Pub Date : 2024-10-01DOI: 10.1109/TSTE.2024.3469448
{"title":"2024 Index IEEE Transactions on Sustainable Energy Vol. 15","authors":"","doi":"10.1109/TSTE.2024.3469448","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3469448","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2830-2874"},"PeriodicalIF":8.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10701079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368243","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 : 2024-09-30DOI: 10.1109/TSTE.2024.3470548
Zhongtian Zhang;Javad Khazaei;Rick S. Blum
This paper introduces a new statistical learning technique based on sparsity promotion for data-driven modeling and control of solar photovoltaic (PV) systems. Compared with conventional sparse regression techniques that might introduce computational complexities when the number of candidate functions increases, an innovative algorithm, named adaptive regulated sparse regression (ARSR) is proposed. The ARSR adaptively regulates the hyperparameter weights of candidate functions to best represent the dynamics of PV systems. This method allows for the application of different sparsity-promoting hyperparameters for each state variable, whereas the conventional approach uses the same hyperparameter for all state variables, which may result in not excluding all the unrelated terms from the dynamics. Consequently, the proposed method can identify more complex dynamics with greater accuracy. Utilizing this algorithm, open-loop and closed-loop models of single-stage and two-stage PV systems are obtained from measurements and are utilized for control design purposes. Moreover, it is demonstrated that the proposed data-driven approach can be successfully employed for fault analysis studies, which distinguishes its capabilities from other data-driven techniques. Finally, the proposed approach is validated through real-time simulations.
{"title":"Adaptive Regulated Sparsity Promoting Approach for Data-Driven Modeling and Control of Grid-Connected Solar Photovoltaic Generation","authors":"Zhongtian Zhang;Javad Khazaei;Rick S. Blum","doi":"10.1109/TSTE.2024.3470548","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3470548","url":null,"abstract":"This paper introduces a new statistical learning technique based on sparsity promotion for data-driven modeling and control of solar photovoltaic (PV) systems. Compared with conventional sparse regression techniques that might introduce computational complexities when the number of candidate functions increases, an innovative algorithm, named adaptive regulated sparse regression (ARSR) is proposed. The ARSR adaptively regulates the hyperparameter weights of candidate functions to best represent the dynamics of PV systems. This method allows for the application of different sparsity-promoting hyperparameters for each state variable, whereas the conventional approach uses the same hyperparameter for all state variables, which may result in not excluding all the unrelated terms from the dynamics. Consequently, the proposed method can identify more complex dynamics with greater accuracy. Utilizing this algorithm, open-loop and closed-loop models of single-stage and two-stage PV systems are obtained from measurements and are utilized for control design purposes. Moreover, it is demonstrated that the proposed data-driven approach can be successfully employed for fault analysis studies, which distinguishes its capabilities from other data-driven techniques. Finally, the proposed approach is validated through real-time simulations.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"560-572"},"PeriodicalIF":8.6,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844493","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}
With the rapid expansion of photovoltaic (PV), grid-forming energy storage systems (GFM-ESS) have been widely employed for inertia response and voltage support to enhance the dynamic characteristics. Converters with different synchronization methods represent significant differences in dynamic behavior. The interactions between grid-forming (GFM) and grid-following (GFL) devices with multi-time scale control may lead to small-signal instability in hybrid systems. This paper investigates a grid-connected system comprising a grid-forming energy storage system and a grid-following PV system (GFL-PV). Based on single-input-single-output (SISO) transfer functions, a dynamic interaction model for the PV-ESS system is established. Combining the open-loop transfer functions of full-loop and sub-loop, the proposed model reveals how GFM-ESS modifies the dynamic characteristics of GFL-PV under weak grid conditions. Subsequently, the impact of different control loops and parameters on the small-signal stability of the system is analyzed. The stability margins of both devices are also compared through the SISO model. Electromagnetic transient simulation results in MATLAB/Simulink and experiments validate the effectiveness of the proposed models and analyses.
