IEEE Transactions on Sustainable Energy最新文献_第9页

Long-Term and Short-Term Coordinated Scheduling for Wind-PV-Hydro-Storage Hybrid Energy System Based on Deep Reinforcement Learning 基于深度强化学习的风-电-水-蓄混合能源系统长期与短期协调调度

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2025-01-13 DOI: 10.1109/TSTE.2025.3529215

Huaiyuan Zhang;Kai Liao;Jianwei Yang;Zhe Yin;Zhengyou He

{"title":"Long-Term and Short-Term Coordinated Scheduling for Wind-PV-Hydro-Storage Hybrid Energy System Based on Deep Reinforcement Learning","authors":"Huaiyuan Zhang;Kai Liao;Jianwei Yang;Zhe Yin;Zhengyou He","doi":"10.1109/TSTE.2025.3529215","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3529215","url":null,"abstract":"For wind-photovoltaic-hydro-storage hybrid energy systems (WPHS-HES) grappling with the complexities of multiple scheduling cycles, traditional long-term strategies often impair short-term regulation capabilities, leading to extensive resource waste and critical power shortages. Thus, this paper introduces a novel framework that intricately nests short-term operational characteristics within long-term operating rules to synchronize multi-timescale scheduling for WPHS-HES. The cornerstone of our approach is the novel formulation of the long-term scheduling as a Markov Decision Process (MDP). It is integrated seamlessly with short-term generation schedules developed through an optimal model embedded at each MDP step. To achieve computational effectiveness and reliability, we propose a hybrid data-model-driven solution that harnesses the synergistic benefits of both data-driven and model-driven methodologies. By leveraging deep reinforcement learning our approach significantly streamlines long-term decision variables, while ensuring strict adherence to short-term operational constraints through mixed integer linear programming. Empirical simulations on an operational WPHS-HES validate the superior efficacy of our method over traditional scenario reduction and robust optimization techniques. The results are striking that it achieves a reduction in sustainable energy curtailment from 11.67% to 0.63% and slashes the load shedding rate from 3.3% to 0.69%, thereby setting a new benchmark for intelligent energy management in complex hybrid systems.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1697-1710"},"PeriodicalIF":8.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331728","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}

引用次数: 0

Dynamics-Incorporated Modeling Framework for Stability Constrained Scheduling Under High-Penetration of Renewable Energy 可再生能源高渗透下稳定约束调度的动态集成建模框架

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2025-01-10 DOI: 10.1109/TSTE.2025.3528027

Jinning Wang;Fangxing Li;Xin Fang;Hantao Cui;Buxin She;Hang Shuai;Qiwei Zhang;Kevin L. Tomsovic

{"title":"Dynamics-Incorporated Modeling Framework for Stability Constrained Scheduling Under High-Penetration of Renewable Energy","authors":"Jinning Wang;Fangxing Li;Xin Fang;Hantao Cui;Buxin She;Hang Shuai;Qiwei Zhang;Kevin L. Tomsovic","doi":"10.1109/TSTE.2025.3528027","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3528027","url":null,"abstract":"In this paper, a modularized modeling framework is designed to enable a dynamics-incorporated power system scheduling under high-penetration of renewable energy. This unique framework incorporates an adapted hybrid symbolic-numeric approach to scheduling models, effectively bridging the gap between device- and system-level optimization models and streamlining the scheduling modeling effort. The adaptability of the proposed framework stems from four key aspects: extensible scheduling formulations through modeling blocks, scalable performance via effective vectorization and sparsity-aware techniques, compatible data structure aligned with dynamic simulators by common power flow data, and interoperable dynamic interface for bi-direction data exchange between steady-state generation scheduling and time-domain dynamic simulation. Through extensive benchmarks with various usage scenarios, the framework's accuracy and scalability are validated. The case studies also demonstrate the efficient interoperation of generation scheduling and dynamics, significantly reducing the modeling conversion work in stability-constrained grid operation towards high-penetration of renewable energy.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1673-1685"},"PeriodicalIF":8.6,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330321","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}

引用次数: 0

Event-Triggered H-Infinity Pitch Control for Floating Offshore Wind Turbines 用于漂浮式近海风力涡轮机的事件触发 H-Infinity 变桨控制

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2025-01-03 DOI: 10.1109/TSTE.2025.3525478

