical two-layer structure: the MIQP layer handles day-ahead scheduling, minimizing operational costs while ensuring system constraint satisfaction, while the CDRL agent makes real-time adjustments. The goal of this framework is to combine the strengths of CDRL in addressing sequential decision-making problems in stochastic systems with the advantages of a mathematical programming model to guide the agent's exploration during the training and reduce the dependency on opaque policies during real-time operation. The system dynamics are modeled as a constrained Markov decision process (CMDP), which is solved by a model-free CDRL agent built upon the constrained policy optimization (CPO) algorithm. Practical test results demonstrate the effectiveness of this framework in improving the optimality and feasibility of the real-time solutions compared to existing stand-alone DRL approaches.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2236-2250"},"PeriodicalIF":8.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331734","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 : 2025-03-07DOI: 10.1109/TSTE.2025.3549225
Ziwen Gu;Yatao Shen;Zijian Wang;Yaqun Jiang;Chun Huang;Peng Li
Accurate photovoltaic power (PVP) prediction is a prerequisite for the efficient and stable operation of new power systems. While existing research has extensively explored the relationship between global factors such as temperature, irradiance, and photovoltaic power, the local dynamic impacts of these factors are often overlooked, which may reduce the accuracy of predictions. To address this issue, this paper considers the dynamic interrelationships among multiple factors and proposes a dynamic locally featured embedding-based broad learning system (DLFE-BLS) algorithm for PVP prediction. Firstly, a novel dynamic phase space reconstruction method (DPSR) is proposed to characterize the dynamic properties of multivariate data. Furthermore, a dynamic local featured embedding (DLFE) algorithm is introduced to extract local dynamic features from multivariate data. Finally, by integrating the dynamic reconstruction and dynamic feature extraction processes into the broad learning system (BLS) framework, we propose the DLFE-BLS algorithm to improve the accuracy of PVP prediction. Case studies have shown that DLFE-BLS outperforms other models in terms of prediction accuracy. Additionally, it has the highest accuracy when applied to transfer prediction.
{"title":"Photovoltaic Power Prediction Considering Multifactorial Dynamic Effects: A Dynamic Locally Featured Embedding-Based Broad Learning System","authors":"Ziwen Gu;Yatao Shen;Zijian Wang;Yaqun Jiang;Chun Huang;Peng Li","doi":"10.1109/TSTE.2025.3549225","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3549225","url":null,"abstract":"Accurate photovoltaic power (PVP) prediction is a prerequisite for the efficient and stable operation of new power systems. While existing research has extensively explored the relationship between global factors such as temperature, irradiance, and photovoltaic power, the local dynamic impacts of these factors are often overlooked, which may reduce the accuracy of predictions. To address this issue, this paper considers the dynamic interrelationships among multiple factors and proposes a dynamic locally featured embedding-based broad learning system (DLFE-BLS) algorithm for PVP prediction. Firstly, a novel dynamic phase space reconstruction method (DPSR) is proposed to characterize the dynamic properties of multivariate data. Furthermore, a dynamic local featured embedding (DLFE) algorithm is introduced to extract local dynamic features from multivariate data. Finally, by integrating the dynamic reconstruction and dynamic feature extraction processes into the broad learning system (BLS) framework, we propose the DLFE-BLS algorithm to improve the accuracy of PVP prediction. Case studies have shown that DLFE-BLS outperforms other models in terms of prediction accuracy. Additionally, it has the highest accuracy when applied to transfer prediction.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2197-2209"},"PeriodicalIF":8.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331780","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 : 2025-03-06DOI: 10.1109/TSTE.2025.3547919
Yuzhen Tang;Qian Xun;Zhuoqun Zheng;Fanqi Min;Chengwei Deng;Jingying Xie;Hengzhao Yang
This paper proposes an optimization framework to address the component sizing and energy management problems in an electric-hydrogen hybrid energy storage system connected to a wind turbine. The total cost of the hybrid system is minimized using a particle swarm optimization (PSO) algorithm. In particular, four decision variables are optimized: the electrolyzer (EL) size, the supercapacitor (SC) size, and two parameters in the energy management strategy (EMS). To determine the power split factor for the wind power, the EMS introduces an artificial potential field (APF) and defines a virtual force based on the SC state of charge (SOC). Two APF parameters are optimized to tune the power allocation between the EL and the SC: the shaping parameter of the virtual force and the basis parameter of the power split factor. Since the cutoff frequency of the low pass filter (LPF) in the EMS is adaptively updated based on the optimized APF parameters, the proposed framework is referred to as the “OP-APF” framework. The effectiveness of the OP-APF framework is validated by performing MATLAB and real-time simulations. Compared to three baseline frameworks, OP-APF is more effective in reducing the system total cost, controlling the SC SOC, and alleviating the EL degradation.
