Pub Date : 2024-03-18DOI: 10.1109/JSYST.2024.3371429
F. Fernando Jurado-Lasso;Mohammadreza Barzegaran;J. F. Jurado;Xenofon Fafoutis
The Internet of Things is shaping the next generation of cyber–physical systems to improve the future industry for smart cities. It has created novel and essential applications that require specific network performance to enhance the quality of services. Since network performance requirements are application-oriented, it is of paramount importance to provide tailored solutions that seamlessly manage the network resources and orchestrate the network to satisfy user requirements. In this article, we propose ELISE, a reinforcement learning (RL) framework to optimize the slotframe size of the time slotted channel hopping protocol in IIoT networks while considering the user requirements. We primarily address the problem of designing a framework that self-adapts to the optimal slotframe length that best suits the user's requirements. The framework takes care of all functionalities involved in the correct functioning of the network, while the RL agent instructs the framework with a set of actions to determine the optimal slotframe size each time the user requirements change. We evaluate the performance of ELISE through extensive analysis based on simulations and experimental evaluations on a testbed to demonstrate the efficiency of the proposed approach in adapting network resources at runtime to satisfy user requirements.
{"title":"ELISE: A Reinforcement Learning Framework to Optimize the Slotframe Size of the TSCH Protocol in IoT Networks","authors":"F. Fernando Jurado-Lasso;Mohammadreza Barzegaran;J. F. Jurado;Xenofon Fafoutis","doi":"10.1109/JSYST.2024.3371429","DOIUrl":"10.1109/JSYST.2024.3371429","url":null,"abstract":"The Internet of Things is shaping the next generation of cyber–physical systems to improve the future industry for smart cities. It has created novel and essential applications that require specific network performance to enhance the quality of services. Since network performance requirements are application-oriented, it is of paramount importance to provide tailored solutions that seamlessly manage the network resources and orchestrate the network to satisfy user requirements. In this article, we propose ELISE, a reinforcement learning (RL) framework to optimize the slotframe size of the time slotted channel hopping protocol in IIoT networks while considering the user requirements. We primarily address the problem of designing a framework that self-adapts to the optimal slotframe length that best suits the user's requirements. The framework takes care of all functionalities involved in the correct functioning of the network, while the RL agent instructs the framework with a set of actions to determine the optimal slotframe size each time the user requirements change. We evaluate the performance of ELISE through extensive analysis based on simulations and experimental evaluations on a testbed to demonstrate the efficiency of the proposed approach in adapting network resources at runtime to satisfy user requirements.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"1068-1079"},"PeriodicalIF":4.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10473707","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140168118","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 : 2024-03-18DOI: 10.1109/JSYST.2024.3363070
{"title":"IEEE Systems Journal Publication Information","authors":"","doi":"10.1109/JSYST.2024.3363070","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3363070","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 1","pages":"C2-C2"},"PeriodicalIF":4.4,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10473688","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140164126","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 : 2024-03-18DOI: 10.1109/JSYST.2024.3363066
{"title":"IEEE Systems Council Information","authors":"","doi":"10.1109/JSYST.2024.3363066","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3363066","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 1","pages":"C3-C3"},"PeriodicalIF":4.4,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10473687","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161299","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 : 2024-03-18DOI: 10.1109/JSYST.2024.3370655
Mohammad Hossein Norouzi;Arman Oshnoei;Behnam Mohammadi-Ivatloo;Mehdi Abapour
Renewable energy sources (RESs) are increasingly used to meet consumer demands in microgrids (MGs). However, high RES integration introduces system frequency stability, inertia, and damping reduction challenges. Virtual inertia (VI) control has been recognized as an effective solution to improve system frequency response in such circumstances. Conventional control techniques for VI control, which rely heavily on specific operating conditions, can lead to flawed performance during contingencies due to their lack of adaptivity. To address these challenges, this article proposes a novel attitude found on brain emotional learning (BEL) to emulate VI and damping for effective frequency control. The BEL-based controller is capable of quickly learning and handling the complexity, nonlinearity, and uncertainty intrinsic to the MGs, and it operates independently of prior knowledge of the system model and parameters. This characteristic enables the controller to adapt to various operating conditions, improving its robustness. The simulation results across three disturbance scenarios show that the proposed BEL-based controller significantly improves the system's response. The absolute maximum deviation of frequency was reduced to 0.0561 Hz in the final scenario, marking performance enhancements of 46.62% and 49.04% when compared with the artificial neural network-based proportional–integral control and the standard proportional control, respectively. This underlines the controller's adaptability and superior effectiveness in varying operating conditions.
