IEEE Transactions on Sustainable Energy最新文献_第3页

A Simplified Probabilistic Framework for Evaluating the Contribution of Solar Photovoltaic Generation to Power Systems 评估太阳能光伏发电对电力系统贡献的简化概率框架

IF 10.3 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2026-07-01 Epub Date: 2025-12-19 DOI: 10.1109/TSTE.2025.3646475

Changhee Han;Kwang Y. Lee;Rajesh Karki;Kyeong-Hee Cho;Kweonhoo Ko;Junzo Watada;Jaeseok Choi

{"title":"A Simplified Probabilistic Framework for Evaluating the Contribution of Solar Photovoltaic Generation to Power Systems","authors":"Changhee Han;Kwang Y. Lee;Rajesh Karki;Kyeong-Hee Cho;Kweonhoo Ko;Junzo Watada;Jaeseok Choi","doi":"10.1109/TSTE.2025.3646475","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3646475","url":null,"abstract":"This paper presents a <bold>probabilistic simulation framework for quantitatively evaluating the contribution of <bold>solar cell <bold>generators (SCGs) to modern power systems. Unlike conventional generators using storable fuels modeled by simple two states, solar generation is <bold>non-storable and inherently uncertain, requiring a more realistic approach. A <bold>multi-state probabilistic model is therefore developed—not as a fixed structure, but as a direct reflection of solar resource variability. By integrating the PV output curve with the <bold>empirical probability density function (pdf) of solar irradiance, the model captures <bold>weather-dependent generation behavior. Case studies on a scaled Jeju Island system verify its effectiveness in evaluating <bold>reliability (LOLE, EENS), <bold>production cost, and <bold>CO2 emissions, and demonstrate its value for <bold>scenario-based planning under increased solar penetration. The proposed framework highlights the transition from <bold>deterministic assumptions to probabilistic realism, offering a robust foundation for <bold>sustainable and data-driven energy planning.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 3","pages":"2305-2318"},"PeriodicalIF":10.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148519416","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

Low-Carbon Energy Scheduling for Hydrogen-Based Steel Heating With Battery Storage 基于电池蓄热的氢基钢加热低碳能源调度

IF 10.3 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2026-07-01 Epub Date: 2025-12-12 DOI: 10.1109/TSTE.2025.3643689

Dafeng Zhu;Bo Yang;Yunyang Zou;Zhao Yang Dong

引用次数: 0

A Novel Hybrid DC/DC Converter With Residual DC Fault Current Blocking Capability for All-DC Offshore Wind Farms 全直流海上风电场中具有剩余故障电流阻断能力的新型混合DC/DC变换器

IF 10.3 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2026-07-01 Epub Date: 2026-02-17 DOI: 10.1109/TSTE.2026.3665796

Yi Liang;Rui Li;Binbin Li;Zhiwen Suo;Lujie Yu;Dianguo Xu

引用次数: 0

Explanation-Enhanced Federated Fine-Tuning of Large Language Models for Photovoltaic Power Forecasting 光伏发电预测大语言模型的解释增强联合微调

IF 10.3 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2026-07-01 Epub Date: 2026-01-20 DOI: 10.1109/TSTE.2026.3655809

Yiyang Song;Tao Li;Yongze Geng;Cong Wu;Yi Wang;Jianxiao Wang

{"title":"Explanation-Enhanced Federated Fine-Tuning of Large Language Models for Photovoltaic Power Forecasting","authors":"Yiyang Song;Tao Li;Yongze Geng;Cong Wu;Yi Wang;Jianxiao Wang","doi":"10.1109/TSTE.2026.3655809","DOIUrl":"https://doi.org/10.1109/TSTE.2026.3655809","url":null,"abstract":"Accurate photovoltaic (PV) power forecasting is essential for the reliable operation of modern power systems. While federated learning (FL) preserves data privacy across geographically distributed PV stations, it suffers from performance degradation under non-independent and identically distributed data. Large language models (LLMs), known for their strong generalization and few-shot learning capabilities, offer a promising solution to this challenge. However, full-model fine-tuning of LLMs incurs considerable communication and computation costs in FL settings. To address this bottleneck, we propose a parameter-efficient fine-tuning framework using low-rank adaptation (LoRA) modules. While reducing overhead, conventional federated LoRA introduces aggregation noise due to the separate averaging of low-rank factors. To mitigate this, we introduce an explanation-enhanced unilateral LoRA paradigm that selectively aggregates generalizable modules while retaining task-specific ones locally. This design reduces noise, accelerates convergence, and improves interpretability. Extensive experiments on real-world PV datasets, using GPT-2 Small and GPT-2 XLarge, validate the effectiveness of the proposed approach in improving prediction accuracy and training efficiency under heterogeneous data conditions.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 3","pages":"2602-2615"},"PeriodicalIF":10.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148515611","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 Weight-Allocation and Privacy-Preserving Federated Learning Approach for the BTM PV Power Forecasting BTM光伏功率预测的权重分配与隐私保护联合学习方法

IF 10.3 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2026-07-01 Epub Date: 2026-01-23 DOI: 10.1109/TSTE.2026.3657101

Jicheng Wu;Shengwei Li;Fanghong Guo;Lei Wang;Dan Zhang

{"title":"A Weight-Allocation and Privacy-Preserving Federated Learning Approach for the BTM PV Power Forecasting","authors":"Jicheng Wu;Shengwei Li;Fanghong Guo;Lei Wang;Dan Zhang","doi":"10.1109/TSTE.2026.3657101","DOIUrl":"https://doi.org/10.1109/TSTE.2026.3657101","url":null,"abstract":"Distributed ultra-short-term photovoltaic (PV) power forecasting plays a vital role in analyzing behind-the-meter (BTM) solar generation and enhancing grid stability. As federated learning (FL) is increasingly applied to distributed BTM PV forecasting on a large scale, its deployment is confronted with two major challenges, namely performance degradation arising from device heterogeneity and privacy risks associated with global communication. To address these challenges, a novel collaborative method, termed AWA-DDP-FL, is proposed, which integrates adaptive weight assignment (AWA) with reinforcement learning-driven dynamic differential privacy (DDP). Specifically, AWA mitigates the impact of device heterogeneity by strategically excluding underperforming clients and dynamically adjusting local model aggregation weights during training. Meanwhile, DDP enables each distributed PV client to autonomously adjust its privacy budget in real time according to data sensitivity, balancing privacy protection and model performance through dynamic noise injection. Experimental results on real-world PV datasets show that the proposed method obtains normalized root mean square error of 5.110% and normalized mean absolute error of 2.630%, indicating that the proposed algorithm achieves higher accuracy even under noise perturbations.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 3","pages":"2662-2675"},"PeriodicalIF":10.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148516735","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

Deep Reinforcement Learning-Based Time-Varying Parameter Identification Method for Grid-Connected Renewable Power Generation Systems 基于深度强化学习的可再生能源并网发电系统时变参数辨识方法

IF 10.3 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2026-07-01 Epub Date: 2025-12-17 DOI: 10.1109/TSTE.2025.3645315

Hanxi Chen;Shilin Gao;Xu Zhou;Ying Chen;Zongsheng Zheng;Yuhong Wang;Bingjie Zhai

{"title":"Deep Reinforcement Learning-Based Time-Varying Parameter Identification Method for Grid-Connected Renewable Power Generation Systems","authors":"Hanxi Chen;Shilin Gao;Xu Zhou;Ying Chen;Zongsheng Zheng;Yuhong Wang;Bingjie Zhai","doi":"10.1109/TSTE.2025.3645315","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3645315","url":null,"abstract":"With the large-scale integration of renewables into the power grid, a number of parameters in power systems exhibit time-varying characteristics, posing new challenges for parameter identification. To address these challenges, this paper proposes a novel identification method for time-varying parameters based on deep reinforcement learning (DRL). First, a DRL environment based on a hierarchical soft-guided reward function is designed, which lays a foundation for parameter identification tasks. Second, to tackle the challenges of multi-parameter coupling, a new DRL algorithm called MMoE-TD3 is proposed. This algorithm applies the concept of the multi-gate mixture-of-experts (MMoE) network to the actor network of the twin delayed deep deterministic policy gradient (TD3) algorithm, aiming to improve its capability in parallel identification of multiple parameters. Last, the identification of varying parameters is formulated as a continual reinforcement learning task, and a training strategy based on knowledge acquisition and retention is proposed. The test results validate the convergence, accuracy, and robustness of the proposed parameter identification method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 3","pages":"2219-2232"},"PeriodicalIF":10.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148517186","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

Pre-Trained Large Model as Distributed PV Power Forecaster: Integrating Scenario-Consistent Retrieval-Augmented Generation and Cross-Modal Semantic Alignment 预训练的大型分布式光伏发电预测模型：集成场景一致检索-增强生成和跨模态语义对齐

IF 10.3 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2026-07-01 Epub Date: 2026-01-05 DOI: 10.1109/TSTE.2025.3650479

Mingyang Gao;Suyang Zhou;Wei Gu;Jili Fan;Haiquan Liu;Aobo Guan;Honghua Xu

{"title":"Pre-Trained Large Model as Distributed PV Power Forecaster: Integrating Scenario-Consistent Retrieval-Augmented Generation and Cross-Modal Semantic Alignment","authors":"Mingyang Gao;Suyang Zhou;Wei Gu;Jili Fan;Haiquan Liu;Aobo Guan;Honghua Xu","doi":"10.1109/TSTE.2025.3650479","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3650479","url":null,"abstract":"The rapid proliferation of distributed photovoltaic (PV) systems presents significant challenges for accurate power generation forecasting due to their inherent intermittency and spatial-temporal variability. This paper proposes a novel framework that leverages pre-trained large models for distributed PV power forecasting by integrating retrieval-augmented generation with cross-modal semantic alignment. To unlock the reasoning capabilities of pre-trained large models, we design a scenario-consistent RAG module that retrieves relevant historical PV sequences guided by long-term dependencies and temporal correlations, and integrates them through a Chain-of-Thought prompting paradigm. Furthermore, we develop a cross-modal semantic alignment mechanism to bridge the representational gap between numerical PV series data and the language semantic space of PLMs, enabling effective pattern recognition across modalities. To ensure physically meaningful forecasting within the training and inference processes, we also establish a constrained activation function that explicitly considers the inherent characteristics of PV power generation. Extensive experiments on nine real-world distributed PV datasets demonstrate that our framework achieves state-of-the-art performance, significantly outperforming six benchmark models.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 3","pages":"2394-2407"},"PeriodicalIF":10.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518386","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

Design of the Electrical Interface of Hydrogen Electrolyzers Limiting the Power Ramp-Rate While Providing Instantaneous Reserves 在提供瞬时储备的同时限制功率斜坡率的氢电解槽电接口设计

IF 10.3 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2026-07-01 Epub Date: 2026-01-12 DOI: 10.1109/TSTE.2026.3652018

Thomas Heynen;Moritz Deitert;Tobias Heins;Marius Kuhn;Antonello Monti;Willem Leterme

{"title":"Design of the Electrical Interface of Hydrogen Electrolyzers Limiting the Power Ramp-Rate While Providing Instantaneous Reserves","authors":"Thomas Heynen;Moritz Deitert;Tobias Heins;Marius Kuhn;Antonello Monti;Willem Leterme","doi":"10.1109/TSTE.2026.3652018","DOIUrl":"https://doi.org/10.1109/TSTE.2026.3652018","url":null,"abstract":"A massive capacity of Hydrogen electrolyzers (HE) is foreseen to be integrated into electrical power systems. Simultaneously, power systems worldwide are facing an increased need for instantaneous reserves (IR) to secure frequency stability. Therefore, provision of IR by HE may highly improve the reliability of future power systems. However, IR provision requires rapid power changes, which pose risks to the operation, and durability of large-scale HE. Consequently, power changes at the HE stack should be limited to practically confirmed ramp-rates. Thus, this paper proposes a novel design method to provide IR while limiting power gradients at the electrolysis stack to fixed ramp-rates. The developed method analytically designs the intermediate DC-link capacity to decouple the grid-side IR power gradients from the HE stack. The resulting designs are verified through EMT-simulations. The analytical analysis reveals that the required energy buffer highly depends on grid code requirements, particularly in case of slower power ramp-rates at the HE stack (<inline-formula><tex-math>$leq {0.4}{frac{text{pu}}{mathrm{s}}}$</tex-math></inline-formula>). Moreover, the results show that the presented design effectively limits rapid power changes at the HE stack for worst-case frequency and phase jump scenarios.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 3","pages":"2487-2500"},"PeriodicalIF":10.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518566","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