{"title":"An Optimization Framework for Component Sizing and Energy Management in Electric-Hydrogen Hybrid Energy Storage Systems","authors":"Yuzhen Tang;Qian Xun;Zhuoqun Zheng;Fanqi Min;Chengwei Deng;Jingying Xie;Hengzhao Yang","doi":"10.1109/TSTE.2025.3547919","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3547919","url":null,"abstract":"This paper proposes an optimization framework to address the component sizing and energy management problems in an electric-hydrogen hybrid energy storage system connected to a wind turbine. The total cost of the hybrid system is minimized using a particle swarm optimization (PSO) algorithm. In particular, four decision variables are optimized: the electrolyzer (EL) size, the supercapacitor (SC) size, and two parameters in the energy management strategy (EMS). To determine the power split factor for the wind power, the EMS introduces an artificial potential field (APF) and defines a virtual force based on the SC state of charge (SOC). Two APF parameters are optimized to tune the power allocation between the EL and the SC: the shaping parameter of the virtual force and the basis parameter of the power split factor. Since the cutoff frequency of the low pass filter (LPF) in the EMS is adaptively updated based on the optimized APF parameters, the proposed framework is referred to as the “OP-APF” framework. The effectiveness of the OP-APF framework is validated by performing MATLAB and real-time simulations. Compared to three baseline frameworks, OP-APF is more effective in reducing the system total cost, controlling the SC SOC, and alleviating the EL degradation.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2182-2196"},"PeriodicalIF":8.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331730","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 : 2025-03-05DOI: 10.1109/TSTE.2025.3548435
Si Lv;Sheng Chen;Tengfei Zhang;Chen Chen;Junjun Xu;Zhinong Wei
Accurately estimating spatial-temporal electric vehicles' (EVs) charging demands is crucial for the secure and economic operation of power systems. At present, the distribution system operator (DSO) relies on historical data collected at each charging station to estimate future EV charging demand. However, the station-level forecast disregards EVs' spatial correlations within traffic networks (TNs) and might suffer significant forecast error, forcing the DSO to make conservative scheduling at the expense of operation economics. To this end, this paper proposes to leverage cross-sector information (i.e., traffic demand data and network parameters in TNs) to enhance forecast accuracy and avoid over-conservative operations. To facilitate the data sharing among the DSO and TN data holders (i.e., traffic authority and navigation App. companies), we adopt the Coalition Game theory to uncover how these entities could cooperate to benefit each other, and to fairly allocate the extra profits (i.e., the operational cost reduction induced by the improved forecasts) among themselves. The conditional value-at-risk theory is adopted to model the risk-averse behavior of the DSO. In case studies, we reveal the non-negligible impact of TN condition variations on EV charging distributions. Moreover, numerical results show that sharing high-quality traffic data contributes to the reduction in DSO's operating cost by utmost 20.8% as compared to the current practice without data sharing.
