Pub Date : 2024-09-17DOI: 10.1109/TSTE.2024.3462476
Xiaochi Ding;Yunfei Du;Xinwei Shen;Qiuwei Wu;Xuan Zhang;Nikos D. Hatziargyriou
The electrical collector system (ECS) plays a crucial role in determining the performance of offshore wind farms (OWFs). Existing research has predominantly restricted ECS cable layouts to conventional radial or ring structures and employed graph theory heuristics for solutions. However, both economic efficiency and reliability of the OWFs heavily depend on their ECS structure, and the optimal ECS cable layout often deviates from typical configurations. In this context, this paper introduces a novel reliability-based ECS cable layout planning method for large-scale OWFs, employing a two-stage stochastic programming approach to address uncertainties of wind power and contingencies. To enhance reliability, the model incorporates optimal post-fault network reconfiguration strategies by adjusting wind turbine power supply paths through link cables. To tackle computation challenges arising from numerous contingency scenarios, a customized progressive contingency incorporation (CPCI) framework is developed to solve the model with higher efficiency by iteratively identifying non-trivial scenarios and solving the simplified problems. The convergence and optimality are theoretically proven. Numerical tests on several real-world OWFs validate the necessity of fully optimizing ECS structures and demonstrate the efficiency of the CPCI algorithm.
{"title":"Reliability-Based Planning of Cable Layout for Offshore Wind Farm Electrical Collector System Considering Post-Fault Network Reconfiguration","authors":"Xiaochi Ding;Yunfei Du;Xinwei Shen;Qiuwei Wu;Xuan Zhang;Nikos D. Hatziargyriou","doi":"10.1109/TSTE.2024.3462476","DOIUrl":"10.1109/TSTE.2024.3462476","url":null,"abstract":"The electrical collector system (ECS) plays a crucial role in determining the performance of offshore wind farms (OWFs). Existing research has predominantly restricted ECS cable layouts to conventional radial or ring structures and employed graph theory heuristics for solutions. However, both economic efficiency and reliability of the OWFs heavily depend on their ECS structure, and the optimal ECS cable layout often deviates from typical configurations. In this context, this paper introduces a novel reliability-based ECS cable layout planning method for large-scale OWFs, employing a two-stage stochastic programming approach to address uncertainties of wind power and contingencies. To enhance reliability, the model incorporates optimal post-fault network reconfiguration strategies by adjusting wind turbine power supply paths through link cables. To tackle computation challenges arising from numerous contingency scenarios, a customized progressive contingency incorporation (CPCI) framework is developed to solve the model with higher efficiency by iteratively identifying non-trivial scenarios and solving the simplified problems. The convergence and optimality are theoretically proven. Numerical tests on several real-world OWFs validate the necessity of fully optimizing ECS structures and demonstrate the efficiency of the CPCI algorithm.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"419-433"},"PeriodicalIF":8.6,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250422","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 : 2024-09-17DOI: 10.1109/TSTE.2024.3462789
Yi Luo;Jun Yao;Dong Yang;Linsheng Zhao;Rongyu Jin
In this article, from the perspective of DC-link voltage (DCV) control, the transient interaction mechanism of multi-paralleled doubly fed induction generator (DFIG)-based wind turbines (WTs) is investigated during asymmetrical grid faults. Firstly, considering the coupling characteristics of positive and negative sequence (PNS) components and the interaction characteristics between the rotor side converter (RSC) and grid side converter (GSC), a large-signal nonlinear model of multiple-parallel DFIG-based WTs in DC-link voltage control time-scale is obtained. Furthermore, by using the energy function method, the dynamic interaction mechanism of multiple-parallel DFIG-based WTs is analyzed. The influence of different parameters on the transient characteristics of DC-link voltage is analyzed by using phase trajectory diagram. The dominant factors affecting the transient stability of the WTs and stability level of DC-link voltage are obtained. In addition, considering the interaction among WTs, the dynamic interaction between RSC and GSC, as well as the requirement of grid codes, a transient stability optimization strategy during asymmetrical grid faults is proposed to improve the transient stability level of the DC-link voltage and the transient stability of multiple-parallel DFIG-based WTs. Finally, simulation and experimental results validate the correctness of theoretical analysis and the effectiveness of the proposed strategy.
