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Integration of Renewable Energy Sources into the Smart Grid Using Enhanced SCA

Shaik Karimulla, K. Ravi*

School of Electrical Engineering-SELECT, Vellore Institute of Technology, VIT-Vellore, 632014, India

* Corresponding Author: K. Ravi. Email:

Intelligent Automation & Soft Computing 2022, 32(3), 1557-1572.


The usage of energy in everyday life is growing day by day as a result of the rapid growth in the human population. One solution is to increase electricity generation to the same extent as the human population, but this is usually practically impossible. As the population is increasing, the need for electrical usage is also increasing. Therefore, smart grids play an important role in making efficient use of existing energy sources like solar, wind and battery storage systems. By managing demand, the minimization of power consumption and its consequent costs. On the load side, residential and commercial types use a large amount of the total energy produced by renewable energy sources. As a result, in this work, we use DSM (Demand-side Management) to schedule various appliances on loads to minimize energy consumption. Smart grid plays a major role in the integration of renewable energy sources as well as in the minimization of cost of energy (COE). Smart meters like advanced metering infrastructure are also used to reduce load demand. Therefore, in this work, an Enhanced sine cosine algorithm (ESCA) is proposed to solve the optimization problem. The proposed method consists of loads like residential and commercial types. The proposed method considered the comparison with the Genetic Algorithm (GA) and Ant colony optimization (ACO). Simulation results were carried out by using MATLAB software. The results show the Enhanced sine cosine algorithm (ESCA) is best when compared to other algorithms like GA and ACO in the minimization of the cost of energy.


Cite This Article

S. Karimulla and K. Ravi, "Integration of renewable energy sources into the smart grid using enhanced sca," Intelligent Automation & Soft Computing, vol. 32, no.3, pp. 1557–1572, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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