Smart dispatching for energy internet with complex cyber‐physical‐social systems: A parallel dispatch perspective

Energy internet (EI) is a complex coupled multienergy system; it is essential to investigate its multienergy dispatching optimization issues. To this end, this paper first proposes a novel conception of smart dispatching for EI with a complex cyber‐physical‐social system (CPSS) network from the perspective of parallel dispatch, called parallel dispatching robot (PDR), and investigates the implementations of PDR based on smart artificial society (SAS) modeling. First, we introduce EI and describe the dispatching issues of EI. Second, we discuss several important concepts supporting the parallel dispatch conception of EI, including knowledge automation (KA), CPSS, and parallel machine learning (PML). On the basis of these, we elaborate the concept of parallel dispatch. Moreover, we construct a large closed‐loop feedback control framework of parallel dispatch for EI integrating a CPSS network based on KA and PML. Third, we establish an experimental platform for PDR research based on the proposed parallel dispatch framework. Fourth, we develop the PML‐based SAS models of a single PDR in centralized dispatching modes and group PDRs in decentralized dispatching modes to achieve crowd wisdom emergence and performance improvement in current cyber‐physical system frameworks of EI. Moreover, we design an external global closed loop for PDR to evaluate its operation stability. Lastly, we conduct a detailed discussion on PDR and offer some prospects for its engineering implementations. The biggest innovation of this paper lies in systematically proposing the smart dispatching concept and framework for complex CPSS‐based EI from the perspective of parallel dispatch and thoroughly investigating how to use SAS modeling to implement parallel dispatching and control for EI considering human and social factors, which is a major extension and theoretical improvement to existing single smart wide area robot concept and a preliminary attempt in investigating a shift from Energy 4.0 to Energy 5.0 in China.

[1]  Tao Yu,et al.  A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems , 2019, International Journal of Energy Research.

[2]  Tao Yu,et al.  Exploration and Exploitation of New Knowledge Emergence to Improve the Collective Intelligent Decision-Making Level of Web-of-Cells With Cyber-Physical-Social Systems Based on Complex Network Modeling , 2018, IEEE Access.

[3]  Shengwei Mei,et al.  Distributed Real-Time Economic Dispatch in Smart Grids: A State-Based Potential Game Approach , 2018, IEEE Transactions on Smart Grid.

[4]  Song Guo,et al.  A Survey on Energy Internet: Architecture, Approach, and Emerging Technologies , 2018, IEEE Systems Journal.

[5]  Yijia CAO,et al.  A comprehensive review of Energy Internet: basic concept, operation and planning methods, and research prospects , 2018 .

[6]  Tao Yu,et al.  Local Energy Management and Optimization: A Novel Energy Universal Service Bus System Based on Energy Internet Technologies , 2018 .

[7]  Tao Yu,et al.  Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey , 2018 .

[8]  Pingkuo Liu,et al.  A review on China's wind power accommodation in the background of “Internet+” strategy , 2018 .

[9]  R. S. Shaji,et al.  A review on the features and technologies for energy efficiency of smart grid , 2018 .

[10]  Yu Tao,et al.  Cyber-physical-social Systems Based Smart Energy Robotic Dispatcher and Its Knowledge Automation: Framework, Techniques and Challenges , 2018 .

[11]  Bo Yang,et al.  Hot Spot Temperature and Grey Target Theory-Based Dynamic Modelling for Reliability Assessment of Transformer Oil-Paper Insulation Systems: A Practical Case Study , 2018 .

[12]  Tao Yu,et al.  Nash Equilibrium-Based Asymptotic Stability Analysis of Multi-Group Asymmetric Evolutionary Games in Typical Scenario of Electricity Market , 2018, IEEE Access.

[13]  Demis Hassabis,et al.  Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm , 2017, ArXiv.

[14]  Xinghuo Yu,et al.  Beyond Smart Grid - Cyber-Physical-Social System in Energy Future , 2017, Proc. IEEE.

[15]  C. Subramani,et al.  A comprehensive review on energy management strategies of hybrid energy storage system for electric vehicles , 2017 .

[16]  Demis Hassabis,et al.  Mastering the game of Go without human knowledge , 2017, Nature.