{"title":"Transient Interaction Mechanism Analysis and Stability Control of Multi-Paralleled DFIG-Based WTs During Asymmetrical Grid Faults","authors":"Yi Luo;Jun Yao;Dong Yang;Linsheng Zhao;Rongyu Jin","doi":"10.1109/TSTE.2024.3462789","DOIUrl":"10.1109/TSTE.2024.3462789","url":null,"abstract":"In this article, from the perspective of DC-link voltage (DCV) control, the transient interaction mechanism of multi-paralleled doubly fed induction generator (DFIG)-based wind turbines (WTs) is investigated during asymmetrical grid faults. Firstly, considering the coupling characteristics of positive and negative sequence (PNS) components and the interaction characteristics between the rotor side converter (RSC) and grid side converter (GSC), a large-signal nonlinear model of multiple-parallel DFIG-based WTs in DC-link voltage control time-scale is obtained. Furthermore, by using the energy function method, the dynamic interaction mechanism of multiple-parallel DFIG-based WTs is analyzed. The influence of different parameters on the transient characteristics of DC-link voltage is analyzed by using phase trajectory diagram. The dominant factors affecting the transient stability of the WTs and stability level of DC-link voltage are obtained. In addition, considering the interaction among WTs, the dynamic interaction between RSC and GSC, as well as the requirement of grid codes, a transient stability optimization strategy during asymmetrical grid faults is proposed to improve the transient stability level of the DC-link voltage and the transient stability of multiple-parallel DFIG-based WTs. Finally, simulation and experimental results validate the correctness of theoretical analysis and the effectiveness of the proposed strategy.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"434-451"},"PeriodicalIF":8.6,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250423","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 : 2024-09-13DOI: 10.1109/TSTE.2024.3454797
Zuan Zhang;Xiaowei Zhao
The Diode-Rectifier (DR) based HVDC has been considered as an economical solution to connect remote offshore wind turbines (WTs) to the onshore power grid. However, the DR is a passive device and cannot support the startup of the WTs. Therefore, it is worth finding an appropriate and cost-effective startup strategy for the DR-connected WTs. This paper investigates to use of wind energy to startup those WTs to avoid the need for additional grid support or the energy storage system, which can reduce the overall cost of such transmission system. The first challenge for this strategy is that the output active power of WTs can be extremely low during the startup process, which puts the WT rotor at a high risk of overspeed. Another challenge is to prevent power surges between synchronizing WTs. To address those issues, the pitch control has been innovated for the DR-connected WTs. In addition, a seamless synchronization control of the DR-connected WTs is proposed, which does not need the phase-locked-loop and can facilitate the whole startup process of the DR-connected WTs. The feasibility of these proposed control strategies for the startup of the DR-connected WTs is verified by comprehensive simulation studies.
