Pub Date : 2025-02-18DOI: 10.1109/TSTE.2025.3543186
Juan Wei;Yuxiang Li;Hanzhi Peng;Sheng Huang;Yu Yang;Shuaifeng Wang;Xueting Cheng;Xiaohui Huang
Rapid power and torque fluctuations in time-varying conditions increase the fatigue load and failure rate of wind turbine (WT) gearboxes. In this study, an optimal power control method is proposed for a wind farm (WF) to improve the power flow and service quality, allowing the WF to track the power demand instructions from the transmission system operator while minimizing the fluctuations of vibration displacements inside the gearbox. A comprehensive dynamic model of the gearbox is developed by analyzing the transmission mechanism of key gearbox components, such as the planet carrier, planet gears, sun gears, and spur gears, describing the correlation between the internal vibration and mechanical torque and power output. Then, an optimal power control problem is formulated based on model predictive control to suppress the fatigue load while tracking power. Furthermore, a fatigue evaluation system is built based on the real-time vibration state inside the gearbox to characterize the service quality of WTs and guide the power generation of the WF. This approach provides a safety-oriented boundary regarding the WT fatigue load in the optimal power dispatch issue of WFs to suppress potential WT failures. Case studies in MATLAB/Simulink demonstrated the effectiveness of the proposed method.
{"title":"Optimal Power Control in Wind Farms for Gearbox Load Reduction","authors":"Juan Wei;Yuxiang Li;Hanzhi Peng;Sheng Huang;Yu Yang;Shuaifeng Wang;Xueting Cheng;Xiaohui Huang","doi":"10.1109/TSTE.2025.3543186","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3543186","url":null,"abstract":"Rapid power and torque fluctuations in time-varying conditions increase the fatigue load and failure rate of wind turbine (WT) gearboxes. In this study, an optimal power control method is proposed for a wind farm (WF) to improve the power flow and service quality, allowing the WF to track the power demand instructions from the transmission system operator while minimizing the fluctuations of vibration displacements inside the gearbox. A comprehensive dynamic model of the gearbox is developed by analyzing the transmission mechanism of key gearbox components, such as the planet carrier, planet gears, sun gears, and spur gears, describing the correlation between the internal vibration and mechanical torque and power output. Then, an optimal power control problem is formulated based on model predictive control to suppress the fatigue load while tracking power. Furthermore, a fatigue evaluation system is built based on the real-time vibration state inside the gearbox to characterize the service quality of WTs and guide the power generation of the WF. This approach provides a safety-oriented boundary regarding the WT fatigue load in the optimal power dispatch issue of WFs to suppress potential WT failures. Case studies in MATLAB/Simulink demonstrated the effectiveness of the proposed method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1990-2001"},"PeriodicalIF":8.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331567","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}
Renewable energyes are highly penetrated in power system through Grid-forming inverter (GFMI). Transient stability of single GFMI system under severe grid fault has been thoroughly analyzed in recent years, but quantitative analysis for parallel grid-tied GFMI system is rarely studied. To fill this gap, the large signal equivalent model of parallel system considering the interaction between inverters is newly built. Based on the model, the Lyapunov function incorporating kinetic energy, potential energy, damping dissipation, and interaction energy is constructed for accurate stability region estimation. Then, the effect of control parameters on stability region is analyzed, indicating that increasing damping, reducing inertia, and lowering reference power of one GFMI can deteriorate stability margin of parallel GFMI system due to the enlarged interaction power, which is distinct from single GFMI system. For this, a decentralized transient control that adaptively adjusts damping, inertia, and reference power is proposed to guarantee transient stability and achieve low-voltage ride-through (LVRT) for parallel system without relying on communication, system information. Finally, simulation and experimental tests validate the correctness of theoretical analysis and the effectiveness of proposed control.
