IEEE Open Access Journal of Power and Energy最新文献_第3页

An Average Power-Based Planning Framework of Transmission Expansion: A New Role for Energy Storage 基于平均功率的输电扩展规划框架：储能的新角色

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-06 DOI: 10.1109/OAJPE.2025.3548911

Qian Zhang;P. R. Kumar;Le Xie

{"title":"An Average Power-Based Planning Framework of Transmission Expansion: A New Role for Energy Storage","authors":"Qian Zhang;P. R. Kumar;Le Xie","doi":"10.1109/OAJPE.2025.3548911","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3548911","url":null,"abstract":"This paper introduces a framework and computational algorithm that utilizes energy storage systems in pairs to improve transmission capacity in electric power systems. Recognizing prolonged development timelines and urgent needs for inter-regional transmission corridors, this paper proposes a near-term supplementary solution that schedules pairs of energy storage systems to increase the throughput of congested transmission lines effectively. We establish a theoretical lower bound on the minimum capacity required for electric power delivery, defined as a function of cumulative power over time. In sharp contrast with conventional transmission planning based on peak power delivery, this new framework allows transmission capacity to be designed around average power delivery needs. This shift would significantly enhance asset utilization in a future grid with large renewable power fluctuations. Numerical experiments demonstrate the proposed method across various grids. In the RTS-GMLC system, the minimum line capacity required was reduced by 36.8% compared to peak-based planning and further decreased by 43.5% when contingency scenarios were considered. In the Texas synthetic grid, the approach achieved a 46.2% reduction in line capacity while maintaining system reliability. These results highlight storage’s potential as a transmission asset, providing practical guidance for planning and policy while enabling insights into future market designs.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"122-134"},"PeriodicalIF":3.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10915681","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data Availability and Integrity Attacks for Power Systems via Nonlinear Spatial-Temporal Modeling 基于非线性时空建模的电力系统数据可用性和完整性攻击

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-05 DOI: 10.1109/OAJPE.2025.3567209

Hongfei Sun;Dongliang Duan;Hongming Zhang;Seong Lok Choi;Jiazi Zhang;Xiaofei Wang;Haonan Wang;Jie Luo;Liuqing Yang

{"title":"Data Availability and Integrity Attacks for Power Systems via Nonlinear Spatial-Temporal Modeling","authors":"Hongfei Sun;Dongliang Duan;Hongming Zhang;Seong Lok Choi;Jiazi Zhang;Xiaofei Wang;Haonan Wang;Jie Luo;Liuqing Yang","doi":"10.1109/OAJPE.2025.3567209","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3567209","url":null,"abstract":"Cybersecurity has become critical for modern power systems as they increasingly rely on digital communication and computational frameworks. Among cyber-attacks, Data Availability and Integrity Attacks (DAIA)—comprising availability attacks such as Denial-of-Service (DoS) and integrity attacks like replay attack—pose severe risks by degrading the reliability and usability of measurement data essential for operational decision-making. This paper presents a data-driven model using the Volterra series to address DAIA in complex power systems. The proposed model effectively captures spatial-temporal relationships, integrating multiple data sources with varying sampling rates while addressing nonlinear dynamics introduced by renewable energy integration. We develop a scalable anomaly detection framework that identifies such attack patterns, including replay attacks leveraging training data. Extensive simulations on the 240-bus WECC system demonstrate the model’s superior performance, achieving robust detection and accurate data recovery with practical computational requirements. Our work provides actionable insights and configuration guidelines for ensuring power system resilience against evolving cyber threats.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"366-377"},"PeriodicalIF":3.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10988696","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Fault Diagnosis of Induction Motor by Using Various Soft Computation Techniques: BESO-RDFA 基于多种软计算技术的异步电动机故障诊断方法：BESO-RDFA

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-04 DOI: 10.1109/OAJPE.2025.3547731

Kapu V. Sri Ram Prasad;K. Dhananjay Rao;Guruvulu Naidu Ponnada;Umit Cali;Taha Selim Ustun

引用次数: 0

Data-Driven Chance-Constrained Capacity Offering for Wind-Electrolysis Joint Systems 数据驱动的机会约束的风力-电解联合系统容量提供

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-03 DOI: 10.1109/OAJPE.2025.3545858

Xuemei Dai;Chunyu Chen;Bixing Ren;Shengfei Yin

{"title":"Data-Driven Chance-Constrained Capacity Offering for Wind-Electrolysis Joint Systems","authors":"Xuemei Dai;Chunyu Chen;Bixing Ren;Shengfei Yin","doi":"10.1109/OAJPE.2025.3545858","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3545858","url":null,"abstract":"An alkaline water electrolyzer (AWE) that converts surplus electricity from fluctuating power of a wind farm (WF) is a promising technology for large-scale and cost-effective hydrogen production. By considering the complementarity of the AWEs and the WF in offering market services, this paper treats the AWE and the WF as a coalition and proposes a joint bidding strategy in the energy and regulation markets to maximize the coalition’s revenue. To overcome the influence of wind and hydrogen uncertainties, we first establish a data-driven distributionally robust chance-constrained bidding model, which reduces market risks by observing uncertainty-related chance constraints for any distribution in the ambiguity set. Then, we use the Shapley value method to evaluate the marginal contribution of the AWE and the WF. Further we propose a game-theory-based bidding revenue allocation scheme. Eventually, case studies based on real-world market data demonstrate that the total profit of the proposed joint bidding strategy increases 27.4% if compared with individual bidding strategy. The average marginal cost of hydrogen production can be reduced by <inline-formula> <tex-math>$5.1~ {$}/$ </tex-math></inline-formula>kg if compared with only participating in the energy market.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"111-121"},"PeriodicalIF":3.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908898","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resident Tolerance to Transitional Temperature Deviation in Smart Communities 智慧社区居民对过渡温度偏差的耐受性

