Pub Date : 2024-07-17DOI: 10.1109/JSYST.2024.3425541
Baya Cherif;Hakim Ghazzai;Ahmad Alsharoa
Light detection and ranging (LiDAR) technology's expansion within the autonomous vehicles industry has rapidly motivated its application in numerous growing areas, such as smart cities, agriculture, and renewable energy. In this article, we propose an innovative approach for enhancing aerial traffic monitoring solutions through the application of LiDAR technology. The objective is to achieve precise and real-time object detection and tracking from aerial perspectives by integrating unmanned aerial vehicles with LiDAR sensors, thereby creating a potent Aerial LiDAR (A-LiD) solution for traffic monitoring. First, we develop a novel deep learning algorithm based on pointvoxel-region-based convolutional neural network (RCNN) to conduct road user detection. Then, we implement advanced LiDAR fusion techniques, including raw data fusion and decision data fusion, in an endeavor to improve detection performance through the combined analysis of multiple A-LiD systems. Finally, we employ the unscented Kalman Filter for object tracking and position estimation. We present selected simulation outcomes to demonstrate the effectiveness of our proposed solution. A comparison between the two fusion methods shows that raw point cloud fusion provides better detection performance than decision fusion.
{"title":"LiDAR From the Sky: UAV Integration and Fusion Techniques for Advanced Traffic Monitoring","authors":"Baya Cherif;Hakim Ghazzai;Ahmad Alsharoa","doi":"10.1109/JSYST.2024.3425541","DOIUrl":"10.1109/JSYST.2024.3425541","url":null,"abstract":"Light detection and ranging (LiDAR) technology's expansion within the autonomous vehicles industry has rapidly motivated its application in numerous growing areas, such as smart cities, agriculture, and renewable energy. In this article, we propose an innovative approach for enhancing aerial traffic monitoring solutions through the application of LiDAR technology. The objective is to achieve precise and real-time object detection and tracking from aerial perspectives by integrating unmanned aerial vehicles with LiDAR sensors, thereby creating a potent Aerial LiDAR (A-LiD) solution for traffic monitoring. First, we develop a novel deep learning algorithm based on pointvoxel-region-based convolutional neural network (RCNN) to conduct road user detection. Then, we implement advanced LiDAR fusion techniques, including raw data fusion and decision data fusion, in an endeavor to improve detection performance through the combined analysis of multiple A-LiD systems. Finally, we employ the unscented Kalman Filter for object tracking and position estimation. We present selected simulation outcomes to demonstrate the effectiveness of our proposed solution. A comparison between the two fusion methods shows that raw point cloud fusion provides better detection performance than decision fusion.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1639-1650"},"PeriodicalIF":4.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Detection systems based on computer vision play important roles in Large-Scale Multiagent Systems. In particular, it can automatically locate and identify key objects and enhance intelligent collaboration and coordination among multiple agents. However, classification and localization in object detection may produce inconsistent prediction results due to different learning focus. Therefore, we propose a Spatial Decoupling and Boundary Feature Aggregation Network (SDBA-Net) to achieve spatial decoupling and task alignment. SDBA-Net includes a spatially sensitive region-aware module (SSRM) and a boundary feature aggregation module (BFAM). SSRM predicts sensitive regions for each task while minimizing computational cost. BFAM extracts valuable boundary features within sensitive regions and aligns them with corresponding anchors. These two modules are combined to spatially decouple and align the features of two tasks. In addition, a significance dependency complementary module (SDCM) is introduced. It enables SSRM to quickly adjust the sensitive region of the classification task to the significant feature region. Experiments are conducted on a large-scale complex real-world dataset MS COCO (Lin et al., 2014). The results show that SDBA-Net achieves better results than the baselines. Using the ResNet-50 backbone, our method improves the average precision (AP) of the single-stage detector VFNet by 1.0 point (from 41.3 to 42.3). In particular, when using the Res2Net-101-DCN backbone, SDBA-Net achieves an AP of 51.8 on the MS COCO test-dev.