[17]  Nanning Zheng,et al.  Parallel learning: a perspective and a framework , 2017, IEEE/CAA Journal of Automatica Sinica.

[18]  Jun Hao,et al.  Social energy: mining energy from the society , 2017, IEEE/CAA Journal of Automatica Sinica.

[19]  Fei-Yue Wang,et al.  From parallel plants to smart plants: intelligent control and management for plant growth , 2017, IEEE/CAA Journal of Automatica Sinica.

[20]  Ji Xiang,et al.  Distributed Consensus Based Supply–Demand Balance Algorithm for Economic Dispatch Problem in a Smart Grid With Switching Graph , 2017, IEEE Transactions on Industrial Electronics.

[21]  Güvenç Şahin,et al.  An agent-based simulation of power generation company behavior in electricity markets under different market-clearing mechanisms , 2017 .

[22]  Seifedine Kadry,et al.  Python for Graph and Network Analysis , 2017, Advanced Information and Knowledge Processing.

[23]  Li Li,et al.  Steps toward Parallel Intelligence , 2016 .

[24]  Zhen Shao,et al.  Energy Internet: The business perspective , 2016 .

[25]  Sajjad Golshannavaz,et al.  A comprehensive stochastic energy management system in reconfigurable microgrids , 2016 .

[26]  Hongbin Sun,et al.  Interactions of district electricity and heating systems considering time-scale characteristics based on quasi-steady multi-energy flow , 2016 .

[27]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[28]  Lei Xi,et al.  A novel multi-agent decentralized win or learn fast policy hill-climbing with eligibility trace algorithm for smart generation control of interconnected complex power grids☆ , 2015 .

[29]  Sanja Vranes,et al.  Energy hub modelling and optimisation: an analytical case-study , 2015, 2015 IEEE Eindhoven PowerTech.

[30]  Chongqing Kang,et al.  Energy-saving generation dispatch toward a sustainable electric power industry in China , 2015 .

[31]  Siddhartha Kumar Khaitan,et al.  Design Techniques and Applications of Cyberphysical Systems: A Survey , 2015, IEEE Systems Journal.

[32]  Qiuwei WU,et al.  Multi-agents modelling of EV purchase willingness based on questionnaires , 2015 .

[33]  Yusheng XUE,et al.  Energy internet or comprehensive energy network? , 2015 .

[34]  Takashi Hikihara,et al.  Router for Power Packet Distribution Network: Design and Experimental Verification , 2015, IEEE Transactions on Smart Grid.

[35]  Wang,et al.  Software-defined systems and knowledge automation: a parallel paradigm shift from newton to merton , 2015 .

[36]  Zhang Dongxi,et al.  Research on Development Strategy for Smart Grid Big Data , 2015 .

[37]  Sun Hongbi,et al.  Energy Internet: Driving Force, Review and Outlook , 2015 .

[38]  Meng Kun,et al.  Information Technology for Energy Internet: A Survey , 2014 .

[39]  Jan Carmeliet,et al.  New formulations of the ‘energy hub’ model to address operational constraints , 2014 .

[40]  Fei-Yue Wang,et al.  A CPSS Approach for Emergency Evacuation in Building Fires , 2014, IEEE Intelligent Systems.

[41]  Q Song.  Evaluating Social Welfare of Smart Grid Development in Pressure-State-Response Framework , 2014 .

[42]  Yang Shengchu.  Architecture and Key Technologies for Situational Awareness Based Automatic Intelligent Dispatching of Power Grid , 2014 .

[43]  Sun Hongbi.  Autonomous-Synergetic Energy Management System Family for Smart Grids: Concept,Architecture and Cases , 2014 .

[44]  Chen Xiao-ji.  Design for Architecture of Intelligent Dispatch Support System of Regional Power Grid , 2014 .

[45]  J. Manyika,et al.  Disruptive technologies: Advances that will transform life, business, and the global economy , 2013 .

[46]  S. A. H. Costa,et al.  Smart grid in the reduction of CO2 emissions in the atmosphere , 2013, 2013 IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Latin America).

[47]  Fei-Yue Wang,et al.  Intelligent systems and technology for integrative and predictive medicine: An ACP approach , 2013, TIST.