{"title":"Learning-Based Virtual Inertia Control of an Islanded Microgrid With High Participation of Renewable Energy Resources","authors":"Mohammad Hossein Norouzi;Arman Oshnoei;Behnam Mohammadi-Ivatloo;Mehdi Abapour","doi":"10.1109/JSYST.2024.3370655","DOIUrl":"10.1109/JSYST.2024.3370655","url":null,"abstract":"Renewable energy sources (RESs) are increasingly used to meet consumer demands in microgrids (MGs). However, high RES integration introduces system frequency stability, inertia, and damping reduction challenges. Virtual inertia (VI) control has been recognized as an effective solution to improve system frequency response in such circumstances. Conventional control techniques for VI control, which rely heavily on specific operating conditions, can lead to flawed performance during contingencies due to their lack of adaptivity. To address these challenges, this article proposes a novel attitude found on brain emotional learning (BEL) to emulate VI and damping for effective frequency control. The BEL-based controller is capable of quickly learning and handling the complexity, nonlinearity, and uncertainty intrinsic to the MGs, and it operates independently of prior knowledge of the system model and parameters. This characteristic enables the controller to adapt to various operating conditions, improving its robustness. The simulation results across three disturbance scenarios show that the proposed BEL-based controller significantly improves the system's response. The absolute maximum deviation of frequency was reduced to 0.0561 Hz in the final scenario, marking performance enhancements of 46.62% and 49.04% when compared with the artificial neural network-based proportional–integral control and the standard proportional control, respectively. This underlines the controller's adaptability and superior effectiveness in varying operating conditions.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"786-795"},"PeriodicalIF":4.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140168347","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 : 2024-03-18DOI: 10.1109/JSYST.2024.3363064
{"title":"IEEE Systems Journal Information for Authors","authors":"","doi":"10.1109/JSYST.2024.3363064","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3363064","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 1","pages":"C4-C4"},"PeriodicalIF":4.4,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10473665","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161140","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 : 2024-03-18DOI: 10.1109/JSYST.2024.3363588
{"title":"List of Reviewers","authors":"","doi":"10.1109/JSYST.2024.3363588","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3363588","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 1","pages":"5-14"},"PeriodicalIF":4.4,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10473605","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140164128","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}
With the growing integration of unpredictable renewable energy sources into the grid, achieving power balance has become an increasingly crucial challenge. To address this challenge, demand response has emerged as a promising solution. This article proposes a new demand-side flexible thermostatically controlled loads response strategy framework. Our method employs a hierarchical control framework that covers three layers of control, which consist of the optimization layer, coordination layer, and local control layer. The optimization layer employs a dynamic average consensus algorithm for economic optimization scheduling to maximize the sum of the aggregators' welfare functions. In the coordination layer, power is distributed fairly based on the comfort state, generating reference signals for the local control layer. The local control layer tracks these reference signals and employs integral sliding mode control to suppress the influence of unknown disturbances. The control objectives of the entire framework can be achieved in a fixed time, and the parameters in the framework are heterogeneous. Furthermore, the relationships between controller parameters and tracking performance are derived, and the upper bounds of settling time are estimated. Finally, we demonstrate the validity of our theoretical results through numerical simulations.
