Pub Date : 2024-08-02DOI: 10.1109/tste.2024.3437758
Jiaqing Zhai, Li Guo, Zhongguan Wang, Xialin Li, Yixin Liu, Chengshan Wang
{"title":"Coordinated Frequency Regulation of Active Distribution Networks Considering Dimension-Augmented Power Flow Constraints","authors":"Jiaqing Zhai, Li Guo, Zhongguan Wang, Xialin Li, Yixin Liu, Chengshan Wang","doi":"10.1109/tste.2024.3437758","DOIUrl":"https://doi.org/10.1109/tste.2024.3437758","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":8.8,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881530","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}
Pub Date : 2024-08-02DOI: 10.1109/tste.2024.3434995
Ke Wang, Yixun Xue, Mohammad Shahidehpour, Xinyue Chang, Zening Li, Yue Zhou, Hongbin Sun
{"title":"Resilience-Oriented Two-Stage Restoration Considering Coordinated Maintenance and Reconfiguration in Integrated Power Distribution and Heating Systems","authors":"Ke Wang, Yixun Xue, Mohammad Shahidehpour, Xinyue Chang, Zening Li, Yue Zhou, Hongbin Sun","doi":"10.1109/tste.2024.3434995","DOIUrl":"https://doi.org/10.1109/tste.2024.3434995","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":8.8,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881463","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}
Accurate wind power prediction (WPP) is crucial to the secure and stable operation of large-scale power systems, and data-driven WPP methods have recently been widely studied and applied. However, existing data-driven methods cannot be applied to new wind farms due to the lack of operational data. This paper presents a novel Bayesian deep learning-based adaptive wind farm power prediction (BDL-AWFPP) method, which is the first time to utilize the computational fluid dynamics (CFD) simulation results as the prior of BDL-based method, thus avoiding the problem that data-driven approaches cannot be applied to newly constructed wind farms. Firstly, a CFD-based wind farm numerical simulation database and a wind turbine power curve database are established to construct a multi-source heterogeneous prior dataset. Then, the BDL-AWFPP model is proposed to utilize the multi-source heterogeneous prior dataset, which can be updated adaptively with newly acquired operational data and saved periodically throughout the life cycle. And an auxiliary aging assessment method for wind turbines is also developed according to the periodically-saved models. Finally, a stochastic variational inference (SVI)-based parameter updating algorithm is derived for the proposed BDL-AWFPP model. Case studies on an actual wind farm validate the effectiveness of the proposed method.
{"title":"A Bayesian Deep Learning-Based Adaptive Wind Farm Power Prediction Method Within the Entire Life Cycle","authors":"Xiaoming Liu;Jun Liu;Yu Zhao;Yongxin Nie;Jiacheng Liu;Tao Ding","doi":"10.1109/TSTE.2024.3435936","DOIUrl":"10.1109/TSTE.2024.3435936","url":null,"abstract":"Accurate wind power prediction (WPP) is crucial to the secure and stable operation of large-scale power systems, and data-driven WPP methods have recently been widely studied and applied. However, existing data-driven methods cannot be applied to new wind farms due to the lack of operational data. This paper presents a novel Bayesian deep learning-based adaptive wind farm power prediction (BDL-AWFPP) method, which is the first time to utilize the computational fluid dynamics (CFD) simulation results as the prior of BDL-based method, thus avoiding the problem that data-driven approaches cannot be applied to newly constructed wind farms. Firstly, a CFD-based wind farm numerical simulation database and a wind turbine power curve database are established to construct a multi-source heterogeneous prior dataset. Then, the BDL-AWFPP model is proposed to utilize the multi-source heterogeneous prior dataset, which can be updated adaptively with newly acquired operational data and saved periodically throughout the life cycle. And an auxiliary aging assessment method for wind turbines is also developed according to the periodically-saved models. Finally, a stochastic variational inference (SVI)-based parameter updating algorithm is derived for the proposed BDL-AWFPP model. Case studies on an actual wind farm validate the effectiveness of the proposed method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866316","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}
Pub Date : 2024-07-26DOI: 10.1109/TSTE.2024.3434493
Derick Mathew;J. Prasanth Ram;Jihun Ha;Jung-Wook Park;Young-Jin Kim
The conventional de-load power tracking algorithm, utilizing a perturb and observe method, manifests deficiencies in terms of speed, stability, and efficacy in identifying operating points within the inverter's voltage range. In this article, the Adaptive Multi-Mode Single-Step Power Tracking (AMSPT) algorithm is introduced, showcasing rapid adaptability to varying solar irradiation conditions, while mitigating energy losses and enhancing overall operational stability. Its key innovation lies in efficiently pinpointing the operating point within the inverter's specified voltage range through a single step. Upon achieving the desired operating point, the algorithm promptly suppresses oscillatory behavior, expediting the settling process and minimizing deviations around the set-point. This article substantiates the superiority of the AMSPT algorithm over existing methods, showcasing remarkable advancements in tracking accuracy, power fluctuations, and energy discrepancies across diverse PV system case studies. Comprehensive validation through theoretical analysis, simulations, and experimental setups meticulously confirms the claimed benefits of the proposed method.