Seamless Grid-Connected/Islanded Transition in Fully Grid-Forming PV Powered Microgrids 完全并网的光伏微电网的无缝并网/孤岛过渡

IF 10.3 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2026-07-01 Epub Date: 2026-01-12 DOI: 10.1109/TSTE.2026.3651357

Zizhen Guo;Wenchuan Wu

{"title":"Seamless Grid-Connected/Islanded Transition in Fully Grid-Forming PV Powered Microgrids","authors":"Zizhen Guo;Wenchuan Wu","doi":"10.1109/TSTE.2026.3651357","DOIUrl":"https://doi.org/10.1109/TSTE.2026.3651357","url":null,"abstract":"This paper investigates the full-state virtual oscillator control (fVOC) method to realize the flexible operation of 100% PV-powered microgrid. In the grid-connected mode, the enhanced DC voltage stability of fVOC enables the grid-forming PVs to provide the dynamic support while being compatible with the maximum power point tracking function. This dual capability is critical to replace the grid-following operation under the very high penetration of renewable sources. In the islanded situation, the reconnection of the microgrid to the bulk grid leads to large phase jumps, which can trigger the overcurrent saturation and induce the DC voltage instability in grid-forming PVs. To address this issue, a phase adaption current limiting method in fVOC is proposed. The full-order system modeling is performed to guide the parameter design of the proposed method. The Lyapunov function is derived to reveal the enhanced transient stability of the grid-forming PV system during the reconnection of the islanded microgrid. The superiority of the proposed method is verified in both simulations and field tests in a 100% PV powered microgrid. The mode transition between grid-connected and islanded is seamless without islanding detection and pre-synchronization.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 3","pages":"2408-2419"},"PeriodicalIF":10.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518846","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

Fast Transient Energy Control Based on Coordination Between GFM Converters During DC FRT in Offshore-Onshore Integration Transmission System 陆海一体化输电系统直流FRT过程中基于GFM变流器协调的快速暂态能量控制

IF 10.3 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2026-07-01 Epub Date: 2025-12-01 DOI: 10.1109/TSTE.2025.3639047

Zixuan Zhang;Qiang Song;Changyue Zou;Zhengxuan Li;Yihong Huang;Biao Zhao;Faxi Peng;Yuxin Lu

{"title":"Fast Transient Energy Control Based on Coordination Between GFM Converters During DC FRT in Offshore-Onshore Integration Transmission System","authors":"Zixuan Zhang;Qiang Song;Changyue Zou;Zhengxuan Li;Yihong Huang;Biao Zhao;Faxi Peng;Yuxin Lu","doi":"10.1109/TSTE.2025.3639047","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3639047","url":null,"abstract":"For offshore wind power transmission via modular multilevel converter-based high voltage dc transmission (MMC-HVdc) system, dc overhead lines have recently been adopted in China for the onshore transmission to the load centers. However, they are prone to short-circuit faults, and the resulting transient energy from both the ac and dc sides flowing into the wind farm-side MMC (WFMMC) may cause severe submodule capacitor overvoltage. To address this issue, this paper proposes a dc fault ride-through (FRT) strategy that coordinates fast transient energy control with conventional low-speed communication. In the proposed approach, both the wind turbines (WTs) and WFMMC adopt grid-forming control. Upon a dc fault, the WFMMC rapidly adjusts the phase of its ac voltage, thereby reducing and even reversing the ac power from the WTs. This effectively suppresses the transient energy and limits the capacitor overvoltage during communication delays. Compared with the existing approaches, the proposed scheme reduces the required safety margin in designing the value of the rated submodule capacitor voltage by suppressing the overvoltage during dc FRT. As a result, it can reduce both the cost and the footprint requirements of the WFMMC. It also offers advantages such as reduced overvoltage duration during FRT and accelerated system restoration. The effectiveness and feasibility of the proposed strategy are verified using detailed simulations conducted in MATLAB/Simulink.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 3","pages":"1983-1998"},"PeriodicalIF":10.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148519165","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