{"title":"Promote Data Sharing in Integrated Power-Traffic Networks: A Coalition Game Approach","authors":"Si Lv;Sheng Chen;Tengfei Zhang;Chen Chen;Junjun Xu;Zhinong Wei","doi":"10.1109/TSTE.2025.3548435","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3548435","url":null,"abstract":"Accurately estimating spatial-temporal electric vehicles' (EVs) charging demands is crucial for the secure and economic operation of power systems. At present, the distribution system operator (DSO) relies on historical data collected at each charging station to estimate future EV charging demand. However, the station-level forecast disregards EVs' spatial correlations within traffic networks (TNs) and might suffer significant forecast error, forcing the DSO to make conservative scheduling at the expense of operation economics. To this end, this paper proposes to leverage cross-sector information (i.e., traffic demand data and network parameters in TNs) to enhance forecast accuracy and avoid over-conservative operations. To facilitate the data sharing among the DSO and TN data holders (i.e., traffic authority and navigation App. companies), we adopt the Coalition Game theory to uncover how these entities could cooperate to benefit each other, and to fairly allocate the extra profits (i.e., the operational cost reduction induced by the improved forecasts) among themselves. The conditional value-at-risk theory is adopted to model the risk-averse behavior of the DSO. In case studies, we reveal the non-negligible impact of TN condition variations on EV charging distributions. Moreover, numerical results show that sharing high-quality traffic data contributes to the reduction in DSO's operating cost by utmost 20.8% as compared to the current practice without data sharing.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2171-2181"},"PeriodicalIF":8.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331704","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}
Maintaining the export power of wind-hydrogen systems within a stable range is critical for power system security. However, this is challenged by the mismatch between large time-scale of alkaline electrolyzer (AWE) scheduling strategies and the short-term fluctuations of wind power. To address this issue, this paper proposes a novel minute-level optimization strategy for AWE operation. Developing effective small time-scale strategies requires a detailed consideration of AWE dynamics. To this end, we first introduce its steady-state electrochemical characteristics and third-order dynamic models for both temperature and Hydrogen-to-Oxygen (HTO) ratio. Based on these refined models, we develop an AWE optimization framework that enables electrolysis power to track minute-level wind power fluctuations by dynamically adjusting fine-grained variables, such as the lye flow rate, cooling flow rate, and pressure, at 1-minute intervals. To overcome the computational challenges posed by the detailed modeling, we propose an improved model predictive control (MPC) framework. This framework incorporates model simplifications to improve computational efficiency, along with an optimization-simulation iterative procedure to ensure operational feasibility. Case studies demonstrate that the proposed strategy extends the AWE load range by 13.8% and reduces wind power curtailment by 15.06%. Additionally, synergies among control variables enable the system to achieve a balance between operational efficiency, stability, and security, highlighting the potential of this approach to enhance the performance of wind-hydrogen integrated systems.
{"title":"A Dynamic Model-Based Minute-Level Optimal Operation Strategy for Alkaline Electrolyzers in Wind-Hydrogen Systems","authors":"Aobo Guan;Suyang Zhou;Wei Gu;Zhi Wu;Xiaomeng Ai;Jiakun Fang;Xiao-ping Zhang","doi":"10.1109/TSTE.2025.3548052","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3548052","url":null,"abstract":"Maintaining the export power of wind-hydrogen systems within a stable range is critical for power system security. However, this is challenged by the mismatch between large time-scale of alkaline electrolyzer (AWE) scheduling strategies and the short-term fluctuations of wind power. To address this issue, this paper proposes a novel minute-level optimization strategy for AWE operation. Developing effective small time-scale strategies requires a detailed consideration of AWE dynamics. To this end, we first introduce its steady-state electrochemical characteristics and third-order dynamic models for both temperature and Hydrogen-to-Oxygen (HTO) ratio. Based on these refined models, we develop an AWE optimization framework that enables electrolysis power to track minute-level wind power fluctuations by dynamically adjusting fine-grained variables, such as the lye flow rate, cooling flow rate, and