Microgrid cost optimization: a case study on Abu Dhabi

NINAN, J., OTHMAN, Y., ALDHUHOORI, S., MEEGAHAPOLA, L. and AKMAL, Muhammad (2019). Microgrid cost optimization: a case study on Abu Dhabi. 2018 8th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), 2018, 120-125.

[img]
Preview
PDF
1570446685 final.pdf - Accepted Version
All rights reserved.

Download (801kB) | Preview
Official URL: https://ieeexplore.ieee.org/document/8699195
Link to published version:: https://doi.org/10.1109/ISMS.2018.00032

Abstract

This paper presents a microgrid cost optimization study specifically focused on the United Arab Emirates (UAE) based on the Genetic and Ant-Bee Colony algorithms. The main objective of the paper is to identify size and amount of power supply sources in Microgrids that result in minimum cost. Specific parameters pertaining to the UAE were employed within the new objective function and constraints. Two different scenarios were tested, and their results have been discussed. During this study, it was evident that solar-PV systems were the second most cost-effective way to reduce cost of microgrids preceded by micro-turbines.

Item Type: Article
Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Genetic algorithms; microgrids; optimization; power-generation dispatch; power systems
Identification Number: https://doi.org/10.1109/ISMS.2018.00032
Page Range: 120-125
Related URLs:
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 22 Jul 2019 13:14
Last Modified: 09 Sep 2019 11:30
URI: http://shura.shu.ac.uk/id/eprint/24890

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics