IEEE Transactions on Smart Grid最新文献_第7页

Online Voltage Regulation of Distribution Networks in Energy Sharing Markets: a Feedback-Based Method With Parameter Estimation 能源共享市场配电网电压在线调节：一种基于反馈的参数估计方法

IF 9.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-28 DOI: 10.1109/tsg.2025.3555320

Wenyue Wang, Yiming Liu, Zhaojian Wang

引用次数: 0

Interoperability Specifications for Multi-Vendor Converter-Dominated Grid: a Robust Stability Perspective 多厂商转换器主导网格的互操作性规范：鲁棒稳定性视角

IF 9.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-28 DOI: 10.1109/tsg.2025.3555402

Federico Cecati, Marco Liserre

引用次数: 0

A Gaussian Process-Regularized Graphical Learning Method for Distribution System State Estimation Using Extremely Scarce State Variable Labels 一种基于高斯过程-正则化图形学习的极稀缺状态变量标签配电系统状态估计方法

IF 9.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-27 DOI: 10.1109/tsg.2025.3552958

Jiaxiang Hu, Qianwen Xu

引用次数: 0

Mode Switching-Induced Instability of Multi-Source Feed DC Microgrid 多源直流微电网模式切换诱导的不稳定性

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-27 DOI: 10.1109/TSG.2025.3554585

Shanshan Jiang;Zelin Sun;Jiankun Zhang;Hua Geng

引用次数: 0

Distributed Accurate Current Sharing for Multi-Bus DC Microgrids with Minimizing Voltage Regulation Deviations: A Game-Theoretic Approach 电压调节偏差最小的多母线直流微电网分布精确电流共享：博弈论方法

IF 9.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-24 DOI: 10.1109/tsg.2025.3553828

Gang Yang, Lei Ding, Maojiao Ye, Sunyuan Xiao, Dong Yue

引用次数: 0

A Unified Model for Smart Meter Data Applications 智能电表数据应用的统一模型

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-21 DOI: 10.1109/TSG.2025.3553173

Zhenyi Wang;Hongcai Zhang;Geert Deconinck;Yonghua Song

{"title":"A Unified Model for Smart Meter Data Applications","authors":"Zhenyi Wang;Hongcai Zhang;Geert Deconinck;Yonghua Song","doi":"10.1109/TSG.2025.3553173","DOIUrl":"10.1109/TSG.2025.3553173","url":null,"abstract":"Making adequate utilization of smart meter data is conducive to improving the energy efficiency of the power system from demand side, especially with booming artificial intelligence (AI) technology. However, most existing AI-based methods are highly incompatible to each other due to unique designs based on their respective tasks. Low compatibility will lead to duplicate modeling among similar tasks and skyrocketing implementation costs, which is not suitable for diverse and changing demand-side tasks. Although large language models provide a promising way to build the general-purpose models, they either need substantial resources for pre-training or case-by-case design for fine-tuning. Hence, there are practically rare task-generic models available for power systems. In this paper, we propose a novel unified model for smart meter data applications. Specifically, we first propose a unified model with mixture-of-expert layers to ensure sufficient model capacity in a cost-effective manner, which makes the training from scratch affordable. Then, we design an information bottleneck-based training scheme to facilitate the unified model to efficiently learn the generic knowledge. Moreover, we develop a general framework based on pre-training paradigm to formulate a uniform objective function and provide a consistent workflow for different tasks. Finally, the effectiveness and superiority of our proposed method are validated on public datasets, where the proposed unified model can be applied to load forecasting, data imputation as well as anomaly detection, and realizes comparable performance to state-of-the-art task-specific methods.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2451-2463"},"PeriodicalIF":8.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672564","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 Progressive Polyhedral Approximation Method for Nonlinear PDE-Constrained Electricity-Water Nexus Dispatch 非线性 PDE 受限水电关联调度的渐进多面体逼近法

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-21 DOI: 10.1109/TSG.2025.3553737

Zhihao Hua;Bin Zhou;Ka Wing Chan;Cong Zhang;Yingping Cao;Pengcheng Wang;Mingchao Xia

引用次数: 0

Industrial Energy Management and Production Decision Making via Lyapunov-Guided Learning 李雅普诺夫引导学习下的工业能源管理与生产决策

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-20 DOI: 10.1109/TSG.2025.3549723

Dafeng Zhu;Bo Yang;Lei Li;Yu Wu;Haoran Deng;Zhaoyang Dong;Kai Ma;Xinping Guan

{"title":"Industrial Energy Management and Production Decision Making via Lyapunov-Guided Learning","authors":"Dafeng Zhu;Bo Yang;Lei Li;Yu Wu;Haoran Deng;Zhaoyang Dong;Kai Ma;Xinping Guan","doi":"10.1109/TSG.2025.3549723","DOIUrl":"10.1109/TSG.2025.3549723","url":null,"abstract":"Energy-intensive industries have to reduce fossil fuel consumption while scheduling production for cost efficiency. It poses the question that how to coordinate renewable energy generation, storage, heat recovery and energy cascade utilization in real time to deal with the low energy efficiency and continuous production problems existing in complex dynamic coupled process production. This question is further complicated while facing difficulties in collaborative modeling and online control by the underlying stochastic process without accurate statistic knowledge. To characterize the above issues, a non-convex operation optimization problem is formulated for coupling production and energy joint scheduling. To obtain a simple online solution with provable performance, a method by combining Lyapunov optimization and actor-critic deep reinforcement learning is proposed. The former is used to decouple the original problem into small-size non-convex subproblems for each time slot and guarantee the long-term constraints. The latter aims at the non-convex part by using model information of the former to obtain accurate evaluations of production actions for fast convergence and high robustness with low computational complexity. The simulation shows that the proposed method can achieve the online optimal benefit while ensuring production tasks and system stability with high scalability.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2184-2196"},"PeriodicalIF":8.6,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672567","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

SecureDec: A Decentralized Scheduling Pipeline With Federated Learning and Efficient Encryption for Electricity-Gas Coupled Systems 基于联邦学习和高效加密的电-气耦合系统分散调度管道

IF 9.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-19 DOI: 10.1109/tsg.2025.3552762

Haizhou Liu, Haitian Liu, Qinran Hu, Xuan Zhang, Hongbin Sun, Mohammad Shahidehpour

引用次数: 0

Improved Control and Stability Analysis of a Microgrid Connector Controller Under Unbalanced Network Conditions 不平衡网络条件下微电网连接器控制器的改进控制及稳定性分析

IF 9.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-19 DOI: 10.1109/tsg.2025.3552770

Hanwen Gu, Behnam Tamimi, Claudio A. Cañizares

引用次数: 0