Nilima R. Das

Work place: Faculty of Engg. & Tech., SOA, Odisha, India



Research Interests:


Nilima R. Das did her M.Tech. in Computer Science and Engineering from Utkal University, Odisha in 2008. She is currently pursuing her Ph.D. from Siksha ‘O’ Anusandhan, Deemed to be University, Bhubaneswar, Odisha.

She is working as an Assistant Professor in the Department of Computer Applications, Institute of Technical Education and Research, Odisha. She has published 3 International Journal papers.

Author Articles
Intelligent Scheduling of Demand Side Energy Usage in Smart Grid Using a Metaheuristic Approach

By Nilima R. Das Satyananda C. Rai Ajit Nayak

DOI:, Pub. Date: 8 Jun. 2018

As the global demand for electricity is growing continuously, the sources use more fossil fuels to generate electricity which in turn increases the level of carbon dioxide in the atmosphere. Moreover the electrical system becomes unreliable during the peak hours if the demand for electricity is very high. So there is a need to have a grid system which can handle these cases in a smarter way. A Smart Grid is such an electrical grid system which can control and manage electricity demand in a more reliable and economic manner using various energy efficient resources and a variety of operational measures like smart meters, smart appliances and smart communication system. The smart grid uses a technique called energy demand management at consumer side which motivates the consumers to control and reduce their demand for energy during peak hours. This makes the whole system more reliable and efficient. The demand side management (DSM) includes various methods such as increasing awareness among the consumers and giving them some financial incentives which can encourage them to be a part of the DSM program. In this paper a novel Demand Side Management technique has been proposed for a typical smart grid scenario which comprises users with energy storage devices using a metaheuristic approach to have an optimal load scheduling that results in reduced peak hour demands.

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