Pub Date : 2024-12-20DOI: 10.1109/TSTE.2024.3520989
Huazhao Ding;Rabi Kar;Zhixin Miao;Lingling Fan
This paper presents the design of a novel grid-forming (GFM) control structure adapted from a typical grid-following (GFL) control structure with minimal edits, thereby enabling a switchable control structure for voltage sourced converters (VSCs) to operate in either GFL or GFM mode by simply switching a flag manually. The VSC is shown to be able to operate in the GFL control mode synchronizing to the main grid through a phase-locked-loop (PLL) and operate as a GFM controller with power-based synchronization for both grid-connected and islanded conditions. To guarantee smooth operation, the control schemes and the mode switching logic have been carefully designed and examined via a series of experiments. The experiment results show that the switchable control structure can fulfill the desired control and operation functions and enable smooth transition between control modes.
{"title":"A Novel Design for Switchable Grid-Following and Grid-Forming Control","authors":"Huazhao Ding;Rabi Kar;Zhixin Miao;Lingling Fan","doi":"10.1109/TSTE.2024.3520989","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3520989","url":null,"abstract":"This paper presents the design of a novel grid-forming (GFM) control structure adapted from a typical grid-following (GFL) control structure with minimal edits, thereby enabling a switchable control structure for voltage sourced converters (VSCs) to operate in either GFL or GFM mode by simply switching a flag manually. The VSC is shown to be able to operate in the GFL control mode synchronizing to the main grid through a phase-locked-loop (PLL) and operate as a GFM controller with power-based synchronization for both grid-connected and islanded conditions. To guarantee smooth operation, the control schemes and the mode switching logic have been carefully designed and examined via a series of experiments. The experiment results show that the switchable control structure can fulfill the desired control and operation functions and enable smooth transition between control modes.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1301-1314"},"PeriodicalIF":8.6,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667490","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}
This paper focuses on the optimized and coordinated operation of a hybrid system comprising wind turbines, a hydrogen electrolyzer, and hydrogen storage. A day-ahead optimized schedule is developed for the hybrid wind-hydrogen system to provide flexibility in meeting the transmission system operator's needs, offering frequency control support through frequency containment reserves (FCR) and managing congestion on nearby transmission lines. The proposed operation strategy enables effective participation in three reserve markets (FCR-N, upward, and downward FCR-D) while robustly managing uncertainties in wind power forecasting by leveraging the flexibility of the hydrogen electrolyzer and hydrogen storage. Utilizing historical data on FCR activation during normal grid operation and disturbances, this strategy robustly addresses frequency-driven uncertainties. The effectiveness of the proposed method is demonstrated through two case studies using real-world data on frequency deviations and market prices in Finland. Additionally, the proposed strategy is compared with two alternative approaches: one based on spot market prices and another prioritizing self-sufficiency over financial gains.
{"title":"Optimized Operation of Hybrid Wind-Hydrogen System to Provide Flexibility for Transmission System Needs","authors":"Hosna Khajeh;Sahar Seyyedeh-Barhagh;Hannu Laaksonen","doi":"10.1109/TSTE.2024.3519953","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3519953","url":null,"abstract":"This paper focuses on the optimized and coordinated operation of a hybrid system comprising wind turbines, a hydrogen electrolyzer, and hydrogen storage. A day-ahead optimized schedule is developed for the hybrid wind-hydrogen system to provide flexibility in meeting the transmission system operator's needs, offering frequency control support through frequency containment reserves (FCR) and managing congestion on nearby transmission lines. The proposed operation strategy enables effective participation in three reserve markets (FCR-N, upward, and downward FCR-D) while robustly managing uncertainties in wind power forecasting by leveraging the flexibility of the hydrogen electrolyzer and hydrogen storage. Utilizing historical data on FCR activation during normal grid operation and disturbances, this strategy robustly addresses frequency-driven uncertainties. The effectiveness of the proposed method is demonstrated through two case studies using real-world data on frequency deviations and market prices in Finland. Additionally, the proposed strategy is compared with two alternative approaches: one based on spot market prices and another prioritizing self-sufficiency over financial gains.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1576-1588"},"PeriodicalIF":8.6,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10806878","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331700","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 : 2024-12-18DOI: 10.1109/TSTE.2024.3519721
Yi-Hua Liu;Yu-Shan Cheng;Yu-Chih Huang
When the photovoltaic generation system (PVGS) operates under partially shaded conditions (PSC), its output power versus voltage (P-V) characteristic curve becomes multimodal, which complicates the search for the global maximum power point (GMPP). This paper proposes a GMPP tracking (GMPPT) method based on machine learning (ML). In the first stage, the regression tree (RT) is used to predict the approximate location of the GMPP. In the second stage, the α-perturb and observe (α-P&O) method is used to obtain the precise GMPP. This study first establishes a PVGS simulation platform and generates the training data required for RT, then optimizes the obtained RT and integrates it into the simulation platform. Finally, this paper compares the proposed method with the state-of-the-art approaches. It can be seen from the results that the proposed method has an average tracking power loss of 2.13 W and an average tracking time of 0.11 seconds under 252 different shading patterns (SPs). It can correctly identify 244 intervals where the exact GMPP is located among the 252 test SPs. The experimental results show that the proposed method outperforms 5 state-of-the-art approaches in terms of tracking accuracy and tracking time under three shading patterns, thus confirming its excellence.