{"title":"Adaptive Multi-Mode Single-Step Power Tracking for Microinverter-Based Photovoltaic System","authors":"Derick Mathew;J. Prasanth Ram;Jihun Ha;Jung-Wook Park;Young-Jin Kim","doi":"10.1109/TSTE.2024.3434493","DOIUrl":"10.1109/TSTE.2024.3434493","url":null,"abstract":"The conventional de-load power tracking algorithm, utilizing a perturb and observe method, manifests deficiencies in terms of speed, stability, and efficacy in identifying operating points within the inverter's voltage range. In this article, the Adaptive Multi-Mode Single-Step Power Tracking (AMSPT) algorithm is introduced, showcasing rapid adaptability to varying solar irradiation conditions, while mitigating energy losses and enhancing overall operational stability. Its key innovation lies in efficiently pinpointing the operating point within the inverter's specified voltage range through a single step. Upon achieving the desired operating point, the algorithm promptly suppresses oscillatory behavior, expediting the settling process and minimizing deviations around the set-point. This article substantiates the superiority of the AMSPT algorithm over existing methods, showcasing remarkable advancements in tracking accuracy, power fluctuations, and energy discrepancies across diverse PV system case studies. Comprehensive validation through theoretical analysis, simulations, and experimental setups meticulously confirms the claimed benefits of the proposed method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141780213","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}
Pub Date : 2024-07-25DOI: 10.1109/tste.2024.3433611
Haihan Ye, Wu Chen, Heng Wu
{"title":"Analysis of the Existence of Stable Equilibrium Points in the WPP-MMC System under Symmetrical AC Fault","authors":"Haihan Ye, Wu Chen, Heng Wu","doi":"10.1109/tste.2024.3433611","DOIUrl":"https://doi.org/10.1109/tste.2024.3433611","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":8.8,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141780214","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}
Pub Date : 2024-07-22DOI: 10.1109/tste.2024.3431616
Changhee Han, Ramesh R. Rao, Seokheon Cho
{"title":"Stochastic Operation of Multi-Terminal Soft Open Points in Distribution Networks with Distributionally Robust Chance-Constrained Optimization","authors":"Changhee Han, Ramesh R. Rao, Seokheon Cho","doi":"10.1109/tste.2024.3431616","DOIUrl":"https://doi.org/10.1109/tste.2024.3431616","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":8.8,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141780215","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}
Pub Date : 2024-07-19DOI: 10.1109/TSTE.2024.3430844
Min Du;Jinning Zhang;Chenghong Gu;Xin Zhang
This paper aims to produce a practical and efficient decision for the system operator to harden critical components in power systems with high wind power penetration against uncertain attacks. Thus, an adjustable robust tri-level defender-attacker-defender (ART-DAD) model is proposed to improve the resilience of power systems by hardening critical transmission lines. The proposed ART-DAD model considers both uncertain attacks and uncertain wind power output, which provides meaningful insights into the resilience improvement of power systems that involve uncertainties. More specifically, the proposed defense model integrates dynamic N-K criterion for attack budgets and the polyhedral uncertainty set for wind power output to develop resilient line hardening strategies. The proposed defense model can be formulated as a mixed integer tri-level programming problem that is decoupled into a master and sub-problem. Then, a constraint-generation based solution algorithm is proposed to solve the overall ART-DAD model with a master and sub-problem scheme. Simulation results on IEEE RTS-79 and RTS-96 systems validate the effectiveness of the proposed resilience improving strategy.