Ya Zhao;Xiyun Yang;Yanfeng Zhang;Qiliang Zhang

{"title":"Event-Triggered H-Infinity Pitch Control for Floating Offshore Wind Turbines","authors":"Ya Zhao;Xiyun Yang;Yanfeng Zhang;Qiliang Zhang","doi":"10.1109/TSTE.2025.3525478","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3525478","url":null,"abstract":"The complex wind and wave environment can lead to increased external disturbances and power fluctuations of floating offshore wind turbines, posing a significant challenge to their stable operation. To cope with this issue, this paper formulates an event-triggered H-infinity pitch control strategy for floating offshore wind turbines. Firstly, a linear parameter varying model of floating offshore wind turbines is proposed, utilizing the dynamic characteristics of subsystems while considering the combined external disturbances from wind and wave. Then, the event-triggered control strategy is introduced into the H-infinity pitch control of floating offshore wind turbines. Based on this, a criterion for the asymptotic stability and H-infinity norm boundedness of floating offshore wind turbines is derived. Furthermore, an algorithm is presented for designing feedback gain matrices of the event-triggered H-infinity pitch control, which can effectively reduce the update frequency of the controller. Finally, a simulation is conducted on the IEA 15 MW Reference Wind Turbine by integrating OpenFAST with MATLAB/Simulink. The simulation results provide a comparative analysis of the event-triggered H-infinity pitch control strategy and the continuous-time pitch control strategy, demonstrating the superiority of the method proposed in this paper.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1329-1339"},"PeriodicalIF":8.6,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667582","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}

引用次数: 0

A Framework of Day-Ahead Wind Supply Power Forecasting by Risk Scenario Perception 基于风险情景感知的风力发电日前预测框架

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2025-01-03 DOI: 10.1109/TSTE.2025.3525498

Mao Yang;Yutong Huang;Zhao Wang;Bo Wang;Xin Su

{"title":"A Framework of Day-Ahead Wind Supply Power Forecasting by Risk Scenario Perception","authors":"Mao Yang;Yutong Huang;Zhao Wang;Bo Wang;Xin Su","doi":"10.1109/TSTE.2025.3525498","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3525498","url":null,"abstract":"Wind power forecasting (WPF) systems are essential to maintain the safe and stable operation of the power system in case of large-scale grid-connected wind farms. However, the current forecasting has the problem of disunity between statistical value and application value, that is, it only pays attention to its forecasting accuracy and ignores the risks caused by it in the power system. In order to solve the above problems, this study proposes a framework of wind supply power forecasting (WSPF) for wind farm cluster, which takes into account the risk scenario perception. First of all, aiming at the predicted risk phenomenon in WPF, TimesNet combined with the fluctuation information of Numerical Weather Prediction (NWP) wind speed is used to identify the corresponding risk scenarios. Secondly, the effective consumption area and power supply risk area evaluation index, as well as the accuracy of WSPF are defined, and the optimal forecasting curve correction scheme is fitted according to the index. Thirdly, taking into account the correction scheme and identification results, a variety of predictors are used to verify the WSPF according to the above framework. Finally, the proposed method is applied to a wind farm cluster in Inner Mongolia Autonomous region of China, the average accuracy of WSPF has increased by 37%, which verifies the effectiveness and universality of this method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1659-1672"},"PeriodicalIF":8.6,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331674","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}

引用次数: 0

Regional Frequency-Constrained Planning for the Optimal Sizing of Power Systems via Enhanced Input Convex Neural Networks 基于增强输入凸神经网络的电力系统最优规模区域频率约束规划

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2025-01-01 DOI: 10.1109/TSTE.2024.3524720

Yi Wang;Goran Strbac

{"title":"Regional Frequency-Constrained Planning for the Optimal Sizing of Power Systems via Enhanced Input Convex Neural Networks","authors":"Yi Wang;Goran Strbac","doi":"10.1109/TSTE.2024.3524720","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3524720","url":null,"abstract":"Large renewable penetration has been witnessed in power systems, resulting in reduced levels of system inertia and increasing requirements for frequency response services. There have been plenty of studies developing frequency-constrained models for power system security. However, most existing literature only considers uniform frequency security, while neglecting frequency spatial differences in different regions. To fill this gap, this paper proposes a novel planning model for the optimal sizing problem of power systems, capturing regional frequency security and inter-area frequency oscillations. Specifically, regional frequency constraints are first extracted via an enhanced input convex neural network (ICNN) and then embedded into the original optimisation for frequency security, where a principled weight initialisation strategy is adopted to deal with the gradient vanishing issues of non-negative weights in traditional ICNNs and enhance its fitting ability. An adaptive genetic algorithm with sparsity calculation and local search is developed to separate the planning model into two stages and effectively solve it iteratively. Case studies have been conducted on three different power systems to verify the effectiveness of the proposed frequency-constrained planning model in ensuring regional system security and obtaining realistic investment decisions.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1644-1658"},"PeriodicalIF":8.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331782","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}