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-01 DOI: 10.1109/OAJPE.2025.3566333

Eve Tsybina;Viswadeep Lebakula;Piljae Im;Helia Zandi;Jeffrey Munk;Justin Hill

引用次数: 0

Coherency-Constrained Spectral Clustering for Power Network Reduction 基于相干约束的频谱聚类算法

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-02-04 DOI: 10.1109/OAJPE.2025.3538619

Mario D. Baquedano-Aguilar;Sean Meyn;Arturo Bretas

{"title":"Coherency-Constrained Spectral Clustering for Power Network Reduction","authors":"Mario D. Baquedano-Aguilar;Sean Meyn;Arturo Bretas","doi":"10.1109/OAJPE.2025.3538619","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3538619","url":null,"abstract":"This paper presents a methodology for reducing the complexity of large-scale power network models using spectral clustering, aggregation of electrical components, and cost function approximation. Two approaches are explored using unconstrained and constrained spectral clustering to determine areas for effective system reduction. Once the system areas are determined, both loads and generators by type are aggregated, and their new cost function is approximated through polynomial curve-fitting or statistical methods. The performance of reduced networks is evaluated in terms of their ability to follow the true daily cost of the original system over a 24-hour period considering a set of several days. Two test systems are taken as test beds. Application of the methodology to a modified version of the IEEE 39-bus system reduces it from 17 generators to a 4-bus system and 9 generators with about 93% of accuracy. Similarly, the IEEE 118-bus system is reduced from 19 generators to a 3-bus system with three aggregated units achieving over 99% of accuracy. These findings address scalability challenges and enhance accuracy for high and mid-loading level conditions, and by aggregating thermal units with similar cost functions.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"88-99"},"PeriodicalIF":3.3,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870381","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning 推进连贯电网划分：包含机器和深度学习的综述

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-28 DOI: 10.1109/OAJPE.2025.3535709

Mohamed Massaoudi;Maymouna Ez Eddin;Ali Ghrayeb;Haitham Abu-Rub;Shady S. Refaat

{"title":"Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning","authors":"Mohamed Massaoudi;Maymouna Ez Eddin;Ali Ghrayeb;Haitham Abu-Rub;Shady S. Refaat","doi":"10.1109/OAJPE.2025.3535709","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3535709","url":null,"abstract":"With the escalating intricacy and expansion of the interconnected electrical grid, the likelihood of power system (PS) collapse has escalated dramatically. There is an increased emphasis on immunizing renewable-dominated power systems from large-scale cascading failures and cyberattacks through optimal power grid partitioning (PGP). By altering the network’s topology, partitioning aims to create areas within the PS that are not only robust but also have increased flexibility in generation and improved controllability over variable demand. This article provides an updated review of the cutting-edge machine learning and data-driven techniques used for PGP in networked PSs. To this end, an in-depth exploration of the basic principles of PGP and performance quantification is provided. The coherency adequacy and controlled islanding within the power network are comprehensively discussed. Subsequently, state-of-the-art research that envisions the use of clustering-based machine learning and deep learning-based solutions for PGP is presented. Finally, key research gaps and future directions for effective PGP are outlined. This paper provides PS researchers with a bird’s eye view of the current state of mainstream PGP implementations. Additionally, it assists stakeholders in selecting the most appropriate clustering algorithms for PGP applications.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"59-75"},"PeriodicalIF":3.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855832","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Open Access Journal of Power and Energy Publication Information IEEE电力与能源开放获取杂志出版信息

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IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-23 DOI: 10.1109/OAJPE.2025.3525881

引用次数: 0

Synergistic Meta-Heuristic Adaptive Real-Time Power System Stabilizer (SMART-PSS) 协同启发式自适应实时电力系统稳定器（SMART-PSS）

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-23 DOI: 10.1109/OAJPE.2025.3532768

Khaled Aleikish;Jonas Kristiansen Nøland;Thomas Øyvang

{"title":"Synergistic Meta-Heuristic Adaptive Real-Time Power System Stabilizer (SMART-PSS)","authors":"Khaled Aleikish;Jonas Kristiansen Nøland;Thomas Øyvang","doi":"10.1109/OAJPE.2025.3532768","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3532768","url":null,"abstract":"Classical fixed-parameter power system stabilizers (PSS) are typically designed to work well for a limited and specific set of operating conditions. However, the integration of low-inertia, inverter-based renewable energy resources (RES) has led to rapid fluctuations in power dispatch, rendering non-adaptive PSSs obsolete. This paper presents a novel hybrid gray-box modeling approach for real-time adaptation of PSS parameters during operation, thereby enabling the PSS to effectively handle a broader range of operating conditions. In our proposed method, we employ a two-stage process. First, we utilize a modified Heffron-Phillips model and meta-heuristics to synthesize the PSS’s compensating transfer function across a broad spectrum of operating conditions independently of external system parameters. Second, we leverage machine learning techniques to extrapolate the tuning results, thus ensuring adaptability across the full range of operating conditions. The effectiveness of this design methodology is rigorously evaluated in multi-machine power systems. Simulation results demonstrate that the proposed SMART-PSS exhibits robust performance compared to conventional fixed-parameter controllers, reducing the maximum phase deviation by 70% to 96%. This makes it highly suitable for modern power systems, which face diverse and dynamic operational challenges.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"36-45"},"PeriodicalIF":3.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10850756","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Information for authors 作者信息

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-23 DOI: 10.1109/OAJPE.2025.3525883

引用次数: 0