As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the...As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the energy efficiency of this CPS.This paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two levels.The high-level establishes a parallel system first,which includes a real powertrain system and an artificial system.Then,the synthesized data from this parallel system is trained by a bidirectional LSTM network.The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)framework.PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules.Finally,real vehicle testing is implemented and relevant experiment data is collected and calibrated.Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.展开更多
Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical components.The greatly interrelated and united characteristics of ...Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical components.The greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems(CPES).At the same time,the rising ubiquity of wireless sensor networks(WSN)in several application areas makes it a vital part of the design of CPES.Since security and energy efficiency are the major challenging issues in CPES,this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms(EASCPSMA).The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion detection.The EASCPSMA technique encompasses two main stages namely improved fruit fly optimization algorithm(IFFOA)based clustering and optimal deep stacked autoencoder(OSAE)based intrusion detection.Besides,the optimal selection of stacked autoencoder(SAE)parameters takes place using root mean square propagation(RMSProp)model.The extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying aspects.The simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.展开更多
A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical processes.The smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physic...A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical processes.The smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical system.At the same time,the machine learning(ML)modelsfind useful for the smart grids integrated into the CPES for effective decision making.Also,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity utilization.In this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in CPES.The proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter optimization.The OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing step.Besides,the MBLSTM model is applied for the prediction of stability level of the smart grids in CPES.At the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM model.To ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several aspects.The experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.展开更多
Recently,with the growth of cyber physical systems(CPS),several applications have begun to deploy in the CPS for connecting the cyber space with the physical scale effectively.Besides,the cloud computing(CC)enabled CP...Recently,with the growth of cyber physical systems(CPS),several applications have begun to deploy in the CPS for connecting the cyber space with the physical scale effectively.Besides,the cloud computing(CC)enabled CPS offers huge processing and storage resources for CPS thatfinds helpful for a range of application areas.At the same time,with the massive development of applica-tions that exist in the CPS environment,the energy utilization of the cloud enabled CPS has gained significant interest.For improving the energy effective-ness of the CC platform,virtualization technologies have been employed for resource management and the applications are executed via virtual machines(VMs).Since effective scheduling of resources acts as an important role in the design of cloud enabled CPS,this paper focuses on the design of chaotic sandpi-per optimization based VM scheduling(CSPO-VMS)technique for energy effi-cient CPS.The CSPO-VMS technique is utilized for searching for the optimum VM migration solution and it helps to choose an effective scheduling strategy.The CSPO algorithm integrates the concepts of traditional SPO algorithm with the chaos theory,which substitutes the main parameter and combines it with the chaos.In order to improve the process of determining the global optimum solutions and convergence rate of the SPO algorithm,the chaotic concept is included in the SPO algorithm.The CSPO-VMS technique also derives afitness function to choose optimal scheduling strategy in the CPS environment.In order to demonstrate the enhanced performance of the CSPO-VMS technique,a wide range of simulations were carried out and the results are examined under varying aspects.The simulation results ensured the improved performance of the CSPO-VMS technique over the recent methods interms of different measures.展开更多
The development of electrical engineering and electronic, communications, smart power grid, and ultra-high voltage transmission technologies have driven the energy system revolution to the next generation: the energy ...The development of electrical engineering and electronic, communications, smart power grid, and ultra-high voltage transmission technologies have driven the energy system revolution to the next generation: the energy internet. Progressive penetration of intermittent renewable energy sources into the energy system has led to unprecedented challenges to the currently wide use of coal-fired power generation technologies. Here, the applications and prospects of advanced coal-fired power generation technologies are analyzed. These technologies can be summarized into three categories:(1) large-scale and higher parameters coal-fired power generation technologies, including 620/650/700 oC ultra-supercritical thermal power and double reheat ultra-supercritical coal-fired power generation technologies;(2) system innovation and specific, highefficiency thermal cycles, which consist of renewable energy-aided coal-fired power generation technologies, a supercritical CO_2 Brayton cycle for coal-fired power plants, large-scale air-cooling coal-fired power plant technologies, and innovative layouts for waste heat utilization and enhanced energy cascade utilization;(3) coal-fired power generation combined with poly-generation technologies, which are represented by integrated gasification combined cycle(IGCC) and integrated gasification fuel cell(IGFC) technologies. Concerning the existing coal-fired power units, which are responsible for peak shaving, possible strategies for enhancing flexibility and operational stability are discussed. Furthermore, future trends for coal-fired power plants coupled with cyber-physical system(CPS) technologies are introduced. The development of advanced, coal-fired power generation technologies demonstrates the progress of science and is suitable for the sustainable development of human society.展开更多
