IEEE Open Access Journal of Power and Energy最新文献_第2页

Fair Cost Allocation in Energy Communities Under Forecast Uncertainty

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2024-12-19 DOI: 10.1109/OAJPE.2024.3520418

Michael Eichelbeck;Matthias Althoff

{"title":"Fair Cost Allocation in Energy Communities Under Forecast Uncertainty","authors":"Michael Eichelbeck;Matthias Althoff","doi":"10.1109/OAJPE.2024.3520418","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3520418","url":null,"abstract":"Energy communities (ECs) are an increasingly studied path toward improving prosumer coordination. A central challenge of ECs is to allocate cost savings fairly to members. While many allocation mechanisms have been developed, existing literature does not account for the implications of inaccurate forecasts on the fairness of the allocation. We introduce a set of fairness conditions for imperfect knowledge allocation and show that these conditions constitute a Pareto front. We demonstrate how a well-established allocation scheme, the Shapley value mechanism (SVM), has unfavorable consequences for flexibility-providing community members and generally does not yield solutions on this Pareto front. In contrast, we interpret dispatch cost under imperfect knowledge as being composed of two components. The first represents the cost under perfect knowledge, and the second represents the cost arising from inaccurate forecasts. Our proposed mechanism extends an SVM-based allocation of the perfect knowledge cost by allocating the remaining cost in a way that guarantees finding solutions on the Pareto front. To this end, we formulate a convex multi-objective optimization problem that can efficiently be solved as a linear or quadratic program.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"2-11"},"PeriodicalIF":3.3,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10807294","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106572","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

Event-Type Identification in Power Grids Using a Spectral Correlation Function-Aided Convolutional Neural Network 基于谱相关函数辅助卷积神经网络的电网事件类型识别

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2024-12-11 DOI: 10.1109/OAJPE.2024.3513776

Ozgur Alaca;Ali Riza Ekti;Jhi-Young Joo;Nils Stenvig

{"title":"Event-Type Identification in Power Grids Using a Spectral Correlation Function-Aided Convolutional Neural Network","authors":"Ozgur Alaca;Ali Riza Ekti;Jhi-Young Joo;Nils Stenvig","doi":"10.1109/OAJPE.2024.3513776","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3513776","url":null,"abstract":"Rapid and accurate identification of events in power grids is critical to ensuring system reliability and security. This study introduces a novel event-type identification method, utilizing a Spectral Correlation Function (SCF)-aided Convolutional Neural Network (CNN). The proposed method employs a six-stage cascaded structure consisting of: (1) data collection, (2) clipping, (3) augmentation, (4) feature extraction (FE), (5) training, and (6) testing. Real-world power grid signals sourced from the Grid Event Signature Library are used for both training and testing. To improve robustness, additive white Gaussian noise (AWGN) is introduced at various signal-to-noise ratio (SNR) levels to augment the dataset. The SCF-based FE method captures distinctive event-type characteristics by exploiting the spectral correlation of signals, allowing the CNN architecture to effectively learn and generalize event patterns. The proposed method is benchmarked against seven conventional techniques, using real-world power grid signals representing four distinct event types: blown fuse, line switching, low amplitude arcing, and transformer energization. Key performance metrics-prediction accuracy, mean absolute error (MAE), precision, recall, F1-score, and confusion matrix—are employed to evaluate the performance. Results demonstrate that the SCF-CNN method outperforms traditional approaches across all metrics and SNR levels, achieving over 99% prediction accuracy and nearly zero error for SNR values above 6 dB. This signifies its efficacy in reliable event-type identification for power grid applications.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"11 ","pages":"653-664"},"PeriodicalIF":3.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10789217","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875107","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

Correction to “Detecting Anomaly Classification Using PCA-Kmeans and Ensembled Classifier for Wind Turbines” 对“利用PCA-Kmeans和集成分类器检测风力发电机异常分类”的修正

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2024-12-09 DOI: 10.1109/OAJPE.2024.3496252

Prince Waqas Khan;Yung-Cheol Byun

引用次数: 0

An Optimal Control of Energy Storage Systems Using Wind Power Prediction 利用风能预测实现储能系统的优化控制

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2024-12-02 DOI: 10.1109/OAJPE.2024.3509964

