IEEE Transactions on Industry Applications最新文献_第2页

Probabilistic Evaluation of Photovoltaic Hosting Capacity in Unbalanced Distribution Network via Polynomial Chaos Based Kriging Model

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2024-12-26 DOI: 10.1109/TIA.2024.3522513

Hongyan Ma;Gan Li;Han Wang;Zheng Yan;Xiaoyuan Xu

{"title":"Probabilistic Evaluation of Photovoltaic Hosting Capacity in Unbalanced Distribution Network via Polynomial Chaos Based Kriging Model","authors":"Hongyan Ma;Gan Li;Han Wang;Zheng Yan;Xiaoyuan Xu","doi":"10.1109/TIA.2024.3522513","DOIUrl":"https://doi.org/10.1109/TIA.2024.3522513","url":null,"abstract":"With the penetration of distributed photovoltaics (PVs) increasing, evaluating PV hosting capacity (PVHC) becomes more and more important for distribution network operations. Traditional evaluation methods ignore the impacts of voltage unbalance in distribution networks and the results are relatively optimistic. Furthermore, the uncertainty of PVs is also required to be considered in the evaluation. Hence, this paper proposes a probabilistic PVHC evaluation method considering unbalanced distribution network (UDN) operations and variable power outputs of PVs. Firstly, the limits of voltage magnitude (VM) and voltage unbalance factor (VUF) are both considered in the proposed method. A probabilistic violation risk (PVR) index is defined to determine the interval of PVHC in UDNs. Then, a polynomial chaos-based Kriging (PCK) method is developed to calculate the probabilistic indices in PVHC evaluation. The PCK method uses a surrogate model to replace the three-phase power flow calculation in UDNs which significantly reduce the computation burdens of probabilistic PVHC evaluation. Finally, an IEEE 123-bus three-phase UDN is used to verify the effectiveness of the proposed method.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 1","pages":"1466-1474"},"PeriodicalIF":4.2,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2024-12-25 DOI: 10.1109/TIA.2024.3522486

Yang Li;Jiankai Gao;Yuanzheng Li;Chen Chen;Sen Li;Mohammad Shahidehpour;Zhe Chen

{"title":"Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling","authors":"Yang Li;Jiankai Gao;Yuanzheng Li;Chen Chen;Sen Li;Mohammad Shahidehpour;Zhe Chen","doi":"10.1109/TIA.2024.3522486","DOIUrl":"https://doi.org/10.1109/TIA.2024.3522486","url":null,"abstract":"To coordinate the interests of operator and users in a microgrid under complex and changeable operating conditions, this paper proposes a microgrid scheduling model considering the thermal flexibility of thermostatically controlled loads and demand response by leveraging physical informed-inspired deep reinforcement learning (DRL) based bi-level programming. To overcome the non-convex limitations of Karush–Kuhn–Tucker (KKT)-based methods, a novel optimization solution method based on DRL theory is proposed to handle the bi-level programming through alternate iterations between levels. Specifically, by combining a DRL algorithm named asynchronous advantage actor-critic (A3C) and automated machine learning-prioritized experience replay (AutoML-PER) strategy to improve the generalization performance of A3C to address the above problems, an improved A3C algorithm, called AutoML-PER-A3C, is designed to solve the upper-level problem; while the DOCPLEX optimizer is adopted to address the lower-level problem. In this solution process, AutoML is used to automatically optimize hyperparameters and PER improves learning efficiency and quality by extracting the most valuable samples. The test results demonstrate that the presented approach manages to reconcile the interests between multiple stakeholders in MG by fully exploiting various flexibility resources. Furthermore, in terms of economic viability and computational efficiency, the proposal vastly exceeds other advanced reinforcement learning methods.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 1","pages":"1488-1500"},"PeriodicalIF":4.2,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Secure Transmission Strategy for Smart Grid Communications Assisted by 5G Base Station

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2024-12-25 DOI: 10.1109/TIA.2024.3522487