{"title":"Stability Region Estimation and Decentralized Transient Control for Parallel Grid-Tied Grid-Forming Inverters","authors":"Cong Luo;Yandong Chen;Shuhan Liao;Zhiwei Xie;Zhijie Lian;Zili Wang;Xiaoke Liu;Mingkun Gao;Jiawei Xie","doi":"10.1109/TSTE.2025.3543595","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3543595","url":null,"abstract":"Renewable energyes are highly penetrated in power system through Grid-forming inverter (GFMI). Transient stability of single GFMI system under severe grid fault has been thoroughly analyzed in recent years, but quantitative analysis for parallel grid-tied GFMI system is rarely studied. To fill this gap, the large signal equivalent model of parallel system considering the interaction between inverters is newly built. Based on the model, the Lyapunov function incorporating kinetic energy, potential energy, damping dissipation, and interaction energy is constructed for accurate stability region estimation. Then, the effect of control parameters on stability region is analyzed, indicating that increasing damping, reducing inertia, and lowering reference power of one GFMI can deteriorate stability margin of parallel GFMI system due to the enlarged interaction power, which is distinct from single GFMI system. For this, a decentralized transient control that adaptively adjusts damping, inertia, and reference power is proposed to guarantee transient stability and achieve low-voltage ride-through (LVRT) for parallel system without relying on communication, system information. Finally, simulation and experimental tests validate the correctness of theoretical analysis and the effectiveness of proposed control.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"2015-2028"},"PeriodicalIF":8.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-17DOI: 10.1109/TSTE.2025.3542549
Shunjiang Yu;Changming Chen;Yunchu Wang;Hongfei Yu;Chuanxun Pei;Jiaorong Ren;Li Yang;Zhenzhi Lin
Heat storage capacity of heat network in urban integrated energy system (UIES) has the potential to significantly improve the operational flexibility of the system. To obtain the optimal UIES planning scheme, a UIES station-network cooperative optimization planning (UIES-SNCOP) method considering heat storage capacity is proposed. First, a heat network operation model under constant flow-variable temperature considering flow direction depiction is established for solving the problem that the existing model cannot be directly applied to UIES-SNCOP because the flow direction of pipeline cannot be predetermined. Then, a radial structure-oriented topology model of distribution and heat networks is developed to ensure the radiality of energy supply network while also reducing planning cost. On this basis, a UIES-SNCOP model based on information gap decision theory and stochastic optimization is constructed to realize the co-optimization of the siting and sizing of energy station and topology of distribution and heat networks. Finally, a solution method of UIES-SNCOP model based on relaxation-contraction coupled McCormick envelope is proposed for effectively improving the accuracy of solution result. The planning of UIES is conducted on an urban topology containing 55 nodes to test the performance of the proposed method, and simulation results indicate that the proposed method outperforms other existing methods in terms of reducing planning cost, ensuring radial structure of the energy supply network, utilizing heat storage capacity and enhancing solution accuracy.
{"title":"Station-Network Cooperative Optimization Planning of Urban Integrated Energy System Considering Heat Storage Capacity of Heat Network","authors":"Shunjiang Yu;Changming Chen;Yunchu Wang;Hongfei Yu;Chuanxun Pei;Jiaorong Ren;Li Yang;Zhenzhi Lin","doi":"10.1109/TSTE.2025.3542549","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3542549","url":null,"abstract":"Heat storage capacity of heat network in urban integrated energy system (UIES) has the potential to significantly improve the operational flexibility of the system. To obtain the optimal UIES planning scheme, a UIES station-network cooperative optimization planning (UIES-SNCOP) method considering heat storage capacity is proposed. First, a heat network operation model under constant flow-variable temperature considering flow direction depiction is established for solving the problem that the existing model cannot be directly applied to UIES-SNCOP because the flow direction of pipeline cannot be predetermined. Then, a radial structure-oriented topology model of distribution and heat networks is developed to ensure the radiality of energy supply network while also reducing planning cost. On this basis, a UIES-SNCOP model based on information gap decision theory and stochastic optimization is constructed to realize the co-optimization of the siting and sizing of energy station and topology of distribution and heat networks. Finally, a solution method of UIES-SNCOP model based on relaxation-contraction coupled McCormick envelope is proposed for effectively improving the accuracy of solution result. The planning of UIES is conducted on an urban topology containing 55 nodes to test the performance of the proposed method, and simulation results indicate that the proposed method outperforms other existing methods in terms of reducing planning cost, ensuring radial structure of the energy supply network, utilizing heat storage capacity and enhancing solution accuracy.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1956-1976"},"PeriodicalIF":8.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-17DOI: 10.1109/TSTE.2025.3542899
Siqi Wang;Xin Zhang;Min Du;Wei Pei
With wind power being extensively integrated into power systems, its inherent uncertainty and variability pose significant challenges to the power system operational security. Traditional robust optimization methods capture the worst-case scenario, which results in overly conservative decisions, with insufficient considerations on AC network constraints in power systems. To overcome this issue, this paper proposes a novel adaptive robust AC network-constrained unit commitment (AC-NCUC) model that considers both the AC network security and the uncertainty of wind power output in power systems. More specifically, a convex polyhedral uncertainty set is constructed to characterize the uncertain wind power output. Here, the conservativeness of UC dispatch decisions can be adjusted by modifying the size of the convex polyhedral uncertainty set. Then, we combine Benders’ decomposition and Newton-Raphson methods to solve the AC-NCUC model for the optimal dispatch decisions. Simulation results on the modified IEEE 6-bus and IEEE RTS 79 systems validate the rationality and validity of our proposed approach. The proposed AC-NCUC model effectively maintains the system security while ensuring economic effectiveness.