{"title":"Startup Control of Grid-Forming Offshore Wind Turbines Connected to the Diode-Rectifier-Based HVDC Link","authors":"Zuan Zhang;Xiaowei Zhao","doi":"10.1109/TSTE.2024.3454797","DOIUrl":"10.1109/TSTE.2024.3454797","url":null,"abstract":"The Diode-Rectifier (DR) based HVDC has been considered as an economical solution to connect remote offshore wind turbines (WTs) to the onshore power grid. However, the DR is a passive device and cannot support the startup of the WTs. Therefore, it is worth finding an appropriate and cost-effective startup strategy for the DR-connected WTs. This paper investigates to use of wind energy to startup those WTs to avoid the need for additional grid support or the energy storage system, which can reduce the overall cost of such transmission system. The first challenge for this strategy is that the output active power of WTs can be extremely low during the startup process, which puts the WT rotor at a high risk of overspeed. Another challenge is to prevent power surges between synchronizing WTs. To address those issues, the pitch control has been innovated for the DR-connected WTs. In addition, a seamless synchronization control of the DR-connected WTs is proposed, which does not need the phase-locked-loop and can facilitate the whole startup process of the DR-connected WTs. The feasibility of these proposed control strategies for the startup of the DR-connected WTs is verified by comprehensive simulation studies.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"407-418"},"PeriodicalIF":8.6,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250424","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}
Power forecasting of newly built photovoltaic (PV) sites faces huge challenges owing to the lack of sufficient training samples. To this end, this paper proposes an unsupervised zero-label learning method for power generation forecasting of newly built PV sites without relying on any historical power output data. The main idea is to extract invariant causal structures across different PV sites and leverage this causality to enhance the power forecasting performance on the newly built ones. In particular, a causality-enabled domain adaptation network (CEDAN) is designed to capture the causal mechanism of PV generation from the multiple fine-grain segments of time-lagged data. It relaxes the causal relationships to an associative structure which is further concretized as attention score vectors through the designed intra- and inter-variable attention mechanisms. To quantify the distribution discrepancies between source and target domain causal structures, a specific domain adaptation loss function is designed for the optimization of CEDAN. It is further extended to a domain adaptation quantile loss to handle the uncertainties of PV power output. By jointly minimizing the domain adaptation loss and power forecasting error/conditional quantile loss, an invariant power generation causal mechanism across data domains for a newly built PV site can be learned. This allows the proposed method to achieve accurate and highly generalized power generation forecasting for newly built PV sites without relying on labeled data. Extensive experiments utilizing real PV generation data demonstrate that the proposed method surpasses state-of-the-art transfer learning methods by 7.57% at least in deterministic forecasting and 8.37% at least in probabilistic forecasting.
{"title":"Causal Mechanism-Enabled Zero-Label Learning for Power Generation Forecasting of Newly-Built PV Sites","authors":"Pengfei Zhao;Weihao Hu;Di Cao;Rui Huang;Xiawei Wu;Qi Huang;Zhe Chen","doi":"10.1109/TSTE.2024.3459415","DOIUrl":"10.1109/TSTE.2024.3459415","url":null,"abstract":"Power forecasting of newly built photovoltaic (PV) sites faces huge challenges owing to the lack of sufficient training samples. To this end, this paper proposes an unsupervised zero-label learning method for power generation forecasting of newly built PV sites without relying on any historical power output data. The main idea is to extract invariant causal structures across different PV sites and leverage this causality to enhance the power forecasting performance on the newly built ones. In particular, a causality-enabled domain adaptation network (CEDAN) is designed to capture the causal mechanism of PV generation from the multiple fine-grain segments of time-lagged data. It relaxes the causal relationships to an associative structure which is further concretized as attention score vectors through the designed intra- and inter-variable attention mechanisms. To quantify the distribution discrepancies between source and target domain causal structures, a specific domain adaptation loss function is designed for the optimization of CEDAN. It is further extended to a domain adaptation quantile loss to handle the uncertainties of PV power output. By jointly minimizing the domain adaptation loss and power forecasting error/conditional quantile loss, an invariant power generation causal mechanism across data domains for a newly built PV site can be learned. This allows the proposed method to achieve accurate and highly generalized power generation forecasting for newly built PV sites without relying on labeled data. Extensive experiments utilizing real PV generation data demonstrate that the proposed method surpasses state-of-the-art transfer learning methods by 7.57% at least in deterministic forecasting and 8.37% at least in probabilistic forecasting.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"392-406"},"PeriodicalIF":8.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219442","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 : 2024-09-11DOI: 10.1109/TSTE.2024.3458916
Xun Shen
Precise wind power curves are pivotal for monitoring the status of wind turbines and predicting wind power, which are important parts of utilizing wind energy in power systems. However, the data sets for training wind power curve models have a critical issue. A considerable proportion of the data sets is abnormal due to communication failure and other factors. Using the data sets with abnormal data will significantly deteriorate the fitting performance. This paper resolves the above issue by proposing a unified way to achieve abnormal data detection and curve fitting. Instead of regression with scalar output, set-valued regression of the wind power curve is considered, giving a set of wind power for a given wind speed. Interval neural network is adopted as the model for set-valued regression. A chance-constrained optimization problem is formulated to train an interval neural network. The obtained interval neural network can specify a subset with the normal data area, which can be used to give the threshold for abnormal data detection. Besides, the center points of the interval can be used as the fitted wind power curve. Since the formulated chance-constrained optimization problem is intractable, a sample-based sigmoidal approximation method is proposed to approximately solve it. The convergence and probabilistic feasibility of the approximation are given. Finally, experimental validations have been conducted to compare the proposed method with several existing methods.
