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Optimal Operation of Distributed Generations Considering Demand Response in a Microgrid Using GWO Algorithm

Shokouhandeh, Hassan; Ahmadi Kamarposhti, Mehrdad; Holderbaum, William; Colak, Ilhami; Thounthong, Phatiphat

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Authors

Hassan Shokouhandeh

Mehrdad Ahmadi Kamarposhti

William Holderbaum

Ilhami Colak

Phatiphat Thounthong



Abstract

The widespread penetration of distributed energy sources and the
use of load response programs, especially in a microgrid, have caused many
power system issues, such as control and operation of these networks, to
be affected. The control and operation of many small-distributed generation
units with different performance characteristics create another challenge for
the safe and efficient operation of the microgrid. In this paper, the optimum
operation of distributed generation resources and heat and power storage in
a microgrid, was performed based on real-time pricing through the proposed
gray wolf optimization (GWO) algorithm to reduce the energy supply cost
with the microgrid. Distributed generation resources such as solar panels,
diesel generators with battery storage, and boiler thermal resources with
thermal storage were used in the studied microgrid. Also, a combined heat
and power (CHP) unit was used to produce thermal and electrical energy
simultaneously. In the simulations, in addition to the gray wolf algorithm,
some optimization algorithms have also been used. Then the results of 20
runs for each algorithm confirmed the high accuracy of the proposed GWO
algorithm. The results of the simulations indicated that the CHP energy
resources must be managed to have a minimum cost of energy supply in the
microgrid, considering the demand response program.

Citation

Shokouhandeh, H., Ahmadi Kamarposhti, M., Holderbaum, W., Colak, I., & Thounthong, P. (2023). Optimal Operation of Distributed Generations Considering Demand Response in a Microgrid Using GWO Algorithm. Computer Systems Science and Engineering, 47(1), 809-822. https://doi.org/10.32604/csse.2023.035827

Journal Article Type Article
Acceptance Date Dec 14, 2022
Online Publication Date May 26, 2023
Publication Date May 26, 2023
Deposit Date Jun 8, 2023
Publicly Available Date Jun 8, 2023
Journal Computer Systems Science and Engineering
Print ISSN 0267-6192
Publisher Tech Science Press
Peer Reviewed Peer Reviewed
Volume 47
Issue 1
Pages 809-822
DOI https://doi.org/10.32604/csse.2023.035827
Keywords General Computer Science; Theoretical Computer Science; Control and Systems Engineering