基于计算机视觉的检测系统在大规模多智能体系统中占有重要地位。特别是，它可以自动定位和识别关键对象，增强多个agent之间的智能协作和协调。然而，在目标检测中的分类和定位，由于学习重点的不同，可能会产生不一致的预测结果。为此，我们提出了一种空间解耦和边界特征聚合网络（SDBA-Net）来实现空间解耦和任务对齐。SDBA-Net包括一个空间敏感区域感知模块（SSRM）和一个边界特征聚合模块（BFAM）。SSRM预测每个任务的敏感区域，同时最小化计算成本。BFAM提取敏感区域内有价值的边界特征，并将其与相应的锚点对齐。将这两个模块结合起来，对两个任务的特征进行空间解耦和对齐。此外，还引入了显著性依赖互补模块（SDCM）。它使SSRM能够快速地将分类任务的敏感区域调整到显著特征区域。实验是在大规模复杂的现实世界数据集MS COCO上进行的（Lin et al., 2014）。结果表明，SDBA-Net取得了比基线更好的效果。利用ResNet-50骨干网，将单级探测器VFNet的平均精度（AP）提高了1.0点（从41.3提高到42.3）。特别是，当使用Res2Net-101-DCN骨干网时，SDBA-Net在MS COCO测试开发上实现了51.8的AP。
{"title":"Multiagent Detection System Based on Spatial Adaptive Feature Aggregation","authors":"Hongbo Wang;He Wang;Xin Zhang;Runze Ruan;Yueyun Wang;Yuyu Yin","doi":"10.1109/JSYST.2024.3423752","DOIUrl":"10.1109/JSYST.2024.3423752","url":null,"abstract":"Detection systems based on computer vision play important roles in Large-Scale Multiagent Systems. In particular, it can automatically locate and identify key objects and enhance intelligent collaboration and coordination among multiple agents. However, classification and localization in object detection may produce inconsistent prediction results due to different learning focus. Therefore, we propose a Spatial Decoupling and Boundary Feature Aggregation Network (SDBA-Net) to achieve spatial decoupling and task alignment. SDBA-Net includes a spatially sensitive region-aware module (SSRM) and a boundary feature aggregation module (BFAM). SSRM predicts sensitive regions for each task while minimizing computational cost. BFAM extracts valuable boundary features within sensitive regions and aligns them with corresponding anchors. These two modules are combined to spatially decouple and align the features of two tasks. In addition, a significance dependency complementary module (SDCM) is introduced. It enables SSRM to quickly adjust the sensitive region of the classification task to the significant feature region. Experiments are conducted on a large-scale complex real-world dataset MS COCO (Lin et al., 2014). The results show that SDBA-Net achieves better results than the baselines. Using the ResNet-50 backbone, our method improves the average precision (AP) of the single-stage detector VFNet by 1.0 point (from 41.3 to 42.3). In particular, when using the Res2Net-101-DCN backbone, SDBA-Net achieves an AP of 51.8 on the MS COCO test-dev.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 4","pages":"1849-1859"},"PeriodicalIF":4.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141718853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-08DOI: 10.1109/JSYST.2024.3420950
Ali Salehpour;Irfan Al-Anbagi
Cascading failures resulting from cyberattacks are one of the main concerns in smart grid systems. The use of machine learning (ML) algorithms has become more relevant in identifying and forecasting such cascading failures. In this article, we develop a real-time early stage mechanism (RESP) to predict cascading failures due to cyberattacks in smart grid systems using supervised ML algorithms. We use a realistic methodology to create a dataset to train the algorithms and predict the state of all components of the system after failure propagation. We utilize the extreme gradient boosting (XGBoost) algorithm and consider the features of both the power and communication networks to improve the failure prediction accuracy. We use the real-time digital simulator (RTDS) to simulate the power system and make the system more applicable. We evaluate the mechanism's effectiveness using the IEEE 14-bus system, which results in the XGBoost algorithm achieving a 96.25% prediction accuracy rate in random attacks. We show that RESP can accurately predict the state of a power system in the early stages of failure propagation using real-time data. Furthermore, we show that RESP can identify the initial failure locations, which can aid in further protection plans and decisions.