{"title":"Set-Valued Regression of Wind Power Curve","authors":"Xun Shen","doi":"10.1109/TSTE.2024.3458916","DOIUrl":"10.1109/TSTE.2024.3458916","url":null,"abstract":"Precise wind power curves are pivotal for monitoring the status of wind turbines and predicting wind power, which are important parts of utilizing wind energy in power systems. However, the data sets for training wind power curve models have a critical issue. A considerable proportion of the data sets is abnormal due to communication failure and other factors. Using the data sets with abnormal data will significantly deteriorate the fitting performance. This paper resolves the above issue by proposing a unified way to achieve abnormal data detection and curve fitting. Instead of regression with scalar output, set-valued regression of the wind power curve is considered, giving a set of wind power for a given wind speed. Interval neural network is adopted as the model for set-valued regression. A chance-constrained optimization problem is formulated to train an interval neural network. The obtained interval neural network can specify a subset with the normal data area, which can be used to give the threshold for abnormal data detection. Besides, the center points of the interval can be used as the fitted wind power curve. Since the formulated chance-constrained optimization problem is intractable, a sample-based sigmoidal approximation method is proposed to approximately solve it. The convergence and probabilistic feasibility of the approximation are given. Finally, experimental validations have been conducted to compare the proposed method with several existing methods.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"350-364"},"PeriodicalIF":8.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219446","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}
Stochastic wind conditions and curtailment lead to a sparse distribution of normal data compared to outliers on the Wind Power Curve (WPC). This results in the removal of sparse normal data during the data cleaning process, hampering short-term wind power assessment and forecasting. To address this issue, this paper proposes a decision boundary construction method that utilizes the wind speed-power correlation trend to retain normal WPC data. First, leveraging the positive correlation between wind speed and power, an incremental trend search strategy is used to obtain the trend curve. Building on this curve, a scatter motion trend algorithm is introduced to eliminate densely clustered curtailed power data. Finally, a kernel function-based 3-sigma boundary construction method is suggested to further reduce the influence of remaining clustered outliers on decision boundaries. The proposed method is compared to eight advanced algorithms using data from 17 wind turbines across three wind farms, demonstrating superior performance, especially in scenarios with sparse normal data.