pressure, at 1-minute intervals. To overcome the computational challenges posed by the detailed modeling, we propose an improved model predictive control (MPC) framework. This framework incorporates model simplifications to improve computational efficiency, along with an optimization-simulation iterative procedure to ensure operational feasibility. Case studies demonstrate that the proposed strategy extends the AWE load range by 13.8% and reduces wind power curtailment by 15.06%. Additionally, synergies among control variables enable the system to achieve a balance between operational efficiency, stability, and security, highlighting the potential of this approach to enhance the performance of wind-hydrogen integrated systems.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2157-2170"},"PeriodicalIF":8.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331741","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 : 2025-03-04DOI: 10.1109/TSTE.2025.3547539
Yushuang Liu;Hua Geng;Geng Yang;Meng Huang;Changjun He;Xiaoming Zha;Wenze Ding;Feng Liu
The operation mode of grid-forming voltage source converters (GFM-VSCs) may switch between voltage source mode (VSM) and current source mode (CSM) under some situations such as grid faults, owing to the current limitation control. During the mode-switching process, there is state transfer from the final state of the last mode to the initial state of the next mode, which impacts the transient synchronization stability (TSS) of GFM-VSCs. This paper primarily focuses on analyzing and improving the TSS of GFM-VSCs by considering the effect of state transfer. A novel transient instability mechanism is revealed through the existence analysis of equilibrium points. It clarifies that the state transfer may cause the operating trajectory during faults to bypass the stable equilibrium point in CSM before diverging to the next cycle, thereby resulting in transient synchronization instability. Besides, to further analyze the TSS of mode-switched VSCs considering the dynamics during faults, multiple Lyapunov functions are adopted to derive the TSS criteria and boundaries. It has been identified that lowering the minimum critical current and adjusting the saturated current phase in accordance with virtual power angle (VPA) dynamics can enhance the TSS. Therefore, a VPA feedback-based current limiting strategy is proposed to safeguard GFM-VSCs against overcurrent and ensure the TSS. The validity of the new transient instability mechanism and the efficacy of the proposed strategy are confirmed through simulations of a GFM-VSC connected to an IEEE 39-bus power grid and hardware-in-the-loop experiments.
{"title":"State Transfer Induced Transient Synchronization Instability of GFM-VSC: Analysis and Improvement","authors":"Yushuang Liu;Hua Geng;Geng Yang;Meng Huang;Changjun He;Xiaoming Zha;Wenze Ding;Feng Liu","doi":"10.1109/TSTE.2025.3547539","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3547539","url":null,"abstract":"The operation mode of grid-forming voltage source converters (GFM-VSCs) may switch between voltage source mode (VSM) and current source mode (CSM) under some situations such as grid faults, owing to the current limitation control. During the mode-switching process, there is state transfer from the final state of the last mode to the initial state of the next mode, which impacts the transient synchronization stability (TSS) of GFM-VSCs. This paper primarily focuses on analyzing and improving the TSS of GFM-VSCs by considering the effect of state transfer. A novel transient instability mechanism is revealed through the existence analysis of equilibrium points. It clarifies that the state transfer may cause the operating trajectory during faults to bypass the stable equilibrium point in CSM before diverging to the next cycle, thereby resulting in transient synchronization instability. Besides, to further analyze the TSS of mode-switched VSCs considering the dynamics during faults, multiple Lyapunov functions are adopted to derive the TSS criteria and boundaries. It has been identified that lowering the minimum critical current and adjusting the saturated current phase in accordance with virtual power angle (VPA) dynamics can enhance the TSS. Therefore, a VPA feedback-based current limiting strategy is proposed to safeguard GFM-VSCs against overcurrent and ensure the TSS. The validity of the new transient instability mechanism and the efficacy of the proposed strategy are confirmed through simulations of a GFM-VSC connected to an IEEE 39-bus power grid and hardware-in-the-loop experiments.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2114-2131"},"PeriodicalIF":8.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331703","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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