{"title":"Interaction Modeling and Stability Analysis of Grid-Forming Energy Storage System Based on SISO Transfer Functions","authors":"Kezan Zhang;Mengxuan Shi;Xia Chen;Dejun Shao;Youping Xu;Yin Chen","doi":"10.1109/TSTE.2024.3471801","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3471801","url":null,"abstract":"With the rapid expansion of photovoltaic (PV), grid-forming energy storage systems (GFM-ESS) have been widely employed for inertia response and voltage support to enhance the dynamic characteristics. Converters with different synchronization methods represent significant differences in dynamic behavior. The interactions between grid-forming (GFM) and grid-following (GFL) devices with multi-time scale control may lead to small-signal instability in hybrid systems. This paper investigates a grid-connected system comprising a grid-forming energy storage system and a grid-following PV system (GFL-PV). Based on single-input-single-output (SISO) transfer functions, a dynamic interaction model for the PV-ESS system is established. Combining the open-loop transfer functions of full-loop and sub-loop, the proposed model reveals how GFM-ESS modifies the dynamic characteristics of GFL-PV under weak grid conditions. Subsequently, the impact of different control loops and parameters on the small-signal stability of the system is analyzed. The stability margins of both devices are also compared through the SISO model. Electromagnetic transient simulation results in MATLAB/Simulink and experiments validate the effectiveness of the proposed models and analyses.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"573-587"},"PeriodicalIF":8.6,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844546","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 : 2024-09-25DOI: 10.1109/TSTE.2024.3468151
Xin Dong;Wenjuan Du;Qiang Fu;Haifeng Wang
Subsynchronous oscillations (SSOs) in renewable power systems have emerged as a major challenge, jeopardizing the stability and safety of power system operations. Thus, it is essential to accurately and timely locate SSO sources. Artificial intelligence (AI)-based methods for locating SSO sources have become increasingly popular, existing AI-based methods usually fail in practical applications due to unavailable or insufficient real-world SSO data for model training, and significant distribution gaps in samples under different operational conditions. They also fail to fully utilize the temporal characteristics of oscillations and the spatial topology of the system. Moreover, these methods only focus on locating either negative-damping-SSO or forced-SSO sources. To overcome these limitations, we introduce a novel strategy termed Spatiotemporal-Adversarial-Domain-Generalization (STADG) to locate oscillation sources in both SSO scenarios of real power systems. This method allows the model to train on multi-source domains (simplified-simulation power systems) with sufficient labeled samples, and to be directly applied to an unseen test target domain (real power system) under unknow operating conditions. The proposed approach employs a graph-attention network and a long-short-term-memory network to fully leverage spatial and temporal features of SSOs. Extensive experiments on the modified IEEE-39 and WECC-179 bus systems confirm the effectiveness of the proposed approach.
{"title":"Spatiotemporal Adversarial Domain Generalization for Locating Subsynchronous Oscillation Sources Under Unseen Conditions in Large-Scale Renewable Power Systems","authors":"Xin Dong;Wenjuan Du;Qiang Fu;Haifeng Wang","doi":"10.1109/TSTE.2024.3468151","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3468151","url":null,"abstract":"Subsynchronous oscillations (SSOs) in renewable power systems have emerged as a major challenge, jeopardizing the stability and safety of power system operations. Thus, it is essential to accurately and timely locate SSO sources. Artificial intelligence (AI)-based methods for locating SSO sources have become increasingly popular, existing AI-based methods usually fail in practical applications due to unavailable or insufficient real-world SSO data for model training, and significant distribution gaps in samples under different operational conditions. They also fail to fully utilize the temporal characteristics of oscillations and the spatial topology of the system. Moreover, these methods only focus on locating either negative-damping-SSO or forced-SSO sources. To overcome these limitations, we introduce a novel strategy termed Spatiotemporal-Adversarial-Domain-Generalization (STADG) to locate oscillation sources in both SSO scenarios of real power systems. This method allows the model to train on multi-source domains (simplified-simulation power systems) with sufficient labeled samples, and to be directly applied to an unseen test target domain (real power system) under unknow operating conditions. The proposed approach employs a graph-attention network and a long-short-term-memory network to fully leverage spatial and temporal features of SSOs. Extensive experiments on the modified IEEE-39 and WECC-179 bus systems confirm the effectiveness of the proposed approach.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"512-529"},"PeriodicalIF":8.6,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844543","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 : 2024-09-25DOI: 10.1109/TSTE.2024.3467686
Shenghu Li;Jianqiao Ye
The increasing wind power decreases power system damping and may intensify low-frequency oscillation (LFO). The LFO are usually damped by the power system stabilizer (PSS) at synchronous generator (SG), and now by the power oscillation damper (POD) at doubly-fed induction generator (DFIG). The existing damping torque analysis (DTA) sets the parameters of the PSS and evaluates its damping capability, but can not be applied to the POD due to the difficulty of finding the damping path related to the DFIG and the coupling between the POD and the DFIG, which are studied in this paper. At first, the analytical expression of the coupling between the POD and DFIG is newly derived with linear fractional transformation (LFT) technique. Then the path damping torque analysis (PDTA) is proposed to reconstruct the damping path of the POD. Thirdly, the damping indicator based on the return difference matrix is proposed to evaluate the contribution of damping path to the LFO. Finally, numerical results of test system are given to validate effectiveness and accuracy of the proposed model, and parameter optimization to the multi-input POD (MIPOD) is performed to show the application value of the proposed model.