引用次数: 0

A Novel Cost-Effective HVDC System With Self Black-Start and Fault Ride-Through Capability 一种具有自黑启动和故障穿越能力的新型高性价比高压直流输电系统

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-12-26 DOI: 10.1109/TSTE.2024.3522167

Haihan Ye;Wu Chen;Tao Li;Xingyu Liu;Guangyue Liu

引用次数: 0

Joint Multi-Stage Planning of Renewable Generation, HESS, and AESS for Deeply Decarbonizing Power Systems With High-Penetration Renewables 高渗透可再生能源深度脱碳电力系统的可再生能源发电、HESS和AESS联合多阶段规划

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-12-25 DOI: 10.1109/TSTE.2024.3521939

Zhipeng Yu;Jin Lin;Feng Liu;Jiarong Li;Yingtian Chi;Yonghua Song;Zhengwei Ren;Chengcheng Lu;Mengbo Ji

{"title":"Joint Multi-Stage Planning of Renewable Generation, HESS, and AESS for Deeply Decarbonizing Power Systems With High-Penetration Renewables","authors":"Zhipeng Yu;Jin Lin;Feng Liu;Jiarong Li;Yingtian Chi;Yonghua Song;Zhengwei Ren;Chengcheng Lu;Mengbo Ji","doi":"10.1109/TSTE.2024.3521939","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3521939","url":null,"abstract":"The further decarbonization of power systems with high renewable energy penetration faces the problem of inter-day intermittence of renewable energy sources (RES) and the seasonal imbalance between RES and load demand, due to the limited regulation ability of conventional units such as thermal generation. Regular solutions based on battery energy storage system (BESS) are too costly to be practical. To address issues above, hydrogen energy storage system (HESS) and ammonia energy storage system (AESS) are introduced to gradually replace thermal generation. Specifically, first, HESS and AESS are incorporated into the multi-stage capacity expansion planning (MSCEP) model with carbon emission reduction constraints. Yearly data with hourly time resolution are utilized for each stage to accurately describe the intermittence of RES. Then, an improved column generation (CG) with Dantzig-Wolfe decomposition (DWD) embedded solution approach is used to efficiently solve the large-scale MSCEP model. Finally, a real-life system in China is studied. The results indicate that the proposed method can guarantee high power supply reliability (PSR) under different renewable energy penetration levels, avoiding the low PSR problem that may be caused by the existing typical scenario-based method (TSM) under high penetration (<inline-formula><tex-math>$geq$</tex-math></inline-formula>30%). Moreover, HESS and AESS are essential to reduce the cost of decarbonization. Especially under the goal of carbon neutrality, the contribution of HESS and AESS in reducing levelized cost of energy (LCOE) reaches 12.28% and 14.59%, respectively, leading to a levelized cost of carbon reduction (LCOCr) of 998 RMB/t.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1613-1628"},"PeriodicalIF":8.6,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331733","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}

引用次数: 0

Identification of Vulnerable Nodes and Sensitivity Analysis of Control Parameters for Multiple Grid-Connected Converter Systems 多并网变流器系统脆弱节点识别及控制参数敏感性分析

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-12-25 DOI: 10.1109/TSTE.2024.3521890

Zhenxiang Liu;Yanbo Chen;Zhi Zhang;Jiahao Ma;Tao Huang

{"title":"Identification of Vulnerable Nodes and Sensitivity Analysis of Control Parameters for Multiple Grid-Connected Converter Systems","authors":"Zhenxiang Liu;Yanbo Chen;Zhi Zhang;Jiahao Ma;Tao Huang","doi":"10.1109/TSTE.2024.3521890","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3521890","url":null,"abstract":"The stability issue of high proportion variable renewable energy (VRE) when connected to the grid has become an important factor limiting the consumption of VRE and seriously threatening the stable operation of the power systems. However, there is still a lack of targeted discrimination theory and compensation mechanisms in areas where the stability margin undergoes drastic changes due to changes in the static operating point of the systems. To this end, based on the small signal stability analysis method, this paper proposes a vulnerable nodes localization method for the multiple grid-connected converter systems (MGCCS) that considers the dynamic response characteristics and static operating point offset of converters. Firstly, a frequency domain negative feedback model is established for MGCCS with passive busbars. Then the sensitivity function for the control parameters of active nodes and the quantitative indicators for identifying vulnerable nodes are derived. Finally, a comprehensive compensation scheme for both active nodes and passive busbars is proposed. Case analysis demonstrates that the proposed vulnerable nodes identification method and comprehensive compensation scheme offer substantial benefits in the realm of stability design and operation planning of MGCCS.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1602-1612"},"PeriodicalIF":8.6,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331744","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}

