IEEE Open Access Journal of Power and Energy最新文献

Data-Driven Chance-Constrained Capacity Offering for Wind-Electrolysis Joint Systems

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

Coherency-Constrained Spectral Clustering for Power Network Reduction

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

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

Synergistic Meta-Heuristic Adaptive Real-Time Power System Stabilizer (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

IEEE Open Access Journal of Power and Energy Publication Information

IF 3.3

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

引用次数: 0

Information for authors

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

2025 Index IEEE Open Access Journal of Power and Energy Vol. 11

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-23 DOI: 10.1109/OAJPE.2025.3532174

引用次数: 0

2024 Best Papers, Outstanding Associate Editors, and Outstanding Reviewers 2024年最佳论文、杰出副编辑和杰出审稿人

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-22 DOI: 10.1109/OAJPE.2025.3528699

Fangxing Fran Li

引用次数: 0

Snake Optimizer Improved Variational Mode Decomposition for Short-Term Prediction of Vehicle Charging Loads

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-20 DOI: 10.1109/OAJPE.2025.3529944

Quanxue Guan;Qinhe Liu;Shaocong Tao;Yunjian Xu;Di Zhou;Haoyong Chen;Xiaojun Tan

{"title":"Snake Optimizer Improved Variational Mode Decomposition for Short-Term Prediction of Vehicle Charging Loads","authors":"Quanxue Guan;Qinhe Liu;Shaocong Tao;Yunjian Xu;Di Zhou;Haoyong Chen;Xiaojun Tan","doi":"10.1109/OAJPE.2025.3529944","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3529944","url":null,"abstract":"The rapid proliferation of electric vehicles (EVs) significantly impacts the power grid, necessitating effective forecasting of charging loads. For ultra short-term load prediction, this paper proposes a Snake Optimization (SO)-Variational Mode Decomposition (VMD)-Long Short-Term Memory (LSTM) algorithm trained by only the historical charging data. Before the prediction starts, the VMD method is utilized to minimize the data complexity, yielding several multiple Intrinsic Mode Functions (IMFs) that correspond to the charging load features at different time scales. The VMD parameters are automatically adjusted using the SO method, instead of the trial-and-error method, to trade off the prediction accuracy against computational overhead. Once the parameters of the VMD are determined, the same number of LSTM networks are employed to forecast the corresponding charging loads from these IMFs, with one LSTM for each IMF. Due to the VMD, IMFs with spanned center frequencies containing few irregularities make the prediction simple. These LSTM outcomes are then summed to obtain the overall load prediction. Experiments are carried out to show that the proposed parallel structure of multiple LSTM networks can achieve high prediction accuracy without requiring complex model structures. Our proposed algorithm outperforms the traditional prediction methods including Gate Recurrent Unit, Extreme Learning Machine, LSTM, and their combination with VMD, significantly reducing the Root Mean Square Error and the Mean Absolute Error by 30.1% and 32.9% in comparison with the optimal VMD-LSTM approach, and by 59.3% and 62.6% with respect to the baseline LSTM method.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"76-87"},"PeriodicalIF":3.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10846941","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422898","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

Proactive Frequency Stability Scheme Based on Bayesian Filters and Spectral Clustering

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-17 DOI: 10.1109/OAJPE.2025.3531240

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

{"title":"Proactive Frequency Stability Scheme Based on Bayesian Filters and Spectral Clustering","authors":"Gian Paramo;Mario D. Baquedano-Aguilar;Arturo Bretas;Sean Meyn","doi":"10.1109/OAJPE.2025.3531240","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3531240","url":null,"abstract":"This work presents a proactive distributed model for power system frequency stability. High-level penetration of renewable energy sources into the grid have introduced unforeseen and unmodeled system dynamics. Underfrequency load shedding state-of-the-art solutions are reactive in design, with efficiency constrained by the modeling error. Being able to detect unstable conditions early makes it possible to generate optimized corrective actions. In this work, phasor measurement units are used to predict frequency values. When a disturbance is detected, the state of frequency is predicted a few seconds into the future via a particle filter algorithm. Corrective actions are modeled through a mixed integer linear programming algorithm within system areas established through spectral clustering. The solution is implemented on Matlab, considering IEEE test systems. The proactive design of the method combined with its multiple layers of optimization deliver results that outperform state-of-the-art solutions. Easy-to-implement model, without hard-to-derive parameters, highlights potential aspects towards real-life implementation.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"100-110"},"PeriodicalIF":3.3,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10844303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611807","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