{"title":"Analytical Evaluation to Power System Oscillation Damping Capability of DFIG-POD Based on Path Damping Torque Analysis","authors":"Shenghu Li;Jianqiao Ye","doi":"10.1109/TSTE.2024.3467686","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3467686","url":null,"abstract":"The increasing wind power decreases power system damping and may intensify low-frequency oscillation (LFO). The LFO are usually damped by the power system stabilizer (PSS) at synchronous generator (SG), and now by the power oscillation damper (POD) at doubly-fed induction generator (DFIG). The existing damping torque analysis (DTA) sets the parameters of the PSS and evaluates its damping capability, but can not be applied to the POD due to the difficulty of finding the damping path related to the DFIG and the coupling between the POD and the DFIG, which are studied in this paper. At first, the analytical expression of the coupling between the POD and DFIG is newly derived with linear fractional transformation (LFT) technique. Then the path damping torque analysis (PDTA) is proposed to reconstruct the damping path of the POD. Thirdly, the damping indicator based on the return difference matrix is proposed to evaluate the contribution of damping path to the LFO. Finally, numerical results of test system are given to validate effectiveness and accuracy of the proposed model, and parameter optimization to the multi-input POD (MIPOD) is performed to show the application value of the proposed model.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"496-511"},"PeriodicalIF":8.6,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844495","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 neighboring wind farms have great frequency support potential. The wind turbine generators (WTGs) in these wind farms are influenced by wake effects and have different frequency support capabilities. In order to fully utilize the WTGs' support capabilities under different operating states, this paper proposes a two-level distributed consensus (TLDC) control to cooperate all the WTGs. Level I is leader-follower control, which is equipped within the wind farms. Level II is leaderless control which is used among the wind farms. This method is able to assign different values of power commands to different WTGs in the system to achieve better frequency support effect and stability. Based on MATLAB/Simulink and Opal-RT real-time simulation platforms, the two-area power system and Guangshui system (100% renewable energy power system) are analyzed, respectively. Simulation results show that the proposed TLDC method has a better effect compared with other frequency support methods. It can also flexibly respond to communication interruptions and delays.
{"title":"Two-Level Distributed Consensus Control of Multiple Wind Farms for Fast Frequency Support","authors":"Kangyi Sun;Hongyu Zhou;Wei Yao;Yongxin Xiong;Yahan Yao;Jinyu Wen","doi":"10.1109/TSTE.2024.3468371","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3468371","url":null,"abstract":"The neighboring wind farms have great frequency support potential. The wind turbine generators (WTGs) in these wind farms are influenced by wake effects and have different frequency support capabilities. In order to fully utilize the WTGs' support capabilities under different operating states, this paper proposes a two-level distributed consensus (TLDC) control to cooperate all the WTGs. Level I is leader-follower control, which is equipped within the wind farms. Level II is leaderless control which is used among the wind farms. This method is able to assign different values of power commands to different WTGs in the system to achieve better frequency support effect and stability. Based on MATLAB/Simulink and Opal-RT real-time simulation platforms, the two-area power system and Guangshui system (100% renewable energy power system) are analyzed, respectively. Simulation results show that the proposed TLDC method has a better effect compared with other frequency support methods. It can also flexibly respond to communication interruptions and delays.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"530-545"},"PeriodicalIF":8.6,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844281","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 : 2024-09-25DOI: 10.1109/TSTE.2024.3467679
Le Su;Xueping Pan;Xiaorong Sun;Jinpeng Guo;Amjad Anvari-Moghaddam
Voltage calculations are critical for assessing photovoltaic hosting capacity; however, acquiring precise parameters and the topology of the medium voltage distribution networks poses a significant challenge, thereby rendering traditional power flow computational methods ineffective. To address this issue, this paper introduces a hybrid method that utilizes a data-driven approach in conjunction with nonlinear functions to determine node voltages. Firstly, a deep neural network model for distribution network's power flow and voltage-power sensitivity analysis is established using historical data. This model captures the data-driven error, which reduces time consumption and increases accuracy. Secondly, a fourth-order Taylor expansion of power to voltage is derived based on the power flow mathematical equation to extrapolate voltage. This is necessary because when photovoltaic generators are connected to the nodes, the load data often exceeds the historical data range, rendering neural networks inapplicable. Finally, the sparrow search algorithm is employed to determine the hosting capacity. The proposed methods are validated using IEEE 33 and IEEE 69 case systems, demonstrating that the data-driven approach, combined with nonlinear functions, can ensure the accuracy in obtaining node voltage and the hosting capacity.