Pei Liu;Yang Zou;Qinglai Guo;Kai Ma;Nianfeng Tian;Yuqian Zhang

{"title":"A Secure Transmission Strategy for Smart Grid Communications Assisted by 5G Base Station","authors":"Pei Liu;Yang Zou;Qinglai Guo;Kai Ma;Nianfeng Tian;Yuqian Zhang","doi":"10.1109/TIA.2024.3522487","DOIUrl":"https://doi.org/10.1109/TIA.2024.3522487","url":null,"abstract":"As the number of Internet of Things (IoT) devices in smart grids grows, security issues arise, including eavesdropping. The fifth generation (5G) wireless technologies are the driving force behind many IoT applications; hence, it is apparent that the broadcast nature of IoT devices makes data security unprecedentedly important. However, the operation of 5G base stations (BSs) incurs more power consumption cost for telecom operator and occupies the majority of the energy consumption in cellular wireless networks. In view of the above, we aim to increase the secrecy rate of smart grid communication networks by joint collaboration between utility companies and telecom operators, while also boosting mutual profitability in this paper. First, the impacts of secrecy rate on communication reliability in demand-side communication networks are evaluated. Next, we propose a secure transmission approach that leases the power of 5G BS to interfere with the eavesdroppers, improving the secrecy rate, and then construct an interference power allocation and price optimization algorithm based on the Stackelberg game to achieve the best results. Numerical results demonstrate that the proposed method can greatly enhance communication secrecy rate while also lowering the costs of the utility companies and telecom operator.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 1","pages":"1695-1703"},"PeriodicalIF":4.2,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lithium-Ion Battery State-of-Charge and State-of-Energy Simultaneous Estimation via Sparse- Quasi Recurrent Neural Networks(S-QRNN)

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2024-12-25 DOI: 10.1109/TIA.2024.3522506

Sakshi Sharma;Bijaya Ketan Panigrahi

{"title":"Lithium-Ion Battery State-of-Charge and State-of-Energy Simultaneous Estimation via Sparse- Quasi Recurrent Neural Networks(S-QRNN)","authors":"Sakshi Sharma;Bijaya Ketan Panigrahi","doi":"10.1109/TIA.2024.3522506","DOIUrl":"https://doi.org/10.1109/TIA.2024.3522506","url":null,"abstract":"This paper presents an innovative methodology for the concurrent estimation of Lithium-Ion Battery (LiB) State-of-Charge (SoC) and State-of-Energy (SoE) employing Sparse Quasi-Recurrent Neural Networks (S-QRNN). The proposed framework is designed to leverage sparse connectivity patterns to efficiently capture intricate long-term dependencies within battery dynamics. Unlike traditional recurrent neural networks and Convolutional Networks, S-QRNN allows for more effective handling of sequential data, making them well-suited for predicting battery behavior, which exhibits complex temporal dynamics. Furthermore, the sparse connectivity structure reduces computational complexity and enhances the interpretability of the model. To validate the effectiveness and accuracy, adequate experimentation was conducted using laboratory-produced battery data. Moreover, the accuracy and computational efficacy of the proposed scheme have been verified in an OPAL-RT-based Real-Time Power Hardware-In-Loop (HIL) environment. The Opal RT platform provides a reliable and flexible environment integrated with MATLAB/Simulink for hardware-in-loop simulation. Experimental results demonstrate that the proposed method achieves robust and accurate estimation of both SoC and SoE, even in dynamic operational conditions of temperatures and battery load profiles.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 1","pages":"774-783"},"PeriodicalIF":4.2,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Knowledge and Data-Driven Hydrogen Trading for Renewable-Dominated Hydrogen Refueling Stations

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2024-12-25 DOI: 10.1109/TIA.2024.3522508