{"title":"Fixed-Time Hierarchical Distributed Control for Flexible Thermostatically Controlled Loads","authors":"Zilong Mi;Zhengmin Kong;Tao Huang;Peng Shi;Zhenwei Yu;Li Ding","doi":"10.1109/JSYST.2024.3366226","DOIUrl":"10.1109/JSYST.2024.3366226","url":null,"abstract":"With the growing integration of unpredictable renewable energy sources into the grid, achieving power balance has become an increasingly crucial challenge. To address this challenge, demand response has emerged as a promising solution. This article proposes a new demand-side flexible thermostatically controlled loads response strategy framework. Our method employs a hierarchical control framework that covers three layers of control, which consist of the optimization layer, coordination layer, and local control layer. The optimization layer employs a dynamic average consensus algorithm for economic optimization scheduling to maximize the sum of the aggregators' welfare functions. In the coordination layer, power is distributed fairly based on the comfort state, generating reference signals for the local control layer. The local control layer tracks these reference signals and employs integral sliding mode control to suppress the influence of unknown disturbances. The control objectives of the entire framework can be achieved in a fixed time, and the parameters in the framework are heterogeneous. Furthermore, the relationships between controller parameters and tracking performance are derived, and the upper bounds of settling time are estimated. Finally, we demonstrate the validity of our theoretical results through numerical simulations.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"1344-1355"},"PeriodicalIF":4.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140168236","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 : 2024-03-17DOI: 10.1109/JSYST.2024.3397301
Shuo Zhang;Jinhai Liu;Wei Wang;Zhigang Zhang
This article concerns the issue of bipartite leader-following consensus for a class of nonlinear switched multiagent systems. The novel model-depended distributed dynamic event-triggered protocols are constructed. Compared to the existing event-triggered rules, different event-triggered functions are designed for different system models to decrease the amount of calculation and communication, and nonnegative model-depended dynamic auxiliary variables are introduced to further enhance the triggering performance. Based on the event-triggered protocols, the novel distributed model-depended bipartite event-triggered control laws are presented, in which different models correspond to different controller gains to ensure better control performance. By employing the Lyapunov theory and the average dwell time method, bipartite leader-following consensus is guaranteed with a global exponential convergence rate. Besides, the Zeno phenomenon is ruled out. Finally, several numerical examples are performed to validate the feasibility and superiority of the proposed theory.
{"title":"Bipartite Leader-Following Consensus of Nonlinear Switched Multiagent Systems Under Model-Depended Dynamic Event-Triggered Control","authors":"Shuo Zhang;Jinhai Liu;Wei Wang;Zhigang Zhang","doi":"10.1109/JSYST.2024.3397301","DOIUrl":"10.1109/JSYST.2024.3397301","url":null,"abstract":"This article concerns the issue of bipartite leader-following consensus for a class of nonlinear switched multiagent systems. The novel model-depended distributed dynamic event-triggered protocols are constructed. Compared to the existing event-triggered rules, different event-triggered functions are designed for different system models to decrease the amount of calculation and communication, and nonnegative model-depended dynamic auxiliary variables are introduced to further enhance the triggering performance. Based on the event-triggered protocols, the novel distributed model-depended bipartite event-triggered control laws are presented, in which different models correspond to different controller gains to ensure better control performance. By employing the Lyapunov theory and the average dwell time method, bipartite leader-following consensus is guaranteed with a global exponential convergence rate. Besides, the Zeno phenomenon is ruled out. Finally, several numerical examples are performed to validate the feasibility and superiority of the proposed theory.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"1044-1055"},"PeriodicalIF":4.0,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141058869","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 : 2024-03-17DOI: 10.1109/JSYST.2024.3398049
Min Hou;Xinrui Liu;Rui Wang;Chaoyu Dong;Qiuye Sun
As the distribution network is affected by the high proportion of renewable energy connected to the grid and the disorderly charging of electric vehicles, how to formulate the optimal scheduling strategy to ensure the safety and stability of the system has become an urgent problem to be solved. Aiming at the uncertainty of the user behavior of the traffic network, a charging pile (station) pricing strategy based on stochastic user equilibrium (SUE) is proposed. The equilibrium electricity price of charging pile (station) is formulated to guide the traffic flow and realize the collaborative optimization of the distribution network. Considering the traffic congestion caused by user behavior, a congestion charging policy is proposed to promote static hybrid SUE. Its feasibility is proved by Karush-Kuhn-Tucker (KKT) condition and variational inequality. In addition, through the introduction of joint pricing center, charging pile (station) electricity price, and congestion charging policy are proposed. Aiming at the uncertainty of system, an enhanced-interval optimal method is established. Finally, the simulation analysis of the power-transportation interconnected system verifies that the congestion charging policy can optimize the unit output, and the enhanced-interval optimal method can solve the uncertain influence, reduce the system cost, and ensure the satisfaction of traffic users.
