IEEE Transactions on Smart Grid最新文献_第10页

IEEE Transactions on Smart Grid Information for Authors IEEE智能电网信息汇刊

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-02-20 DOI: 10.1109/TSG.2025.3539058

引用次数: 0

IEEE Transactions on Smart Grid Publication Information IEEE智能电网出版信息汇刊

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-02-20 DOI: 10.1109/TSG.2025.3539056

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Predictive Health Management of Smart Meters: Daily Measurement Error Forecasting Under Complex Environmental Conditions 智能电表的预测健康管理：复杂环境条件下的日常测量误差预测

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-02-17 DOI: 10.1109/TSG.2025.3542786

Junfeng Duan;Qiu Tang;Ning Li;Wei Qiu;Wenxuan Yao

引用次数: 0

A “Smart Model-Then-Control” Strategy for the Scheduling of Thermostatically Controlled Loads 温控负荷调度的“智能先模型后控制”策略

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-02-17 DOI: 10.1109/TSG.2025.3542544

Xueyuan Cui;Boyuan Liu;Yehui Li;Yi Wang

{"title":"A “Smart Model-Then-Control” Strategy for the Scheduling of Thermostatically Controlled Loads","authors":"Xueyuan Cui;Boyuan Liu;Yehui Li;Yi Wang","doi":"10.1109/TSG.2025.3542544","DOIUrl":"10.1109/TSG.2025.3542544","url":null,"abstract":"Model predictive control (MPC) has been widely adopted for indoor temperature control and building energy management. There are two steps in traditional MPC: 1) modeling thermal dynamics as the state space function to represent the temperature variation influenced by thermostatically controlled loads (TCLs); 2) formulating an optimization problem for optimal scheduling of TCLs within the control horizon. However, such a “model-then-control” strategy could result in biased control because of the unaligned modeling error and control cost, i.e., minimization of model errors may not necessarily lead to minimal costs against actual thermal dynamics in buildings. To tackle this problem, we advocate for a “smart model-then-control” (SMC) strategy that incorporates thermal dynamics modeling into the temperature control task. In particular, instead of using mean squared errors (MSE), we adopt the control objective as the task-specific loss function to guide the model training. We further formulate an Input Convex Neural Network (ICNN)-based surrogate loss function, which is differentiable and convex for effective training. In this way, the objectives of both model training and temperature control in MPC are well-aligned to obtain cost-effective decisions. We validate the performance of the SMC strategy in single-zone and multi-zone buildings. The simulation results show that it can reduce control costs by 5.97% and 2.10% respectively when compared with traditional MPC.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2246-2260"},"PeriodicalIF":8.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443519","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

Privacy-Enhanced Safe Reinforcement Learning for the Dispatch of a Local Energy Community 本地能源社区调度的隐私增强安全强化学习

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-02-13 DOI: 10.1109/TSG.2025.3536211

Haoyuan Deng;Ershun Du;Yi Wang

{"title":"Privacy-Enhanced Safe Reinforcement Learning for the Dispatch of a Local Energy Community","authors":"Haoyuan Deng;Ershun Du;Yi Wang","doi":"10.1109/TSG.2025.3536211","DOIUrl":"10.1109/TSG.2025.3536211","url":null,"abstract":"Local Energy Community (LEC) has emerged as a viable community-focus framework to enhance local reliability and energy efficiency by integrating different energy sectors and managing local distributed energy resources (DERs). However, the difficulties associated with handling model complexity, along with privacy concerns arising from interactions between energy operators within the LEC, pose challenges for traditional algorithms in achieving coordinated dispatch. To this end, we develop a novel privacy-enhanced, safe, coordinated dispatch framework that integrates reinforcement learning (RL), the perturbation module, and the safety module. The private states of each energy sector within the LEC are concealed by the independent perturbation module before sharing. A central RL agent is then trained on the concealed state space to learn the optimal policy for coordinated dispatch under the complex and uncertain environment. Furthermore, dispatch actions are evaluated and refined by the safety module before the operators execute them. In this way, we can obtain an optimal policy without disclosing any sector’s private state while ensuring the safe operation of the LEC. Extensive experiments are carried out to validate the superior performance and scalability of the proposed method.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2169-2183"},"PeriodicalIF":8.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418373","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

Multi-Time Scale Frequency Regulation Control of Virtual Power Plant Based on Fuzzy Sets 基于模糊集的虚拟电厂多时间尺度调频控制

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-02-13 DOI: 10.1109/TSG.2025.3541544

Lili Mo;Junkun Lan;Zhizhong Chen;Hao Yang;Xin Liao;Haoyong Chen;Sibei Chen

{"title":"Multi-Time Scale Frequency Regulation Control of Virtual Power Plant Based on Fuzzy Sets","authors":"Lili Mo;Junkun Lan;Zhizhong Chen;Hao Yang;Xin Liao;Haoyong Chen;Sibei Chen","doi":"10.1109/TSG.2025.3541544","DOIUrl":"10.1109/TSG.2025.3541544","url":null,"abstract":"With the continuous development of the power system, in the face of the frequency deviation caused by the randomness and volatility of renewable energy sources such as photovoltaic and wind power, considering the use of air conditioning, electric vehicle charging piles, and energy storage to form a distributed resource cluster that takes into account economy and efficiency to participate in frequency regulation auxiliary services to reduce frequency deviation. In the process of a virtual power plant (VPP) participating in frequency regulation auxiliary service, a multi-time scale frequency regulation control strategy of VPP is proposed, which can cope with frequency deviation on different time scales. By establishing a three-stage frequency regulation process, the collaborative optimization of distributed resources is realized. The priority principle is adopted to prioritize the scheduling of higher-value resources to participate in frequency regulation, which helps to allocate resources. In the process of tertiary frequency regulation, the fuzzy sets are used to optimize the frequency regulation strategy of energy storage, which reduces the switching times of energy storage and improves the stability of frequency regulation. The effectiveness of the proposed algorithm and the feasibility of distributed resources participating in frequency regulation are verified by a case.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2361-2374"},"PeriodicalIF":8.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417163","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

