IEEE Transactions on Smart Grid最新文献_第8页

Model-Based Safe Reinforcement Learning for Active Distribution Network Scheduling 基于模型的配电网主动调度安全强化学习

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-17 DOI: 10.1109/TSG.2025.3547843

Yuxiang Guan;Wenhao Ma;Liang Che;Mohammad Shahidehpour

引用次数: 0

A Lightweight Framework for Measurement Causality Extraction and FDIA Localization 测量因果关系提取和 FDIA 定位的轻量级框架

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-14 DOI: 10.1109/TSG.2025.3548097

Shengyang Wu;Chen Yang;Jingyu Wang;Dongyuan Shi

{"title":"A Lightweight Framework for Measurement Causality Extraction and FDIA Localization","authors":"Shengyang Wu;Chen Yang;Jingyu Wang;Dongyuan Shi","doi":"10.1109/TSG.2025.3548097","DOIUrl":"10.1109/TSG.2025.3548097","url":null,"abstract":"False Data Injection Attack (FDIA) has become a growing concern in modern cyber-physical power systems. Many learning-based approaches have utilized the statistical correlation patterns between measurements to facilitate the detection and localization of FDIA. However, these correlation patterns are susceptible to the distribution drift of measurement data, which can be induced by changes in system operating points or variations in attack strength, leading to degraded model performance. Causal inference serves as a promising solution to this problem, as it can embed the physical relationship between measurements as causal patterns that are robust against data distribution drifts. However, causal inference is also computationally demanding. To leverage its advantages and address the computational cost issue, this paper proposes a lightweight framework based on causal inference and Graph Attention Networks (GATs) to extract causal patterns between measurements and locate FDIAs. The proposed framework consists of two levels. The lower level uses an X-learner algorithm to estimate the causality strength between measurements and generate Measurement Causality Graphs (MCGs). The upper level then applies a GAT to identify the anomaly patterns in the MCGs. Since the extracted causality patterns are intrinsically related to the measurements, it is easier for the upper level model to identify the attacked nodes than the existing FDIA localization approaches. A physical neighbor masking strategy is implemented to cut down the computational cost of both levels. The performance of the proposed framework is evaluated on the IEEE 39-bus and 118-bus systems. Experimental results show that the causality-based FDIA localization mechanism provides a lightweight solution to interpretable measurement causality extraction and robust FDIA localization.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2587-2598"},"PeriodicalIF":8.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631362","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

Performance-Based Capacity Allocation of DERs to Improve Aggregated Flexibility Provision 基于性能的 DER 容量分配，以改善总体灵活性的提供

IF 9.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-12 DOI: 10.1109/tsg.2025.3550846

Amir Hamidi, Mohsen Hamzeh, Mokhtar Bozorg, Rachid Cherkaoui

引用次数: 0

Network Topology Flexibility-Aware Robust Electricity Trading for Distribution System Survivability Enhancement 基于网络拓扑灵活性的鲁棒电力交易增强配电系统生存性

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-11 DOI: 10.1109/TSG.2025.3550363

Mingze Xu;Shunbo Lei;Canqi Yao;Weimin Wu;Cheng Ma;Chong Wang

{"title":"Network Topology Flexibility-Aware Robust Electricity Trading for Distribution System Survivability Enhancement","authors":"Mingze Xu;Shunbo Lei;Canqi Yao;Weimin Wu;Cheng Ma;Chong Wang","doi":"10.1109/TSG.2025.3550363","DOIUrl":"10.1109/TSG.2025.3550363","url":null,"abstract":"Extreme weather events significantly threaten power system security and market operations, causing either substantial load fluctuations or grid line failures. Current distribution-level electricity market mechanisms often prove inadequate during such events. Furthermore, limited research has explored electricity market mechanisms for improving distribution system survivability. To fill this gap, this study proposes a double-auction mechanism to facilitate distribution-level transactions of non-utility distributed energy resources through market incentives for proactive system resilience enhancement. This mechanism aims to utilize a market-driven approach to achieve the highest system survivability in disasters. This trading mechanism is divided into two stages to address the direct changes in the tradability caused by line failures: clearing and delivery. During market clearing, the DSO operates as an auctioneer to concurrently optimize social welfare and prior-event network topology. The pricing rule employs an enhanced Vickrey-Clarke-Groves mechanism to ensure truthful bidding and incentive compatibility. During delivery, the DSO performs redispatch to mitigate economic losses and compensates for energy curtailments through established settlement protocols. This market-driven resilience enhancement method formulates a bi-level two-stage robust optimization problem, solved using a customized column-and-constraint generation algorithm. Modified IEEE 13-node and 123-node systems are used to verify the effectiveness of the proposed approach.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2504-2517"},"PeriodicalIF":8.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599532","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

Anomaly Identification of Synchronized Voltage Waveform for Situational Awareness of Low Inertia Systems 用于低惯性系统态势感知的同步电压波形异常识别