{"title":"Research on PV Hosting Capacity of Distribution Networks Based on Data-Driven and Nonlinear Sensitivity Functions","authors":"Le Su;Xueping Pan;Xiaorong Sun;Jinpeng Guo;Amjad Anvari-Moghaddam","doi":"10.1109/TSTE.2024.3467679","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3467679","url":null,"abstract":"Voltage calculations are critical for assessing photovoltaic hosting capacity; however, acquiring precise parameters and the topology of the medium voltage distribution networks poses a significant challenge, thereby rendering traditional power flow computational methods ineffective. To address this issue, this paper introduces a hybrid method that utilizes a data-driven approach in conjunction with nonlinear functions to determine node voltages. Firstly, a deep neural network model for distribution network's power flow and voltage-power sensitivity analysis is established using historical data. This model captures the data-driven error, which reduces time consumption and increases accuracy. Secondly, a fourth-order Taylor expansion of power to voltage is derived based on the power flow mathematical equation to extrapolate voltage. This is necessary because when photovoltaic generators are connected to the nodes, the load data often exceeds the historical data range, rendering neural networks inapplicable. Finally, the sparrow search algorithm is employed to determine the hosting capacity. The proposed methods are validated using IEEE 33 and IEEE 69 case systems, demonstrating that the data-driven approach, combined with nonlinear functions, can ensure the accuracy in obtaining node voltage and the hosting capacity.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"483-495"},"PeriodicalIF":8.6,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844545","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 : 2024-09-25DOI: 10.1109/TSTE.2024.3468992
Stefan Borozan;Goran Strbac
The shift towards a decentralized paradigm in power systems in response to decarbonization and deregulation efforts necessitates stronger coordination between transmission and distribution operators for cost-effective operation and planning. However, long-term uncertainties in the transition to net-zero are posing major challenges for decision-making. Moreover, literature has traditionally focused on the transmission and distribution expansion planning problems independently, as is customary in industry, leading to a lack of sophisticated integrated planning methods and inefficient expansion decisions in practice. This paper proposes a novel multi-stage stochastic programming framework for the integrated transmission and active distribution networks expansion planning under multi-dimensional uncertainties. Infrastructure investments are co-optimized with non-network alternatives with diverse techno-economic characteristics to support flexible planning. To manage the increased computational complexities, a machine learning-assisted multi-cut Benders decomposition approach is implemented. The case studies firstly highlight the strategic and economic advantages of the proposed multi-stage formulation, and then demonstrate the significant role and value of smart investment options in managing uncertainty. Lastly, the application of the proposed model on a study involving a 229-bus test system and 18 long-term scenarios validates its scalability and practical applicability.
{"title":"Multi-Stage Integrated Transmission and Distribution Expansion Planning Under Uncertainties With Smart Investment Options","authors":"Stefan Borozan;Goran Strbac","doi":"10.1109/TSTE.2024.3468992","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3468992","url":null,"abstract":"The shift towards a decentralized paradigm in power systems in response to decarbonization and deregulation efforts necessitates stronger coordination between transmission and distribution operators for cost-effective operation and planning. However, long-term uncertainties in the transition to net-zero are posing major challenges for decision-making. Moreover, literature has traditionally focused on the transmission and distribution expansion planning problems independently, as is customary in industry, leading to a lack of sophisticated integrated planning methods and inefficient expansion decisions in practice. This paper proposes a novel multi-stage stochastic programming framework for the integrated transmission and active distribution networks expansion planning under multi-dimensional uncertainties. Infrastructure investments are co-optimized with non-network alternatives with diverse techno-economic characteristics to support flexible planning. To manage the increased computational complexities, a machine learning-assisted multi-cut Benders decomposition approach is implemented. The case studies firstly highlight the strategic and economic advantages of the proposed multi-stage formulation, and then demonstrate the significant role and value of smart investment options in managing uncertainty. Lastly, the application of the proposed model on a study involving a 229-bus test system and 18 long-term scenarios validates its scalability and practical applicability.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"546-559"},"PeriodicalIF":8.6,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10694803","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844496","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}
The speed of the pitch action is one of the primary constraints that limit the power ramp rate of wind turbines (WTs). By storing kinetic energy (KE) within the wind wheel, blades, and generator rotor, the output power of the WT can be regulated more rapidly with less pitch action. This capability is beneficial in specific situations where additional power injection is required by the external system, such as during the system restoration process after a blackout. In this paper, a novel fast power regulation method for direct-drive PMSG-based (D-PMSG-based) WTs is proposed. This method allows for the early completion of the most time-consuming pitch reduction process and ensures that KE can be stored within the WT before the external system becomes available. As a result, the WT will be able to achieve maximum power output before the system restoration is fully completed. The potential operation boundary and the maximum external power support capability of D-PMSG-based WTs are analyzed. By following the operation boundary, converter modulation problem caused by high KE reserve can be avoided. The proposed fast power regulation method can significantly reduce the power increase speed and maximize the output power capability of D-PMSG-base WTs.
