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

Reinforcement Learning for Stability-Guaranteed Adaptive Optimal Primary Frequency Control of Power Systems Using Partially Monotonic Neural Networks 部分单调神经网络用于电力系统保稳定自适应最优一次频率控制的强化学习

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-04-02 DOI: 10.1109/OAJPE.2025.3556142

Hamad Alduaij;Yang Weng

{"title":"Reinforcement Learning for Stability-Guaranteed Adaptive Optimal Primary Frequency Control of Power Systems Using Partially Monotonic Neural Networks","authors":"Hamad Alduaij;Yang Weng","doi":"10.1109/OAJPE.2025.3556142","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3556142","url":null,"abstract":"Deepening the deployment of distributed energy resources requires the large-scale integration of inverter-based resources, which can deteriorate the frequency stability. Recent studies propose using neural Lyapunov-based reinforcement learning for control. While this method can be trained offline with performance guarantees, it is only optimal for specific values of system parameters, as it omits critical modeling factors like decreasing inertia and damping variation over time. To maintain the performance at varying operation points, we consider an adaptive neural Lyapunov framework that adapts the controller’s output in the presence of varying parameters. Neural networks require flexibility to maximize adaptive control performance, while stability demands monotonicity, creating an inherent conflict. In this paper, we design a partially monotonic controller that maintains stability with maximal representation capacity for parameter adaptation. Stability is ensured by having monotonicity retained for frequency while non-monotonicity is allowed for the system parameters, such as damping and inertia. The structural form of partially monotonic neural networks is used for the controller design to that end. Flexibility is allowed by the design when adaptation to changes to the system parameters is made, while the Lyapunov stability guarantee is retained. The non-monotonic layers are chosen through an adaptive layer that is designed for damping and inertia based on their relationship to control in the system equation, by which optimized output for different operating conditions is allowed.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"259-269"},"PeriodicalIF":3.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947581","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073416","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 Learning-Based Power System Health Assessment Model 基于图学习的电力系统健康评估模型

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-31 DOI: 10.1109/OAJPE.2025.3556004

Koji Yamashita;Nanpeng Yu;Evangelos Farantatos;Lin Zhu

{"title":"Graph Learning-Based Power System Health Assessment Model","authors":"Koji Yamashita;Nanpeng Yu;Evangelos Farantatos;Lin Zhu","doi":"10.1109/OAJPE.2025.3556004","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3556004","url":null,"abstract":"As the power transmission system’s energy sources become increasingly diversified, the grid stability is experiencing increased fluctuations, thereby necessitating more frequent and near real-time monitoring by grid operators. The power system security has been monitored through real-time contingency analysis and dynamic security assessment framework, both of which are typically based on time-domain simulations or power flow calculations. Achieving higher accuracy in grid health level prediction often requires time-consuming simulation and analysis. To improve computational efficiency, this paper develops machine learning models with phasor measurement unit (PMU) data to monitor the power system health index, focusing on rotor angle stability and frequency stability. The proposed machine learning models accurately predict frequency and angle stability indicators, essential for evaluating grid health considering various contingencies, even when dealing with limited PMU deployment in transmission grids. The proposed framework leverages a physics-informed graph convolution network and graph attention network with ordinal encoders, which are benchmarked with multi-layer perceptron models. These models are trained on dataset derived from an augmented IEEE 118-bus system with different demand levels and fuel mix, including tailored dynamic generator models, generator controller models, and grid protection models. The numerical studies explored the performance of the proposed and baseline machine learning models under both full PMU coverage and various partial PMU coverage conditions, where different data imputation methods are used for substations without PMUs. The findings from this study offer valuable insights, such as machine learning model selection and critical PMU locations regarding power equipment, into the design of data-driven grid health index prediction models for power systems.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"181-193"},"PeriodicalIF":3.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10945887","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800742","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

Two-Timescale Coordination of Discretely and Continuously Adjustable Devices in ADNs With DRL and Physical Convex Optimization 基于DRL和物理凸优化的ADNs中离散和连续可调器件的双时间尺度协调

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-27 DOI: 10.1109/OAJPE.2025.3573961

Jian Zhang;Yigang He

{"title":"Two-Timescale Coordination of Discretely and Continuously Adjustable Devices in ADNs With DRL and Physical Convex Optimization","authors":"Jian Zhang;Yigang He","doi":"10.1109/OAJPE.2025.3573961","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3573961","url":null,"abstract":"High penetration of electrical vehicles (EVs) and renewable distributed generators (DGs) into active distribution networks (ADNs) lead to frequent, rapid and fierce voltages magnitudes violations. A novel two-timescale coordination scheme for different types of adjustable devices in ADNs is put forward in this article by organically integrating data-driven deep reinforce-ment learning (DRL) into physical convex model. A Markov Decision Process (MDP) is formulated on slow timescale, in which ratios/statuses of on load transformer changers (OLTCs) and switchable capacitors reactors (SCRs) and ESSs charging/ discharging power are set hourly to optimize network losses while regulating voltages magnitudes. An improved DRL with relaxation-prediction-correction strategies is proposed for eradicating discrete action components dimension curses. Whereas, on fast timescale (e.g., several seconds or minutes), the optimal reactive power of DGs inverters and static VAR compensators (SVCs) in balanced and unbalanced ADNs are set with physical convex optimization to minimize network losses while respecting physical constraints. Five simulations cases with IEEE 33-node balanced and 123-node unbalanced feeders are carried out to verify capabilities of put forward method.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"391-403"},"PeriodicalIF":3.3,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11016130","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144264188","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