{"title":"RESP: A Real-Time Early Stage Prediction Mechanism for Cascading Failures in Smart Grid Systems","authors":"Ali Salehpour;Irfan Al-Anbagi","doi":"10.1109/JSYST.2024.3420950","DOIUrl":"10.1109/JSYST.2024.3420950","url":null,"abstract":"Cascading failures resulting from cyberattacks are one of the main concerns in smart grid systems. The use of machine learning (ML) algorithms has become more relevant in identifying and forecasting such cascading failures. In this article, we develop a real-time early stage mechanism (RESP) to predict cascading failures due to cyberattacks in smart grid systems using supervised ML algorithms. We use a realistic methodology to create a dataset to train the algorithms and predict the state of all components of the system after failure propagation. We utilize the extreme gradient boosting (XGBoost) algorithm and consider the features of both the power and communication networks to improve the failure prediction accuracy. We use the real-time digital simulator (RTDS) to simulate the power system and make the system more applicable. We evaluate the mechanism's effectiveness using the IEEE 14-bus system, which results in the XGBoost algorithm achieving a 96.25% prediction accuracy rate in random attacks. We show that RESP can accurately predict the state of a power system in the early stages of failure propagation using real-time data. Furthermore, we show that RESP can identify the initial failure locations, which can aid in further protection plans and decisions.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1593-1604"},"PeriodicalIF":4.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study delves into the distributed optimal coordination (DOC) problem, where a network comprises agents with different relative degrees. Each agent is equipped with a private cost function. The goal is to steer these agents towards minimizing the global cost function, which aggregates their individual costs. Existing literature often leans on known agent dynamics, which may not faithfully represent real-world scenarios. To bridge this gap, we delve into the DOC problem within a network of linear time-invariant (LTI) agents, where the system matrices remain entirely unknown. Our proposed solution introduces a novel distributed two-layer control policy: the top layer endeavors to find the minimizer and generates tailored reference signals for each agent, while the bottom layer equips each agent with an adaptive controller to track these references. Key assumptions include strongly convex private cost functions with local Lipschitz gradients. Under these conditions, our control policy guarantees asymptotic consensus on the global minimizer within the network. Moreover, the control policy operates fully distributedly, relying solely on private and neighbor information for execution. Theoretical insights are substantiated through simulations, encompassing both numerical and practical examples involving speed control of a multimotor network, thereby affirming the efficacy of our approach in practical settings.
{"title":"Heterogeneous Unknown Multiagent Systems of Different Relative Degrees: A Distributed Optimal Coordination Design","authors":"Hossein Noorighanavati Zadeh;Reza Naseri;Mohammad Bagher Menhaj;Amir Abolfazl Suratgar","doi":"10.1109/JSYST.2024.3417255","DOIUrl":"10.1109/JSYST.2024.3417255","url":null,"abstract":"This study delves into the distributed optimal coordination (DOC) problem, where a network comprises agents with different relative degrees. Each agent is equipped with a private cost function. The goal is to steer these agents towards minimizing the global cost function, which aggregates their individual costs. Existing literature often leans on known agent dynamics, which may not faithfully represent real-world scenarios. To bridge this gap, we delve into the DOC problem within a network of linear time-invariant (LTI) agents, where the system matrices remain entirely unknown. Our proposed solution introduces a novel distributed two-layer control policy: the top layer endeavors to find the minimizer and generates tailored reference signals for each agent, while the bottom layer equips each agent with an adaptive controller to track these references. Key assumptions include strongly convex private cost functions with local Lipschitz gradients. Under these conditions, our control policy guarantees asymptotic consensus on the global minimizer within the network. Moreover, the control policy operates fully distributedly, relying solely on private and neighbor information for execution. Theoretical insights are substantiated through simulations, encompassing both numerical and practical examples involving speed control of a multimotor network, thereby affirming the efficacy of our approach in practical settings.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1570-1580"},"PeriodicalIF":4.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-26DOI: 10.1109/JSYST.2024.3406698
