This paper is published in Volume-6, Issue-4, 2020
Area
Electrical Engineering
Author
Kirtanpal Singh, Puneet Jain
Org/Univ
Adesh Institute of Engineering and Technology, Faridkot, Punjab, India
Pub. Date
13 August, 2020
Paper ID
V6I4-1377
Publisher
Keywords
Bat, Firefly Algorithm, Load Data Management, Smart Grid

Citationsacebook

IEEE
Kirtanpal Singh, Puneet Jain. Smart grid demand side management using hybrid combination of bat and firefly algorithm, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Kirtanpal Singh, Puneet Jain (2020). Smart grid demand side management using hybrid combination of bat and firefly algorithm. International Journal of Advance Research, Ideas and Innovations in Technology, 6(4) www.IJARIIT.com.

MLA
Kirtanpal Singh, Puneet Jain. "Smart grid demand side management using hybrid combination of bat and firefly algorithm." International Journal of Advance Research, Ideas and Innovations in Technology 6.4 (2020). www.IJARIIT.com.

Abstract

A smart grid is the advanced power grid, and electricity demand is managed and controlled by it. The electricity demand is increased during the peak hours of the day. Thus, to fulfill the electricity demand either generate the extra electricity in the peak hours that increase the cost or reduce the load in the peak hours. The load during peak hours is reduced by adopting a demand-side management technique. These techniques reduce the generation cost, and the performance of the smart grid is improved. This paper provides proposed a new/latest approach known as a hybrid of BAT, and Firefly optimization is used to control the switching time of devices so that the overall load can be minimized. The experimental results are performed for different devices (3 and 5). The simulation results show that the proposed algorithm reduces the cost as compared to the original cost. In the last, we have compared the performance of the proposed algorithm over the existing Particle Swarm Optimization (PSO) algorithm and found that 47.02% and 36.48% cost reduction for 3 and 5 devices, respectively.
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