State-of-the-art technologies such as the Internet of Things(IoT),cloud computing(CC),big data analytics(BDA),and artificial intelligence(AI)have greatly stimulated the development of smart manufacturing.An important ...State-of-the-art technologies such as the Internet of Things(IoT),cloud computing(CC),big data analytics(BDA),and artificial intelligence(AI)have greatly stimulated the development of smart manufacturing.An important prerequisite for smart manufacturing is cyber-physical integration,which is increasingly being embraced by manufacturers.As the preferred means of such integration,cyber-physical systems(CPS)and digital twins(DTs)have gained extensive attention from researchers and practitioners in industry.With feedback loops in which physical processes affect cyber parts and vice versa,CPS and DTs can endow manufacturing systems with greater efficiency,resilience,and intelligence.CPS and DTs share the same essential concepts of an intensive cyber-physical connection,real-time interaction,organization integration,and in-depth collaboration.However,CPS and DTs are not identical from many perspectives,including their origin,development,engineering practices,cyber-physical mapping,and core elements.In order to highlight the differences and correlation between them,this paper reviews and analyzes CPS and DTs from multiple perspectives.展开更多
Smart grids are the developmental trend of power systems and they have attracted much attention all over the world.Due to their complexities,and the uncertainty of the smart grid and high volume of information being c...Smart grids are the developmental trend of power systems and they have attracted much attention all over the world.Due to their complexities,and the uncertainty of the smart grid and high volume of information being collected,artificial intelligence techniques represent some of the enabling technologies for its future development and success.Owing to the decreasing cost of computing power,the profusion of data,and better algorithms,AI has entered into its new develop-mental stage and AI 2.0 is developing rapidly.Deep learning(DL),reinforcement learning(RL)and their combination-deep reinforcement learning(DRL)are representative methods and relatively mature methods in the family of AI 2.0.This article introduces the concept and status quo of the above three methods,summarizes their potential for application in smart grids,and provides an overview of the research work on their application in smart grids.展开更多
Due to increasing complexity, uncertainty and data dimensions in power systems, conventional methods often meet bottlenecks when attempting to solve decision and control prob- lems. Therefore, data-driven methods towa...Due to increasing complexity, uncertainty and data dimensions in power systems, conventional methods often meet bottlenecks when attempting to solve decision and control prob- lems. Therefore, data-driven methods toward solving such prob- lems are being extensively studied. Deep reinforcement learning (DRL) is one of these data-driven methods and is regarded as real artificial intelligence (AI). DRL is a combination of deep learning (DL) and reinforcement learning (RL). This field of research has been applied to solve a wide range of complex sequential decision-making problems, including those in power systems. This paper firstly reviews the basic ideas, models, algorithms and techniques of DRL. Applications in power systems such as energy management, demand response, electricity market, operational control, and others are then considered. In addition, recent advances in DRL including the combination of RL with other classical methods, and the prospect and challenges of applications in power systems are also discussed.展开更多
Internet of Things (IoT) are being adopted for industrial and manufacturing applications such as manufacturing automation, remote machine diagnostics, prognostic health management of industrial machines and supply cha...Internet of Things (IoT) are being adopted for industrial and manufacturing applications such as manufacturing automation, remote machine diagnostics, prognostic health management of industrial machines and supply chain management. Cloud-Based Manufacturing is a recent on-demand model of manufacturing that is leveraging IoT technologies. While Cloud-Based Manufacturing enables on-demand access to manufacturing resources, a trusted intermediary is required for transactions between the users who wish to avail manufacturing services. We present a decentralized, peer-to-peer platform called BPIIoT for Industrial Internet of Things based on the Block chain technology. With the use of Blockchain technology, the BPIIoT platform enables peers in a decentralized, trustless, peer-to-peer network to interact with each other without the need for a trusted intermediary.展开更多
What needs to be developed from the concept of"Smart Grid"is that:when renewable energy sources are absolutely prevailing in power generation,distributed power generation and distributed energy storage syste...What needs to be developed from the concept of"Smart Grid"is that:when renewable energy sources are absolutely prevailing in power generation,distributed power generation and distributed energy storage systems are widespread across the grid,and electric vehicle charging loads are prevailing in power load demands,how can the power grid support electric power as a core secondary energy source,undertake the role of a bridge between primary energy and end-use energy,and achieve the coordination and the optimization in macro energy perspective;how to guarantee the security of both macro energy and environment as well as the reliability of electricity.If a new term is needed,it should be Comprehensive Energy Network,not Energy Internet.展开更多
Due to the challenge of climate and energy crisis,renewable energy generation including solar generation has experienced significant growth.Increasingly high penetration level of photovoltaic(PV)generation arises in s...Due to the challenge of climate and energy crisis,renewable energy generation including solar generation has experienced significant growth.Increasingly high penetration level of photovoltaic(PV)generation arises in smart grid.Solar power is intermittent and variable,as the solar source at the ground level is highly dependent on cloud cover variability,atmospheric aerosol levels,and other atmosphere parameters.The inherent variability of large-scale solar generation introduces significant challenges to smart grid energy management.Accurate forecasting of solar power/irradiance is critical to secure economic operation of the smart grid.This paper provides a comprehensive review of the theoretical forecasting methodologies for both solar resource and PV power.Applications of solar forecasting in energy management of smart grid are also investigated in detail.展开更多
基金This work is financially supported by the National Key Research and Development Program of China(2016YFB1101700)the National Natural Science Foundation of China(51875030)the Academic Excellence Foundation of BUAA for PhD Students.