{"title":"Two-Stage Adaptive Robust Model for AC Network-Constrained Unit Commitment in Power Systems With Uncertain Wind Power","authors":"Siqi Wang;Xin Zhang;Min Du;Wei Pei","doi":"10.1109/TSTE.2025.3542899","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3542899","url":null,"abstract":"With wind power being extensively integrated into power systems, its inherent uncertainty and variability pose significant challenges to the power system operational security. Traditional robust optimization methods capture the worst-case scenario, which results in overly conservative decisions, with insufficient considerations on AC network constraints in power systems. To overcome this issue, this paper proposes a novel adaptive robust AC network-constrained unit commitment (AC-NCUC) model that considers both the AC network security and the uncertainty of wind power output in power systems. More specifically, a convex polyhedral uncertainty set is constructed to characterize the uncertain wind power output. Here, the conservativeness of UC dispatch decisions can be adjusted by modifying the size of the convex polyhedral uncertainty set. Then, we combine Benders’ decomposition and Newton-Raphson methods to solve the AC-NCUC model for the optimal dispatch decisions. Simulation results on the modified IEEE 6-bus and IEEE RTS 79 systems validate the rationality and validity of our proposed approach. The proposed AC-NCUC model effectively maintains the system security while ensuring economic effectiveness.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1977-1989"},"PeriodicalIF":8.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-14DOI: 10.1109/TSTE.2025.3542296
Paula B. Garcia-Rosa;Rene A. Barrera-Cardenas;Giacomo Alessandri;Federico Gallorini;Mairead A. Cruz;Joao Cruz;Salvatore D'Arco
This paper presents a design methodology for integrating an electrical energy storage unit into a hardware-in-the-loop (HIL) test rig for wave energy converters (WECs). Typically, the power production from WECs is characterised by pronounced fluctuations at low frequency and high peaks compared to the average. Wave energy test rigs should be able to reproduce these variations to impose realistic conditions to the device under test. Thus, the grid connection of the rig must be sized to cope with high peaks, and additional measures may be required to avoid disturbances on nearby loads and negative effects on voltage quality. The integration of electrical energy storage can smoothen power fluctuations and mitigate these drawbacks, while resulting in lower installation and operating costs. The design methodology indicates how to effectively size the storage unit and which technology to favour based on the type and duration of test campaigns. Numerical simulation results are presented for a dual HIL test rig and operational profiles of three different WEC technologies. For designs with energy storage lifetime shorter than the calendar life, sensitivity analyses indicate that the rig's annual utilisation rate and the level of accelerated testing have a significant effect on the storage energy requirements.