{"title":"Preserving Normal Power Curve Data With Sparse Density via Wind Speed-Power Correlation Trend Cleaning Method","authors":"Hongrui Li;Shuangxin Wang;Jiading Jiang;Jun Liu;Junmei Ou;Ziang Zhou","doi":"10.1109/TSTE.2024.3459005","DOIUrl":"10.1109/TSTE.2024.3459005","url":null,"abstract":"Stochastic wind conditions and curtailment lead to a sparse distribution of normal data compared to outliers on the Wind Power Curve (WPC). This results in the removal of sparse normal data during the data cleaning process, hampering short-term wind power assessment and forecasting. To address this issue, this paper proposes a decision boundary construction method that utilizes the wind speed-power correlation trend to retain normal WPC data. First, leveraging the positive correlation between wind speed and power, an incremental trend search strategy is used to obtain the trend curve. Building on this curve, a scatter motion trend algorithm is introduced to eliminate densely clustered curtailed power data. Finally, a kernel function-based 3-sigma boundary construction method is suggested to further reduce the influence of remaining clustered outliers on decision boundaries. The proposed method is compared to eight advanced algorithms using data from 17 wind turbines across three wind farms, demonstrating superior performance, especially in scenarios with sparse normal data.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"365-376"},"PeriodicalIF":8.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219445","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 : 2024-09-11DOI: 10.1109/TSTE.2024.3450608
Yuan Du;Yixun Xue;Mohammad Shahidehpour;Wenchuan Wu;Xinyue Chang;Zening Li;Hongbin Sun
Unit commitment (UC) is a key player in the coordinated operation of integrated energy systems. However, the participation of multiple market entities with widely different characteristics in large-scale energy systems has urged the critical need for the application of a distributed scheme to the UC problem. The NP-hard UC problem is a challenging mixed-integer programming problem. The presence of a large number of binary variables in the UC subproblems, which are solved by each participating entity after implementing the UC decomposition, fails to guarantee the convergence and the optimality of existing solution methods. To bridge this gap, this paper proposes a distributed method, using logic-based Benders decomposition (LBBD), for the UC problem in a typical multi-entity system, i.e., integrated electric and heating system (IEHS). By searching the branch and bound tree of the district heating system (DHS) subproblem, the lower bound of its objective function is rigorously derived as a valid Benders cut to ensure the convergence to global optimal results. This distributed method is suitable for both deterministic and robust UC solutions. Numerical simulations are conducted on two test systems to demonstrate the performance of the proposed model and its distributed solution method.
{"title":"Globally Optimal Distributed Operation of Integrated Electric and Heating Systems","authors":"Yuan Du;Yixun Xue;Mohammad Shahidehpour;Wenchuan Wu;Xinyue Chang;Zening Li;Hongbin Sun","doi":"10.1109/TSTE.2024.3450608","DOIUrl":"10.1109/TSTE.2024.3450608","url":null,"abstract":"Unit commitment (UC) is a key player in the coordinated operation of integrated energy systems. However, the participation of multiple market entities with widely different characteristics in large-scale energy systems has urged the critical need for the application of a distributed scheme to the UC problem. The NP-hard UC problem is a challenging mixed-integer programming problem. The presence of a large number of binary variables in the UC subproblems, which are solved by each participating entity after implementing the UC decomposition, fails to guarantee the convergence and the optimality of existing solution methods. To bridge this gap, this paper proposes a distributed method, using logic-based Benders decomposition (LBBD), for the UC problem in a typical multi-entity system, i.e., integrated electric and heating system (IEHS). By searching the branch and bound tree of the district heating system (DHS) subproblem, the lower bound of its objective function is rigorously derived as a valid Benders cut to ensure the convergence to global optimal results. This distributed method is suitable for both deterministic and robust UC solutions. Numerical simulations are conducted on two test systems to demonstrate the performance of the proposed model and its distributed solution method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"336-349"},"PeriodicalIF":8.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219444","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}
Fast frequency response of wind turbine generators (WTGs) is achieved by injecting incremental power to the grid followed by power reductions to avoid over-deceleration and ensure secure rotor speed recovery. Second frequency deeps (SFDs) are the results of such power reductions that are challenging during abrupt frequency transients that may lead to under-frequency load shedding, or cascading events leading to blackouts. To address this issue, this paper presents an adaptive inertial control (AIC) scheme for WTGs designed to maximize the improvement in frequency nadir without causing SFD. The proposed method is developed through an assessment of power reduction period of WTGs during fast frequency response. This analysis investigates the impacts on the system frequency of a) injecting different shares of disturbance size (SoDSs) by WTGs and b) latency/delay in power injection. Derived from this analysis, the AIC is proposed to inject the maximum possible SoDS during the over-production period and successfully stabilize and recover the rotor speed during the assigned optimal power reduction period with SFDs disabled. This is achieved by adaptively adjusting the AIC in the reduction period based on the SoDS injected by WTGs during the over-production stage. Also, the AIC is modified to adapt against wind speed deviations. To evaluate the performance of the AIC, a comprehensive verification is carried out by comparing AIC with thirteen existing inertial control schemes and maximum power point tracking control in various cases using wind-integrated IEEE 39-bus system in Digsilent PowerFactory and real-time experimental tests. The results confirm the effectiveness of AIC in terms of achieving maximum improvement in frequency nadir without generating SFD.