Zuqing Zheng;Guo Chen;Zixiang Shen
The dramatic increase in renewable energy sources has created significant uncertainties in the operation of power systems. This article investigates a day-ahead economic dispatch problem for a typical microgrid, considering the uncertainties of renewable energy sources and load demand. An interval-partitioned and temporal-correlated uncertainty set based robust optimization model is proposed, which allows a more accurate characterization of the distribution of uncertainties. The proposed robust optimization model can reduce the conservativeness of the optimal solution by avoiding scenarios that are low-probability or even impossible in reality. The model is then decomposed into a master problem and a nonlinear bi-level subproblem and solved by the $C & CG$ method and Big-M method. However, this method requires the introduction of a large number of auxiliary variables and related constraints, significantly increasing the computation burden. To tackle this problem, an efficient solution method, Improved- $C & CG$ , is developed by integrating an outer approximation method into the $C & CG$ method. Finally, case studies verify the effectiveness of the proposed model, uncertainty set, and solution methods.
{"title":"Interval-Partitioned and Correlated Uncertainty Set Based Robust Optimization of Microgrid","authors":"Zuqing Zheng;Guo Chen;Zixiang Shen","doi":"10.1109/JSYST.2024.3406698","DOIUrl":"10.1109/JSYST.2024.3406698","url":null,"abstract":"The dramatic increase in renewable energy sources has created significant uncertainties in the operation of power systems. This article investigates a day-ahead economic dispatch problem for a typical microgrid, considering the uncertainties of renewable energy sources and load demand. An interval-partitioned and temporal-correlated uncertainty set based robust optimization model is proposed, which allows a more accurate characterization of the distribution of uncertainties. The proposed robust optimization model can reduce the conservativeness of the optimal solution by avoiding scenarios that are low-probability or even impossible in reality. The model is then decomposed into a master problem and a nonlinear bi-level subproblem and solved by the \u0000<inline-formula><tex-math>$C & CG$</tex-math></inline-formula>\u0000 method and Big-M method. However, this method requires the introduction of a large number of auxiliary variables and related constraints, significantly increasing the computation burden. To tackle this problem, an efficient solution method, Improved-\u0000<inline-formula><tex-math>$C & CG$</tex-math></inline-formula>\u0000, is developed by integrating an outer approximation method into the \u0000<inline-formula><tex-math>$C & CG$</tex-math></inline-formula>\u0000 method. Finally, case studies verify the effectiveness of the proposed model, uncertainty set, and solution methods.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1516-1527"},"PeriodicalIF":4.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-20DOI: 10.1109/JSYST.2024.3380721
{"title":"IEEE Systems Journal Information for Authors","authors":"","doi":"10.1109/JSYST.2024.3380721","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3380721","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"C4-C4"},"PeriodicalIF":4.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10566080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-20DOI: 10.1109/JSYST.2024.3380715
{"title":"IEEE Systems Journal Publication Information","authors":"","doi":"10.1109/JSYST.2024.3380715","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3380715","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"C2-C2"},"PeriodicalIF":4.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10566083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-20DOI: 10.1109/JSYST.2024.3380719
{"title":"IEEE Systems Council Information","authors":"","doi":"10.1109/JSYST.2024.3380719","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3380719","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"C3-C3"},"PeriodicalIF":4.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10566075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-20DOI: 10.1109/JSYST.2024.3407428
Amir Aghdam
There have been many events and much news since our first issue last March. Notably, the 2024 IEEE Systems Journal Best Paper Award was selected. As stated on the journal's website, the Systems Journal Best Paper Award is given annually to the papers deemed the best among those published in the IEEE Systems Journal during the preceding calendar year. The journal's Editorial Board participates in the selection process. This year, the paper by Klar et al., [A1] published in the first issue of 2023, was selected; the award was presented by Walter Downing, President of the IEEE Systems Council , to one of the authors of the paper at the 2024 IEEE SysCon in Montreal.