Open Power System Datasets and Open Simulation Engines: A Survey Toward Machine Learning Applications 开放电力系统数据集和开放仿真引擎：机器学习应用综述

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-26 DOI: 10.1109/OAJPE.2025.3573958

Ignacio Aravena;Chih-Che Sun;Ranyu Shi;Subir Majumder;Weihang Yan;Jhi-Young Joo;Le Xie;Jiyu Wang

{"title":"Open Power System Datasets and Open Simulation Engines: A Survey Toward Machine Learning Applications","authors":"Ignacio Aravena;Chih-Che Sun;Ranyu Shi;Subir Majumder;Weihang Yan;Jhi-Young Joo;Le Xie;Jiyu Wang","doi":"10.1109/OAJPE.2025.3573958","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3573958","url":null,"abstract":"A major factor behind the success of machine learning (ML) models in multiple domains is the availability and accessibility of large, labeled, and well-organized datasets for training and benchmarking. In comparison, power grid datasets face three major challenges: (i) real-world data is often restricted by regulatory constraints, privacy reasons, or security concerns, making it difficult to obtain and work with; (ii) synthetic datasets, which are created to address these limitations, often have incomplete information and are released using specialized tools, making them inaccessible to the broader community; and, (iii) input-output datasets are difficult to generate through simulation for non-experts because open-source simulators are not known outside the power system community. This survey addresses these challenges by serving as an entry point to publicly available datasets and simulators for researchers venturing in this area. We review the current landscape of open-source power network data, machine models, consumer demand profiles, renewable generation data, and inverter models. We also examine open-source power system simulators, which are crucial for generating high-quality, high-fidelity power grid datasets. We aim to provide a foundation for overcoming data scarcity and advance towards a structured web of datasets and simulators to support the development of ML for power systems.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"353-365"},"PeriodicalIF":3.3,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11015807","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206069","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

Design of Large-Scale Hybrid, Hydrogen and Battery, and Energy Storage Systems for Grid Applications 面向电网应用的大型混合动力、氢电池和储能系统的设计

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-22 DOI: 10.1109/OAJPE.2025.3572590

Marvin Dorn;Jonas Lotze;Uwe Këhnapfel;André Weber;Veit Hagenmeyer

{"title":"Design of Large-Scale Hybrid, Hydrogen and Battery, and Energy Storage Systems for Grid Applications","authors":"Marvin Dorn;Jonas Lotze;Uwe Këhnapfel;André Weber;Veit Hagenmeyer","doi":"10.1109/OAJPE.2025.3572590","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3572590","url":null,"abstract":"Due to the energy transition, which involves phasing out base load power plants such as coal, there is a need to establish storage systems within the energy system to compensate for fluctuations of renewable energies. Batteries are suitable for day-night cycles and particularly for short-cycle applications. To address the problem of dark-doldrums, when neither wind nor solar energy is available, gas and, in the more distant future, hydrogen power plants are to be used. By combining batteries and hydrogen power plants in a hybrid energy storage system, further advantages and application possibilities arise regarding grid stability and system design. This work illustrates interrelationships between the subsystems, optimizes proportions, and demonstrates logical system sizes, technologies, and their costs. A central part of the work are the self-derived methods for system design and the justification of these. Storage pressure, running times, availability time, annual cycles and design of the subsystems are described. Systems of this scale are difficult to imagine. A program developed as part of this work to implement the methods, visualizes the system, displays the system parameters, and shows the best-case and worst-case capital expenditures. An optimized system design is presented. Different combinations in the system design show the effects on capital expenditures. Starting from 2 to 4 hours of availability time, the hybrid system becomes cheaper than a pure battery system in terms of capital expenditures.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"341-352"},"PeriodicalIF":3.3,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11010119","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206070","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 Driven Reduced Pi-Model of Feeders for Distribution Network Representation With DERs for Fast Reconfiguration 数据驱动的带der的配电网络馈线简化pi模型

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-22 DOI: 10.1109/OAJPE.2025.3572718

