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}
In recent years, researchers and practitioners have focused on Industry 4.0, emphasizing the role of cyber-physical systems (CPSs) in manufacturing. However, the operationalization of Industry 4.0 has presented many implementation challenges caused by the inability of available technologies to meet industry needs effectively. Furthermore, Industry 4.0 has been criticized for the absence of focus on the human component in CPSs impacting the concept of sustainability in the long run. Responding to this critique and building on the foundation of the Industry 5.0 concept, this article proposes a holistic methodology empowered by human expert knowledge for human-cyber-physical system (HCPS) implementation. The proposed novel HCPS methodology represents a more sustainable solution for companies that consists of five phases to promote the integration of human expert knowledge and cyber and physical parts empowered by big data analytics for real-time anomaly detection. Specifically, real-time anomaly detection is enabled by industrial edge computing for big data optimization, data processing, and the industrial Internet of Things (IIoTs) real-time product quality control. Finally, we implement the developed HCPS solution in a case study from the process industry, where automated system decision-making is achieved. The results obtained indicate that an HCPS, as a strategy for companies, must augment human capabilities and require human involvement in final decision-making, foster meaningful human impact, and create new employment opportunities.
{"title":"Toward a Human-Cyber-Physical System for Real-Time Anomaly Detection","authors":"Bojana Bajic;Aleksandar Rikalovic;Nikola Suzic;Vincenzo Piuri","doi":"10.1109/JSYST.2024.3402978","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3402978","url":null,"abstract":"In recent years, researchers and practitioners have focused on Industry 4.0, emphasizing the role of cyber-physical systems (CPSs) in manufacturing. However, the operationalization of Industry 4.0 has presented many implementation challenges caused by the inability of available technologies to meet industry needs effectively. Furthermore, Industry 4.0 has been criticized for the absence of focus on the human component in CPSs impacting the concept of sustainability in the long run. Responding to this critique and building on the foundation of the Industry 5.0 concept, this article proposes a holistic methodology empowered by human expert knowledge for human-cyber-physical system (HCPS) implementation. The proposed novel HCPS methodology represents a more sustainable solution for companies that consists of five phases to promote the integration of human expert knowledge and cyber and physical parts empowered by big data analytics for real-time anomaly detection. Specifically, real-time anomaly detection is enabled by industrial edge computing for big data optimization, data processing, and the industrial Internet of Things (IIoTs) real-time product quality control. Finally, we implement the developed HCPS solution in a case study from the process industry, where automated system decision-making is achieved. The results obtained indicate that an HCPS, as a strategy for companies, must augment human capabilities and require human involvement in final decision-making, foster meaningful human impact, and create new employment opportunities.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"1308-1319"},"PeriodicalIF":4.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10555342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435544","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 : 2024-06-12DOI: 10.1109/JSYST.2024.3403500
Khaled Alanezi;Shivakant Mishra
Modern Internet of Things (IoT) environments with thousands of low-end and diverse IoT nodes with complex interactions among them and often deployed in remote and/or wild locations present some unique challenges that make traditional node compromise detection services less effective. This article presents the design, implementation, and evaluation of a fog-based architecture that utilizes the concept of a digital twin to detect compromised IoT nodes exhibiting malicious behaviors by either producing erroneous data and/or being used to launch network intrusion attacks to hijack other nodes eventually causing service disruption. By defining a digital twin of an IoT infrastructure at a fog server, the architecture is focused on monitoring relevant information to save energy and storage space. This article presents a prototype implementation for the architecture utilizing malicious behavior datasets to perform misbehaving node classification. An extensive accuracy and system performance evaluation was conducted based on this prototype. Results show good accuracy and negligible overhead especially when employing deep learning techniques, such as multilayer perceptron.