With the access to large amounts of renewable energy sources(RES),operation uncertainty of distribution networks increases significantly.Fortunately,adopting advanced information and communication technology,a cyber-p...With the access to large amounts of renewable energy sources(RES),operation uncertainty of distribution networks increases significantly.Fortunately,adopting advanced information and communication technology,a cyber-physical distribution network(CPDS)provides the possibility to solve this problem via aggregative management of decentralized controllable loads.However,information flow in cyber space deeply interacts with energy flow in physical space,leading to a complexity in modeling,design and analysis of the whole control process.To deal with this problem,a general hybrid flow model of CPDS is first proposed in this paper.In this model,the control process is abstracted into interactions among three types of cyber nodes through cyber branches.The mathematic model of cyber nodes and branches is developed as well as that of the controlled physical object for hybrid flow computation.Then,based on the hybrid model,an instantiated application to compensate feeder power deviation caused by RES fluctuation through aggregative control of large-scale air-conditioners(ACs)is investigated.In this application,coordinative control of the AC cluster is achieved through a decentralized control strategy with very little communication cost and very good privacy protection.Results of numerical examples verify the correctness and flexibility of the hybrid flow model in reflecting interactions between cyber flow and energy flow as well as system operations.The proposed decentralized control strategy of the AC cluster is also proven to be effective and robust in FCE compensation.展开更多
基金supported by National Natural Science Foundation of China(61100159,61233007)National High Technology Research and Development Program of China(863 Program)(2011AA040103)+2 种基金Foundation of Chinese Academy of Sciences(KGCX2-EW-104)Financial Support of the Strategic Priority Research Program of Chinese Academy of Sciences(XDA06021100)the Cross-disciplinary Collaborative Teams Program for Science,Technology and Innovation,of Chinese Academy of Sciences-Network and System Technologies for Security Monitoring and Information Interaction in Smart Grid Energy Management System for Micro-smart Grid
基金supported in part by the National Natural Science Foundation of China(61533019,91720000)Beijing Municipal Science and Technology Commission(Z181100008918007)the Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles(pICRI-IACVq)
文摘As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the energy efficiency of this CPS.This paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two levels.The high-level establishes a parallel system first,which includes a real powertrain system and an artificial system.Then,the synthesized data from this parallel system is trained by a bidirectional LSTM network.The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)framework.PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules.Finally,real vehicle testing is implemented and relevant experiment data is collected and calibrated.Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.
基金This study was funded by the Deanship of Scientific Research,Taif University Researchers Supporting project number(TURSP-2020/195)Taif University,Taif,Saudi Arabia.The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/25/43)+1 种基金The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR02)The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges(APC)of this publication.
文摘Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical components.The greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems(CPES).At the same time,the rising ubiquity of wireless sensor networks(WSN)in several application areas makes it a vital part of the design of CPES.Since security and energy efficiency are the major challenging issues in CPES,this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms(EASCPSMA).The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion detection.The EASCPSMA technique encompasses two main stages namely improved fruit fly optimization algorithm(IFFOA)based clustering and optimal deep stacked autoencoder(OSAE)based intrusion detection.Besides,the optimal selection of stacked autoencoder(SAE)parameters takes place using root mean square propagation(RMSProp)model.The extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying aspects.The simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.
基金supported by the Researchers Supporting Program(TUMA-Project-2021-27)Almaarefa University,Riyadh,Saudi ArabiaTaif University Researchers Supporting Project number(TURSP-2020/161),Taif University,Taif,Saudi Arabia。
文摘A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical processes.The smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical system.At the same time,the machine learning(ML)modelsfind useful for the smart grids integrated into the CPES for effective decision making.Also,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity utilization.In this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in CPES.The proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter optimization.The OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing step.Besides,the MBLSTM model is applied for the prediction of stability level of the smart grids in CPES.At the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM model.To ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several aspects.The experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.