{"title":"Resilience Improving Strategy for Power Systems With High Wind Power Penetration Against Uncertain Attacks","authors":"Min Du;Jinning Zhang;Chenghong Gu;Xin Zhang","doi":"10.1109/TSTE.2024.3430844","DOIUrl":"10.1109/TSTE.2024.3430844","url":null,"abstract":"This paper aims to produce a practical and efficient decision for the system operator to harden critical components in power systems with high wind power penetration against uncertain attacks. Thus, an adjustable robust tri-level defender-attacker-defender (ART-DAD) model is proposed to improve the resilience of power systems by hardening critical transmission lines. The proposed ART-DAD model considers both uncertain attacks and uncertain wind power output, which provides meaningful insights into the resilience improvement of power systems that involve uncertainties. More specifically, the proposed defense model integrates dynamic N-K criterion for attack budgets and the polyhedral uncertainty set for wind power output to develop resilient line hardening strategies. The proposed defense model can be formulated as a mixed integer tri-level programming problem that is decoupled into a master and sub-problem. Then, a constraint-generation based solution algorithm is proposed to solve the overall ART-DAD model with a master and sub-problem scheme. Simulation results on IEEE RTS-79 and RTS-96 systems validate the effectiveness of the proposed resilience improving strategy.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740371","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}
Pub Date : 2024-07-19DOI: 10.1109/TSTE.2024.3430972
Yubo Zhang;Songhao Yang;Zhiguo Hao;Baohui Zhang
The fast frequency support (FFS) towards frequency trajectory optimization provides a system view for the frequency regulation of wind farms (WFs). However, the existing frequency trajectory optimization-based FFS generally relies on the accurate governor dynamics model of synchronous generators (SGs), which aggrandizes the difficulty of controller implementation. In this paper, a proportional-integral (PI) based FFS of WFs is designed for tracking the optimal frequency trajectory, which gets rid of the dependence on the governor model. Firstly, the prototypical PI-based FFS of WFs is proposed and its feasibility for tracking the optimal frequency trajectory is analyzed and demonstrated. Then, based on the “frequency-RoCoF” form of the optimal frequency trajectory, a more practical PI controller is constructed, avoiding the time dependence of the prototypical PI controller. Besides, an adaptive gain associated with PI parameters is designed for multi-WF coordination. Finally, the validity of the proposed method is verified in both the single-WF system and the multi-WF system.
面向频率轨迹优化的快速频率支持（FFS）为风电场（WFs）的频率调节提供了系统视图。然而，现有的基于频率轨迹优化的 FFS 通常依赖于同步发电机 (SG) 的精确调速器动力学模型，这增加了控制器实现的难度。本文设计了一种基于比例积分（PI）的风力发电机 FFS，用于跟踪最优频率轨迹，摆脱了对调速器模型的依赖。首先，提出了基于 PI 的 WFs FFS 原型，并分析和论证了其跟踪最佳频率轨迹的可行性。然后，根据最佳频率轨迹的 "频率-RoCoF "形式，构建了一个更实用的 PI 控制器，避免了原型 PI 控制器的时间依赖性。此外，还为多 WF 协调设计了与 PI 参数相关的自适应增益。最后，在单 WF 系统和多 WF 系统中验证了所提方法的有效性。
{"title":"Model-Free Fast Frequency Support of Wind Farms for Tracking Optimal Frequency Trajectory","authors":"Yubo Zhang;Songhao Yang;Zhiguo Hao;Baohui Zhang","doi":"10.1109/TSTE.2024.3430972","DOIUrl":"10.1109/TSTE.2024.3430972","url":null,"abstract":"The fast frequency support (FFS) towards frequency trajectory optimization provides a system view for the frequency regulation of wind farms (WFs). However, the existing frequency trajectory optimization-based FFS generally relies on the accurate governor dynamics model of synchronous generators (SGs), which aggrandizes the difficulty of controller implementation. In this paper, a proportional-integral (PI) based FFS of WFs is designed for tracking the optimal frequency trajectory, which gets rid of the dependence on the governor model. Firstly, the prototypical PI-based FFS of WFs is proposed and its feasibility for tracking the optimal frequency trajectory is analyzed and demonstrated. Then, based on the “frequency-RoCoF” form of the optimal frequency trajectory, a more practical PI controller is constructed, avoiding the time dependence of the prototypical PI controller. Besides, an adaptive gain associated with PI parameters is designed for multi-WF coordination. Finally, the validity of the proposed method is verified in both the single-WF system and the multi-WF system.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740370","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}
Pub Date : 2024-07-18DOI: 10.1109/TSTE.2024.3430321
Douglas M. Logan;Fred Beasom;Murty P. Bhavaraju;Francis J. Bell;Lee Thaubald;Kai Jiang;Chris J. Dent
IEEE Standard Definitions for Use in Reporting Electric Generating Unit Reliability, Availability, and Productivity has recently been revised to extend the standard to variable energy resource (VER) generating facilities. The revision includes definitions of energy resource and resource unavailability, indexes that distinguish between equipment performance and the availability of a generator for system reliability analysis purposes, and generation-based reliability indexes for VER units corresponding to the traditional time-based indexes in previous versions of the standard. The revision also includes definition of critical period indexes, where the critical period is specified as hours of high system need. This paper describes the new terms and outlines issues with their definition.