{"title":"An IoT Architecture Leveraging Digital Twins: Compromised Node Detection Scenario","authors":"Khaled Alanezi;Shivakant Mishra","doi":"10.1109/JSYST.2024.3403500","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3403500","url":null,"abstract":"Modern Internet of Things (IoT) environments with thousands of low-end and diverse IoT nodes with complex interactions among them and often deployed in remote and/or wild locations present some unique challenges that make traditional node compromise detection services less effective. This article presents the design, implementation, and evaluation of a fog-based architecture that utilizes the concept of a digital twin to detect compromised IoT nodes exhibiting malicious behaviors by either producing erroneous data and/or being used to launch network intrusion attacks to hijack other nodes eventually causing service disruption. By defining a digital twin of an IoT infrastructure at a fog server, the architecture is focused on monitoring relevant information to save energy and storage space. This article presents a prototype implementation for the architecture utilizing malicious behavior datasets to perform misbehaving node classification. An extensive accuracy and system performance evaluation was conducted based on this prototype. Results show good accuracy and negligible overhead especially when employing deep learning techniques, such as multilayer perceptron.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"1224-1235"},"PeriodicalIF":4.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435546","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 : 2024-06-07DOI: 10.1109/JSYST.2024.3406800
Rongxing Guan;Huabo Liu;Keke Huang;Haisheng Yu
This article is concerned with the $H_infty$ performance problems for large-scale networked systems comprising many subsystems. The connections among these subsystems with different dynamics are arbitrary and linear time-invariant. Necessary and sufficient conditions have been derived for $H_infty$ performance, in which the system structure is sufficiently utilized and higher computational efficiency is obtained. Furthermore, several analysis conditions that rely solely on individual subsystem parameters are obtained. The effectiveness and ascendancy of the derived conditions are verified by some numerical simulations.
{"title":"$H_infty$ Performance Analysis of Large-Scale Networked Systems","authors":"Rongxing Guan;Huabo Liu;Keke Huang;Haisheng Yu","doi":"10.1109/JSYST.2024.3406800","DOIUrl":"10.1109/JSYST.2024.3406800","url":null,"abstract":"This article is concerned with the \u0000<inline-formula><tex-math>$H_infty$</tex-math></inline-formula>\u0000 performance problems for large-scale networked systems comprising many subsystems. The connections among these subsystems with different dynamics are arbitrary and linear time-invariant. Necessary and sufficient conditions have been derived for \u0000<inline-formula><tex-math>$H_infty$</tex-math></inline-formula>\u0000 performance, in which the system structure is sufficiently utilized and higher computational efficiency is obtained. Furthermore, several analysis conditions that rely solely on individual subsystem parameters are obtained. The effectiveness and ascendancy of the derived conditions are verified by some numerical simulations.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1528-1537"},"PeriodicalIF":4.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932828","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 article focuses on covert communication in a distributed network with multiple nonorthogonal multiple access (NOMA) systems, where each NOMA system is consisted of a transmitter, a legitimate public user, a covert user, and a warden. Power allocation for multiple transmitters in such network is a highly tricky problem, since it needs to addresses the issues of complex inter-NOMA system interference, constraints from both public users and covert users, and the optimization of overall network performance. We first conduct a theoretical analysis to depict the inherent relationship between the inter-NOMA system interference and transmit power of transmitters. With the help of the interference analysis, we then develop a theoretical framework for the modeling of detection error probability, covert rate, and public rate in each NOMA system. Based on these results and the constraints from both public users and covert users, we formulate the concerned power allocation problem as a Markov decision process, and further develop multiagent reinforcement learning (RL) algorithms to identify the optimal power allocation among transmitters to maximize the sum-rate of the overall network. Finally, numerical results are provided to illustrate the efficiency of our RL algorithms for power allocation in multi-NOMA networks.
{"title":"RL-Assisted Power Allocation for Covert Communication in Distributed NOMA Networks","authors":"Jiaqing Bai;Ji He;Yanping Chen;Yulong Shen;Xiaohong Jiang","doi":"10.1109/JSYST.2024.3406035","DOIUrl":"10.1109/JSYST.2024.3406035","url":null,"abstract":"This article focuses on covert communication in a distributed network with multiple nonorthogonal multiple access (NOMA) systems, where each NOMA system is consisted of a transmitter, a legitimate public user, a covert user, and a warden. Power allocation for multiple transmitters in such network is a highly tricky problem, since it needs to addresses the issues of complex inter-NOMA system interference, constraints from both public users and covert users, and the optimization of overall network performance. We first conduct a theoretical analysis to depict the inherent relationship between the inter-NOMA system interference and transmit power of transmitters. With the help of the interference analysis, we then develop a theoretical framework for the modeling of detection error probability, covert rate, and public rate in each NOMA system. Based on these results and the constraints from both public users and covert users, we formulate the concerned power allocation problem as a Markov decision process, and further develop multiagent reinforcement learning (RL) algorithms to identify the optimal power allocation among transmitters to maximize the sum-rate of the overall network. Finally, numerical results are provided to illustrate the efficiency of our RL algorithms for power allocation in multi-NOMA networks.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1504-1515"},"PeriodicalIF":4.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932720","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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