{"title":"Integration of Electrical Energy Storage in Wave Energy Hardware-in-the-Loop Test Rigs","authors":"Paula B. Garcia-Rosa;Rene A. Barrera-Cardenas;Giacomo Alessandri;Federico Gallorini;Mairead A. Cruz;Joao Cruz;Salvatore D'Arco","doi":"10.1109/TSTE.2025.3542296","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3542296","url":null,"abstract":"This paper presents a design methodology for integrating an electrical energy storage unit into a hardware-in-the-loop (HIL) test rig for wave energy converters (WECs). Typically, the power production from WECs is characterised by pronounced fluctuations at low frequency and high peaks compared to the average. Wave energy test rigs should be able to reproduce these variations to impose realistic conditions to the device under test. Thus, the grid connection of the rig must be sized to cope with high peaks, and additional measures may be required to avoid disturbances on nearby loads and negative effects on voltage quality. The integration of electrical energy storage can smoothen power fluctuations and mitigate these drawbacks, while resulting in lower installation and operating costs. The design methodology indicates how to effectively size the storage unit and which technology to favour based on the type and duration of test campaigns. Numerical simulation results are presented for a dual HIL test rig and operational profiles of three different WEC technologies. For designs with energy storage lifetime shorter than the calendar life, sensitivity analyses indicate that the rig's annual utilisation rate and the level of accelerated testing have a significant effect on the storage energy requirements.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1944-1955"},"PeriodicalIF":8.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1109/TSTE.2025.3541326
Wen Gao;Kaishun Xiahou;Yang Liu;Zhigang Li;Q. H. Wu;Dongxu Chang;Yihua Zhu
To address the transient frequency and voltage stability challenges posed by low-inertia offshore wind farms (OWFs) connected through voltage source converter (VSC) based multi-terminal high voltage direct current (VSC-MTDC) system, a nonlinear perturbation observer and funnel control-based transient frequency and voltage support (PFTFVS) control strategy is proposed for VSC-MTDC integrated OWFs system. This approach utilizes improved estimation ability of the observer and adaptive feature of the funnel controller to enhance transient support capability and disturbance rejection performance of the system. The strategy comprises three parts: 1) an adaptive transient frequency support and rotor speed control method for wind turbines is devised to enhance the frequency support capability of OWFs; 2) considering the energy storage capability of DC capacitor in VSC-MTDC system, a transient frequency support controller is designed for VSC station to swiftly manage transient frequency variations; and 3) utilizing the rapid power regulation ability of VSC-MTDC, a transient voltage support controller is developed for VSC station to enhance voltage stability and boost the power transmission capacity. Finally, dynamic simulations of VSC-MTDC integrated OWFs system are built to verify the validity and robustness of the proposed strategy.
{"title":"Transient Frequency-Voltage Support Strategy for VSC-MTDC Integrated Offshore Wind Farms Based on Perturbation Observer and Funnel Control","authors":"Wen Gao;Kaishun Xiahou;Yang Liu;Zhigang Li;Q. H. Wu;Dongxu Chang;Yihua Zhu","doi":"10.1109/TSTE.2025.3541326","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3541326","url":null,"abstract":"To address the transient frequency and voltage stability challenges posed by low-inertia offshore wind farms (OWFs) connected through voltage source converter (VSC) based multi-terminal high voltage direct current (VSC-MTDC) system, a nonlinear perturbation observer and funnel control-based transient frequency and voltage support (PFTFVS) control strategy is proposed for VSC-MTDC integrated OWFs system. This approach utilizes improved estimation ability of the observer and adaptive feature of the funnel controller to enhance transient support capability and disturbance rejection performance of the system. The strategy comprises three parts: 1) an adaptive transient frequency support and rotor speed control method for wind turbines is devised to enhance the frequency support capability of OWFs; 2) considering the energy storage capability of DC capacitor in VSC-MTDC system, a transient frequency support controller is designed for VSC station to swiftly manage transient frequency variations; and 3) utilizing the rapid power regulation ability of VSC-MTDC, a transient voltage support controller is developed for VSC station to enhance voltage stability and boost the power transmission capacity. Finally, dynamic simulations of VSC-MTDC integrated OWFs system are built to verify the validity and robustness of the proposed strategy.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1931-1943"},"PeriodicalIF":8.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331783","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}
Hydrogen has drawn significant attention due to its long-term storage capability and wide industrial applications. How to efficiently utilize renewable energy to maximize hydrogen production of a group of spatially distributed electrolyzers is a fundamental problem urgently needed to be solved. This paper is the first to attempt to address the problem by proposing a hydrogen production dispatch (HPD) model for hydrogen-based microgrids with proton exchange membrane electrolyzers. Considering the limited communication and privacy requirement of distributed energy systems, a distributed hydrogen production dispatch framework is constructed. The original nonconvex optimization problem is transformed into a convex form. Furthermore, it is proven that the marginal hydrogen production benefit of each electrolyzer should be equal for the optimal hydrogen production dispatch via Lagrangian duality. By setting the marginal hydrogen production benefit as a consensus variable, a novel distributed consensus-based dispatch algorithm is developed, in which an event-triggered communication scheme is introduced to alleviate the communication burden. It is demonstrated that the proposed algorithm achieves linear convergence. Results of the case study indicate that the proposed strategy yields the optimal hydrogen production benefit, which is increased by 9.43% compared to on-site hydrogen production and demonstrates excellent solving efficiency especially for large-scale systems.