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-10 DOI: 10.1109/TSG.2025.3549476

He Yin;Wei Qiu;Yuru Wu;Wenpeng Yu;Jin Tan;Andy Hoke;Cameron J. Kruse;Brad W. Rockwell;Yilu Liu

{"title":"Anomaly Identification of Synchronized Voltage Waveform for Situational Awareness of Low Inertia Systems","authors":"He Yin;Wei Qiu;Yuru Wu;Wenpeng Yu;Jin Tan;Andy Hoke;Cameron J. Kruse;Brad W. Rockwell;Yilu Liu","doi":"10.1109/TSG.2025.3549476","DOIUrl":"10.1109/TSG.2025.3549476","url":null,"abstract":"Inverter-based resources (IBRs) such as photovoltaics (PVs), wind turbines, and battery energy storage systems (BESSs) are widely deployed in low-carbon power systems. However, these resources typically do not provide the inertia needed for grid stability, resulting in a low-inertia power system. IBRs and lack of inertia have been known to cause anomalies such as waveform distortions and wideband oscillations in power systems due to the limited inertia level, leading to increased generation trips and load shedding. To achieve effective anomaly identification, this paper proposes a synchro-waveform-based algorithm utilizing real-time synchronized voltage waveform measurements from waveform measurement units (WMUs). In the proposed method, different physical characteristics, as well as statistical features, are extracted from synchronized voltage waveform measurements to filter anomalies. Then, the anomaly identification approach based on the random forest is developed and deployed into the FNET/GridEye system considering trade-offs among accuracy, computational burden, and deployment cost. Moreover, four WMUs are specially designed and deployed on Kauai Island to receive instantaneous synchronized voltage waveform measurements. To verify the performance of the proposed algorithm, different experiments are carried out with collected field test data. The result demonstrates that the performance of the proposed synchro-waveform-based anomaly categorization algorithm can accurately identify anomalies 95.35% of the time, which has comparable performance among benchmarking algorithms.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2416-2428"},"PeriodicalIF":8.6,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640592","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

Implicit Enhanced Distributed Heavy-Ball Energy Management Strategy for Microgrids With Time-Varying Social Welfare and Delay 具有时变社会福利和延迟的微电网隐式增强分布式重球能量管理策略

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-10 DOI: 10.1109/TSG.2025.3548975

Mingqi Xing;Dazhong Ma;Kai Ma;Qiuye Sun

{"title":"Implicit Enhanced Distributed Heavy-Ball Energy Management Strategy for Microgrids With Time-Varying Social Welfare and Delay","authors":"Mingqi Xing;Dazhong Ma;Kai Ma;Qiuye Sun","doi":"10.1109/TSG.2025.3548975","DOIUrl":"10.1109/TSG.2025.3548975","url":null,"abstract":"Diverse energy sources and fluctuating energy demands highlight the criticality of microgrid energy management (MEM). This paper develops a general model of social welfare maximization in the presence of time-varying social welfare with quadratic transmission losses, which implies that the fitting coefficients of the transmission losses, cost function, and utility function may vary over time. A significant difference with existing works is the dependence of the problem’s optimal solution on time variation, making higher requirements on the algorithm’s performance, especially the convergence rate. To address these challenges, we extend the conventional heavy-ball method and propose a novel implicit enhanced distributed heavy-ball algorithm. The algorithm incorporates multiple heavy-ball terms to accelerate convergence. Notably, the second heavy-ball term is implicitly implemented via an acceleration term that contains historical information and consensus error. Furthermore, the algorithm obviates the necessity for the sharing of additional auxiliary variables, thereby reducing the communication overhead. We demonstrate that the algorithm converges asymptotically to the neighborhood of the time-varying optimal solution even with arbitrarily large but bounded communication delays. Finally, detailed case studies illustrate that the algorithm can improve the convergence rate by 15.02% over conventional method, followed by the validation of its scalability and the discussion of the negative impact of delay.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2492-2503"},"PeriodicalIF":8.6,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599463","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

Model-Free Aggregation for Virtual Power Plants Using Input Convex Neural Networks 基于输入凸神经网络的虚拟电厂无模型聚合

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-07 DOI: 10.1109/TSG.2025.3548026

