TY - GEN
T1 - A Cyber-Physical-Social System with Parallel Learning for Distributed Energy Management of a Microgrid
AU - Zhang, Xiaoshun
AU - Xu, Zhao
AU - Yu, Tao
PY - 2018/9/18
Y1 - 2018/9/18
N2 - A novel cyber-physical-social system (CPSS) with parallel learning is presented for distributed energy management (DEM) of a microgrid. CPSS is developed by extending the conventional cyber-physical system to the social space with human participation and interaction. Each energy supplier or each energy demander is regarded as a human in the social space, who is able to learn the knowledge, cooperate with others, and make a decision with various preference behaviors. The correlated equilibrium (CE) based general-sum game is employed for realizing the human interaction on the complex optimization subtask, while the novel adaptive consensus algorithm (ACA) is used for achieving that on the simple optimization subtask with multi-energy balance constraints. A real-world system and multiple virtual artificial systems are introduced for parallel and interactive execution based on the small world network, thus a higher quality optimum of DEM can be rapidly emerged with a high probability. Case studies of a microgrid demonstrate that the proposed technique can effectively achieve the human-computer collaboration and rapidly obtain a higher quality optimum of DEM.
AB - A novel cyber-physical-social system (CPSS) with parallel learning is presented for distributed energy management (DEM) of a microgrid. CPSS is developed by extending the conventional cyber-physical system to the social space with human participation and interaction. Each energy supplier or each energy demander is regarded as a human in the social space, who is able to learn the knowledge, cooperate with others, and make a decision with various preference behaviors. The correlated equilibrium (CE) based general-sum game is employed for realizing the human interaction on the complex optimization subtask, while the novel adaptive consensus algorithm (ACA) is used for achieving that on the simple optimization subtask with multi-energy balance constraints. A real-world system and multiple virtual artificial systems are introduced for parallel and interactive execution based on the small world network, thus a higher quality optimum of DEM can be rapidly emerged with a high probability. Case studies of a microgrid demonstrate that the proposed technique can effectively achieve the human-computer collaboration and rapidly obtain a higher quality optimum of DEM.
KW - adaptive consensus algorithm
KW - correlated equilibrium
KW - cyber-physical-social system
KW - distributed energy management
KW - parallel learning
UR - http://www.scopus.com/inward/record.url?scp=85055519592&partnerID=8YFLogxK
U2 - 10.1109/ISGT-Asia.2018.8467970
DO - 10.1109/ISGT-Asia.2018.8467970
M3 - Conference article published in proceeding or book
AN - SCOPUS:85055519592
T3 - International Conference on Innovative Smart Grid Technologies, ISGT Asia 2018
SP - 1294
EP - 1298
BT - International Conference on Innovative Smart Grid Technologies, ISGT Asia 2018
A2 - Hao, Quan
A2 - Sharma, Anurag
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Innovative Smart Grid Technologies, ISGT Asia 2018
Y2 - 22 May 2018 through 25 May 2018
ER -