Document Type
Article
Publication Date
10-2017
Publication Title
International Journal of Smart Grid and Clean Energy
Volume
6
Abstract
The futuristic smart grid require creative architectures for Information and Communication technologies (ICT) networks. One such architecture must need some sort of topological control for the next generation grid. To assess architectural impact and effectiveness, simulation models are important. Communication networks are expected to be tightly coupled-integrated element for smart grid systems. The co-existence of communication and control and their joint operation necessitate accurate modeling of communication events. While at the first glance, it is tempting to model the communication network as a black box that introduces delay/s between its input and output, the complex interactions among grid network and its components and between data sources and the network make it less tractable. The paper focuses a software-based smart grid architecture modeling using an OMNET++, a discrete event simulator. It consists of layers of independent management modules for communication, and control events that represent realworld cases using generators, circuit breakers, switches or relays, transmission lines and loads. A topology sorting algorithm is presented using modified Dijkstra‟s Algorithm, and several contingency scenarios for an IEEE 14 bus system was carried out emulating real electric test-bed conditions.
Issue
4
First Page
233
Last Page
251
DOI
10.12720/sgce.6.4.233-251
ISSN
23154462
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Eric Horton and Prakash Ranganathan. "A Software Based Smart Grid Framework for Automated Decision on Resource Allocation, Topological Control using OMNET++" (2017). Electrical Engineering Faculty Publications. 14.
https://commons.und.edu/ee-fac/14
Comments
This work is supported by NSF award #: 1537565 titled “Topology Aware Resource Optimization and Uncertainty Quantification Energy Models for the Power Grid”.