Kuan Zhang;Junyu Xie;Nian Liu

{"title":"Hybrid Knowledge and Data-Driven Hydrogen Trading for Renewable-Dominated Hydrogen Refueling Stations","authors":"Kuan Zhang;Junyu Xie;Nian Liu","doi":"10.1109/TIA.2024.3522508","DOIUrl":"https://doi.org/10.1109/TIA.2024.3522508","url":null,"abstract":"This paper proposes a hybrid knowledge and data-driven predict-then-optimize paradigm for green hydrogen (H2) trading among renewable-dominated hydrogen refueling stations (HRSs). Firstly, a data-driven H2 load forecasting method is formulated where the key influencing features are captured by XGBoost and the Informer algorithm with encoder and decoder processes is utilized to generate the predicted time series of hydrogen load. Then, a bi-level hybrid knowledge and data-driven H2 trading model with rolling horizon optimization is proposed to determine the optimal trading quantity of H2 and dynamically optimize the transportation routes for the traded H2 based on the cell transmission model and traffic state. Moreover, a fully distributed solution algorithm is developed to decompose the complex multi-period H2 trading problem into local electricity and hydrogen dispatch subproblems of HRSs for efficiently obtaining the optimal H2 trading amount. Comparative studies have demonstrated the superior performance of the proposed methodology on the improvement of the distributed renewable energy accommodation and economic benefits for HRSs.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 1","pages":"1658-1674"},"PeriodicalIF":4.2,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bi-Level Planning of Microgrid Considering Seasonal Hydrogen Storage and Efficiency Degradation of Electrolyzer

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2024-12-25 DOI: 10.1109/TIA.2024.3522458

Yanhui Xu;Zilin Deng

{"title":"Bi-Level Planning of Microgrid Considering Seasonal Hydrogen Storage and Efficiency Degradation of Electrolyzer","authors":"Yanhui Xu;Zilin Deng","doi":"10.1109/TIA.2024.3522458","DOIUrl":"https://doi.org/10.1109/TIA.2024.3522458","url":null,"abstract":"Microgrids that contain a high percentage of renewable energy face the challenge of having insufficient resources for long-term regulation of the energy balance. Seasonal hydrogen storage emerges as a promising option. To analyze the feasibility and economic viability of seasonal hydrogen storage in microgrids, this paper proposes a bi-level planning approach. First, considering the dynamic characteristics of electrolyzer power-efficiency-degradation, a linearized method for calculating the dynamic efficiency is proposed. Taking into account the randomness of the data, a data-driven Markov Chain Monte Carlo method is used to generate typical daily time series. The upper level of the bi-level model focuses on investment decisions for electrolyzer capacity and seasonal hydrogen storage, while the lower level optimizes system operational revenue and electrolyzer usage costs. Then, addressing the nonlinear term issue in optimization calculations, a piecewise McCormick envelope method considering the power characteristics of the electrolyzer is proposed to convexify the optimization problem. The results of the case study show that the proposed planning method can increase the annual revenue by 11% and 595% compared to only considering constant efficiency and fixed electrolyzer lifespan. Additionally, the convex relaxation method enhances convergence speed while maintaining solution accuracy.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 1","pages":"1385-1398"},"PeriodicalIF":4.2,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Knowledge-Inspired Data-Aided Robust Power Flow in Distribution Networks With ZIP Loads and High DER Penetration

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2024-12-25 DOI: 10.1109/TIA.2024.3522496

Sungjoo Chung;Ying Zhang;Yuanshuo Zhang

{"title":"Knowledge-Inspired Data-Aided Robust Power Flow in Distribution Networks With ZIP Loads and High DER Penetration","authors":"Sungjoo Chung;Ying Zhang;Yuanshuo Zhang","doi":"10.1109/TIA.2024.3522496","DOIUrl":"https://doi.org/10.1109/TIA.2024.3522496","url":null,"abstract":"Characterized by increasing penetration of distributed energy resources, active distribution networks necessitate developing uncertainty-adaptive power flow (PF) algorithms to cover broad operating conditions. Despite the success of data-driven methods in improving such adaptivity, the efficacy of these methods relies heavily on large, precise, and outlier-free datasets, which limits their materialization in practical grids. To address these dual issues, this paper proposes a knowledge-inspired data-aided robust PF algorithm in unbalanced distribution systems with ZIP load models and high penetration of distributed energy resources. The proposed method first uses Taylor expansion to derive an explicitly analytical linear solution for the PF calculation. A data-driven support vector regression-based method is further proposed to mitigate the approximation loss of the linearized PF model, which might surge in widening voltage variations. Inspired by physical knowledge of distribution system operation, the proposed method can adapt to a wide range of operating conditions without retraining and thus can be applied to passive/active distribution networks. Case studies in the IEEE 13- and 123-bus unbalanced feeders illustrate that the proposed algorithm exhibits superior computation efficiency and guaranteed accuracy, under variable penetration levels and lightweight datasets.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 1","pages":"1523-1532"},"PeriodicalIF":4.2,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Objective Optimization of Permanent Magnet Assisted Synchronous Reluctance Motor for Industrial Drive Using Three-Step Optimization Method

