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Development of smart controller for demand side management in smart grid using reactive power optimization

Authors :
E. Muthukumaran
S. Kalyani
Source :
Soft Computing. 25:1581-1594
Publication Year :
2020
Publisher :
Springer Science and Business Media LLC, 2020.

Abstract

Reactive power optimization is one of the major problems of concern in smart grid (SG) environment. Although several techniques have been proposed for reactive power optimization, demand side management (DSM) plays a vital role in smart grid networks. The main idea of this work is to address reactive power optimization by developing a smart controller for DSM by effective monitoring of real power loss in the smart grid network. The proposed smart controller is developed by formulating the DSM as an optimization problem and obtaining its solution by applying elephant herd optimization–firefly (EHO–FF) evolutionary algorithm. Further, the proposed smart controller for DSM aims to meet the power demand and limit the power flow in transmission network by adding distributed generation (DGs) units at optimal locations. The proposed work aims to improve the energy efficiency and voltage profile in the power grid network when operating under different load scenarios. The benchmark IEEE 30 bus system consisting of 6 generating units, 41 transmission lines with total load of 283.4 MW and 126.2 MVAR is used as the test system in this work. The test system is subjected to varying load pattern for 24 h in a typical day. The performance of the proposed smart controller is evaluated on the bench mark IEEE 30 bus system through program code developed in MATLAB environment. The simulation results have proved that the proposed smart controller for DSM minimizes the power loss and improves the voltage profile significantly by incorporating DG units in optimal locations. The effectiveness of the EHO–FF algorithm is analyzed by comparing the results obtained with PSO and bAT algorithm.

Details

ISSN :
14337479 and 14327643
Volume :
25
Database :
OpenAIRE
Journal :
Soft Computing
Accession number :
edsair.doi...........ddb13aa0373c6c06da0ad8de359822ed
Full Text :
https://doi.org/10.1007/s00500-020-05246-3