{"title":"Editorial GDOP-Based Low-Complexity LEO Satellite Subset Selection for Positioning","authors":"Amir Aghdam","doi":"10.1109/JSYST.2024.3407428","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3407428","url":null,"abstract":"There have been many events and much news since our first issue last March. Notably, the 2024 \u0000<italic>IEEE Systems Journal Best Paper Award</i>\u0000 was selected. As stated on the journal's website, the \u0000<italic>Systems Journal Best Paper Award</i>\u0000 is given annually to the papers deemed the best among those published in the \u0000<italic>IEEE Systems Journal</i>\u0000 during the preceding calendar year. The journal's Editorial Board participates in the selection process. This year, the paper by Klar et al., [A1] published in the first issue of 2023, was selected; the award was presented by Walter Downing, President of the \u0000<italic>IEEE Systems Council</i>\u0000, to one of the authors of the paper at the 2024 \u0000<italic>IEEE SysCon</i>\u0000 in Montreal.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"785-785"},"PeriodicalIF":4.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10566084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-17DOI: 10.1109/JSYST.2024.3404600
Arghya Mallick;Abhishek Mishra;Ashish R. Hota;Prabodh Bajpai
With the phenomenal growth in renewable energy generation, the conventional synchronous generator-based power plants are gradually getting replaced by renewable energy sources-based microgrids. Such transition gives rise to the challenges of procuring various ancillary services from microgrids. We propose a distributed optimization framework that coordinates multiple microgrids in an active distribution network for provisioning passive voltage support-based ancillary services while satisfying operational constraints. Specifically, we exploit the reactive power support capability of the inverters and the flexibility offered by storage systems available with microgrids for provisioning ancillary service support to the transmission grid. We develop novel mixed-integer inequalities to represent the set of feasible active and reactive power exchange with the transmission grid that ensures passive voltage support. The proposed alternating direction method of multipliers-based algorithm is fully distributed, and does not require the presence of a centralized entity to achieve coordination among the microgrids. We present detailed numerical results on the IEEE 33-bus distribution test system to demonstrate the effectiveness of the proposed approach and examine the scalability and convergence behavior of the distributed algorithm for different choice of hyperparameters and network sizes.
{"title":"Distributed Coordination of Multi-microgrids in Active Distribution Networks for Provisioning Ancillary Services","authors":"Arghya Mallick;Abhishek Mishra;Ashish R. Hota;Prabodh Bajpai","doi":"10.1109/JSYST.2024.3404600","DOIUrl":"10.1109/JSYST.2024.3404600","url":null,"abstract":"With the phenomenal growth in renewable energy generation, the conventional synchronous generator-based power plants are gradually getting replaced by renewable energy sources-based microgrids. Such transition gives rise to the challenges of procuring various ancillary services from microgrids. We propose a distributed optimization framework that coordinates multiple microgrids in an active distribution network for provisioning passive voltage support-based ancillary services while satisfying operational constraints. Specifically, we exploit the reactive power support capability of the inverters and the flexibility offered by storage systems available with microgrids for provisioning ancillary service support to the transmission grid. We develop novel mixed-integer inequalities to represent the set of feasible active and reactive power exchange with the transmission grid that ensures passive voltage support. The proposed alternating direction method of multipliers-based algorithm is fully distributed, and does not require the presence of a centralized entity to achieve coordination among the microgrids. We present detailed numerical results on the IEEE 33-bus distribution test system to demonstrate the effectiveness of the proposed approach and examine the scalability and convergence behavior of the distributed algorithm for different choice of hyperparameters and network sizes.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1492-1503"},"PeriodicalIF":4.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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