Biogeography based optimization for home energy management in smart grid

15Citations
Citations of this article
10Readers
Mendeley users who have this article in their library.
Get full text

Abstract

In this paper, we use meta-heuristic algorithm: Genetic Algorithm (GA) and Biogeography based Optimization (BBO) integrated in Energy Management Controller (EMC) to evaluate the performance of home energy management in residential area. EMC is introduced with the objective of cost reduction and to manage high peak demand problem. Time of use tariff model is used for electricity bill calculation. Simulation results show the effectiveness and efficiency of proposed scheme by load management and cost reduction. BBO based EMC performs better than GA based EMC. We also perform comparison both GA based EMC and BBO based EMC with unscheduled scheme and results show both outperform than unscheduled. BBO based EMC is more efficient in electricity cost minimization and peak to average ratio minimization as compared to GA based EMC.

Cite

CITATION STYLE

APA

Iftikhar, H., Asif, S., Maroof, R., Ambreen, K., Khan, H. N., & Javaid, N. (2018). Biogeography based optimization for home energy management in smart grid. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 7, pp. 177–190). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-319-65521-5_16

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free