Title
Overview of the co-simulation methods for power and communication system
Abstract
Smart grid is a space-time multidimensional heterogeneous system integrated with cyber system and power system. Because of event-driven discrete characteristic of communication system and continuous characteristic of power system, it is difficult to study power and communication hybrid system. In traditional researches, the model of either system was simplified and run in one single platform, while the accuracy and practicability of study results can't be guaranteed. Therefore, co-simulation methods have become a research hotspot which can be used to analyze complex dynamic performance of power and communication hybrid system. This paper summarizes existing co-simulation methods by classifying them into three types: unified simulation methods, non-real-time simulation methods and real-time simulation methods. Simulation tools, system architecture and data synchronization problems are also overviewed and analyzed in detail. In order to increase simulation precision and efficiency, a new time synchronization method based on state cache is proposed.
Year
DOI
Venue
2016
10.1109/RCAR.2016.7784007
2016 IEEE International Conference on Real-time Computing and Robotics (RCAR)
Keywords
Field
DocType
co-simulation method,smart grid,space-time multidimensional heterogeneous system,cyber system,power system,event-driven discrete characteristic,continuous characteristic,power system,complex dynamic performance analysis,hybrid power-and-communication system,unified simulation method,nonreal-time simulation method,real-time simulation method,system architecture,data synchronization,state cache
Smart grid,Computer science,Data synchronization,Power system simulation,Communications system,Electric power system,Real-time computing,Systems architecture,Co-simulation,Hybrid system,Computer engineering
Conference
ISBN
Citations 
PageRank 
978-1-4673-8960-0
0
0.34
References 
Authors
6
5
Name
Order
Citations
PageRank
Yi Tang135.71
Feng Li233849.66
Qi Wang37340.49
Chen Bin400.34
Ming Ni513615.17