Kenta Koiwa;Tomonori Tashiro;Tomoya Ishii;Tadanao Zanma;Kang-Zhi Liu

{"title":"An Optimal Control of Energy Storage Systems Using Wind Power Prediction","authors":"Kenta Koiwa;Tomonori Tashiro;Tomoya Ishii;Tadanao Zanma;Kang-Zhi Liu","doi":"10.1109/OAJPE.2024.3509964","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3509964","url":null,"abstract":"Wind power plants (WPPs) have been rapidly installed worldwide as an alternative source to thermal power plants. Nevertheless, since the outputs of WPPs constantly fluctuates due to variations in wind speed, WPPs expose power systems to power quality degradation, such as frequency fluctuation. This paper develops an optimal control method of energy storage systems (ESSs) that utilizes WPP output prediction to mitigate WPP output fluctuation. In the proposed method, an output reference of ESS can be obtained as the solution of an optimization problem. Specifically, the proposed method regulates the state of charge of ESS within its appropriate range by minimizing a cost function. At the same time, the minimization of ESS output and multiple grid codes related to the mitigation of WPP output fluctuation are considered as constraints. As a result, the proposed method enables us to mitigate the output fluctuation of WPP sufficiently by an ESS with small rated power. The effectiveness of the proposed method is demonstrated through comparative analysis with conventional methods via scenario simulations.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"11 ","pages":"637-652"},"PeriodicalIF":3.3,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772215","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825855","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

Resilient Space Operations With Digital Twin for Solar PV and Storage 弹性空间操作与数字孪生太阳能光伏和存储

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2024-11-27 DOI: 10.1109/OAJPE.2024.3508576

Shayan Ebrahimi;Mohammad Seyedi;S. M. Safayet Ullah;Farzad Ferdowsi

{"title":"Resilient Space Operations With Digital Twin for Solar PV and Storage","authors":"Shayan Ebrahimi;Mohammad Seyedi;S. M. Safayet Ullah;Farzad Ferdowsi","doi":"10.1109/OAJPE.2024.3508576","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3508576","url":null,"abstract":"Space missions would not be possible without an available, reliable, autonomous, and resilient power system. Space-based power systems differ from Earth’s grid in generation sources, needs, structure, and controllability. This research introduces a groundbreaking approach employing digital twin (DT) technology to emulate and enhance the performance of a physical system representing a space-based system. The system encompasses three DC converters, a DC source, and a modular battery storage unit feeding a variable load. Rigorous testing across diverse operating points establishes the real-time high-fidelity DT, with root mean square error (RMSE) values consistently below 5%. The principal innovation leverages this DT to fortify system resilience against unforeseen events, surpassing the capabilities of existing controllers and autonomy levels. The approach offers an invaluable tool for scenarios where the system may not be primed for or physical access to components is limited. This research introduces a modular battery storage solution that seamlessly compensates for power shortages due to dust effects on the Lunar surface or unexpected system faults. This holistic approach validates the DT’s fidelity and underscores its potential to revolutionize system operation, safeguard against uncertainties, and expedite response strategies during unexpected contingencies. The proposed approach also paves the way for future development.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"11 ","pages":"624-636"},"PeriodicalIF":3.3,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770281","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810337","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

Graph Theory-Based Fault Location Method for Transmission Systems With Renewable Energy Sources 基于图论的可再生能源输电系统故障定位方法

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2024-11-27 DOI: 10.1109/OAJPE.2024.3507537

Victor Gonzalez;V. Torres-García;Daniel Guillen;Luis M. Castro

{"title":"Graph Theory-Based Fault Location Method for Transmission Systems With Renewable Energy Sources","authors":"Victor Gonzalez;V. Torres-García;Daniel Guillen;Luis M. Castro","doi":"10.1109/OAJPE.2024.3507537","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3507537","url":null,"abstract":"Fault location has been crucial in minimizing fault restoration time. Various techniques and methodologies have been deployed to enhance the performance of fault location algorithms, especially in light of the increasing integration of renewable energy sources. In this context, this paper describes a graph-theory-based method for fault location in power networks with renewable energy sources. This novel technique is designed to provide accurate fault distance estimates, even in the presence of severe noise and fault resistance. It takes advantage of graph theory and equivalent impedances applying Kirchhoff’s laws systematically to ensure accurate fault location even in the presence of fault resistances. To showcase the improved accuracy of the proposed methodology, a comparison with typical impedance-based two-terminal fault location methods is carried out. The effectiveness of the proposed algorithm was proven with different electrical systems. Average errors inferior to 0.22% and 0.48% were obtained for single-phase faults and three-phase faults with resistances up to \u0000<inline-formula> <tex-math>$200~Omega $ </tex-math></inline-formula>\u0000 respectively, which confirms the improved performance with respect to conventional algorithms implemented in typical impedance relays.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"11 ","pages":"611-623"},"PeriodicalIF":3.3,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10769500","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798055","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