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2024-12-20 DOI: 10.1109/TIA.2024.3520881

Shushu Zhu;Xun Li;Junqi Hu;Renhua Jiang;Chuang Liu;Kai Wang

{"title":"Multi-Objective Optimization of Permanent Magnet Assisted Synchronous Reluctance Motor for Industrial Drive Using Three-Step Optimization Method","authors":"Shushu Zhu;Xun Li;Junqi Hu;Renhua Jiang;Chuang Liu;Kai Wang","doi":"10.1109/TIA.2024.3520881","DOIUrl":"https://doi.org/10.1109/TIA.2024.3520881","url":null,"abstract":"Because of the advantages of high power factor and high power, and has been gradually applied in the field of industrial drive. The permanent magnet assisted synchronous reluctance motor has been widely applied in the field of industrial drive. The drive motor requires large output torque, high power factor and small torque ripple, thereby imposing more stringent demands on motor optimization. However, due to the complex rotor structure with the complex magnetic barrier, the optimization parameters of the permanent magnet assisted synchronous reluctance motor is large. Aiming at the above-mentioned problems, a three-step optimization method is studied. The relationship between the rotor structural dimensions is studied to reduce the number of parameters to be optimized. The parameter sensitivity is used to optimize the structure parameters. The response surface method and genetic algorithm are combined used to realize the comprehensive optimization of multi-objective. Then, the parameters with high sensitivity of single target are optimized by the single parameter scanning method. Finally, the structural detail of the magnetic barrier tip is precisely optimized to reduce the torque ripple. A 15 kW/1500 rpm permanent magnet assisted synchronous reluctance motor is optimized by the three-step optimization method. The simulation and experimental results are presented to verify the improvement of the motor performance.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 1","pages":"218-230"},"PeriodicalIF":4.2,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Guest Editorial: Knowledge-and Data-Driven Smart Energy Management in Distribution Networks

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2024-12-02 DOI: 10.1109/TIA.2024.3510472

Yuan-Kang Wu;Ming Yang;Jianxiao Wang;Chin-Woo Tan;Guannan He;Zhenfei Tan;Javad Mohammadi;Leijiao Ge

{"title":"Guest Editorial: Knowledge-and Data-Driven Smart Energy Management in Distribution Networks","authors":"Yuan-Kang Wu;Ming Yang;Jianxiao Wang;Chin-Woo Tan;Guannan He;Zhenfei Tan;Javad Mohammadi;Leijiao Ge","doi":"10.1109/TIA.2024.3510472","DOIUrl":"https://doi.org/10.1109/TIA.2024.3510472","url":null,"abstract":"Distribution networks (DN) are gradually transformed into their active form due to increasing penetration of distributed generation and fast development of use-side flexible resources. Due to limited measurements and communication capability, conventional power system analysis methods based on analytical formulation become inadequate for the management of DNs with high uncertainties and complex interactions. The advancement of the Internet of Things and artificial intelligence (AI) technologies enables data-driven approaches for the forecasting, modeling, operation, and control of DNs. To address challenges in practical industrial applications, such as interpretability, reliability, security, portability, and lack of high-quality training data, the nexus of data-driven and knowledge-based analysis methods have attracted growing research interest. The objective of this special issue is to identify and disseminate cutting-edge research focusing on integrating data-driven and knowledge-based technologies to tackle emerging challenges in smart management of active distribution systems.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 1","pages":"1326-1328"},"PeriodicalIF":4.2,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Get published in the New IEEE Open Journal of Industry Applications 在新的《IEEE 工业应用开放期刊》上发表文章

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2024-11-13 DOI: 10.1109/TIA.2024.3488395

引用次数: 0