{"title":"A Machine Learning-Based Global Maximum Power Point Tracking Technique for a Photovoltaic Generation System Under Complicated Partially Shaded Conditions","authors":"Yi-Hua Liu;Yu-Shan Cheng;Yu-Chih Huang","doi":"10.1109/TSTE.2024.3519721","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3519721","url":null,"abstract":"When the photovoltaic generation system (PVGS) operates under partially shaded conditions (PSC), its output power versus voltage (P-V) characteristic curve becomes multimodal, which complicates the search for the global maximum power point (GMPP). This paper proposes a GMPP tracking (GMPPT) method based on machine learning (ML). In the first stage, the regression tree (RT) is used to predict the approximate location of the GMPP. In the second stage, the α-perturb and observe (α-P&O) method is used to obtain the precise GMPP. This study first establishes a PVGS simulation platform and generates the training data required for RT, then optimizes the obtained RT and integrates it into the simulation platform. Finally, this paper compares the proposed method with the state-of-the-art approaches. It can be seen from the results that the proposed method has an average tracking power loss of 2.13 W and an average tracking time of 0.11 seconds under 252 different shading patterns (SPs). It can correctly identify 244 intervals where the exact GMPP is located among the 252 test SPs. The experimental results show that the proposed method outperforms 5 state-of-the-art approaches in terms of tracking accuracy and tracking time under three shading patterns, thus confirming its excellence.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1562-1575"},"PeriodicalIF":8.6,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331554","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 existing rotor current control methods, despite achieving simultaneous control on the positive and negative sequence rotor currents for the doubly-fed induction generator (DFIG)-based wind turbine, are still facing challenges. Specifically, some works introduce the sequence current decomposition into the classical control structure, which can deteriorate the dynamic performance. While others with high-order regulator embedded into the classical control structure can increase the risk of instability. To this end, this paper proposes a novel current reference transformation-based positive and negative sequence rotor current control method. Firstly, the negative sequence response of the DFIG under the classical single dq-PI rotor current control method is studied, pointing out its satisfactory dynamic performance but poor steady-state performance. Based on which, a transformation formula for the negative sequence rotor current reference is analytically derived to compensate for the steady-state performance. The corresponding analysis indicates that negative sequence rotor current static errors from parameter deviations can be well limited. Comparative simulations illustrated an improved dynamic performance and stability of the DFIG rotor current control with the proposed method. The experimental test of a prototype DFIG system has also been conducted to verify the feasibility of the proposed method in practical implementation.
{"title":"A Novel Current Reference Transformation-Based Positive and Negative Sequence Rotor Current Control Method of DFIGs","authors":"Xuesong Gao;Shiyao Qin;Xianzhuo Sun;Zhihao Wang;Rongde Cui;Shuai Xu;Lei Ding","doi":"10.1109/TSTE.2024.3520182","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3520182","url":null,"abstract":"The existing rotor current control methods, despite achieving simultaneous control on the positive and negative sequence rotor currents for the doubly-fed induction generator (DFIG)-based wind turbine, are still facing challenges. Specifically, some works introduce the sequence current decomposition into the classical control structure, which can deteriorate the dynamic performance. While others with high-order regulator embedded into the classical control structure can increase the risk of instability. To this end, this paper proposes a novel current reference transformation-based positive and negative sequence rotor current control method. Firstly, the negative sequence response of the DFIG under the classical single dq-PI rotor current control method is studied, pointing out its satisfactory dynamic performance but poor steady-state performance. Based on which, a transformation formula for the negative sequence rotor current reference is analytically derived to compensate for the steady-state performance. The corresponding analysis indicates that negative sequence rotor current static errors from parameter deviations can be well limited. Comparative simulations illustrated an improved dynamic performance and stability of the DFIG rotor current control with the proposed method. The experimental test of a prototype DFIG system has also been conducted to verify the feasibility of the proposed method in practical implementation.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1283-1300"},"PeriodicalIF":8.6,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667240","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 : 2024-12-17DOI: 10.1109/TSTE.2024.3508513
{"title":"Share Your Preprint Research with the World!","authors":"","doi":"10.1109/TSTE.2024.3508513","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3508513","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"730-730"},"PeriodicalIF":8.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10805486","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844327","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 : 2024-12-17DOI: 10.1109/TSTE.2024.3506259
{"title":"IEEE Transactions on Sustainable Energy Information for Authors","authors":"","doi":"10.1109/TSTE.2024.3506259","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3506259","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"C4-C4"},"PeriodicalIF":8.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10805488","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844494","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}
With the rapid development of global offshore wind power, the scale and capacity of offshore wind farms (OWF) are continuously expanding, making it crucial to enhance the overall economic efficiency of OWFs. However, previous studies on DC collector systems of OWFs mainly focus on the DC series-parallel (SP) topology, which escalates the overall costs. To optimize the collector system layout, this paper proposes a novel hierarchical reinforcement learning (HRL) based framework for improving the overall economic efficiency by leveraging an advanced DC series-parallel-parallel (SPP) topology. In the proposed framework, a hierarchical open-loop multiple travelling salesman problem (HOMTSP) is utilized to model the SPP topology, decomposing the collector system layout optimization (CSLO) problem into sub-problems for resolution. Subsequently, a hierarchical double Q-learning (DQL) is employed to solve these sub-problems, with a topology-guided mechanism to refine the routing results and correct crossed cables by incorporating topological characteristics. Furthermore, this study acquires the GIS data and the connection scheme of wind turbines in a real OWF for the case study. Numerical results show the SPP-based framework significantly improves the economic efficiency compared to the DC SP topology and the AC double-sided ring topology.