[48]  Alan N. Fish.  Knowledge Automation: How to Implement Decision Management in Business Processes , 2012 .

[49]  Hans-Jürgen Appelrath,et al.  Internet of Energy , 2012, Bus. Inf. Syst. Eng..

[50]  Robert A. Hefner,et al.  The Grand Energy Transition , 2012 .

[51]  Zhengshuo Li,et al.  Current energy management technologies research in China considering EVs integration , 2012, 2012 IEEE Power and Energy Society General Meeting.

[52]  Tao Huang,et al.  Congestion management impacts on bilateral electricity markets under strategic negotiation , 2011 .

[53]  Takashi Hiyama,et al.  Intelligent Automatic Generation Control , 2011 .

[54]  Zhang Yang.  Interpretation of the Latest US Policy on Smart Grid , 2011 .

[55]  He Guangyu.  Smart Grid and Smart Wide Area Robot , 2011 .

[56]  Jeremy Rifkin,et al.  The third industrial revolution : how lateral power is transforming energy, the economy, and the world , 2011 .

[57]  A Q Huang,et al.  The Future Renewable Electric Energy Delivery and Management (FREEDM) System: The Energy Internet , 2011, Proceedings of the IEEE.

[58]  Fei-Yue Wang,et al.  Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications , 2010, IEEE Transactions on Intelligent Transportation Systems.

[59]  Fei-Yue Wang,et al.  The Emergence of Intelligent Enterprises: From CPS to CPSS , 2010, IEEE Intelligent Systems.

[60]  Chris Arney,et al.  Hot, Flat, and Crowded: Why We Need a Green Revolution - and How It Can Renew America , 2010 .

[61]  Kevin P. Schneider,et al.  The Smart Grid: An Estimation of the Energy and CO2 Benefits , 2010 .

[62]  T. O'Brien,et al.  PJM Advanced Control Center - a Service Oriented Architecture , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[63]  Christopher M. Bishop,et al.  A New Framework for Machine Learning , 2008, WCCI.

[64]  Thomas L. Friedman,et al.  Hot, Flat, and Crowded: Why We Need a Green Revolution—And How It Can Renew America , 2008 .

[65]  Fei-Yue Wang,et al.  Toward a Paradigm Shift in Social Computing: The ACP Approach , 2007, IEEE Intell. Syst..

[66]  M. Nilsson,et al.  Red light for Green Paper : The EU policy on energy efficiency , 2007 .

[67]  M. Nowak,et al.  Stochastic dynamics of invasion and fixation. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[68]  M. Nowak,et al.  Evolutionary game dynamics in a Wright-Fisher process , 2006, Journal of mathematical biology.

[69]  Ann Nowé,et al.  Evolutionary game theory and multi-agent reinforcement learning , 2005, The Knowledge Engineering Review.

[70]  C. Hauert,et al.  Coevolutionary dynamics: from finite to infinite populations. , 2004, Physical review letters.

[71]  D. Fudenberg,et al.  Emergence of cooperation and evolutionary stability in finite populations , 2004, Nature.

[72]  Wang Feiyue,et al.  From Artificial Life to Artificial Societies——New Methods for Studies of Complex Social Systems , 2004 .

[73]  Wang Feiyue,et al.  Parallel system methods for management and control of complex systems , 2004 .

[74]  M. Newman,et al.  Mean-field solution of the small-world network model. , 1999, Physical review letters.

[75]  M. Newman,et al.  Renormalization Group Analysis of the Small-World Network Model , 1999, cond-mat/9903357.

[76]  T. E. Dy-Liacco.  Enhancing power system security control , 1997 .

[77]  K. E. Bollinger,et al.  Artificial intelligence assisted voltage stability enhancement , 1996, Proceedings of 1996 Canadian Conference on Electrical and Computer Engineering.

[78]  G. Irisarri,et al.  Integration of artificial intelligence applications in the EMS: issues and solutions , 1995, Proceedings of Power Industry Computer Applications Conference.

[79]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[80]  Dai Ruwei,et al.  A new discipline of science — The study of open complex giant system and its methodology , 1993 .

[81]  H. Peyton Young,et al.  Stochastic Evolutionary Game Dynamics , 1990 .