{"title":"Optimal Hydrogen Production Dispatch of Networked Hydrogen-Based Microgrids via a Distributed Method","authors":"Wangli He;Jiawei Yu;Chenxi Cao;Honggang Wang;Feng Qian","doi":"10.1109/TSTE.2025.3541097","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3541097","url":null,"abstract":"Hydrogen has drawn significant attention due to its long-term storage capability and wide industrial applications. How to efficiently utilize renewable energy to maximize hydrogen production of a group of spatially distributed electrolyzers is a fundamental problem urgently needed to be solved. This paper is the first to attempt to address the problem by proposing a hydrogen production dispatch (HPD) model for hydrogen-based microgrids with proton exchange membrane electrolyzers. Considering the limited communication and privacy requirement of distributed energy systems, a distributed hydrogen production dispatch framework is constructed. The original nonconvex optimization problem is transformed into a convex form. Furthermore, it is proven that the marginal hydrogen production benefit of each electrolyzer should be equal for the optimal hydrogen production dispatch via Lagrangian duality. By setting the marginal hydrogen production benefit as a consensus variable, a novel distributed consensus-based dispatch algorithm is developed, in which an event-triggered communication scheme is introduced to alleviate the communication burden. It is demonstrated that the proposed algorithm achieves linear convergence. Results of the case study indicate that the proposed strategy yields the optimal hydrogen production benefit, which is increased by 9.43% compared to on-site hydrogen production and demonstrates excellent solving efficiency especially for large-scale systems.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1919-1930"},"PeriodicalIF":8.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331785","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}
Medium-term planning of cascaded hydropower (CHP) determines appropriate carryover storage levels in reservoirs to optimize the usage of available water resources. This optimization seeks to maximize the hydropower generated in the current period (i.e., immediate benefit) plus the potential hydropower generation in the future period (i.e., future value). Thus, in the medium-term planning, properly quantifying the future value deposited in carryover storage is essential to achieve a balanced trade-off between immediate benefit and future value. To this end, this paper presents a framework to quantify the future value of carryover storage, which consists of three major steps: i) constructing a deterministic model to calculate the maximum possible hydropower generation that a given level of carryover storage can deliver in the future period; ii) extracting the implicit locational marginal water value (LMWV) of carryover storage for each reservoir by applying a partition-then-extract algorithm to the constructed model; and iii) developing a set of analytical rules based on the extracted LMWV to effectively calculate the future value. These rules can be seamlessly integrated into medium-term CHP planning models as tractable mixed-integer linear constraints to quantify the future value properly, and can be easily visualized to offer valuable insights for CHP operators. Finally, numerical results on Portland General Electric's CHP demonstrate the effectiveness of the presented framework in aiding medium-term CHP planning to identify suitable carryover storage strategies.