{"title":"Adaptive Inertial Control for Wind Turbine Generators in Fast Frequency Response Based on the Power Reduction Period Assessment","authors":"Mahdi Heidari;Lei Ding;Mostafa Kheshti;Xiaowei Zhao;Vladimir Terzija","doi":"10.1109/TSTE.2024.3459729","DOIUrl":"10.1109/TSTE.2024.3459729","url":null,"abstract":"Fast frequency response of wind turbine generators (WTGs) is achieved by injecting incremental power to the grid followed by power reductions to avoid over-deceleration and ensure secure rotor speed recovery. Second frequency deeps (SFDs) are the results of such power reductions that are challenging during abrupt frequency transients that may lead to under-frequency load shedding, or cascading events leading to blackouts. To address this issue, this paper presents an adaptive inertial control (AIC) scheme for WTGs designed to maximize the improvement in frequency nadir without causing SFD. The proposed method is developed through an assessment of power reduction period of WTGs during fast frequency response. This analysis investigates the impacts on the system frequency of a) injecting different shares of disturbance size (SoDSs) by WTGs and b) latency/delay in power injection. Derived from this analysis, the AIC is proposed to inject the maximum possible SoDS during the over-production period and successfully stabilize and recover the rotor speed during the assigned optimal power reduction period with SFDs disabled. This is achieved by adaptively adjusting the AIC in the reduction period based on the SoDS injected by WTGs during the over-production stage. Also, the AIC is modified to adapt against wind speed deviations. To evaluate the performance of the AIC, a comprehensive verification is carried out by comparing AIC with thirteen existing inertial control schemes and maximum power point tracking control in various cases using wind-integrated IEEE 39-bus system in Digsilent PowerFactory and real-time experimental tests. The results confirm the effectiveness of AIC in terms of achieving maximum improvement in frequency nadir without generating SFD.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"377-391"},"PeriodicalIF":8.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219443","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 : 2024-09-06DOI: 10.1109/TSTE.2024.3456068
Ruihao Song;Vladimir Terzija;Thomas Hamacher;Vedran S. Perić
Fast frequency response services, designed to quickly balance the electrical grid within seconds, have a critical importance for managing sudden anomalies in low-inertia power systems. Battery systems often serve as versatile prosumers on the demand side to facilitate fast frequency response services. However, the nature of fast frequency response services leads to a highly fluctuating power profile for batteries, which can shorten their lifetime. In contrast, distributed air-source heat pumps in residential areas have a substantial untapped potential to support fast frequency response services. This paper seeks to integrate them into the existing services through a controller upgrade. We analyze the influence of air-source heat pumps' inherent complex thermal dynamics on fast frequency response services, revealing control challenges posed by unpredictable operating condition changes. Such a challenge is tackled with a standard droop control structure which is tuned through ${{H}_infty }$ method, guaranteeing practical and stable operations within the permitted operating condition range. Finally, the proposed fast frequency response service scheme is tested through multiphysics simulations on a small-size low-inertia residential microgrid. The obtained results strongly supported the proposed new service.
