Smart Grid

Traditional power grid can be upgraded into smart grids by incorporating two-way

integrated communications and smart computing capabilities for improved efficiency, reliability

and decision support.

Smart grid enables us to use both conventional energy source e.g. carbon based fuels and

renewable energy source e.g. wind and solar energy. Carbon fuel based power plants can

cooperate with renewable energy plants to reduce the carbon fuel consumption and pollution

caused by it.

A smart grid delivers electricity between supplier and consumer using two-way digital

technology. To increase reliability, transparency and to reduce cost and save energy smart grids

can be used.

It incorporates modernization of traditional electricity network by providing real time

monitoring of power consumption which helps consumer to minimize their expense on

conventional energy by adjusting their home appliances operation to avoid peak hours and

utilize the renewable resources.

KEY AREAS IN SMART GRID TECHNOLOGIES

1) Sensing and measurement e.g. advanced metering infrastructure system

2) Advanced components e.g. efficient and reliable electric components.

3) Integrated communications e.g. advanced communication protocols

4) Data management and Decision support e.g. supply and demand control system.

Our focus is to work on data management and decision support system. The smart grid

infrastructure delivers vast amount of data coming from its applications. This data can be

harnessed to build an efficient supply and demand control system and next generation

distribution control system.

Currently we are focusing on supply and demand control system by predicting the power

consumption through

i) Time-series pattern matching using statistical analysis.

ii) Time-series analysis using ARMA and ARIMA models.

iii) Artificial neural network.

References: 1) Ye Yan, Yi Qian, Hamid Sharif, David Tipper, “A survey on smart grid communication infrastructures:

Motivations, Requirements and Challenges” IEEE communication surveys and tutorials, vol. 15, No. 1, 1st

quarter 2013.

2) Andreas Reinhardt, Delphine Christin, Salil S. Kanhere, “Predicting the power consumption of electric

appliances through time series pattern matching” Proceedings of the 5th

ACM Workshop on Embedded

Systems for Energy-Efficient Buildings (BuildSys), pp. 1-2, ACM Press, November 2013.

By: -

Kamal Pradhan

Santanu Patel