{"title":"A Carryover Storage Valuation Framework for Medium-Term Cascaded Hydropower Planning: A Portland General Electric System Study","authors":"Xianbang Chen;Yikui Liu;Zhiming Zhong;Neng Fan;Zhechong Zhao;Lei Wu","doi":"10.1109/TSTE.2025.3540923","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3540923","url":null,"abstract":"Medium-term planning of cascaded hydropower (CHP) determines appropriate carryover storage levels in reservoirs to optimize the usage of available water resources. This optimization seeks to maximize the hydropower generated in the current period (i.e., immediate benefit) plus the potential hydropower generation in the future period (i.e., future value). Thus, in the medium-term planning, properly quantifying the future value deposited in carryover storage is essential to achieve a balanced trade-off between immediate benefit and future value. To this end, this paper presents a framework to quantify the future value of carryover storage, which consists of three major steps: <italic>i)</i> constructing a deterministic model to calculate the maximum possible hydropower generation that a given level of carryover storage can deliver in the future period; <italic>ii)</i> extracting the implicit locational marginal water value (LMWV) of carryover storage for each reservoir by applying a partition-then-extract algorithm to the constructed model; and <italic>iii)</i> developing a set of analytical rules based on the extracted LMWV to effectively calculate the future value. These rules can be seamlessly integrated into medium-term CHP planning models as tractable mixed-integer linear constraints to quantify the future value properly, and can be easily visualized to offer valuable insights for CHP operators. Finally, numerical results on Portland General Electric's CHP demonstrate the effectiveness of the presented framework in aiding medium-term CHP planning to identify suitable carryover storage strategies.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1903-1918"},"PeriodicalIF":8.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1109/TSTE.2025.3540541
Guiyan Liu;Yajuan Zhang;Ping Zhang;Junhua Gu
Accurate and robust wind power forecasting plays a crucial role in ensuring the safety and stability of the power system. Hybrid spatiotemporal forecasting models based on graph convolutional networks have received widespread attention due to their advantages in spatial feature extraction. However, these methods are susceptible to the quality of the generated graph due to data noise and missing issues, resulting in suboptimal performance. In this paper, we propose a hybrid deep learning model based on spatiotemporal graph contrastive learning to address the above issues. Specifically, the model's encoder combines an adaptive graph convolutional network with LSTM to capture fine-grained spatiotemporal dependencies. To enhance the robustness of the encoder against data noise, we apply feature-level and topology-level data augmentation techniques to the model's input and design two contrastive learning auxiliary tasks from the temporal and spatial dimensions, respectively. Furthermore, to capture more comprehensive spatial correlations, we construct an adaptive graph by fusing the static graph with a learnable parameter matrix. Extensive experimental results on two real-world datasets demonstrate that our proposed model significantly outperforms other state-of-the-art methods.
{"title":"Spatiotemporal Graph Contrastive Learning for Wind Power Forecasting","authors":"Guiyan Liu;Yajuan Zhang;Ping Zhang;Junhua Gu","doi":"10.1109/TSTE.2025.3540541","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3540541","url":null,"abstract":"Accurate and robust wind power forecasting plays a crucial role in ensuring the safety and stability of the power system. Hybrid spatiotemporal forecasting models based on graph convolutional networks have received widespread attention due to their advantages in spatial feature extraction. However, these methods are susceptible to the quality of the generated graph due to data noise and missing issues, resulting in suboptimal performance. In this paper, we propose a hybrid deep learning model based on spatiotemporal graph contrastive learning to address the above issues. Specifically, the model's encoder combines an adaptive graph convolutional network with LSTM to capture fine-grained spatiotemporal dependencies. To enhance the robustness of the encoder against data noise, we apply feature-level and topology-level data augmentation techniques to the model's input and design two contrastive learning auxiliary tasks from the temporal and spatial dimensions, respectively. Furthermore, to capture more comprehensive spatial correlations, we construct an adaptive graph by fusing the static graph with a learnable parameter matrix. Extensive experimental results on two real-world datasets demonstrate that our proposed model significantly outperforms other state-of-the-art methods.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1889-1902"},"PeriodicalIF":8.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-10DOI: 10.1109/TSTE.2025.3540599
Jiajie Xiao;Peiqiang Li;Zhiyu Mao;Chunming Tu
This paper presents a hierarchical coordinated con-trol strategy designed to enhance the overall performance of the energy storage system (ESS) in secondary frequency regulation (SFR). The strategy includes three layers: the system layer, the ESS operation layer, and the coordination control layer. In the system layer, a detailed frequency response model of the multi-area interconnected system is developed. The intrinsic mech-anisms of timing, depth, and the effect of ESS and conventional generating unit (CGU) in SFR are revealed through the sen-sitivity analysis of the power allocation factor. Furthermore, a sensitivity-based adaptive power allocation strategy for ESS and CGU is proposed, which improves the SFR effect while reducing the ESS power and maintaining the state of charge (SOC). In the ESS operation layer, the ESS is divided into two components for integration, employing a monotonic charge-discharge strategy to reduce lifetime degradation caused by frequent charging and discharging, thereby enhancing operational efficiency. In the coordination control layer, considering the power prediction and the ESS operating state, a SOC optimization strategy based on the double-input fuzzy control (DIFC) is proposed. It further dynamically corrects the power allocation factor based on fuzzy rules, optimizing the SOC level to ensure the bidirectional SFR capability of ESS under all conditions. The case studies validate the overall SFR performance of the proposed strategy with different scenarios.
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