由于配电网受到高比例可再生能源并网以及电动汽车无序充电的影响，如何制定最优调度策略以确保系统安全稳定成为亟待解决的问题。针对交通网络用户行为的不确定性，提出了一种基于随机用户均衡（SUE）的充电桩（站）定价策略。通过制定充电桩（站）的均衡电价来引导交通流，实现配电网的协同优化。考虑到用户行为导致的交通拥堵，提出了促进静态混合 SUE 的拥堵收费政策。其可行性由 Karush-Kuhn-Tucker (KKT) 条件和变分不等式证明。此外，通过引入联合定价中心、充电桩（站）电价，提出了拥堵充电政策。针对系统的不确定性，建立了增强的区间优化方法。最后，通过对电力-交通互联系统的仿真分析，验证了拥堵收费策略可以优化单位产出，增强间隔最优方法可以解决不确定性影响，降低系统成本，确保交通用户的满意度。
{"title":"Enhanced-Interval Optimal Scheduling of Power-Transportation Interconnected System Considering Pile (Station) Equilibrium Price","authors":"Min Hou;Xinrui Liu;Rui Wang;Chaoyu Dong;Qiuye Sun","doi":"10.1109/JSYST.2024.3398049","DOIUrl":"10.1109/JSYST.2024.3398049","url":null,"abstract":"As the distribution network is affected by the high proportion of renewable energy connected to the grid and the disorderly charging of electric vehicles, how to formulate the optimal scheduling strategy to ensure the safety and stability of the system has become an urgent problem to be solved. Aiming at the uncertainty of the user behavior of the traffic network, a charging pile (station) pricing strategy based on stochastic user equilibrium (SUE) is proposed. The equilibrium electricity price of charging pile (station) is formulated to guide the traffic flow and realize the collaborative optimization of the distribution network. Considering the traffic congestion caused by user behavior, a congestion charging policy is proposed to promote static hybrid SUE. Its feasibility is proved by Karush-Kuhn-Tucker (KKT) condition and variational inequality. In addition, through the introduction of joint pricing center, charging pile (station) electricity price, and congestion charging policy are proposed. Aiming at the uncertainty of system, an enhanced-interval optimal method is established. Finally, the simulation analysis of the power-transportation interconnected system verifies that the congestion charging policy can optimize the unit output, and the enhanced-interval optimal method can solve the uncertain influence, reduce the system cost, and ensure the satisfaction of traffic users.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"1320-1331"},"PeriodicalIF":4.0,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141058599","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 work proposes a rate-splitting (RS) strategy for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided massive multiple-input multiple-output (mMIMO) systems to reduce the interference among multiple users and enhance the spectral efficiency (SE) while improving the coverage degraded by blockages. Specifically, we use the RS to design the precoder for the common part by solving the asymptotic problem. Also, unlike traditional RIS-aided systems, receivers can be positioned on either side of the RIS panel in the proposed system. We derive the sum-rate based on statistical channel state information (CSI) to reduce the signal overhead. Next, we optimize the rate through a projected gradient ascent method algorithm simultaneously with respect to the amplitudes and phase shifts of the STAR-RIS. Simulations show the advantages of the RS strategy compared with the broadcasting strategy in improving the sum-rate. We further evaluate the efficiency of the STAR-RIS system against the traditional RIS-aided system. In our analysis, we employ energy splitting and mode switching protocols to fine-tune the transmission and reflection coefficients of the outgoing and incoming signals.
{"title":"A Rate-Splitting Strategy for STAR-RIS-Aided Massive MIMO Systems With Joint Optimization","authors":"Hanxiao Ge;Anastasios Papazafeiropoulos;Navneet Garg;Tharmalingam Ratnarajah","doi":"10.1109/JSYST.2024.3398249","DOIUrl":"10.1109/JSYST.2024.3398249","url":null,"abstract":"This work proposes a rate-splitting (RS) strategy for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided massive multiple-input multiple-output (mMIMO) systems to reduce the interference among multiple users and enhance the spectral efficiency (SE) while improving the coverage degraded by blockages. Specifically, we use the RS to design the precoder for the common part by solving the asymptotic problem. Also, unlike traditional RIS-aided systems, receivers can be positioned on either side of the RIS panel in the proposed system. We derive the sum-rate based on statistical channel state information (CSI) to reduce the signal overhead. Next, we optimize the rate through a projected gradient ascent method algorithm simultaneously with respect to the amplitudes and phase shifts of the STAR-RIS. Simulations show the advantages of the RS strategy compared with the broadcasting strategy in improving the sum-rate. We further evaluate the efficiency of the STAR-RIS system against the traditional RIS-aided system. In our analysis, we employ energy splitting and mode switching protocols to fine-tune the transmission and reflection coefficients of the outgoing and incoming signals.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"977-988"},"PeriodicalIF":4.0,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141058658","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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