文摘State-of-the-art technologies such as the Internet of Things(IoT),cloud computing(CC),big data analytics(BDA),and artificial intelligence(AI)have greatly stimulated the development of smart manufacturing.An important prerequisite for smart manufacturing is cyber-physical integration,which is increasingly being embraced by manufacturers.As the preferred means of such integration,cyber-physical systems(CPS)and digital twins(DTs)have gained extensive attention from researchers and practitioners in industry.With feedback loops in which physical processes affect cyber parts and vice versa,CPS and DTs can endow manufacturing systems with greater efficiency,resilience,and intelligence.CPS and DTs share the same essential concepts of an intensive cyber-physical connection,real-time interaction,organization integration,and in-depth collaboration.However,CPS and DTs are not identical from many perspectives,including their origin,development,engineering practices,cyber-physical mapping,and core elements.In order to highlight the differences and correlation between them,this paper reviews and analyzes CPS and DTs from multiple perspectives.
文摘Smart grids are the developmental trend of power systems and they have attracted much attention all over the world.Due to their complexities,and the uncertainty of the smart grid and high volume of information being collected,artificial intelligence techniques represent some of the enabling technologies for its future development and success.Owing to the decreasing cost of computing power,the profusion of data,and better algorithms,AI has entered into its new develop-mental stage and AI 2.0 is developing rapidly.Deep learning(DL),reinforcement learning(RL)and their combination-deep reinforcement learning(DRL)are representative methods and relatively mature methods in the family of AI 2.0.This article introduces the concept and status quo of the above three methods,summarizes their potential for application in smart grids,and provides an overview of the research work on their application in smart grids.
基金This work is supported by National Natural Science Foundation of China under Grant No.61571296the National Key Research and Development Program of China under 2018YFF0214705.
文摘Due to increasing complexity, uncertainty and data dimensions in power systems, conventional methods often meet bottlenecks when attempting to solve decision and control prob- lems. Therefore, data-driven methods toward solving such prob- lems are being extensively studied. Deep reinforcement learning (DRL) is one of these data-driven methods and is regarded as real artificial intelligence (AI). DRL is a combination of deep learning (DL) and reinforcement learning (RL). This field of research has been applied to solve a wide range of complex sequential decision-making problems, including those in power systems. This paper firstly reviews the basic ideas, models, algorithms and techniques of DRL. Applications in power systems such as energy management, demand response, electricity market, operational control, and others are then considered. In addition, recent advances in DRL including the combination of RL with other classical methods, and the prospect and challenges of applications in power systems are also discussed.
文摘Internet of Things (IoT) are being adopted for industrial and manufacturing applications such as manufacturing automation, remote machine diagnostics, prognostic health management of industrial machines and supply chain management. Cloud-Based Manufacturing is a recent on-demand model of manufacturing that is leveraging IoT technologies. While Cloud-Based Manufacturing enables on-demand access to manufacturing resources, a trusted intermediary is required for transactions between the users who wish to avail manufacturing services. We present a decentralized, peer-to-peer platform called BPIIoT for Industrial Internet of Things based on the Block chain technology. With the use of Blockchain technology, the BPIIoT platform enables peers in a decentralized, trustless, peer-to-peer network to interact with each other without the need for a trusted intermediary.
基金This work is supported by National High Technology Research and Development Program of China(863 Program)(No.2011AA05A105)and SGCC Projects.
文摘What needs to be developed from the concept of"Smart Grid"is that:when renewable energy sources are absolutely prevailing in power generation,distributed power generation and distributed energy storage systems are widespread across the grid,and electric vehicle charging loads are prevailing in power load demands,how can the power grid support electric power as a core secondary energy source,undertake the role of a bridge between primary energy and end-use energy,and achieve the coordination and the optimization in macro energy perspective;how to guarantee the security of both macro energy and environment as well as the reliability of electricity.If a new term is needed,it should be Comprehensive Energy Network,not Energy Internet.
基金This work was partially sup-ported by National High-tech R&D Program of China(863 Program,grant no.2014AA051901)Nature Science Foundation of China grant no.2014DFG62670,51207077 and 51261130472+1 种基金China Postdoctoral Science Foundation grant no.2015M580097Hong Kong RGC Theme Based Research Scheme grant no.T23-407/13-N.
文摘Due to the challenge of climate and energy crisis,renewable energy generation including solar generation has experienced significant growth.Increasingly high penetration level of photovoltaic(PV)generation arises in smart grid.Solar power is intermittent and variable,as the solar source at the ground level is highly dependent on cloud cover variability,atmospheric aerosol levels,and other atmosphere parameters.The inherent variability of large-scale solar generation introduces significant challenges to smart grid energy management.Accurate forecasting of solar power/irradiance is critical to secure economic operation of the smart grid.This paper provides a comprehensive review of the theoretical forecasting methodologies for both solar resource and PV power.Applications of solar forecasting in energy management of smart grid are also investigated in detail.