{"title":"Fast Power Regulation Method During System Restoration for D-PMSG-Based Wind Turbines","authors":"Guohang Huang;Sheng Huang;Juan Wei;Hesong Cui;Lei Liu;Xueting Cheng;Jinhao Wang;Shoudao Huang","doi":"10.1109/TSTE.2024.3465529","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3465529","url":null,"abstract":"The speed of the pitch action is one of the primary constraints that limit the power ramp rate of wind turbines (WTs). By storing kinetic energy (KE) within the wind wheel, blades, and generator rotor, the output power of the WT can be regulated more rapidly with less pitch action. This capability is beneficial in specific situations where additional power injection is required by the external system, such as during the system restoration process after a blackout. In this paper, a novel fast power regulation method for direct-drive PMSG-based (D-PMSG-based) WTs is proposed. This method allows for the early completion of the most time-consuming pitch reduction process and ensures that KE can be stored within the WT before the external system becomes available. As a result, the WT will be able to achieve maximum power output before the system restoration is fully completed. The potential operation boundary and the maximum external power support capability of D-PMSG-based WTs are analyzed. By following the operation boundary, converter modulation problem caused by high KE reserve can be avoided. The proposed fast power regulation method can significantly reduce the power increase speed and maximize the output power capability of D-PMSG-base WTs.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"469-482"},"PeriodicalIF":8.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844497","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 : 2024-09-18DOI: 10.1109/TSTE.2024.3463177
Tao Zhang;Tianjiao Pu;Lei Dong;Xin Yuan;Yunfei Mu;Hongjie Jia
The flexible soft open point (SOP) connected to active distribution networks (ADNs) offers a promising manner of improving voltage and VAR control (VVC) by providing flexible power regulation. Due to the expansion of interconnective networks, the centralized optimization method has recently faced various challenges. This paper thus proposes a novel distributed coordinated reactive power optimization strategy for SOP-based multiregional ADNs based on local model decoupling and iterative interactions. By applying the alternating direction method of multipliers (ADMM), the centralized VVC optimization problem is divided into several subproblems, allowing each area to optimize its local subproblem in a fully distributed manner. Multiple resources are thereby coordinated by the VVC, including both discrete and continuous devices. To ensure computability of both integer and non-convex problems, the relaxation iteration and successive linear approximation methods are nested to the ADMM framework, within this approach to allow ready solution to the distributed VVC optimization problem to be generated using a relaxation iterative algorithm, which significantly improves algorithm convergence and computational efficiency. The effectiveness of the proposed method is demonstrated in this work using a modified IEEE standard interconnection system.
{"title":"Distributed Reactive Power Optimization for Flexible Distribution Networks With Successive Relaxation Iteration Method","authors":"Tao Zhang;Tianjiao Pu;Lei Dong;Xin Yuan;Yunfei Mu;Hongjie Jia","doi":"10.1109/TSTE.2024.3463177","DOIUrl":"10.1109/TSTE.2024.3463177","url":null,"abstract":"The flexible soft open point (SOP) connected to active distribution networks (ADNs) offers a promising manner of improving voltage and VAR control (VVC) by providing flexible power regulation. Due to the expansion of interconnective networks, the centralized optimization method has recently faced various challenges. This paper thus proposes a novel distributed coordinated reactive power optimization strategy for SOP-based multiregional ADNs based on local model decoupling and iterative interactions. By applying the alternating direction method of multipliers (ADMM), the centralized VVC optimization problem is divided into several subproblems, allowing each area to optimize its local subproblem in a fully distributed manner. Multiple resources are thereby coordinated by the VVC, including both discrete and continuous devices. To ensure computability of both integer and non-convex problems, the relaxation iteration and successive linear approximation methods are nested to the ADMM framework, within this approach to allow ready solution to the distributed VVC optimization problem to be generated using a relaxation iterative algorithm, which significantly improves algorithm convergence and computational efficiency. The effectiveness of the proposed method is demonstrated in this work using a modified IEEE standard interconnection system.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"452-468"},"PeriodicalIF":8.6,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250421","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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