Tharmini Thavaratnam;Bala Venkatesh

{"title":"Data Driven Reduced Pi-Model of Feeders for Distribution Network Representation With DERs for Fast Reconfiguration","authors":"Tharmini Thavaratnam;Bala Venkatesh","doi":"10.1109/OAJPE.2025.3572718","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3572718","url":null,"abstract":"Deep electrification by 2050 is expected to increase distribution systems by three to five times and include innumerable distributed energy resources (DERs). Robust methods for operations will be required. Reconfigurations, well researched for 50+ years, are created given the size and importance of present distribution systems. This paper proposes a network configuration method which is significantly dense, heavily loaded, societally important, and has innumerable loads and DERs. This method reduces sections of feeders with DERs to equivalent reduced Pi-Model representations. It then uses a regression model to correlate loading scenarios of the distribution to reduced Pi-Model parameters feeder sections. A regression model yields reduced Pi-Models of feeder sections, and they are used to construct a complete distribution system representation, with this reduced model used for reconfiguration. The proposed method was tested on modified 33-, 69- and 123-Bus data networks and reduced the number of buses to around 50%. Computing time was reduced by 26.30%, 58.54% and 67.33%, respectively while providing accuracy of 97.35%, 97.30%, and 99.05%, respectively. The computation time was lowered by 46.45% when the methodology was expanded to the North Dakota 880-Bus network. As the method scales for larger distribution systems, it should increasingly perform better.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"330-340"},"PeriodicalIF":3.3,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11010128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170809","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

Efficient, Robust, and Comprehensive Fault Calculation of IBR-Rich Systems Considering Diverse Controls 考虑多种控制的富ibr系统的高效、鲁棒和综合故障计算

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-22 DOI: 10.1109/OAJPE.2025.3572769

Xinquan Chen;Aboutaleb Haddadi;Zhe Yang;Evangelos Farantatos;Ilhan Kocar

引用次数: 0

Assessing Oscillatory Stability With Dominant Grid-Forming Power Systems for Active Power Imbalances 有功不平衡下优势并网电力系统振荡稳定性评估

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-21 DOI: 10.1109/OAJPE.2025.3571108

Sander Lid Skogen;José Luis Rueda Torres

{"title":"Assessing Oscillatory Stability With Dominant Grid-Forming Power Systems for Active Power Imbalances","authors":"Sander Lid Skogen;José Luis Rueda Torres","doi":"10.1109/OAJPE.2025.3571108","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3571108","url":null,"abstract":"As the integration of renewable energy accelerates, ensuring power system stability becomes increasingly critical. This research utilized a Root Mean Square (RMS) synthetic model of the future 380 kV Dutch power system towards 2050 to analyze its oscillatory stability under high renewable penetration and the impact of grid-forming converters under various parametrizations. The presented case study shows that grid-forming (GFM) converters significantly improve frequency stability and damping performance across different perturbations, particularly at higher GFM penetration levels, improving frequency and damping parameters. However, various oscillatory modes present potential stability risks at high penetration levels. The case study also shows minimal differences in controller selection in large-scale models, except under certain conditions. Additionally, the analysis of controller parameters highlighted the critical importance of tuning active power parameters to ensure system stability. The investigation provides essential insights for future power systems, where large-scale integration of renewable energy will necessitate the implementation of converters able to provide ancillary services. The findings emphasize the importance of optimizing GFM converter settings and penetration levels to maintain system resilience, offering valuable guidance for future system planning and regulatory frameworks.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"318-329"},"PeriodicalIF":3.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11008671","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170986","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

Energy-Efficient Direct Instantaneous Torque Control of Switched Reluctance Generator at Low Speeds 低速开关磁阻发电机的节能直接瞬时转矩控制

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-20 DOI: 10.1109/OAJPE.2025.3553408

Elmer O. Hancco Catata;Marcelo Vinícius De Paula;Ernesto Ruppert Filho;Tárcio André Dos Santos Barros

{"title":"Energy-Efficient Direct Instantaneous Torque Control of Switched Reluctance Generator at Low Speeds","authors":"Elmer O. Hancco Catata;Marcelo Vinícius De Paula;Ernesto Ruppert Filho;Tárcio André Dos Santos Barros","doi":"10.1109/OAJPE.2025.3553408","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3553408","url":null,"abstract":"An efficient switching method is proposed for Direct Instantaneous Torque Control (DITC) in Switched Reluctance Generators (SRG) operating at low speeds, aiming to enhance system efficiency and reduce torque ripple. In the traditional DITC strategy, the magnetization state in the outgoing phase is enabled at low operating speeds, leading to decreased efficiency and unnecessary torque ripple. The proposed DITC strategy improves efficiency at low speeds while maintaining low torque ripple levels. It prioritizes the freewheeling and demagnetization states during the outgoing period. When the back electromotive force (back EMF) is small, the magnetization state is disabled, using the freewheeling state to smoothly increase torque and the demagnetization state to decrease torque. The magnetization state is reintroduced as the back EMF increases. To implement the modified DITC, an artificial neural network is used to estimate electromagnetic torque. Experimental tests were conducted for both fixed and variable SRG speeds. The proposed method is compared with other methods in the literature. Experimental tests carried out at fixed and variable SRG speeds show that the proposed method significantly enhances efficiency by up to 20% and reduces torque ripple by up to 21% compared to existing methods.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"171-180"},"PeriodicalIF":3.3,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10935298","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740298","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

Composite Index for Identifying Anomalies in Low Voltage Systems Using Smart Meter Measurement Data 利用智能电表测量数据识别低压系统异常的综合指数

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-16 DOI: 10.1109/OAJPE.2025.3570834

Felipe B. B. Rolim;Fernanda C. L. Trindade;Vinicius C. Cunha

引用次数: 0