{"title":"Reliability Indexes for Variable Energy Resource Generation in IEEE Standard 762-2023","authors":"Douglas M. Logan;Fred Beasom;Murty P. Bhavaraju;Francis J. Bell;Lee Thaubald;Kai Jiang;Chris J. Dent","doi":"10.1109/TSTE.2024.3430321","DOIUrl":"10.1109/TSTE.2024.3430321","url":null,"abstract":"<italic>IEEE Standard Definitions for Use in Reporting Electric Generating Unit Reliability, Availability, and Productivity</i>\u0000 has recently been revised to extend the standard to variable energy resource (VER) generating facilities. The revision includes definitions of energy resource and resource unavailability, indexes that distinguish between equipment performance and the availability of a generator for system reliability analysis purposes, and generation-based reliability indexes for VER units corresponding to the traditional time-based indexes in previous versions of the standard. The revision also includes definition of critical period indexes, where the critical period is specified as hours of high system need. This paper describes the new terms and outlines issues with their definition.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740372","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}
Pub Date : 2024-07-17DOI: 10.1109/TSTE.2024.3429781
Xueqian Fu;Chunyu Zhang;Xiurong Zhang;Hongbin Sun
The stochastic production simulation of photovoltaic (PV) power is crucial for the analysis of power balance in power planning, annual or monthly operational planning, and long-term transactions in the electricity market, especially in power systems with a high share of PVs. To model the uncertainty and temporal characteristics inherent in PV power, this letter introduces the style transfer and innovatively establishes bi-directional long short-term memory generative adversarial networks (GAN). Simulation results confirm the advantages of the proposed GAN over traditional convolutional neural network-based GANs in simulating the diversity and temporal characteristics of PV power.
光伏（PV）电力的随机生产模拟对于电力规划中的电力平衡分析、年度或月度运营规划以及电力市场中的长期交易至关重要，尤其是在光伏占比较高的电力系统中。为模拟光伏发电固有的不确定性和时间特性，本文引入了样式转移，并创新性地建立了双向长短期记忆生成式对抗网络（GAN）。仿真结果证实，与传统的基于卷积神经网络的 GAN 相比，所提出的 GAN 在模拟光伏发电的多样性和时间特性方面更具优势。
{"title":"A Novel GAN Architecture Reconstructed Using Bi-LSTM and Style Transfer for PV Temporal Dynamics Simulation","authors":"Xueqian Fu;Chunyu Zhang;Xiurong Zhang;Hongbin Sun","doi":"10.1109/TSTE.2024.3429781","DOIUrl":"10.1109/TSTE.2024.3429781","url":null,"abstract":"The stochastic production simulation of photovoltaic (PV) power is crucial for the analysis of power balance in power planning, annual or monthly operational planning, and long-term transactions in the electricity market, especially in power systems with a high share of PVs. To model the uncertainty and temporal characteristics inherent in PV power, this letter introduces the style transfer and innovatively establishes bi-directional long short-term memory generative adversarial networks (GAN). Simulation results confirm the advantages of the proposed GAN over traditional convolutional neural network-based GANs in simulating the diversity and temporal characteristics of PV power.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740424","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}
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