文摘Recently,with the growth of cyber physical systems(CPS),several applications have begun to deploy in the CPS for connecting the cyber space with the physical scale effectively.Besides,the cloud computing(CC)enabled CPS offers huge processing and storage resources for CPS thatfinds helpful for a range of application areas.At the same time,with the massive development of applica-tions that exist in the CPS environment,the energy utilization of the cloud enabled CPS has gained significant interest.For improving the energy effective-ness of the CC platform,virtualization technologies have been employed for resource management and the applications are executed via virtual machines(VMs).Since effective scheduling of resources acts as an important role in the design of cloud enabled CPS,this paper focuses on the design of chaotic sandpi-per optimization based VM scheduling(CSPO-VMS)technique for energy effi-cient CPS.The CSPO-VMS technique is utilized for searching for the optimum VM migration solution and it helps to choose an effective scheduling strategy.The CSPO algorithm integrates the concepts of traditional SPO algorithm with the chaos theory,which substitutes the main parameter and combines it with the chaos.In order to improve the process of determining the global optimum solutions and convergence rate of the SPO algorithm,the chaotic concept is included in the SPO algorithm.The CSPO-VMS technique also derives afitness function to choose optimal scheduling strategy in the CPS environment.In order to demonstrate the enhanced performance of the CSPO-VMS technique,a wide range of simulations were carried out and the results are examined under varying aspects.The simulation results ensured the improved performance of the CSPO-VMS technique over the recent methods interms of different measures.
基金supported by the National Nature Science Foundation of China(Grant No.51821004)supported by National Soft Science Projects:"Frontier tracking research on science and technology in the field of energy" program
文摘The development of electrical engineering and electronic, communications, smart power grid, and ultra-high voltage transmission technologies have driven the energy system revolution to the next generation: the energy internet. Progressive penetration of intermittent renewable energy sources into the energy system has led to unprecedented challenges to the currently wide use of coal-fired power generation technologies. Here, the applications and prospects of advanced coal-fired power generation technologies are analyzed. These technologies can be summarized into three categories:(1) large-scale and higher parameters coal-fired power generation technologies, including 620/650/700 oC ultra-supercritical thermal power and double reheat ultra-supercritical coal-fired power generation technologies;(2) system innovation and specific, highefficiency thermal cycles, which consist of renewable energy-aided coal-fired power generation technologies, a supercritical CO_2 Brayton cycle for coal-fired power plants, large-scale air-cooling coal-fired power plant technologies, and innovative layouts for waste heat utilization and enhanced energy cascade utilization;(3) coal-fired power generation combined with poly-generation technologies, which are represented by integrated gasification combined cycle(IGCC) and integrated gasification fuel cell(IGFC) technologies. Concerning the existing coal-fired power units, which are responsible for peak shaving, possible strategies for enhancing flexibility and operational stability are discussed. Furthermore, future trends for coal-fired power plants coupled with cyber-physical system(CPS) technologies are introduced. The development of advanced, coal-fired power generation technologies demonstrates the progress of science and is suitable for the sustainable development of human society.
基金supported in part by the National Key Research and Development Program of China(Basic Research Class 2017YFB0903000)the National Natural Science Foundation of China(51677116)the Science and Technology Project of State Grid Corporation of China:Basic Theory and Method of Analysis and Control of Cyber Physical System for Power Grid(Supporting Project).
文摘With the access to large amounts of renewable energy sources(RES),operation uncertainty of distribution networks increases significantly.Fortunately,adopting advanced information and communication technology,a cyber-physical distribution network(CPDS)provides the possibility to solve this problem via aggregative management of decentralized controllable loads.However,information flow in cyber space deeply interacts with energy flow in physical space,leading to a complexity in modeling,design and analysis of the whole control process.To deal with this problem,a general hybrid flow model of CPDS is first proposed in this paper.In this model,the control process is abstracted into interactions among three types of cyber nodes through cyber branches.The mathematic model of cyber nodes and branches is developed as well as that of the controlled physical object for hybrid flow computation.Then,based on the hybrid model,an instantiated application to compensate feeder power deviation caused by RES fluctuation through aggregative control of large-scale air-conditioners(ACs)is investigated.In this application,coordinative control of the AC cluster is achieved through a decentralized control strategy with very little communication cost and very good privacy protection.Results of numerical examples verify the correctness and flexibility of the hybrid flow model in reflecting interactions between cyber flow and energy flow as well as system operations.The proposed decentralized control strategy of the AC cluster is also proven to be effective and robust in FCE compensation.