Wei Lin;Yi Wang;Jianghua Wu;Fei Feng

{"title":"Model-Free Aggregation for Virtual Power Plants Using Input Convex Neural Networks","authors":"Wei Lin;Yi Wang;Jianghua Wu;Fei Feng","doi":"10.1109/TSG.2025.3548026","DOIUrl":"10.1109/TSG.2025.3548026","url":null,"abstract":"The virtual power plant (VPP) has been advocated as a promising way to aggregate massive distributed energy resources (DERs) in a distribution system (DS) for their participation in transmission-level operations. This requires identifying the feasible set of VPP power transfers fulfilling DS operational constraints. The identification task is completed using the existing methods by relying on constraint parameters (e.g., line impedance). However, the constraint parameters in a DS may be inaccurate and even missing in practice. Consequently, this paper develops a model-free aggregation method for VPPs. The proposed method first develops an input convex neural network (ICNN)-based surrogate for the feasible set of VPP power transfers. Our ICNN-based surrogate can be determined in a model-free manner by historical data. Furthermore, it is proven by leveraging the convexity and epigraph relaxation of an ICNN that our ICNN-based surrogate can be reformulated as a linear programming model without binary variables. This allows efficiently embedding our ICNN-based surrogate in transmission-level operations so that numerous VPPs can be efficiently coordinated at the transmission level. The proposed method is verified by numerical experiments in the IEEE 33-bus and IEEE 136-bus test systems.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2404-2415"},"PeriodicalIF":8.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575270","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

Distributed Deep Reinforcement Learning for Data-Driven Water Heater Model in Smart Grid 智能电网中数据驱动热水器模型的分布式深度强化学习

IF 9.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-06 DOI: 10.1109/tsg.2025.3548653

Jiangjiao Xu, Ting Zheng, Yibing Dang, Fan Yang, Dongdong Li

引用次数: 0

Perturbed Decision-Focused Learning for Modeling Strategic Energy Storage 基于摄动决策的战略储能建模

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-06 DOI: 10.1109/TSG.2025.3548009

Ming Yi;Saud Alghumayjan;Bolun Xu

{"title":"Perturbed Decision-Focused Learning for Modeling Strategic Energy Storage","authors":"Ming Yi;Saud Alghumayjan;Bolun Xu","doi":"10.1109/TSG.2025.3548009","DOIUrl":"10.1109/TSG.2025.3548009","url":null,"abstract":"This paper presents a novel decision-focused framework integrating the physical energy storage model into machine learning pipelines. Motivated by the model predictive control for energy storage, our end-to-end method incorporates the prior knowledge of the storage model and infers the hidden reward that incentivizes energy storage decisions. This is achieved through a dual-layer framework, combining a prediction layer with an optimization layer. We introduce the perturbation idea into the designed decision-focused loss function to ensure the differentiability over linear storage models, supported by a theoretical analysis of the perturbed loss function. We also develop a hybrid loss function for effective model training. We provide two challenging applications for our proposed framework: energy storage arbitrage, and energy storage behavior prediction. The numerical experiments on real price data demonstrate that our arbitrage approach achieves the highest profit against existing methods. The numerical experiments on synthetic and real-world energy storage data show that our approach achieves the best behavior prediction performance against existing benchmark methods, which shows the effectiveness of our method.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2574-2586"},"PeriodicalIF":8.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570407","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

An Adaptive MARL Large Model for Dispatch Strategy Generation in Logistics-Energy Spatiotemporal Coordination of Container Seaports 集装箱港口物流-能源时空协调调度策略生成的自适应MARL大模型

IF 8.6 1区工程技术

IEEE Transactions on Smart Grid Pub Date : 2025-03-06 DOI: 10.1109/TSG.2025.3547830

Yiwen Huang;Wentao Huang;Ran Li;Tao Huang;Canbing Li;Nengling Tai

{"title":"An Adaptive MARL Large Model for Dispatch Strategy Generation in Logistics-Energy Spatiotemporal Coordination of Container Seaports","authors":"Yiwen Huang;Wentao Huang;Ran Li;Tao Huang;Canbing Li;Nengling Tai","doi":"10.1109/TSG.2025.3547830","DOIUrl":"10.1109/TSG.2025.3547830","url":null,"abstract":"Logistics-energy coordination significantly enhances energy efficiency in electrified seaports. However, daily changes in environment data necessitate the re-implementation of optimization procedures, causing huge computational burdens. This paper proposes an adaptive multi-agent reinforcement learning (MARL) large model for logistics-energy spatiotemporal coordination of container seaports. The well-trained large model can directly generate optimal policy for each operating day from environment data without re-solving. To achieve this, a comprehensive logistics-energy coordination model is first established considering the spatial and temporal constraints of all-electric ships (AESs), quay cranes (QCs), auto guided vehicles (AGVs), and the seaport power distribution network (SPDN). The model is formulated as a Markov Decision Process (MDP). Then a MARL large model is developed, involving a hypernetwork mapping environment data to optimal policy, and special structures for both hypernetwork and agent policy networks to adapt to any number of daily arrival AESs. Additionally, a cascading action modification layer is designed to ensure correct action outputs within complex spatiotemporal constraints. A tailored training method with two acceleration strategies are developed for the large model. Case studies illustrate that the large model after training can automatically generate optimal policies with little to no fine-tuning, outperforming existing methods that require extensive solution time.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 3","pages":"2261-2277"},"PeriodicalIF":8.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570390","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