DocumentCode :
3762140
Title :
Optimized scalable decentralized hybrid advanced metering infrastructure for smart grid
Author :
Alireza Ghasempour
Author_Institution :
Department of Electrical and Computer Engineering, Utah State University, USA
fYear :
2015
Firstpage :
223
Lastpage :
228
Abstract :
Advanced Metering Infrastructure (AMI) is one of the most important components of smart grid (SG) which aggregates data from smart meters (SMs) and sends the collected data to the utility center (UC) to be analyzed and stored. In traditional centralized AMI architecture, there is one meter data management system to process all gathered information in the UC, therefore, by increasing the number of SMs and their data rates, this architecture is not able to satisfy SG requirements (e.g., delay and reliability) and it would not be scalable. Since scalability is one of most important characteristics of AMI architecture in SG, in this paper, we have investigated scalability of different AMI architectures and propose a scalable hybrid AMI architecture. We have introduced three performance metrics: monthly, fixed, and total deployment cost of AMI architecture as objective functions of corresponding optimization problems. Based on these metrics, we formulated each AMI architecture and used a genetic-based algorithm to minimize them and find their near optimal solutions. Based on the requirements of five demographic regions (i.e., different density or number of SMs) and range of SMs data rates, we simulated our proposed AMI architecture and the results proved that our proposed AMI hybrid architecture has better performance compared with centralized and decentralized AMI architectures and it has a good load and geographic scalability.
Keywords :
"Computer architecture","Scalability","Smart grids","Reliability","Aggregates","Optimization","Substations"
Publisher :
ieee
Conference_Titel :
Smart Grid Communications (SmartGridComm), 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/SmartGridComm.2015.7436304
Filename :
7436304
Link To Document :
بازگشت