{"title":"Integrating Air-Source Heat Pumps into the Demand-Side Fast Frequency Response Service: A Study Based on Thermal Dynamic Uncertainty","authors":"Ruihao Song;Vladimir Terzija;Thomas Hamacher;Vedran S. Perić","doi":"10.1109/TSTE.2024.3456068","DOIUrl":"10.1109/TSTE.2024.3456068","url":null,"abstract":"Fast frequency response services, designed to quickly balance the electrical grid within seconds, have a critical importance for managing sudden anomalies in low-inertia power systems. Battery systems often serve as versatile prosumers on the demand side to facilitate fast frequency response services. However, the nature of fast frequency response services leads to a highly fluctuating power profile for batteries, which can shorten their lifetime. In contrast, distributed air-source heat pumps in residential areas have a substantial untapped potential to support fast frequency response services. This paper seeks to integrate them into the existing services through a controller upgrade. We analyze the influence of air-source heat pumps' inherent complex thermal dynamics on fast frequency response services, revealing control challenges posed by unpredictable operating condition changes. Such a challenge is tackled with a standard droop control structure which is tuned through \u0000<inline-formula><tex-math>${{H}_infty }$</tex-math></inline-formula>\u0000 method, guaranteeing practical and stable operations within the permitted operating condition range. Finally, the proposed fast frequency response service scheme is tested through multiphysics simulations on a small-size low-inertia residential microgrid. The obtained results strongly supported the proposed new service.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"323-335"},"PeriodicalIF":8.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219447","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 : 2024-09-06DOI: 10.1109/TSTE.2024.3455759
Mao Yang;Yunfeng Guo;Fulin Fan
Ultra-short-term prediction of wind farm cluster power (UPWFCP) is of great significance for the development of intra-day power generation plan, and the power prediction accuracy is difficult to be further improved due to the chaotic effect of the weather system and the incompleteness of the information. In this regard, this paper proposes an embedded graph structure learning method for wind farm cluster (WFC) that incorporates spatiotemporal information gain (STIG) theory. The graph structure describing the spatiotemporal evolution relationship of information between wind farms (WFs) is constructed based on the spatiotemporal transfer relationship of power waveforms between WFs. An embedded graph attention network (EGAN) is proposed to learn STIG adjacency relationship among WFs, and a dynamic grouping scheme of redundant nodes in WFs based on STIG distance is constructed to reduce the modeling complexity. The proposed method is applied to the WFC of Inner Mongolia, China, and the results show that the NRMSE, NMAE, and MAPE of the proposed method are on average 2.63%, 2.09%, and 20.95% lower, and the R 2 and Pr are on average 7.66% and 6.64% higher, respectively, compared with the rest of the comparison methods at all time scales.
{"title":"Ultra-Short-Term Prediction of Wind Farm Cluster Power Based on Embedded Graph Structure Learning With Spatiotemporal Information Gain","authors":"Mao Yang;Yunfeng Guo;Fulin Fan","doi":"10.1109/TSTE.2024.3455759","DOIUrl":"10.1109/TSTE.2024.3455759","url":null,"abstract":"Ultra-short-term prediction of wind farm cluster power (UPWFCP) is of great significance for the development of intra-day power generation plan, and the power prediction accuracy is difficult to be further improved due to the chaotic effect of the weather system and the incompleteness of the information. In this regard, this paper proposes an embedded graph structure learning method for wind farm cluster (WFC) that incorporates spatiotemporal information gain (STIG) theory. The graph structure describing the spatiotemporal evolution relationship of information between wind farms (WFs) is constructed based on the spatiotemporal transfer relationship of power waveforms between WFs. An embedded graph attention network (EGAN) is proposed to learn STIG adjacency relationship among WFs, and a dynamic grouping scheme of redundant nodes in WFs based on STIG distance is constructed to reduce the modeling complexity. The proposed method is applied to the WFC of Inner Mongolia, China, and the results show that the NRMSE, NMAE, and MAPE of the proposed method are on average 2.63%, 2.09%, and 20.95% lower, and the R\u0000<sup>2</sup>\u0000 and Pr are on average 7.66% and 6.64% higher, respectively, compared with the rest of the comparison methods at all time scales.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"308-322"},"PeriodicalIF":8.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219448","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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