Multi-Timescale Modeling Framework of Hybrid Power Plants Providing Secondary Frequency Regulation 提供二次调频的混合电厂多时间尺度建模框架

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2024-11-22 DOI: 10.1109/OAJPE.2024.3504835

Yuxin Deng;Xin Fang;Ningchao Gao;Jin Tan

{"title":"Multi-Timescale Modeling Framework of Hybrid Power Plants Providing Secondary Frequency Regulation","authors":"Yuxin Deng;Xin Fang;Ningchao Gao;Jin Tan","doi":"10.1109/OAJPE.2024.3504835","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3504835","url":null,"abstract":"Hybrid power plants (HPPs) present a promising solution to address the significant challenges posed by the rapid integration of variable renewable energy sources (VREs) into power systems, particularly in maintaining power balance and frequency stability. Therefore, there is a pressing need for system operators and HPP owners to effectively manage both the energy and regulation services of HPPs within the current system operational framework. Existing studies on HPP modeling often separate dynamic control from steady-state scheduling and lack coordinated integration of self-scheduling of HPPs with the system-level scheduling, leading to over/under estimation of the flexibility of HPPs. To address this challenge, this paper presents a generic modeling framework for HPPs that integrates steady-state optimization with dynamic control across multiple timescales, enabling seamless HPP participation in day-ahead and real-time markets and real-time control. Additionally, the framework facilitates comprehensive economic and frequency performance evaluations. Case studies on a modified IEEE 39-bus system demonstrate the framework’s ability to ensure frequency performance with flexible HPP operation modes, align BESS state-of-charge (SOC) with dispatch targets, and optimize reliability and economic outcomes under various scenarios.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"11 ","pages":"595-609"},"PeriodicalIF":3.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10764748","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777750","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

Numerical Algorithm for Solving Market Clearing Problem in Power Electronics-Based Power Distribution Systems 电力电子配电系统市场出清问题的数值求解

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2024-11-18 DOI: 10.1109/OAJPE.2024.3501575

Musharrat Sabah;Aaron M. Cramer;Yuan Liao

引用次数: 0

A Robust Multi-Modal Deep Learning-Based Fault Diagnosis Method for PV Systems 基于鲁棒多模态深度学习的光伏系统故障诊断方法

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2024-11-13 DOI: 10.1109/OAJPE.2024.3497880

Shahabodin Afrasiabi;Sarah Allahmoradi;Mousa Afrasiabi;Xiaodong Liang;C. Y. Chung;Jamshid Aghaei

引用次数: 0

Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model 利用单粒子模型在线和离线识别电池传感器中的虚假数据注入攻击

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2024-11-07 DOI: 10.1109/OAJPE.2024.3493757

Victoria A. O’Brien;Vittal S. Rao;Rodrigo D. Trevizan

{"title":"Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model","authors":"Victoria A. O’Brien;Vittal S. Rao;Rodrigo D. Trevizan","doi":"10.1109/OAJPE.2024.3493757","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3493757","url":null,"abstract":"The cells in battery energy storage systems are monitored, protected, and controlled by battery management systems whose sensors are susceptible to cyberattacks. False data injection attacks (FDIAs) targeting batteries’ voltage sensors affect cell protection functions and the estimation of critical battery states like the state of charge (SoC). Inaccurate SoC estimation could result in battery overcharging and over discharging, which can have disastrous consequences on grid operations. This paper proposes a three-pronged online and offline method to detect, identify, and classify FDIAs corrupting the voltage sensors of a battery stack. To accurately model the dynamics of the series-connected cells a single particle model is used and to estimate the SoC, the unscented Kalman filter is employed. FDIA detection, identification, and classification was accomplished using a tuned cumulative sum (CUSUM) algorithm, which was compared with a baseline method, the chi-squared error detector. Online simulations and offline batch simulations were performed to determine the effectiveness of the proposed approach. Throughout the batch simulations, the CUSUM algorithm detected attacks, with no false positives, in 99.83% of cases, identified the corrupted sensor in 97% of cases, and determined if the attack was positively or negatively biased in 97% of cases.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"11 ","pages":"571-582"},"PeriodicalIF":3.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10746526","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672078","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