Network Intrusion Detection for Modern Smart Grids Based on Adaptive Online Incremental Learning 基于自适应在线增量学习的现代智能电网网络入侵检测

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-02-13 DOI: 10.1109/TSG.2025.3535949

Qiuyu Lu;Kexin An;Jun’e Li;Jin Wang

{"title":"Network Intrusion Detection for Modern Smart Grids Based on Adaptive Online Incremental Learning","authors":"Qiuyu Lu;Kexin An;Jun’e Li;Jin Wang","doi":"10.1109/TSG.2025.3535949","DOIUrl":"https://doi.org/10.1109/TSG.2025.3535949","url":null,"abstract":"New and evolving cyber attacks against smart grids are emerging. This necessitates that the network intrusion detection systems (IDSs) have online learning ability. However, most existing methods struggle to handle new and evolving attacks while retaining old attack knowledge, and many of them employ deep models requiring long update periods. Therefore, we propose an IDS based on adaptive online incremental learning (AdaOIL-IDS). 1) A class-correlated broad learning system (CC-BLS) is proposed for intrusion detection. A weighted CC-factor derived from intra- and inter-class correlations is introduced in CC-BLS to improve classification accuracy. CC-incremental learnings are designed to quickly add new inputs and additional nodes without retraining. The CC-factor for new inputs is adjusted based on correlations of new and old classes, which enables simultaneous adaptation to new attacks and new observations of old attacks while retaining more old knowledge. 2) An adaptive learning framework is proposed for online-offline combined learning of models. Online learning and offline retraining are adaptive switched based on the real-time loss to achieve efficient lifelong learning. Experiment results show that CC-BLS has better performance than selected state-of-the-art incremental broad and deep models, and the proposed adaptive learning framework behaviors better effectiveness and efficiency than selected existing frameworks.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2541-2553"},"PeriodicalIF":8.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860909","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

Two-Timescale Online Optimization of Behind-the-Meter Battery Storage for Stacked Revenue by Providing Multi-Services 表后电池储能的双倍在线优化，通过提供多种服务实现叠加收益

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-02-13 DOI: 10.1109/TSG.2025.3538014

Shibo Chen;Suhan Zhang;Shangyang He;Haosen Yang

{"title":"Two-Timescale Online Optimization of Behind-the-Meter Battery Storage for Stacked Revenue by Providing Multi-Services","authors":"Shibo Chen;Suhan Zhang;Shangyang He;Haosen Yang","doi":"10.1109/TSG.2025.3538014","DOIUrl":"10.1109/TSG.2025.3538014","url":null,"abstract":"Behind-the-meter (BTM) battery energy storage systems (BESS) are becoming increasingly important in the power system with the proliferation of intermittent distributed renewable energy sources. Stacked revenue can be achieved by providing multi-services to the power grid, justifying the substantial upfront cost of BTM BESS and promoting their future adoption. This paper focuses on optimizing the operation strategy of BTM BESS to maximize the time average stacked revenue obtained from multiple service markets, including energy arbitrage, frequency regulation, photovoltaic (PV) power smoothing and reactive power compensation. Challenges arise from the coupling of operation decisions both among multiple services and over the temporal dimension, considering the different decision timescales of service markets as well as the battery dynamics. The uncertainty of stochastic parameters further complicates the optimization process. To address these challenges, this paper proposes a novel two timescale online optimization scheme based on the Lyapunov optimization framework. The uncertainties are tackled by making decisions online, and the computation complexity is highly relieved by relaxing the temporal coupling with a drift-plus-penalty technique. Theoretic analyses are conducted to prove that the solution of this relaxed online decision problem is always feasible for the original one, and it can achieve near-optimum with a constant optimality gap. Extensive simulations utilizing the energy and frequency regulation data from the real market validate the effectiveness of our proposed scheme.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2222-2233"},"PeriodicalIF":8.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417162","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 Hybrid LSTM-Transformer Model for Power Load Forecasting 电力负荷预测的lstm -变压器混合模型

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-02-13 DOI: 10.1109/TSG.2025.3535407

Vasileios Pentsos;Spyros Tragoudas;Jason Wibbenmeyer;Nasser Khdeer

引用次数: 0

Distributed Two-Layer Predictive Control of AC Microgrid Clusters With Communication Delays 具有通信延迟的交流微电网集群分布式两层预测控制

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-02-12 DOI: 10.1109/TSG.2025.3539789

Tao Yang;Jingang Lai;Chang Yu;Xiaoping Wang;Qiang Xiao

引用次数: 0