引用次数: 0

Intraday Wind Power Forecasting by Ensemble of Overlapping Historical Numerical Weather Predictions 利用重叠历史数值天气预报集合进行日内风力预报

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-12-23 DOI: 10.1109/TSTE.2024.3521384

Yongning Zhao;Shiji Pan;Yanxu Chen;Haohan Liao;Yingying Zheng;Lin Ye

{"title":"Intraday Wind Power Forecasting by Ensemble of Overlapping Historical Numerical Weather Predictions","authors":"Yongning Zhao;Shiji Pan;Yanxu Chen;Haohan Liao;Yingying Zheng;Lin Ye","doi":"10.1109/TSTE.2024.3521384","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3521384","url":null,"abstract":"The numerical weather prediction (NWP) is crucial to improve intraday wind power forecasting (WPF) accuracy. However, conventional WPF methods relied solely on a latest reported single NWP, overlooking hidden information from sequentially reported multiple historical NWPs that are partially overlapped over time. Additionally, it's challenging to tackle intraday WPF as it involves both ultra-short-term and short-term horizons with different characteristics. Therefore, a novel spatio-temporal representation learning network is proposed for intraday WPF by ensemble of overlapping historical NWPs. Initially, an integrated mask-reconstruction representation learning pretraining strategy is employed to extract hidden representations of historical wind power measurements and overlapping historical NWPs, providing contextual information for the subsequent intraday WPF task. Then, the output layer is trained and end-to-end fine-tuning of the entire network is conducted to adapt to the specific forecasting task. Moreover, a multi-task learning strategy based on hard parameter sharing is adopted to ensure balanced predictive accuracy across each of forecasted wind farms. Case study and detailed ablation tests based on 5 real-world wind farms demonstrate that the proposed method enhances the forecasting accuracy of most wind farms by leveraging spatio-temporal correlation, achieving the best average performance across all time horizons compared to the baseline models.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1315-1328"},"PeriodicalIF":8.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667508","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}

引用次数: 0

Modeling of Power Support Capability and Frequency Coordinated Control of DFIG-Based Wind Farm Considering Coupling Constraints of Multi-State Variables 考虑多状态变量耦合约束的dfig风电场功率支持能力建模与频率协调控制

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-12-23 DOI: 10.1109/TSTE.2024.3521509

Jinxin Ouyang;Jianfeng Yu;Shoudong Xu;Shuqi Bi

{"title":"Modeling of Power Support Capability and Frequency Coordinated Control of DFIG-Based Wind Farm Considering Coupling Constraints of Multi-State Variables","authors":"Jinxin Ouyang;Jianfeng Yu;Shoudong Xu;Shuqi Bi","doi":"10.1109/TSTE.2024.3521509","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3521509","url":null,"abstract":"The operational states of wind turbines are different in large-scale wind farms, which presents serious challenges to the frequency control of power systems. The doubly fed induction generator-based wind turbine (DFIG) can be coordinated to participate in frequency control by altering the rotational kinetic energy. The coordinated control of wind farms is usually conditioned by the accurate assessment of the power support capability (PSC) of DFIG. However, the assessment mainly focuses on the influence of single variables such as wind speed and rotor speed. The coupling constraints of rotor speed, pitch angle and rotor current on the PSC are ignored. The PSC of wind farms is still difficult to accurately assess and fully utilize. Therefore, the power characteristics of DFIG under dynamic variations of mechanical power are analyzed. The PSC of DFIG considering the coupling constraints of multi-state variables is modeled. Then the assessment method of PSC considering the coupling constraints of rotor speed, pitch angle and rotor current is proposed. The allocation method of the contribution of DFIG considering different operational states of DFIG is proposed, and the frequency coordinated control method of DFIG-based wind farm is proposed. The effectiveness of the proposed method is verified by case studies.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1589-1601"},"PeriodicalIF":8.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329504","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}

引用次数: 0