{"title":"DC Collector System Layout Optimization for Offshore Wind Farm With SPP Topology","authors":"Chunyang Pan;Shuli Wen;Miao Zhu;Jianjun Ma;Chuanchuan Hou","doi":"10.1109/TSTE.2024.3519432","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3519432","url":null,"abstract":"With the rapid development of global offshore wind power, the scale and capacity of offshore wind farms (OWF) are continuously expanding, making it crucial to enhance the overall economic efficiency of OWFs. However, previous studies on DC collector systems of OWFs mainly focus on the DC series-parallel (SP) topology, which escalates the overall costs. To optimize the collector system layout, this paper proposes a novel hierarchical reinforcement learning (HRL) based framework for improving the overall economic efficiency by leveraging an advanced DC series-parallel-parallel (SPP) topology. In the proposed framework, a hierarchical open-loop multiple travelling salesman problem (HOMTSP) is utilized to model the SPP topology, decomposing the collector system layout optimization (CSLO) problem into sub-problems for resolution. Subsequently, a hierarchical double Q-learning (DQL) is employed to solve these sub-problems, with a topology-guided mechanism to refine the routing results and correct crossed cables by incorporating topological characteristics. Furthermore, this study acquires the GIS data and the connection scheme of wind turbines in a real OWF for the case study. Numerical results show the SPP-based framework significantly improves the economic efficiency compared to the DC SP topology and the AC double-sided ring topology.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1269-1282"},"PeriodicalIF":8.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667666","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 : 2024-12-17DOI: 10.1109/TSTE.2024.3508517
{"title":"Get Published in the New IEEE Open Access Journal of Power and Energy","authors":"","doi":"10.1109/TSTE.2024.3508517","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3508517","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"732-732"},"PeriodicalIF":8.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10805554","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844326","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 : 2024-12-17DOI: 10.1109/TSTE.2024.3506255
{"title":"IEEE Transactions on Sustainable Energy Publication Information","authors":"","doi":"10.1109/TSTE.2024.3506255","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3506255","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"C2-C2"},"PeriodicalIF":8.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10805484","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859180","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}
Under extreme weather events represented by severe convective weather (SCW), the adaptability of power system and service restoration have become paramount. To this end, this paper presents a novel planning method of stationary-mobile integrated battery energy storage system (SMI-BESS) capable of spatial flexibility. This designed system can flexibly switch between stationary and mobile modes to cope with normal operation and extreme weather events. Considering the multitude of threats posed of SCW, such as extreme wind speed, lightning strikes, and hail, a comprehensive fragility model of the distribution network is established to quantify adverse impacts of the extreme event. Uncertainties in renewable energy generation and distribution network failures are characterized using two types of ambiguity sets. A two-stage adaptive distributionally robust optimization (2S-ADRO) model is developed to plan the SMI-BESS in detail, meeting the requirements of mobile energy storage. Finally, case studies are conducted using weather and grid data from some regions in China to validate the effectiveness of the proposed structure and method.
{"title":"Planning of Stationary-Mobile Integrated Battery Energy Storage Systems Under Severe Convective Weather","authors":"Qian Wang;Xueguang Zhang;Ying Xu;Zhongkai Yi;Dianguo Xu","doi":"10.1109/TSTE.2024.3513295","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3513295","url":null,"abstract":"Under extreme weather events represented by severe convective weather (SCW), the adaptability of power system and service restoration have become paramount. To this end, this paper presents a novel planning method of stationary-mobile integrated battery energy storage system (SMI-BESS) capable of spatial flexibility. This designed system can flexibly switch between stationary and mobile modes to cope with normal operation and extreme weather events. Considering the multitude of threats posed of SCW, such as extreme wind speed, lightning strikes, and hail, a comprehensive fragility model of the distribution network is established to quantify adverse impacts of the extreme event. Uncertainties in renewable energy generation and distribution network failures are characterized using two types of ambiguity sets. A two-stage adaptive distributionally robust optimization (2S-ADRO) model is developed to plan the SMI-BESS in detail, meeting the requirements of mobile energy storage. Finally, case studies are conducted using weather and grid data from some regions in China to validate the effectiveness of the proposed structure and method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1253-1268"},"PeriodicalIF":8.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667665","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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